Methods in Bioengineering Nanoscale Bioengineering and Nanomedicine
The Artech House Methods in Bioengineering Series Series Editors-in-Chief Martin L. Yarmush, M.D., Ph.D. Robert S. Langer, Sc.D. Methods in Bioengineering: Biomicrofabrication and Biomicrofluidics, Jeffrey D. Zahn and Luke P. Lee, editors Methods in Bioengineering: Microdevices in Biology and Medicine, Yaakov Nahmias and Sangeeta N. Bhatia, editors Methods in Bioengineering: Nanoscale Bioengineering and Nanomedicine, Kaushal Rege and Igor Medintz, editors Methods in Bioengineering: Stem Cell Bioengineering, Biju Parekkadan and Martin L. Yarmush, editors Methods in Bioengineering: Systems Analysis of Biological Networks, Arul Jayaraman and Juergen Hahn, editors
Series Editors Martin L. Yarmush, Harvard Medical School Christopher J. James, University of Southampton
Advanced Methods and Tools for ECG Data Analysis, Gari D. Clifford, Francisco Azuaje, and Patrick E. McSharry, editors Advances in Photodynamic Therapy: Basic, Translational, and Clinical, Michael Hamblin and Pawel Mroz, editors Biomedical Surfaces, Jeremy Ramsden Intelligent Systems Modeling and Decision Support in Bioengineering, Mahdi Mahfouf Translational Approaches in Tissue Engineering and Regenerative Medicine, Jeremy Mao, Gordana Vunjak-Novakovic, Antonios G. Mikos, and Anthony Atala, editors
Methods in Bioengineering Nanoscale Bioengineering and Nanomedicine Kaushal Rege Department of Chemical Engineering Arizona State University
Igor L. Medintz Center for Biomolecular Science and Engineering U.S. Naval Research Laboratory
Editors
artechhouse.com
Library of Congress Cataloging-in-Publication Data A catalog record for this book is available from the U. S. Library of Congress.
British Library Cataloguing in Publication Data A catalogue record for this book is available from the British Library.
ISBN-13: 978-1-59693-410-8 Text design by Darrell Judd Cover design by Igor Valdman
© 2009 Artech House. All rights reserved. Printed and bound in the United States of America. No part of this book may be reproduced or utilized in any form or by any means, electronic or mechanical, including photocopying, recording, or by any information storage and retrieval system, without permission in writing from the publisher. All terms mentioned in this book that are known to be trademarks or service marks have been appropriately capitalized. Artech House cannot attest to the accuracy of this information. Use of a term in this book should not be regarded as affecting the validity of any trademark or service mark.
10 9 8 7 6 5 4 3 2 1
Contents Preface
xv
CHAPTER 1 Preparation and Characterization of Carbon Nanotube-Protein Conjugates
1
1.1 Introduction
2
1.2 Materials
3
1.3 Methods
3
1.3.1 Physical Adsorption of Proteins on Carbon Nanotubes
3
1.3.2 Protein Assisted Solubilization of Carbon Nanotubes
4
1.3.3 Covalent Attachment of Proteins onto Carbon Nanotubes
5
1.4 Data Acquisition, Anticipated Results, and Interpretation of Data
7
1.4.1 Characterization of Proteins Physically Adsorbed onto 1.4.1 Carbon Nanotubes
7
1.4.2 Characterization of Protein-Solubilized Carbon Nanotubes
11
1.4.3 Characterization of Covalently Attached Carbon 1.4.1 Nanotube-Protein Conjugates
13
1.5 Discussion and Commentary
18
1.6 Applications Notes
19
1.7 Summary Points
21
Acknowledgments
21
References
21
CHAPTER 2 Peptide-Nanoparticle Assemblies
25
2.1 Introduction
26
2.2 Materials
27
2.3 Methods
28
2.3.1 Coil-Coil Peptide Mediated NP Assembly
28
2.3.2 Synthesis of Hybrid Structures Using Multifunctional Peptides
31
2.4 Assembly Mediated by Metal Ion-Peptide Recognition
32
2.5 Peptides as Antibody Epitopes for Nanoparticle Assembly
33
2.6 DATA Acquisition, Anticipated Results, and Interpretation
34
2.7 Discussion and Commentary
35 v
Contents
2.8 Application Notes
36
2.9 Summary Points
36
Acknowledgments
36
References
37
CHAPTER 3 Nanoparticle-Enzyme Hybrids as Bioactive Materials
39
3.1 Introduction
40
3.2 Materials
40
3.3 Methods
41
3.3.1 Enzyme-Attached Polystyrene Nanoparticles
41
3.3.2 Polyacrylamide Hydrogel Nanoparticles for 3.3.2 Entrapment of Enzymes
41
3.3.3 Magnetic Nanoparticles with Porous Silica Coating for 3.3.3 Enzyme Attachment
42
3.3.4 Enzyme Loading and Activity Assay
42
3.4 Results
44
3.4.1 Polystyrene-Enzyme Hybrid Nanoparticles
44
3.4.2 Polyacrylamide Hydrogel Nanoparticles with 3.4.2 Entrapped Enzymes
45
3.4.3 Magnetic Nanoparticles for Enzyme Attachment
46
3.5 Discussion and Commentary
47
3.6 Troubleshooting
49
3.7 Application Notes
49
3.8 Summary Points
49
Acknowledgments
50
References
50
CHAPTER 4 Self-Assembled QD-Protein Bioconjugates and Their Use in Fluorescence Resonance Energy Transfer 4.1 Introduction
54
4.2 Materials
56
4.2.1 Reagents
56
4.2.2 Equipment
56
4.3 Methods 4.3.1
Quantum Dot Synthesis
56 56
4.3.2 Surface Ligand Exchange
58
4.3.3 Biomolecule Conjugation
61
4.3.4 Fluorescence Measurements
65
4.4 Data Analysis and Interpretation
vi
53
66
4.4.1 Calculating Donor-Acceptor Distances
68
4.4.2 Calculating Reaction Rates of Surface-Bound Substrates
70
Contents
4.5 Summary Points
72
4.6 Conclusions
72
References
72
Annotated References
74
CHAPTER 5 Tracking Single Biomolecules in Live Cells Using Quantum Dot Nanoparticles
75
5.1 Introduction
76
5.2 Materials
78
5.2.1 Reagents
78
5.2.2 Imaging Equipment
79
5.3 Methods
79
5.3.1 Forming QD Bioconjugates
79
5.3.2 Treating Cells with QD Bioconjugates
79
5.4 Data Acquisition, Anticipated Results, and Interpretation
79
5.4.1 Imaging QD-Bound Complexes in Cells
79
5.4.2 Analysis of the Real-Time QD Dynamics
80
5.5 Discussion and Commentary References
81 82
CHAPTER 6 Nanoparticles as Biodynamic Substrates for Engineering Cell Fates
85
6.1 Introduction
86
6.2 Experimental Design
88
6.3 Materials
88
6.3.1 Cell Culture, Fixing, Staining, and Analysis Reagents
88
6.3.2 Nanoparticle Fabrication and Functionalization
89
6.3.3 Microscale Plasma Initiated Patterning
89
6.4 Methods
89
6.4.1 Albumin Nanoparticle Fabrication
89
6.4.2 Albumin Nanoparticle Functionalization
91
6.4.3 Albumin Nanoparticle Pattern Creation—Microscale 6.4.3 Plasma Initiated Patterning (μPIP)
93
6.4.4 Cell Culture
94
6.4.5 Keratinocyte Morphology and Migration
94
6.4.6 Fibroblast Extracellular Matrix Assembly
94
6.4.7 Cell Attachment Assay
95
6.5 Results 6.5.1 Enhanced Cell Migration 6.5.2 Enhanced Extracellular Matrix Assembly 6.6 Discussion of Pitfalls 6.6.1 Spatial Guidance of Cell Attachment—Microscale Plasma 6.6.1 Initiated Patterning
95 95 97 100 100 vii
Contents
6.6.2 Three-Dimensional Presentation of Albumin Nanoparticles 6.7 Summary Points
101 102
Acknowledgments
103
References
103
CHAPTER 7 Magnetic Cell Separation to Enrich for Rare Cells 7.1 Introduction
107 108
7.1.1 Principle
110
7.1.2 Examples of Cell Magnetic Separation Applications
115
7.2 Materials and Methods
116
7.2.1 Enrichment Process
116
7.2.2 Red Cell Lysis Step
117
7.2.3 Immunomagnetic Labeling
117
7.2.4 Magnetic Cell Separation Step
117
7.3 Data Acquisition, Results, and Interpretation
117
7.4 Discussion and Commentary
120
7.5 Summary Points to Obtain High-Performance, 7.5 Magnetic Cell Separations
120
Acknowledgments
120
References
121
CHAPTER 8 Magnetic Nanoparticles for Drug Delivery 8.1 Introduction
124
8.2 Experimental Design
124
8.3 Materials
126
8.3.1 Reagents
126
8.3.2 Facilities and Equipment
127
8.4 Methods
viii
123
128
8.4.1 Synthesis of Magnetic Nanoparticles
128
8.4.2 Physical Characterization of Magnetic Nanoparticles
129
8.4.3 Conversion of DOX•HCl
129
8.4.4 Drug Loading and Release Kinetics
129
8.4.5 Kinetics of DOX Release from Magnetic Nanoparticles
130
8.4.6 Antiproliferative Activity of Doxorubicin Loaded Magnetic 8.4.6 Nanoparticles on MCF-7 Cells
131
8.4.7 Antiproliferative Activity of Doxorubicin Loaded Magnetic 8.4.6 Nanoparticles on MCF-7 Cells in the Presence of a 8.4.6 Magnetic Field
131
8.5 Data Acquisition, Anticipated Results, and Interpretation
132
8.6 Discussion and Commentary
133
8.7 Application Notes
134
Contents
8.8 Summary Points
134
Acknowledgments
135
References
135
CHAPTER 9 Imaging and Therapy of Atherosclerotic Lesions with Theranostic Nanoparticles
137
9.1 Introduction
138
9.2 Experimental Design
139
9.3 Materials
140
9.3.1 Reagents
140
9.3.2 Facilities/Equipment
140
9.3.3 Animal Model
141
9.3.4 Alternate Reagents and Equipment
141
9.4 Methods
141
9.4.1 Synthesis of Theranostic Nanoparticles
141
9.4.2 Intravital Fluorescence Microscopy
143
9.4.3 Light-Based Therapy
144
9.5 Data Acquisition, Anticipated Results, and Interpretation
145
9.5.1 Characterization of Theranostic Nanoparticles
145
9.5.2 Animal Experimentation
146
9.5.3 Intravital Fluorescence Microscopy
146
9.5.4 Statistical Analyses
147
9.5.5 Anticipated Results
148
9.6 Discussion and Commentary
148
9.7 Summary Points
149
Acknowledgments
150
References
150
CHAPTER 10 Biomedical Applications of Metal Nanoshells 10.1 Introduction
153 154
10.1.1 Biomedical Applications of Metal Nanoshells
154
10.1.2 Nanoshells for Combined Optical Contrast and 10.1.2 Therapeutic Application
155
10.2 Experimental Design
156
10.3 Materials
156
10.3.1 Nanoparticle Production
156
10.3.2 Protein Conjugation to Nanoshells Surface
156
10.3.3 Cell Culture
157
10.3.4 In Vitro Assays
157
10.4 Methods
157
10.4.1 Fabrication of Gold/Silica Core Nanoshells
157
10.4.2 Nanoshells for Combined Imaging and Therapy In Vivo
158 ix
Contents
10.4.3 Passivation of Nanoshells with PEG
159
10.4.4 Conjugation of Biomolecules to Nanoshells
160
10.4.5 Quantification of Antibodies on Nanoshells
160
10.5 Results
161
10.5.1 Gold/Silica Nanoshells Allow Both Imaging Contrast Increase 10.5.1 and Therapeutic Benefit
161
10.5.2 Evaluation of Antibody Concentration per Nanoshell
163
10.6 Discussion of Pitfalls
163
10.7 Statistical Analysis
165
Acknowledgments
166
References
166
CHAPTER 11 Environmentally Responsive Multifunctional Liposomes 11.1 Introduction
170
11.1.1 Cis-Aconityl Linkage
171
11.1.2 Trityl Linkage
172
11.1.3 Acetal Linkage
172
11.1.4 Polyketal Linkage
172
11.1.5 Vinyl Ether Linkage
172
11.1.6 Hydrazone Linkage
173
11.1.7 Poly(Ortho-Esters)
173
11.1.8 Thiopropionates
173
11.2 Materials
174
11.2.1 Chemicals
174
11.2.2 Syntheses
175
11.2.3 Preparation of the TATp-Bearing, Rhodamine-Labeled 11.2.3 Liposomal Formulations
175
11.2.4 Preparation of the TAtp-Bearing, Rhodamine Labeled, 11.2.3 pGFP Complexed Liposomal Formulations
175
11.3 Methods
176
11.3.1 Synthesis of Hydrazone-Based mPEG-HZ-PE Conjugates
176
11.3.2 Synthesis of PE-PEG1000-TATp Conjugate
183
11.3.3 In Vitro pH-Dependant Degradation of PEG-HZ-PE 11.3.3 Conjugates
184
11.3.4 Avidin-Biotin Affinity Chromatography
184
11.3.5 In Vitro Cell-Culture Study
184
11.3.6 In Vivo Study
185
11.3.7 In Vivo Transfection with pGFP
185
11.4 Discussion and Commentary
x
169
185
11.4.1 Synthesis of Hydrazone-Based mPEG-HZ-PE Conjugates
185
11.4.2 Synthesis of PE-PEG1000-TATp Conjugate
186
11.4.3 In Vitro pH-Dependant Degradation of PEG-HZ-PE 11.4.3 Conjugates
186
Contents
11.4.4 Avidin-Biotin Affinity Chromatography
188
11.4.5 In Vitro Cell Culture Study
188
11.4.6 In Vivo Study
188
11.4.7 In Vivo pGFP Transfection Experiment
189
11.5 Conclusion
191
11.7 Summary Points
192
Acknowledgments
192
References
192
CHAPTER 12 Biodegradable, Targeted Polymeric Nanoparticle Drug Delivery Formulation for Cancer Therapy
197
12.1 Introduction
198
12.2 Materials
200
12.2.1 Polymer Synthesis of PLA-PEG and PLGA-PEG
200
12.2.2 Nanoparticle Formation
201
12.2.3 Ligand Conjugation
201
12.2.4 Quantification of Drug Encapsulation
201
12.2.5 Release Experiments
202
12.2.6 Postformulation Treatment
202
12.2.7 Cell Binding and Uptake Experiments
202
12.2.8 Cytotoxicity Experiments
203
12.3 Methods
203
12.3.1 Polymer Synthesis of PLA-PEG and PLGA-PEG
204
12.3.2 Nanoparticle Formation
207
12.3.3 Conjugation of Targeting Ligand
209
12.3.4 Quantification of Drug Encapsulation
211
12.3.5 Drug Release Studies
212
12.3.6 Postformulation Treatment
213
12.3.7 In Vitro Experiments: Cell Binding and Uptake Studies
214
12.3.8 In Vitro Experiments: Cytotoxicity Studies
215
12.4 Data Acquisition, Results, and Interpretation
216
12.4.1 Polymer Characterization
216
12.4.2 Nanoparticle characterization
217
12.4.3 In Vitro Experiments
220
12.5 Discussion and Commentary
222
12.5.1 Particle Size
222
12.5.2 Particle Shape
224
12.5.3 Surface Chemistry
224
12.5.4 Drug Loading
225
12.5.5 Drug Release
226
12.5.6 Active Targeting and Ligand Conjugation
228
12.6 Troubleshooting Tips
230 xi
Contents
12.7 Application Notes
230
12.8 Summary Points
231
Acknowledgments
231
References
231
CHAPTER 13 Porous Silicon Particles for Multistage Delivery 13.1 Introduction
238
13.2 Fabrication of PSPs
245
13.2.1 Materials
245
13.2.2 Methods
247
13.2.3 Characterization
251
13.3 Oxidation and Surface Modification with APTES of PSPs
252
13.3.1 Reagents
252
13.3.2 Methods
252
13.4 Fluorescent Dye Conjugation of PSPs
254
13.4.1 Reagents
254
13.4.2 Methodology
254
13.5 Zeta Potential Measurement
254
13.5.1 Equipment
254
13.5.2 Reagents
254
13.5.3 Methodology
254
13.5.4 Results
255
13.6 Count and Size Analysis of PSPs
255
13.6.1 Materials
255
13.6.2 Methods
255
13.6.3 Data Acquisition, Anticipated Results, and Interpretation
256
13.7 Using Inductively Coupled Plasma–Atomic Emission Spectroscopy 13.7 (ICP-AES) to Determine the Amount of Degraded Silicon in Solution
257
13.7.1 Materials
257
13.7.2 Methods
258
13.7.3 Data Acquisition, Anticipated Results, and Interpretation
258
13.8 Flow Cytometry to Characterize PSP Shape, Size, and 13.8 Fluorescence Intensity
260
13.8.1 Materials
262
13.8.2 Methods
262
13.8.3 Data Acquisition, Anticipated Results, and Interpretation
263
13.9 Loading and Release of Second-Stage NPs from PSPs
264
13.9.1 Loading of NP into PSPs
264
13.9.2 Release of NPs from PSPs
265
13.9.3 Data Acquisition, Anticipated Results, and Interpretation
265
13.10 Discussion and Commentary Acknowledgments xii
237
267 271
Contents
References
271
CHAPTER 14 Mathematical Modeling of Nanoparticle Targeting
275
14.1 Introduction
276
14.2 Molecular/Cellular Scale
277
14.2.1 Methods
277
14.2.2 Data Acquisition, Anticipated Results, and Interpretation
280
14.2.3 Discussion and Commentary
280
14.3 Tissue Scale
282
14.3.1 Methods
282
14.3.2 Data Acquisition, Anticipated Results, and Interpretation
284
14.3.3 Discussion and Commentary
284
14.4 Organism Scale 14.4.1 Methods
285 285
14.4.2 Data Acquisition, Anticipated Results, and Interpretation
286
14.4.3 Discussion and Commentary
287
14.5 Model Validation and Application 14.5.1 Statistical Guidelines 14.6 Summary Points
287 287 289
Acknowledgments
290
References
290
CHAPTER 15 Techniques for the Characterization of Nanoparticle-Bioconjugates
293
15.1 Introduction
294
15.2 Methods
296
15.2.1 Separation-Based Techniques
296
15.2.2 Scattering Techniques
300
15.2.3 Microscopy
308
15.2.4 Spectroscopic
312
15.2.5 Mass Spectroscopy
317
15.2.6 Thermal Techniques
318
15.3 Summary Points Acknowledgments
320
References
321
About the Editors List of Contributors Index
319
333 334 337
xiii
Preface As a research field, nanotechnology is already spinning off numerous stand-alone subdisciplines including bionanotechnology, nanomedicine, nanophotonics, nanoplasmonics, and nanotoxicology. Concomitant with this, the materials, especially the nanoparticles, utilized in these fields are steadily moving into the mainstream and becoming known to researchers pursuing other endeavors including most particularly the myriad areas of biological research. For example, biologists who commonly utilize fluorescent or molecular imaging techniques have heard of quantum dots and are most likely curious if these nanocrystalline fluorophores can further enhance their capabilities. Alternatively, many in the pharmaceutical industry are excited by the potential benefits offered by nanoparticle-mediated drug delivery which may help improve drug-targeting and potentially mitigate systemic toxicity issues. Although there are many more examples, the common thread among all the researchers is the need for a source of methods to synthesize, characterize, biofunctionalize, and apply the nanomaterial that is most suitable to tackle the problem at hand. They may wonder how hard it would be to make and characterize a particular nanoparticle or attach a biomolecule to a nanoparticle. How will they know if the materials they have prepared have the properties they would like? This method-based focus of this book serves to fill this critical gap. Following the cross-disciplinary nature of nanotechnology itself, the contributors of each of the chapters found in this book are drawn from among many different fields including materials science, chemistry, chemical engineering, molecular biology, physics, imaging, and medicine to name but a few. They represent the best scientists and engineers in their respective fields and have been drawn together in this book to provide biomedical scientists and others with the tools and methods they need to pursue the further biological applications of nanoparticles. This book describes many of the methods needed to synthesize, biofunctionalize and apply nanoparticles at bimolecular, cellular, and tissue/organism scales. Chapters 1 through 4 describe the interface between nanoparticles including quantum dots and carbon nanotubes with biomolecules such as peptides and proteins for biosensing and biocatalytic applications. Chapters 5 through 8 describe the use of nanoparticles and nanoassemblies for cellular applications including intracellular trafficking, engineering cell fates, tissue engneering, and cell separations. Chapters 9 through 14 focus on the emerging field of nanomedicine and focus on the use of magnetic, polymeric, metal, and multifunctional nanoparticles as potential therapeutics and imaging agents for devastating diseases including cancer and atherosclerosis. Chapter 15 focuses on the modeling of interactions between nanoparticles and cells and tissues. We have also asked a group at the US FDA to put together a comprehensive review of the available methods
xv
Preface
for characterizing nanoparticle-bioconjugates for inclusion in this book (Chapter 16). The pressing need for the method described in this book is intended to be of use to all who already use or are planning to use nanoparticles in their respective applications. We hope that well-worn copies of this book will find a place in your laboratory. Kaushal Rege and Igor L. Medintz
xvi
CHAPTER
1 Preparation and Characterization of Carbon Nanotube-Protein Conjugates Jonathan S. Dordick,* Dhiral A. Shah, Ravindra C. Pangule, Shyam Sundhar Bale, Prashanth Asuri, Amit Joshi, Akhilesh Banerjee, David Vance, and Ravi S. Kane* Department of Chemical and Biological Engineering, Rensselaer Polytechnic Institute, Troy, NY *Corresponding Authors: Prof. Ravi S. Kane, Department of Chemical and Biological Engineering, Rensselaer Polytechnic Institute, 110 8th Street, Troy, NY 12180, Phone: 518-276-2536, Fax: 518-276-4030, e-mail:
[email protected]; Prof. Jonathan S. Dordick, Department of Chemical and Biological Engineering, Rensselaer Polytechnic Institute, 110 8th Street, Troy, NY 12180, Phone: 518-276-2899, Fax: 518-276-2207, e-mail:
[email protected] Abstract This chapter describes methods of immobilizing proteins on carbon nanotubes, using two different routes—physical adsorption and covalent attachment. We also provide an overview on how such conjugates can be characterized with the help of various techniques, such as Raman, Fourier transform infrared (FT-IR), circular dichroism (CD), and fluorescence spectroscopies, in addition to the standard enzyme kinetic analyses of activity and stability. Both the attachment routes—covalent and noncovalent—could be used to prepare protein conjugates that retained a significant fraction of their native structure and function; furthermore, the protein conjugates were operationally stable, reusable, and functional even under harsh denaturing conditions. These studies therefore corroborate the use of these immobilization methods to engineer functional carbon nanotube-protein hybrids that are highly active and stable.
Key terms
enzyme immobilization carbon nanotubes physical adsorption covalent attachment nanotube solubilization
1
Preparation and Characterization of Carbon Nanotube-Protein Conjugates
1.1 Introduction Nanomaterials, such as carbon nanotubes (CNTs) offer a unique combination of electrical, mechanical, thermal, and optical properties [1] that make them promising materials for various applications ranging from sensing [2] and diagnostics to biotransformations and the cellular delivery of peptides and proteins [3, 4]. For instance, Barone et al. [5] have developed carbon nanotube-glucose oxidase conjugates that can act as glucose sensors. Recently, Dai and coworkers [2] demonstrated the recognition of monoclonal antibodies by a recombinant human antigen immobilized onto carbon nanotubes. Carbon nanotubes have also been used for both biomolecule delivery and targeted therapy. Pantarotto et al. [3] demonstrated that carbon nanotubes functionalized with peptides can penetrate cell membranes of human and murine fibroblasts, and serve as carriers for biomolecule delivery. Dai and coworkers [4] observed internalization of nanotube-protein conjugates in nonadherent human cancer cells as well as adherent cell lines. Kam et al. [6] demonstrated that functionalized CNTs could be used to selectively target cancer cells and destroy them by irradiating CNTs with near-infrared (NIR) light. These studies represent a fraction of the exciting opportunities at the interface of nanotechnology and biotechnology. It is, however, important to interface carbon nanotubes with biomolecules, such as proteins, to realize some of these applications. As a result, various methods of functionalization have been developed recently to functionalize CNTs with proteins. In this chapter, we describe three methods of preparing carbon nanotube-protein conjugates, each of them possessing distinct structural, mechanical, and functional characteristics. Noncovalent attachment is probably the simplest technique for attaching proteins onto carbon nanotubes. The adsorption of proteins onto CNTs is hypothesized to be a result of the attractive hydrophobic interactions between carbon nanotubes and proteins [7]. This method has been found to preserve a significant fraction of the native structural and functional properties of several proteins as well as the physicochemical properties of nanotubes [2, 8–10]. The resulting formulations prevail in the form of aggregates, which can be easily separated from other solution components. However, the limited solubility of these conjugates in water limits their attractiveness for many applications in biotechnology [11, 12]. Nevertheless, such conjugates have been used for biosensing, diagnostics and preparing antifouling nanocomposites films [13]. To overcome the aforementioned limitation of water solubility, Karajanagi et al. have described a simple method that uses proteins to solubilize single-walled carbon nanotubes (SWNTs) in water [14]. Efficient solubilization of SWNTs has previously been achieved using surfactants [15, 16], polymers [17, 18], single stranded DNA [19], peptides [20], and polysaccharides [12, 21]. The direct solubilization of SWNTs using a variety of proteins differing in size and structure is a simple and scalable alternative that enables the generation of individual nanotube solutions. Moreover, proteins are rich in structure and function and have numerous reactive groups, such as hydroxyls, amines, thiols, carboxylic acids, and others, which can be used as orthogonal reactive handles for further functionalization of SWNTs. Finally, Asuri et al. have developed an alternative method of preparing water-soluble conjugates of carbon nanotubes with a broad range of proteins [22]. CNTs can be acid oxidized to produce hydrophilic carboxylic acid and hydroxyl groups along their sidewalls [23, 24], thereby leading to water solubility. Proteins can then be covalently attached to oxidized water-soluble CNTs using carbodiimide activation of the carboxylic 2
1.2
Materials
acid groups. These water-soluble conjugates not only display low diffusional resistance [25] and high activity with stable protein attachment [26], but also have added advantages of high stability and reusability, thereby overcoming the traditional limitations of water-soluble proteins. Though the covalent immobilization of proteins onto CNTs leads to stable protein attachment, the chemical modification of the CNTs surface may compromise the desirable electronic properties of CNTs. Such water-soluble CNT-protein conjugates may find application in fields other than biosensing, for example, biotransformations, biomaterials, medicine, and self-assembled materials. It is, therefore, clear that many methods have been explored to prepare functional nanotube-protein conjugates. Each of these methods possesses its own unique set of advantages and disadvantages, and the best choice of the method depends on the desired end application of the hybrid conjugates.
1.2 Materials Raw and purified HIPCO single-walled carbon nanotubes (SWNTs) (1–1.5 nm diameter, ca. 10 μm length, 10 M) to ensure long term stability in water. 7. Test the pH of the solution. The pH should be maintained around 10 to 12 to ensure QD stability in water. Add dilute NaOH to the sample if the pH is low. 8. Purify the aqueous suspension by using an ultra-free centrifugal filter (50 kDa MW cutoff). 9. Repeat the centrifugal filtration cycle using the above filtration device three to four times and resuspend the QD solution in deionized or ultrafiltered water. 10. If the solution is slightly turbid, verify that the pH is basic. If the pH is 9 or above (ideally 9–10), an additional filtration step may be required. Use a 0.45- m disposable syringe filter to remove any large aggregates that may have formed during the cap exchange process. 11. The water-soluble QD solutions should be refrigerated in tightly sealed vials at 4°C. Over time, samples may begin to dry out, precipitate, and/or sustain bacterial growth. If properly handled, however, samples have been shown to last many months. It is recommended to periodically check samples to ensure they haven’t degraded. Some of these issues can be resolved by simply adding base to the solution (final pH >9), sonicating, or filtering with a 0.45- m disposable syringe filter. If the solution is filtered, an absorption measurement should be taken to re-evaluate the concentration. This is especially important for quantitative studies that rely on accurate estimates of concentration as some QDs are invariably lost during filtration. 12. The quantum yield of the QDs should be measured after transfer into water. It is known that a cap exchange using thiols will result in a decreased quantum yield (compared with the native hydrophobic sample); however, a value of ~5–15% is considered to be reasonable for water-soluble QDs. Higher values can be achieved with better passivating ligands. Visual inspection of brightness is not a good indicator of the quantum yield since brightness is also a function of concentration.
4.3.3
Biomolecule Conjugation
There are several ways to stably attach biomolecules to QDs; however, we will highlight two specific methods here. The first is based on electrostatic self-assembly in which biomolecules having a net charge interact with oppositely charged QDs (e.g., avidin associating with DHLA-capped QDs). This process typically results in rapid and stable self-assembly, even in high salt conditions. If direct protein self-assembly is desired, it is often necessary to engineer a region of charged amino acids (e.g., lysine) that have an affinity for the oppositely charged QD surface [20, 29]. If the biomolecule itself does not have a dense region of charge, an alternative is to use avidin as a bridging molecule [30, 31]. Avidin has sufficient positive charge to associate with negatively charged QDs, 61
Self-Assembled QD-Protein Bioconjugates and Their Use in Fluorescence Resonance Energy Transfer
and additionally allows any biotinylated molecule (e.g., biotin-labeled antibody) to be readily attached to an avidin-coated QD. This is a versatile means of building nanocrystal-biomolecule conjugates using noncovalent self-assembly; however, one obvious drawback is the prohibitive size of these conjugates. The calculated Förster distance for most QD-dye pairs is much too small (~30–60Å) to expect reasonable energy transfer efficiency in these systems unless multiple acceptors are used or the energy can be relayed to distal acceptor dyes via an intermediate dye (a two-step FRET mechanism) [12]. The second attachment method is specific to peptides and proteins that have engineered terminal polyhistidine tags (His-tags, shown in Figure 4.5). Engineered His-tagged proteins are commonly purified using a nickel-nitrilotriacetic acid (Ni-NTA) resin where they are later eluted from the column with an imidazole solution. Histidine has affinity for other metals as well (e.g., Zn, Co, Fe), which makes it a convenient route for self-assembly on metallic and semiconductor nanoparticle surfaces [12, 32].One caveat, however, is that the His-tag must have access the metal surface and not be significantly impeded by a steric barrier due to ligands on the surface. For example, if the nanoparticle is solublized by a polyethylene glycol (PEG) ligand, it is not likely that a His-tagged protein will be able to bind to its surface due to the bulkiness of the ligand. Peptide oligomers bearing terminal His-tags are more likely to bind to these surfaces, but in general the His-tag self-assembly method greatly favors an accessible surface. Extensive work with DHLA has shown it to be a suitable ligand for the His-tag self-assembly strategy suggesting that it does not sterically inhibit attachment or overpopulate nanocrystal binding sites. Other compact thiols have similar features to DHLA and are appropriate candidates for the His-tag procedure. Since the affinity of a His-tagged protein or peptide depends on both surface coverage and size of the ligand, it is difficult to determine a priori which ligands are compatible with this self-assembly procedure. However, most thiols having short carbon chains appear to be appropriate candidates based on previous studies. Either self-assembly procedure provides a general and flexible
HN
HN
N
N
O
O
H2C
O
CH 2
H N
H N
H N
N H CH2
N
N H CH2
O
N NH
OH O
CH2
N NH
NH
Figure 4.5 C-terminal pentahistidine (5×His) tag of a protein or peptide. The multiple imidazole rings on the His residues provide high affinity for metals including Zn. The His-tag is a common method for purifying engineered proteins on Ni-NTA resins.
62
4.3
Methods
platform for attaching biomolecules to the surface of QDs (see Figure 4.6). In this chapter, we will focus on the His-tag attachment method since it is a more direct approach and typically minimizes the overall size of the conjugate, which is critical for FRET applications. Organic dyes are ideal acceptors for QD-based FRET experiments due to their small size, narrow absorption spectra, and versatile labeling schemes. QDs have nearly opposite features making them poor acceptor candidates. In the case of peptides and proteins, the location of the dye can be specified by using reactive derivatives that target certain amino acids. For example, dyes modified with maleimide groups preferentially react with thiol groups found on cysteine (Cys). If the peptide or protein has only one Cys residue in its structure, a dye can be labeled at unique locations within the primary structure. Peptide synthesis and site-directed mutagenesis can therefore be used to control of the position of the dye. Other dye derivatives may be used to label proteins and peptides more generally where the location and number of dyes per molecule are less important. For example, dyes modified with N-hydroxysuccinimide (NHS) ester groups react readily with primary amines found on lysine (Lys). There are many other options available, and the choice of labeling method largely depends on the specific application. In some cases acceptors may be linked to molecules not directly bound to the QD. For example, Medintz et al. reported a nanosensor that generated enhanced fluorescence following the displacement of a sugar analog from QD-bound maltose binding protein (MBP) [12]. In that assay designed to detect maltose in solution, acceptor dye was covalently attached to a cyclic oligosaccharide that was later preassociated to the binding pocket of MBP prior to self-assembly on the nanocrystal surface. Depending on the application, labeling may need to be performed on separate molecules followed by an annealing or binding reaction to establish the desired association. Oligonucleotide labeling is achieved through similar covalent methods that attach dyes directly to bases within the sequence. Intercalating dyes such as ethidium bromide (EtBr) can also be used to label double stranded oligonucleotides that do not require
(a) Avidin b-lgG
His-tagged protein
(b)
Figure 4.6 A schematic showing two examples for attaching biomolecules to negatively charged QD surfaces. (a) Electrostatic self-assembly of avidin and attachment of a biotinylated antibody (b-IgG). (b) Metal affinity coordination using a His-tagged protein for direct attachment.
63
Self-Assembled QD-Protein Bioconjugates and Their Use in Fluorescence Resonance Energy Transfer
covalent attachment. Some of these dyes are highly sensitive to local conditions, which can drastically change their emission properties. EtBr in particular is only weakly fluorescent in water but becomes ~30-fold brighter when bound to DNA [6]. While most acceptor dyes are emissive, quencher dyes with very low quantum yields also have utility where a secondary fluorescence signal is undesirable [33]. However, in most cases the enhanced acceptor emission provides conclusive evidence of a FRET interaction even if the acceptor signal is not directly used to quantify the efficiency. Although measuring changes in donor fluorescence intensity and/or lifetime is preferable for estimating FRET efficiency, in some situations it is difficult or impossible to carry out necessary controls that are needed to evaluate the extent of quenching from the donor signal alone. A generic protocol for generating dye-labeled QD bioconjugates is provided below. While applications will vary, many of the basic features for producing self-assembled bioconjugates are preserved regardless of use. Here we present a method for direct self-assembly of labeled biomolecules (proteins, peptides, DNA, etc.) to a QD surface. Choice of the appropriate biomolecule-QD ratio is usually guided by steric limitations; however, the number acceptors associated to each QD depends on the desired FRET efficiency. In many cases the number of labeled biomolecules per QD is varied in each sample in order to understand the relationship between FRET efficiency and the number of dyes per donor. This information can later be used to understand how biomolecules are arranged on the nanocrystal surface or guide the choice of an optimal starting condition for a biosensing arrangement.
4.3.3.1 Biomolecule Conjugation Protocol 1. Prepare a sterile 1.5 mL microcentrifuge tube by adding 100 L of sodium tetraborate buffer (pH 9.5). Note: While basic buffer solutions are recommended for DHLAcapped QDs, neutral buffers such as HEPES and PBS have been shown to work as well. The stability of these QDs in neutral buffers is probably on the scale of hours to a few days; however, this is often acceptable for many applications. 2. Add to the buffer the desired molar quantities of labeled and unlabeled biomolecules appropriate for binding 20 pmol of QD. Ensure the solution is well-mixed prior to continuing. 3. Add 20 pmol of QD (determine the appropriate volume based on the QD stock concentration). Mix the solution thoroughly with the pipetter. 4. Allow the biomolecules and QDs to self-assemble at room temperature (preferably in the dark) for at least 15 minutes. A longer incubation time may be required to ensure complete association (30–60 minutes). 5. If desired, additional samples should be assembled in separate microcentrifuge tubes in parallel and allowed to incubate. For example, samples having varying numbers of labeled proteins or peptides per QD might be useful. A good practice is to maintain the average total number of biomolecules (labeled and unlabeled) per QD constant. 6. One of the samples should be a control containing a fixed average number of unlabeled biomolecules per QD. This is critical for assessing the influence of biomolecule binding on the quantum yield of the QD and is required for subsequent FRET calculations. 7. Add buffer to the microcentrifuge tube(s) such that the final volume is appropriate for sampling by a spectrofluorimeter or plate reader (typically 0.5–1 mL total 64
4.3
Methods
volume). If a larger volume is required (e.g., 3-mL cuvette), add additional buffer to the cuvette. 8. Fluorescence measurements should be taken soon after the incubation period. The self-assembled bioconjugates are stable for many hours (and perhaps longer) at room temperature; however, for consistency it is recommended to take fluorescence measurements as soon as possible.
4.3.4
Fluorescence Measurements
The spectroscopic measurements required to characterize FRET can be made in either a time-resolved mode or with continuous excitation to induce steady-state fluorescence. While the former is considered to be superior due to the ability to resolve the lifetimes of fluorescent subpopulations, it requires more expensive and sophisticated equipment including pulsed laser sources (on the order of ps or shorter) and time correlated single photon counting (TCSPC) detectors. The latter method is more common and yields similar results. Even within the context of steady-state measurements, fluorescence spectra can be measured in a variety of ways; however, for simplicity we will describe a protocol using a dual monochromator spectrofluorimeter that allows precise control over the excitation wavelength and produces high-resolution spectra. The basic considerations for spectral data acquisition are largely independent of the specific instrumentation used. However, the quantitative nature of the measurements requires careful selection of detector settings, appropriate control samples, background subtraction, and repeatable conditions. FRET measurements often rely on comparisons between samples, which require careful sample preparation and subsequent measurements.
4.3.4.1 Fluorescence Spectra Acquisition Protocol 1. An appropriate excitation wavelength is chosen that efficiently excites the QD donor yet minimally excites the acceptor dye. Consult the QD and dye absorption spectra to identify a suitable excitation wavelength. As an example, a QD population having a peak emission wavelength of 530 nm is a good donor for a Cy3 acceptor. An excitation wavelength of 400 nm will preferentially excite the QD donor while minimally exciting the Cy3 acceptor. Identifying the minimum in the dye absorption spectrum is a reasonable starting point. The broad absorption of QDs makes this a relatively flexible choice as any wavelength shorter than the emission maximum wavelength will suffice. 2. Choose appropriate detector settings (such as slit width and integration time) to ensure a high signal-to-noise ratio and high resolution. These settings should remain unchanged over the course of all measurements. The quantitative nature of these measurements requires consistent sampling conditions. Do not change these settings between measurements. If possible, the spectral resolution should be 2 nm or less to ensure accurate data analysis later. 3. Collect a fluorescence spectrum from a blank sample consisting of aqueous buffer only and subtract this from subsequent spectral measurements. Also record spectra from two separate control samples containing only QD and only dye. The former should contain a concentration exactly equal to the concentration of QDs in subsequent conjugate samples. The QD control is used to quantify the extent of QD 65
Self-Assembled QD-Protein Bioconjugates and Their Use in Fluorescence Resonance Energy Transfer
quenching due to FRET and calculate the energy transfer efficiency. The dye control is used to determine the amount of dye fluorescence generated via direct excitation (in this case, excited at 400 nm). If using the dye signal to corroborate FRET efficiency estimates (not as common), a dye control spectrum should be subtracted from each subsequent QD-dye spectra. Since the concentration of dye can vary by sample, the control sample must match the concentration precisely. 4. Record a comprehensive emission scan that measures the fluorescence intensity from the QD and dye in a given bioconjugate. In the example of 530-nm emitting QDs and Cy3 (using 400-nm excitation), the recorded emission spectrum should extend from 450 to 700 nm. This captures the complete signal from both donor and acceptor and eliminates recording the excitation source entirely. The blank and control spectra should also cover this range. 5. If measuring other samples later in the same vessel, rinse it thoroughly with water and/or buffer before proceeding with the next sample. In particular, a quartz cuvette should be rinsed with water multiple times and lastly with buffer prior to drying and subsequent use. Note: Glass cuvettes will often be stained by fluorescent dyes or proteins with repeated use. Occasionally the cuvette will need to be cleaned with dilute HCl to remove contaminants. This condition can be monitored by periodically measuring the fluorescence signal from a blank sample containing only buffer. The best way to avoid this problem is to clean the vessel immediately following use. 6. Measured spectra are saved in a spreadsheet format (CSV or ASCII) and readied for data analysis.
4.4 Data Analysis and Interpretation In order to produce quantitative information, the measured spectra must be further processed. This can be accomplished in a number of ways ranging from a manual spreadsheet approach to an automated algorithm. In most cases the end goal is to obtain information such as donor-acceptor distance (r) or the number of digested biomolecules following an enzymatic reaction. It is also possible to generate more advanced measurements such as proteolysis rates [16] or protein orientation [15] by including additional information and using proper models. To begin, consider the QD control spectrum where the nanocrystal is patterned with biomolecules lacking fluorescent tags (i.e., unlabeled). If we numerically integrate this spectrum over all measured wavelengths, this provides a value for the starting intensity of QD photoluminescence (PL) to which we will compare other measurements. We expect the QD signal to be reduced when there is an efficient exchange of energy from the donor to acceptor. Next, we need to spectrally separate (deconvolute) the individual signals consisting of the QD donor and dye acceptor from the measured composite spectrum. This is necessary to accurately quantify the individual signal changes in each, and ultimately calculate the FRET efficiency. For this we assume that the measured composite spectrum is the linear combination of two individual signals that have the same shape as the pure QD and dye control samples [34]. Using these known shapes, we can use a simple fitting procedure to identify the magnitude (or proportional contribution) of the donor and acceptor to the composite spectrum. Figure 4.7 shows a deconvolution example where the QD 66
4.4
2.5
× 10
Data Analysis and Interpretation
6
2
Cy3
Photoluminescence (a.u.)
QD Composite 1.5
1
0.5
0 450
500
550 600 Wavelength (nm)
650
700
Figure 4.7 Deconvolution of a composite QD-dye signal into its constituent spectra. In this example, the QD donor is significantly quenched by the Cy3 acceptor. Proper decoupling of donor and acceptor signals is critical to accurately estimating FRET efficiency.
and dye spectra are isolated. The QD best fit (i.e., isolated QD signal) can then be numerically integrated to give an overall intensity that is directly compared with the (dye-free) QD control sample. For the fit to work, we further allow for some slight spectral shifting to occur when the conjugates form. This means that the location of the peak emission from the donor and acceptor can independently translate slightly to the blue or red (usually only a fraction of a nanometer in wavelength): I fit ( λ) = a1 I QD ( λ + b1 ) + a2 I dye ( λ + b2 )
(4.1)
where the various I( ) represent spectra for the QD and dye samples, respectively; a1 and a2 are proportionality constants for the overall intensity of QD and dye, respectively; b1 and b2 are spectral shifts. This linear four-parameter model typically provides an excellent fit to the data (using a least squares regression) and allows us to accurately estimate the FRET efficiency. At this point, the energy transfer efficiency can be measured in several ways; however, the most common and straightforward method is to detect changes in the steady-state emission intensity of the donor fluorophore: E = 1−
I DA = 1 − a1 ID
(4.2)
where E is the observed FRET efficiency, and ID and IDA are the integrated intensities of the donor alone and the donor in the presence of acceptor (i.e., dye), respectively. From the above expression, it follows that a donor (QD) that is completely quenched indicates 100% FRET efficiency. In order to calculate efficiency, we turn to the fit provided in
67
Self-Assembled QD-Protein Bioconjugates and Their Use in Fluorescence Resonance Energy Transfer
equation 1. The parameter a1 is an estimate of the fractional intensity the QD donor maintains in the presence of acceptor. As we see, parameter a1 provides a nearly direct estimate of the FRET efficiency.
4.4.1
Calculating Donor-Acceptor Distances
A suitable deconvolution algorithm provides an accurate estimate of the FRET efficiency that can then be applied to a particular application. For example, if a QD conjugate is formed with multiple acceptors surrounding it, this will have a profound effect on what the overall measured FRET efficiency means. If the goal is to estimate the average donor-acceptor distance, we must use a model that accounts for multiple interactions. In most FRET experiments, there is a pair-wise interaction between a donor and acceptor, but with QDs it is entirely possible to have multiple acceptors per donor. The usual relationship between FRET efficiency and distance for a donor-acceptor pair is: E(r ) =
R06 R + r6
(4.3)
6 0
where R0 is the calculated Förster distance for the interacting pair [6]. Equation (4.3) suggests that there is a precipitous drop in the efficiency as the distance exceeds R0. The Förster distance depends on a multitude of physical parameters and merits some further discussion in the context of QD-based FRET. The Förster distance (in angstroms) is calculated as follows: ⎡9000( ln 10)κ 2 Q D R0 = ⎢ 5 4 ⎣ NA 128π n D
∫
∞ 0
1 6
⎤ FD ( λ)ε A ( λ) λ4 dλ ⎥ ⎦
(4.4)
where κ2 is a dipole orientation factor, QD is the quantum yield of the donor, NA is Avogadro’s number, nD is the refractive of the media between donor and acceptor, FD is the emission spectrum of the donor, and A is the absorption spectrum (expressed as the extinction coefficient) of the acceptor [6]. The integrated quantity is referred to as the overlap integral J(λ) and is related to the spectral overlap between donor and acceptor as depicted for a sample QD-dye FRET pair in Figure 4.8. The tunability of QD emission allows optimization of the spectral overlap and is a convenient way to improve FRET efficiency in a given system. Also note that the integrand in (4.4) is weighted by 4, which means that donor-acceptor pairs with longer wavelength emission and absorption spectra will increase R0, all else constant. Accurate calculation of Förster distance requires careful consideration of each parameter value. When using QDs as donors, the quantum yield should be measured just prior to an experiment to account for changes in the sample over time. This is far more important than for typical dye donors where the quantum yield does not vary significantly. It is also critical to measure the quantum yield of QDs with biomolecules attached to the surface as this can dramatically influence passivation and thus QD. In some cases, the quantum yield can increase three-fold or more with attached proteins [29]. The orientation factor κ2 typically receives little scrutiny as most references suggest using a standard value of 2/3 consistent with random dipole orientation [6]. This is likely a good estimate for QDs due to 2-D polarization at room temperature [35], and the ran68
4.4
1
Data Analysis and Interpretation
Cy3 abs QD PL
Normalized abs, PL
0.8
0.6
0.4
0.2
0 400
Figure 4.8
450
500
550 600 Wavelength (nm)
650
700
Spectral overlap between a 530-nm max emission QD sample and Cy3 dye.
dom orientation of labeled biomolecules on the QD surface. While the orientation cannot be ignored, it is probable that the random circular polarization of the QD and the random positioning of the dye in an ensemble of QD bioconjugates will not exhibit an appreciable orientation effect. Lastly, we consider the refractive index which is most often estimated as 1.4 for biomolecules in aqueous solutions (slightly above the 1.33 value for pure water). Likewise, more refined estimates of nD are rare even though the 3 Förster distance is somewhat sensitive to this parameter (varying as n−2 ). The use of a D QD donor complicates matters further if we consider that the refractive index accounts for the average electrical permittivity of the material between the donor and acceptor dipoles. In the case of most commonly used QDs (type I, core-shell), the exciton is effectively confined within the core and thus the dipole interactions occur through a crystalline shell layer in addition to surface ligands, biomolecules, and the aqueous medium. This likely means that a generic refractive index value of 1.4 cited for biomolecules in water underestimates the true value; however, there has been little effort given to determining a more appropriate estimate of nD in these QD-based systems. If we ignore the possibility of multiple acceptors, the variation of FRET efficiency with distance described by (4.3) will certainly be in error. However, we can apply an intuitive modification, which accounts for multiple acceptors surrounding a central QD donor: E(r , n) =
nR06 nR06 + r 6
(4.5)
where n is the average number of acceptors surrounding each donor [13]. This accounts for multiple energy transfer channels (each having the same transfer rate) between the QD donor and nearby acceptor dyes. Rearranging the above expression for the distance r gives:
69
Self-Assembled QD-Protein Bioconjugates and Their Use in Fluorescence Resonance Energy Transfer
⎡ n(1 − E) ⎤ r = R0 ⎢ ⎥ E ⎣ ⎦
1 6
(4.6)
The efficiency estimate in (4.5) has two further assumptions inherent to it. First, it assumes that every donor has precisely n acceptors surrounding it, which is essentially never true. A self-assembly process will invariably lead to a Poisson distribution of acceptors; however a plurality of conjugates will have n acceptors per QD. In many cases, the potential error due to this distribution is small so long as r > R0 and diminishes quickly in all cases as n increases [16]. Second, the equation assumes that every acceptor is the exact same distance from the donor’s center (as depicted in Figure 4.9). This may be nominally true for some bioconjugate systems, but for others it may be a poor assumption; the validity of the assumption must be individually assessed. Overall, (4.6) is a rather simple yet powerful model for estimating distances for multiple acceptor systems, but only under certain conditions. It is possible to develop more sophisticated models relating distance to efficiency that account for the complications described above, however this is beyond the scope of this chapter.
4.4.2
Calculating Reaction Rates of Surface-Bound Substrates
In some applications, such as monitoring proteolysis, estimates of FRET efficiency can be used to determine the rate at which QD-bound substrate is cleaved by soluble enzymes [16, 17]. This requires a quantitative relationship between efficiency (an observable quantity) and the number of intact substrate molecules that is obtained by generating a standard curve where the average number of dye-labeled substrate molecules per QD is systematically varied. In this case it is not necessary to stipulate a centrosymmetric arrangement of dyes surrounding a central QD donor; here, we are merely considering how the efficiency changes with n and assuming that this ensemble relationship is repeatable for this bioconjugate system. Figure 4.10 shows such a
Figure 4.9 QD bioconjugate with six proteins (three dye-labeled and three unlabeled). This example shows an idealized uniform arrangement of the biomolecules around the central QD where the donor acceptor distance, r, is approximately uniform.
70
4.4
Data Analysis and Interpretation
standard curve for a QD bioconjugate system that has numerous dye-labeled peptides bound to its surface. Fitting the standard curve to a suitable interpolating function (a hyperbola is used in Figure 4.10) provides a means for estimating the average number of intact dye-labeled biomolecules per QD given a measurement of the FRET efficiency. One of these arrangements (of n molecules per QD) is chosen as a starting experimental condition. A maximum change in efficiency following digestion is most desirable (larger slope); from the data in Figure 4.10, a starting point of five dyes per QD is a reasonable choice. The bioconjugate (consisting of five labeled peptides per QD) is then briefly exposed to excess enzyme (~10 minutes) in order to cleave some of the dyes from the QD surface and obtain an estimate of the initial digestion rate (or “velocity”). Following addition of an inhibitor to arrest the reaction, the efficiency is again measured to determine the average number of intact molecules per QD. Because the concentration and reaction time are known, this allows calculation the reaction rate (expressed in mol L-1s-1). The preceding experiment is repeated over a range of substrate concentrations in order to produce a saturation curve that shows the initial reaction rate (velocity) versus substrate concentration. Analysis of this behavior using Michaelis-Menten or similar models reveals information about the mechanism of enzymatic activity and provides relevant kinetic parameters.16 It should be noted that, since the substrate is bound to the surface of a nanocrystal, the general assumptions inherent to homogeneous catalysis models like Michaelis-Menten may not be strictly valid in these systems. Previous work has shown that a homogeneous model appears to fit the data well when analyzing reactions occurring on QD-bound substrates, however the substrate diffuses as a confined bundle rather than as individual molecules which, in a rigorous sense, requires a more complex model.
1.0
Efficiency (E)
0.8
0.6
0.4
0.2
0.0 0 Figure 4.10
5 10 15 Dye-labeled peptides per QD (n)
20
Standard curve relating the average dyes per QD and FRET efficiency.
71
Self-Assembled QD-Protein Bioconjugates and Their Use in Fluorescence Resonance Energy Transfer
4.5 Summary Points •
Bright, stable nanocrystals are critical to the success of these experiments. The quality of the QD preparation influences the ability of biomolecules to bind the surface as well as the efficiency of energy transfer.
•
QDs provide a unique and flexible platform for developing new classes of FRETbased biosensors. The design of these materials takes advantage of the nanocrystal surface area which can accommodate a variety of ligands and multiple biomolecules simultaneously.
•
Suitable spectral deconvolution algorithms are necessary to separate composite steady-state spectra into constituent donor and acceptor signals. The FRET efficiency is best estimated by calculating the intensity loss in the presence of acceptors.
•
Proper distance measurements require appropriate models that describe the FRET efficiency. The validity of these models depends principally on the particular biomolecules used. Additionally, critical physical parameters must be accurately measured or estimated.
4.6 Conclusions QD-based FRET is a powerful spectroscopic technique that has many notable advantages over more traditional donor-acceptor systems composed of organic dyes. In the context of biological studies, the ability to easily generate self-assembled bioconjugates allows a versatile method for detecting enzymatic activity, biomolecule association/dissociation, structural rearrangements, and soluble analytes. In addition to well-known brightness and stability benefits, QDs allow multiple interactions with surface-bound acceptors and can significantly extend the effective interaction distance between donor and acceptors. By tuning surface ligands, QDs can be tailored for stability in a variety of environments and interfaced with nearly any functional biomolecule. While many of the applications outlined in this chapter are carried out in vitro, there is a growing focus on live cell imaging and in vivo studies. The stability and functionality of QDs in these more complex environments is clearly a substantial challenge.
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[4] [5] [6] [7]
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Bruchez, M., Jr., Moronne, M., Gin, P., Weiss, S., and Alivisatos, A. P., “Semiconductor nanocrystals as fluorescent biological labels,” Science, Vol. 281, 1998, pp. 2013-16. Chan, W. C. W., and Nie, S., “Quantum dot bioconjugates for ultrasensitive nonisotopic detection,” Science, Vol. 281, 1998, pp. 2016-18. Michalet, X., Pinaud, F. F., Bentolila, L. A., Tsay, J. M., Doose, S., Li, J. J., Sundaresan, G., Wu, A. M., Gambhir, S. S., and Weiss, S., “Quantum dots for live cells, in vivo imaging, and diagnostics,” Science, Vol. 307, 2005, pp. 538-44. Medintz, I., Uyeda, H., Goldman, E., and Mattoussi, H., “Quantum dot bioconjugates for imaging, labelling and sensing,” Nature Materials, Vol. 4, 2005, pp. 435-46. Klimov, V. I. Semiconductor and Metal Nanocrystals: Synthesis and Electronic and Optical Properties, New York: Marcel Dekker, 2004 Lakowicz, J. R. Principles of Fluorescence Spectroscopy, Singapore: Springer, 2006 Stryer, L., “Fluorescence Energy Transfer as a Spectroscopic Ruler,” Annual Review of Biochemistry, Vol. 47, 1978, pp. 819-46.
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G., and Mattoussi, H., “Fluorescence resonance energy transfer between quantum dot donors and dye-labeled protein acceptors,” Journal of the American Chemical Society, Vol. 126, 2004, pp. 301-10. Clapp, A. R., Medintz, I. L., and Mattoussi, H., “Förster resonance energy transfer investigations using quantum dot fluorophores,” ChemPhysChem, Vol. 7, 2006, pp. 47-57. Medintz, I. L., Konnert, J. H., Clapp, A. R., Stanish, I., Twigg, M. E., Mattoussi, H., Mauro, J. M., and Deschamps, J. R., “A fluorescence resonance energy transfer derived structure of a quantum dot-protein bioconjugate nanoassembly,” Proceedings of the National Academy of Sciences, Vol. 101, 2004, pp. 9612-17. Medintz, I. L., Clapp, A. R., Brunel, F. M., Tiefenbrunn, T., Uyeda, H. T., Chang, E. L., Deschamps, J. R., Dawson, P. E., and Mattoussi, H., “Proteolytic activity monitored by fluorescence resonance energy transfer through quantum-dot-peptide conjugates,” Nature Materials, Vol. 5, 2006, pp. 581-89. Clapp, A. R., Goldman, E. R., Uyeda, H. T., Chang, E. L., Whitley, J. L., and Medintz, I. L., “Monitoring of Enzymatic Proteolysis Using Self-Assembled Quantum Dot-Protein Substrate Sensors,” Journal of Sensors, Vol. 2008, 2008, pp. 10. Murray, C. B., Norris, D. J., and Bawendi, M. G., “Synthesis and characterization of nearly monodisperse CdE (E = sulfur, selenium, tellurium) semiconductor nanocrystallites,” Journal of the American Chemical Society, Vol. 115, 1993, pp. 8706-15. Dabbousi, B. O., Rodriguez-Viejo, J., Mikulec, F. V., Heine, J. R., Mattoussi, H., Ober, R., Jensen, K. F., and Bawendi, M. G., “(CdSe)ZnS Core-Shell Quantum Dots: Synthesis and Optical and Structural Characterization of a Size Series of Highly Luminescent Materials,” Journal of Physical Chemistry B, Vol. 101, 1997, pp. 9463-75. Mattoussi, H., Mauro, J. M., Goldman, E. R., Anderson, G. P., Sundar, V. C., Mikulec, F. V., and Bawendi, M. G., “Self-Assembly of CdSe-ZnS Quantum Dot Bioconjugates Using an Engineered Recombinant Protein,” Journal of the American Chemical Society, Vol. 122, 2000, pp. 12142-50. Peng, Z. A., and Peng, X., “Formation of high-quality CdTe, CdSe, and CdS nanocrystals using CdO as precursor,” Journal of the American Chemical Society, Vol. 123, 2001, pp. 183-84. Clapp, A. R., Goldman, E. R., and Mattoussi, H., “Capping of CdSe-ZnS quantum dots with DHLA and subsequent conjugation with proteins,” Nature Protocols, Vol. 1, 2006, pp. 1258-66. Talapin, D. V., Rogach, A. L., Kornowski, A., Haase, M., and Weller, H., “Highly Luminescent Monodisperse CdSe and CdSe/ZnS Nanocrystals Synthesized in a Hexadecylamine-Trioctylphosphine Oxide-Trioctylphosphine Mixture,” Nano Letters, Vol. 1, 2001, pp. 207-11. Hines, M. A., and Guyot-Sionnest, P., “Synthesis and characterization of strongly luminescing ZnS-Capped CdSe nanocrystals,” Journal of Physical Chemistry, Vol. 100, 1996, pp. 468-71. Dubertret, B., Skourides, P., Norris, D. J., Noireaux, V., Brivanlou, A. H., and Libchaber, A., “In vivo imaging of quantum dots encapsulated in phospholipid micelles,” Science, Vol. 298, 2002, pp. 1759-62. Aldana, J., Wang, Y. A., and Peng, X., “Photochemical Instability of CdSe Nanocrystals Coated by Hydrophilic Thiols,” Journal of the American Chemical Society, Vol. 123, 2001, pp. 8844-50. Uyeda, H. T., Medintz, I. L., Jaiswal, J. K., Simon, S. M., and Mattoussi, H., “Synthesis of compact multidentate ligands to prepare stable hydrophilic quantum dot fluorophores,” Journal of the American Chemical Society, Vol. 127, 2005, pp. 3870-78. Susumu, K., Uyeda, H. T., Medintz, I. L., Pons, T., Delehanty, J. B., and Mattoussi, H., “Enhancing the Stability and Biological Functionalities of Quantum Dots via Compact Multifunctional Ligands,” Journal of the American Chemical Society, Vol. 129, 2007, pp. 13987-96. Mattoussi, H., Mauro, J. M., Goldman, E. R., Green, T. M., Anderson, G. P., Sundar, V. C., and Bawendi, M. G., “Bioconjugation of highly luminescent colloidal CdSe-ZnS quantum dots with an engineered two-domain recombinant protein,” Physica Status Solidi B, Vol. 224, 2001, pp. 277-83.
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Self-Assembled QD-Protein Bioconjugates and Their Use in Fluorescence Resonance Energy Transfer
[30]
[31] [32]
[33]
[34]
[35]
Goldman, E. R., Balighian, E. D., Mattoussi, H., Kuno, M. K., Mauro, J. M., Tran, P. T., and Anderson, G. P., “Avidin: A Natural Bridge for Quantum Dot-Antibody Conjugates,” Journal of the American Chemical Society, Vol. 124, 2002, pp. 6378-82. Jaiswal, J. K., Goldman, E., R., Mattoussi, H., and Simon, S. M., “Use of quantum dots for live cell imaging,” Nature Methods, Vol. 1, 2004, pp. 73-78. Sapsford, K. E., Pons, T., Medintz, I. L., Higashiya, S., Brunel, F. M., Dawson, P. E., and Mattoussi, H., “Kinetics of Metal-Affinity Driven Self-Assembly between Proteins or Peptides and CdSe-ZnS Quantum Dots,” Journal of Physical Chemistry C, Vol. 111, 2007, pp. 11528-38. Clapp, A. R., Medintz, I. L., Uyeda, H. T., Fisher, B. R., Goldman, E. R., Bawendi, M. G., and Mattoussi, H., “Quantum dot-based multiplexed fluorescence resonance energy transfer,” Journal of the American Chemical Society, Vol. 127, 2005, pp. 18212-21. Goldman, E. R., Clapp, A. R., Anderson, G. P., Uyeda, H. T., Mauro, J. M., Medintz, I. L., and Mattoussi, H., “Multiplexed Toxin Analysis Using Four Colors of Quantum Dot Fluororeagents,” Analytical Chemistry, Vol. 76, 2004, pp. 684-88. Chung, I. H., Shimizu, K. T., and Bawendi, M. G., “Room temperature measurements of the 3D orientation of single CdSe quantum dots using polarization microscopy,” Proceedings of the National Academy of Sciences of the United States of America, Vol. 100, 2003, pp. 405-08.
Annotated References Lakowicz, J. R. Principles of Fluorescence Spectroscopy, Singapore: Springer, 2006 An indispensible and comprehensive resource for the field of fluorescence spectroscopy, this book covers the fundamentals of FRET interactions, novel fluorophores, labeling chemistry, and data analysis. Mattoussi, H., Mauro, J. M., Goldman, E. R., Anderson, G. P., Sundar, V. C., Mikulec, F. V., and Bawendi, M. G., “Self-Assembly of CdSe-ZnS Quantum Dot Bioconjugates Using an Engineered Recombinant Protein,” Journal of the American Chemical Society, Vol. 122, 2000, pp. 12142-50. A seminal paper describing the use of DHLA as a capping ligand and electrostatic self-assembly to build stable QD bioconjugates. Medintz, I. L., Clapp, A. R., Mattoussi, H., Goldman, E. R., Fisher, B., and Mauro, J. M., “Self-assembled nanoscale biosensors based on quantum dot FRET donors,” Nature Materials, Vol. 2, 2003, pp. 630-38. An early paper that describes development of a FRET-based nanosensor sensitive for maltose sugar using a His-tagged protein self-assembly procedure. The paper also details an alternate biosensing scheme using a two-step QD FRET process with Cy3 and Cy3.5 dyes. Michalet, X., Pinaud, F. F., Bentolila, L. A., Tsay, J. M., Doose, S., Li, J. J., Sundaresan, G., Wu, A. M., Gambhir, S. S., and Weiss, S., “Quantum dots for live cells, in vivo imaging, and diagnostics,” Science, Vol. 307, 2005, pp. 538-44. An excellent review of the biological applications of quantum dots focusing on live cells. Clapp, A. R., Goldman, E. R., and Mattoussi, H., “Capping of CdSe-ZnS quantum dots with DHLA and subsequent conjugation with proteins,” Nature Protocols, Vol. 1, 2006, pp. 1258-66. A specific protocol for the synthesis of CdSe-ZnS QDs is presented with additional details of a DHLA cap exchange and conjugation of antibodies for a multiplexed detection assay. Clapp, A. R., Medintz, I. L., and Mattoussi, H., “Förster resonance energy transfer investigations using quantum dot fluorophores,” ChemPhysChem, Vol. 7, 2006, pp. 47-57. A recent review of QD-based FRET applications.
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CHAPTER
5 Tracking Single Biomolecules in Live Cells Using Quantum Dot Nanoparticles Katye M. Fichter, Ardalan Ardeshiri, and Tania Q. Vu Department of Biomedical Engineering, Oregon Health and Science University Portland, OR 97239
Abstract A monumental challenge of live-cell imaging is the ability to determine the trafficking behavior of single biomolecules. Current methods allow tracking of populations of molecules, but the subtle behavior of an individual molecule has increased potential to reveal biology’s most well-guarded secrets. Here, we introduce a versatile method for tracking single biomolecules using quantum dot nanoparticles. Because of their intense brightness, photostability, and unique blinking pattern, the identification of single molecules can be observed. An extensive variety of conjugation techniques exist to conjugate quantum dots with biochemical tags. Furthermore, although current research in this area focuses on the tracking of receptors, an untapped well of potential exists for the study of intracellular processes, including, but not limited to: apoptosis, nuclear import, and pathogenic responses. The application of single molecule trafficking has potential for breakthroughs in many biomedical areas such as neurochemistry, cancer research, and drug delivery. Key terms
single particle tracking quantum dots nanotechnology live-cell imaging intracellular trafficking fluorescent bioconjugates
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Tracking Single Biomolecules in Live Cells Using Quantum Dot Nanoparticles
5.1 Introduction The ability to track a single molecule inside a living cell is a highly sought-after technique that may play a significant role in uncovering biology’s most elusive and fundamental mechanisms. Considered a cutting-edge technique, the field of single molecule tracking is currently experiencing rapid growth. Using this technique may allow the researcher to elucidate countless types of cellular mechanisms such as uptake, movement, and fate at a molecular level.[1–5] Moreover, in addition to basic biological applications, initial strides have been made to apply single molecule tracking to biomedical problems. Furthermore, single molecule tracking has been used to visualize and measure the efficacy of therapeutic agents such as nucleic acids, proteins, and other drug compounds, based on dynamic uptake and intracellular fate [6, 7]. Fluorescence live cell imaging has revolutionized biology and medicine since it was introduced during the 1980s [8, 9]. Fluorescence microscopy overcomes the diffraction limits of transmitted light microscopy and provides high contrast imaging of specific populations of cellular biomolecules. However, while the use of fluorescence live cell imaging has rapidly accelerated the understanding of many cellular processes in biology and biomedicine, many challenges still remain. One prominent challenge is to gain finer spatiotemporal resolution of individual molecules, or small groups of molecules. Currently, the averaged behavior of the total population of fluorescently tagged molecules is commonly observed and studied. A second challenge is to track an individual biomolecule undergoing successive stages of its lifetime, such as membrane internalization, membrane recycling, or interorganelle transport. If these two main challenges can be overcome, then the subtle behavior of single biomolecules (currently unattainable because of population averaging) can be examined. Such subtle behaviors may yield critical information able to elucidate fundamental cellular mechanisms currently inaccessible to investigators. Organic fluorescence dyes such as fluorescein and rhodamine were often used in early fluorescence live cell imaging studies. However, investigators faced major problems such as photobleaching and phototoxicity, especially in long-duration live cell experiments. Such experiments routinely exposed these organic fluorophores inside live cells to repetitious pulses of light over long time points and, unlike fixed (dead) cell experiments, the use of fade-resistant mounting media was not possible. More photostable organic dyes, such as the Cyan and Alexa series dyes provided some improvements [10] but were still troublesome in longer-term live cell imaging experiments. The development of “living” enhanced fluorescent proteins (EFPs) [11] was a revolutionary turn in the field of fluorescence live cell imaging [12–15]. EFPs permitted the visualization of proteins transcribed in live cells. However, this technique also suffered from drawbacks. Photobleaching of the EFPs also remained an issue of concern over longer time-lapse experiments. Additionally, large amounts of non-endogenous EFPs can be cytotoxic to cells, as they accumulate in the cytosol and other intracellular compartments [16]. Finally, while some researchers were able to use EFPs to measure the movements of single proteins in cells under short durations (minutes) [17, 18], their relatively dim fluorescence made single EFPs extremely difficult to visualize for practical use. As a result, photobleaching, phototoxicity, and photostability of fluorescent tags continue to be major hurdles in fluorescence live cell imaging. These limit the observation of single molecules or small groups of molecules undergoing cellular processes over longer time points. 76
5.1
Introduction
Fluorescent quantum dot nanoparticles provide promising potential and address the call for a brighter, more photostable fluorophore for following the movements of single molecules. The most commonly used quantum dots consist of a core nanocrystal semiconductor, CdSe, with a ZnS shell that enhances the optical properties of the fluorophore. Although not fully understood, these nanoparticles undergo intermittent nonradiant states, which cause them to “blink” [19]. For biological studies, the QDs are typically covered with a layer of amphiphilic polymer to increase water solubility. The surface of the nanoparticle can then be conjugated to various molecules such as poly(ethyleneglycol) (PEG), and/or chemical handles such as affinity tags and antibodies, making them versatile conjugation reagents [20] (Figure 5.1). Quantum dots have the advantage of being extremely photostable, allowing for hours of imaging without photobleaching [21]. Additionally, they are extremely bright, with extinction coefficients that are about an order of magnitude higher than organic dyes [22]. Finally, because of their blinking patterns and brightness, the detection of single quantum dots are possible [23], allowing researchers to follow the trajectories of single molecules in live cells. Quantum dots were introduced to biological applications as substitutes for organic fluorophores in immunofluorescence-type experiments using fixed samples [24–26]. The benefits of the inherent multifunctional properties of QDs were, and continue to be, successfully demonstrated in a wide range of cellular applications. Quantum dots have emission wavelengths that are dependent on their size, in narrow bandwidths approximately ranging from 511 to 800 nm [27]. This huge selection of colors has opened a floodgate of multicolor experiments that were not previously possible. Additionally QDs have utility as detectors of pH and divalent cations [28], and long luminescent lifetimes similar to that of lanthanides [29]. It was not long before quantum dots were investigated and used in live cell imaging experiments [30, 31]. Most of these studies focused of the diffusion of lipids in membranes [32] as well as membrane receptors on cell surfaces [33, 34].
Chemical handle
PEG
CdSe ZnS
Polymer coating
Figure 5.1
Exemplary structure of a functionalized quantum dot nanoparticle.
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Tracking Single Biomolecules in Live Cells Using Quantum Dot Nanoparticles
Although quantum dots are superior and versatile fluorophores for cellular imaging, there are a few limitations to their usage that should be addressed. A major drawback is the size of the nanoparticles, which can range from approximately 5 to 30 nm in diameter, depending on the size of the QD itself, and the type of conjugation. This has raised questions about the effects of their size on some intracellular trafficking pathways [35]. However, this issue is still in debate and more experiments must be completed to determine what, if any, influence size has on the cell’s natural mechanisms. Furthermore, the “blinking” of quantum dots can cause difficulties in continuity of time-lapse image series. However, software programs exist to regain this continuity based on the location and blinking pattern of the nanoparticle. Furthermore, groups are currently working to synthesize QDs that do not “blink”[36]. In this chapter, we introduce the use of quantum dot conjugates to study the intracellular trafficking of biomolecules. Numerous conjugation techniques are available to attach quantum dots to just about any biomolecule of interest [20, 37]. Covalent bioconjugation schemes have been successfully used for generating QDs that carry ligands, antibodies, affinity tags (such as biotin and hemmaglutinin (HA))[38–40] as well as other chemical handles such as azides [39, 41–43]. Currently the biomolecules most often studied in live cell imaging experiments using QDs are proteins with an extracellular domain, such as receptors, that are capable of extracellular QD-conjugation through ligands [44, 45], antibodies [46], or affinity tags [47]. However, microinjection and liposomal delivery are possible ways to introduce quantum dots to cytosolic or nuclear proteins for subsequent imaging [48–50]. Although the intracellular study of nucleic acids [51, 52] and lipids is within capabilities of QD tracking techniques, they are underrepresented and hold promise for future research. Here, we outline a technique for studying the single-particle trafficking of membrane-expressed receptors inside live neurons. This is illustrated using a protocol for QD-nerve growth factor bioconjugates (QD-NGFs) to image different modes of movement that surface-expressed NGF receptors undergo after activation and internalization (Figure 5.2) [53]. These examples illustrate the application of this technique and the methodology below is offered as a starting point for customization to user-specific applications.
5.2 Materials 5.2.1
Reagents
1. Streptavidin 655 QDots (Invitrogen, Carlsbad, CA) Store at 4°C. Do not freeze. 2. Nerve Growth Factor (β-NGF), (R&D Systems, Minneapolis, MN). 3. NHS-PEO4-biotin (Pierce, Rockford IL). 4. D-MEM (4500 mg/L glucose, 862 mg/L glutamine, and 110 mg/L sodium pyruvate) (Invitrogen, Carlsbad, CA). 5. Cell imaging solution: Add 10 μL B-27 serum-free supplement (Invitrogen, Carlsbad, CA) to 490 μl Hibernate E (Brain Bits, Springfield, IL). This provides enough for 5 samples. Store at 4°C.
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5.3
5.2.2
Methods
Imaging Equipment
1. Fluorescence microscope equipped with a high magnification (x63 or x100) objective. 2. Appropriate fluorescence filter set for QDs (available from Chroma or Semrock). 3. Sensitive digital CCD camera. 4. Appropriate computing software for acquiring digital images and analysis, such as ImageJ.
5.3 Methods 5.3.1
Forming QD Bioconjugates
A number of chemical cross-linkers (i.e., EDC) or biochemical affinity tags (i.e., biotin, HA peptide) can be used to conjugate QDs to biomolecules. Here we form QD-nerve growth factor (QD-NGFs) bioconjugates using biotin-streptavidin conjugation. 1. Prepare biotinylated NGF: Add a 30-fold molar excess of NHS-PEO4-biotin to β-NGF (200 ug/mL). Allow reaction to proceed for 1 hour at room temperature. To purify the conjugates, dialyze the solution (7 kDa MWCO Slide-Alyzer, Pierce, Rockford, IL) against 500 mL of PBS (pH 7.2) for 3 hours. 2. To form QD-NGF bioconjugates, add streptavidin-QDs to biotinylated NGF at a 1:1 molar ratio (typically 1 nM, 100 μL streptavidin-QD: 1 nM, 100 μL biotinylated NGF) in PBS at 4°C for 1 hour. Store at 4°C and use within 24 hours to minimize aggregation.
5.3.2
Treating Cells with QD Bioconjugates
1. Plate neurons on a No. 1 glass coverslip at a density of about 360 cells/mm2. Allow cells to culture for about 1 week before QD treatment. 2. Prior to QD treatment, wash neurons twice with 1 mL D-MEM using a 3-cc syringe. Wash gently to minimize detachment of cells from the culture dish. 3. Treat cells with QD-NGF: Incubate with 10–200 pM QD-NGF at 37°C, 5% CO2 for 15 minutes. Note: QD-NGF concentrations will need to be optimized. Use low concentrations to simplify single particle tracking and to minimize multiple QD interactions. 4. Remove unbound QD-NGF from cells: Gently wash five times with 1 mL D-MEM using a 3-cc syringe. 5. Add imaging media to cells.
5.4 Data Acquisition, Anticipated Results, and Interpretation 5.4.1
Imaging QD-Bound Complexes in Cells
1. Place cells on the stage of an inverted fluorescent microscope. A heating stage can be used to keep cells at 37°C. Cover the culture dish or imaging chamber with a glass coverslip to prevent evaporation of media. 79
Tracking Single Biomolecules in Live Cells Using Quantum Dot Nanoparticles
2. Use a high magnification objective (such as 63- or 100-x) to image QD-NGF complexes on cells. Select cells with an optimal number of QDs (typically 10–20 QDs/field of view). Optimize exposure times to obtain the fastest capture rates with lowest amount of background. Capture a time-series stack of the quantum dots of the cells using a digital CCD camera. Note: If QD blinking is observed, this is a good indication that single or small groups of QDs are present. Bright QD clusters and very slow blinking rates may indicate QD aggregation. If this occurs, check the QD bioconjugates for blinking prior to introduction to cells.
5.4.2
Analysis of the Real-Time QD Dynamics
1. To obtain quantitative and detailed movement information from the time series, single particle tracking can be used to outline the trajectory of QD-bound cellular complexes. Software such as the ImageJ particle tracking plug-in can be used [54, 55]. If a large number of QDs are in view, a single field of view may be segmented and processed in quadrants to increase processing speed. The following parameters can serve as starting points for this plug-in: •
Particle radius w [pixel]:3
•
Intensity r [%]:0.05
•
Cutoff score Ts [-]:0.0
•
Maximum step length L [pixels]:1.0
•
Link range R [frames]:1 or 10.
2. After running the automated QD tracking program, confirm that the tracked QD trajectories are accurate. Compare, frame by frame, the movement of each QD with its assigned trajectory. Discard trajectories that have incorrect position assignments. These artifacts may occur due to multiple QD interactions, disappearance of the QD from the plane of focus, or QD blinking. Blinking may cause some trajectories to lose QDs. This can be minimized by increasing the image capture rate and/or extending the link range in the analysis program. This optimization allows QD blinking to be a useful feature of indicating single or low numbers of QDs in a complex while still retaining accurate trajectory information. 3. Graph the 2-D trajectory of each QD complex. These 2-D trajectories contain qualitative features that can be used to estimate the trafficking mechanisms of the QDs. For example, linear displacements may suggest active transport, whereas diffusive movement in confined locations may suggest containment of QD-complexes in vesicular compartments [53]. Further experiments using pharmacological compounds (e.g., nocodazole to disrupt microtubule-based transport) or immunochemistry techniques can be used verify transport mechanisms [53]. 4. The text file containing positional information for each QD trajectory can be imported into graphing/analysis software such as MATLAB or Excel to obtain quantitative positional/temporal information. Quantitative parameters such as average velocity, length of active motor steps, and mean square displacements can be computed from this positional information.
80
(a)
(b)
(c)
(d)
6.5 6 5.5 5 4.5 4 3.5 3
X position (μm)
Y position (μm)
5.5
0
5
10 Time(s)
15
20
Discussion and Commentary
4.5 4 3.5 3 2.5 2 1.5 1 0
(e)
5
10 Time(s)
15
20
(f)
Figure 5.2 Molecular dynamics of single QD molecules in cortical neurons. (a) DIC image of a cortical neuron (5 days in vitro). Scale bar: 10 μm (b) Corresponding fluorescence image of the cortical neuron in (a) containing bound QD-NGFs. Scale bar: 10 μm (c) Single particle tracking reveals the motion of a QD-NGF complex undergoing linearly-directed active transport. Scale bar: 100 nm. (d) Single particle tracking reveals the motion of a QD-NGF complex undergoing restricted diffusive movement. Scale bar: 100 nm. (e) Position plot of the QD-NGF complex in (c) shows linear translation on the order of a few micrometers. (f) Position plot of the QD-NGF complex in (e), shows restricted movement on the order of ~0.5 μm.
5.5 Discussion and Commentary Quantum dots are very bright nanoparticles that can overcome the drawbacks of photostability inherent in organic dyes. A wide variety of conjugation techniques exist to tag biomolecules to these nanocrystals. Both chemical covalent conjugation and biotin-streptavidin binding are widely used as QD conjugation techniques. Because of their brightness and blinking pattern, individual QDs can be used to record the cellular location and distribution of their biomolecule conjugates. This type of information is very difficult, if not impossible to gain from the use of traditional organic fluorophores, and opens up a tremendous opportunity to increase the knowledge in many areas of cell biology. Many analysis programs, such as ImageJ, contain software to analyze the trajectory of QDs. These trajectories may allow the observation of very distinctive movement that may indicate the transport mechanism. This type of single particle analysis allows the researcher to gain fine details about the locomotion of individual biomolecules inside living cells.
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Tracking Single Biomolecules in Live Cells Using Quantum Dot Nanoparticles
Troubleshooting Table Problem
Solution
Poor cell health
Ensure that cells are in good health before QD incubation. Use an imaging media containing antioxidants to minimize phototoxicity. Use QDs that have a biocompatible coating such as PEG. Streptavidin and amine-functionalized Qdots, available from Invitrogen, contain PEG derivatives. Excess conjugation reagents may cause toxicity. Ensure that these reagents are removed via dialysis or other purification. Image conjugates separately in solution to check for aggregation. The presence of very large or bright particles with very slow blinking rates may indicate an aggregation problem. Increase the concentration of QD-conjugates or duration of QD incubation. Check the ligand to ensure bioactivity. For instance, free NGF should cause increased growth of processes and differentiation. Ensure the ligand is bound to the QD. A coimmunoprecipitation assay may be used to determine this. Do not freeze QDs or QD-conjugates. Freezing may cause QDs to aggregate. Store QD stock solutions and ligand solutions only at high concentrations (100 nM or higher). Dilute QD conjugates into solutions containing 10% BSA. Use 0.1M borate buffer to store QD solutions for extended periods of time. Decrease the number of QDs in the field of view by incubating with a lower concentration of QDs. If QDs are allowed to come near each other, the trajectory data may not be accurate. Use a heated stage and/or objective heater to help stabilize the focal plane and keep QDs from going into and out of focus. Adjust the linkage rate in the ImageJ particle tracking software to account for blinking of QDs. Use a heated stage and objective heater to bring the system to physiological temperature. Ensure cells are in good health. Ensure QDs are not nonspecifically bound to the substrate. Check for specific binding of the ligand. An immunochemistry experiment can be used to determine that QD-ligand-conjugates are binding specifically to the receptor.
Lack of QDs bound to cells
Aggregation of conjugates
Insufficient trajectory data
No QD movement
References [1]
[2] [3] [4] [5] [6] [7] [8] [9]
82
Groc, L., Lafourcade, M., Heine, M., Renner, M., Racine, V., Sibarita, J.-B., Lounis, B., Choquet, D., Cognet, L., “Surface Trafficking of Neurotransmitter Receptor: Comparison between Single-Molecule/Quantum Dot Strategies,” Journal of Neuroscience, Vol. 27, No. 46 2007, pp. 12433–12437. Levi, V., Gratton, E., “Exploring Dynamics in Living Cells by Tracking Single Particles,” Cell Biochemistry and Biophysics, Vol. 48, No. 1 2007, pp. 1–15. Moerner, W.E., “New Directions in Single-Molecule Imaging and Analysis,” Proc. Natl. Acad. Sci. USA, Vol. 104, No. 31 2007, pp. 12596–12602. Greenleaf, W.J., Woodside, M. T., Block, S. M., “High-Resolution Single-Molecule Measurements of Biomolecular Motion,” Annu. Rev. Biophys, Biomol. Struct., Vol. 36, No. 2007, pp. 171–190. Xie, X.S., Trautman, J. K., “Optical Studies of Single Molecules at Room Temperature,” Annu. Rev. Phys. Chem., Vol. 49, No. 1998, pp. 441–480. Babcock, H.P., Zhuang, X., “Using Single Particle Tracking to Study Nuclear Trafficking of Viral Genes,” Biophysical Journal, Vol. 87, No. 4 2004, pp. 2749–2758. Suh, J., Dawson, M., Hanes, J., “Real-Time Multiple-Particle Tracking: Applications to Drug and Gene Delivery,” Adv. Drug Del. Rev., Vol. 57, No. 1 2005, pp. 63–78. Wang, Y.-L., Taylor, D. L., Fluorescence Microscopy of Living Cells in Culture, Part A, San Diego: Academic Press, 1989, p. 328. Wang, Y.-L., Taylor, D. L., Fluorescence Microscopy of Living Cells in Culture, Part. B, San Diego: Academic Press, 1989, p. 498.
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[20]
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CHAPTER
6 Nanoparticles as Biodynamic Substrates for Engineering Cell Fates 1, 2
2†
3
4
María Pía Rossi , Ram I. Sharma , Emily Pawelski , Jean E. Schwarzbauer, and Prabhas V. Moghe2, 3* 1
2
3
New Jersey Center for Biomaterials, Department of Chemical and Biochemical Engineering and Department of Biomedical Engineering, Rutgers University, Piscataway, NJ 08854, 4Department of Molecular Biology, Princeton University, Princeton, NJ 08544 *
Corresponding Author: Professor Prabhas V. Moghe, Director, Rutgers NSF IGERT on Integrated Science & Engineering of Stem Cells, Department of Chemical and Biochemical Engineering, Rutgers University, 599 Taylor Road, Piscataway, NJ 08854, Phone: 732-445-4500 x 6315, Fax: 732-445-3753, e-mail:
[email protected] †
Currently at Orthopedic Biomechanics Laboratory, Universität Zürich, Zürich, Switzerland
Abstract Cell behavior traditionally has been manipulated via biochemical cues. The use of nanoscale biointerfaces is particularly attractive because these could be used to manipulate cell functions at their natural scale, and induce cell behaviors that had not been possible through “bulk” presentation of pharmaceutical or biological factors. One of the advantages that nanomaterials can provide is to mimic the presentation of ligands and peptides in a clustered fashion via nanoparticles. In this work, we utilized albumin nanoparticles functionalized with extracellular matrix ligands to activate and alter cell behavior. Focusing on the effect of biofunctional nanoparticles on skin cells such as keratinocytes and fibroblasts, we show the enhanced migration and matrix assembly by cells. Additionally, we show spatial guidance of cell processes by nanoparticles. Finally, the presentation of functionalized nanoparticles on three-dimensional structures is discussed.
Key terms
biodegradable nanoparticles, cell migration, cell patterning, extracellular matrix assembly, ligand clustering, nanotechnology
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Nanoparticles as Biodynamic Substrates for Engineering Cell Fates
6.1 Introduction The extracellular matrix, commonly abbreviated as the ECM, is vital for many cell functions, as it provides not only biochemical cues that direct cell behavior but also the structural support to cells [1]. The ECM is composed of a variety of proteins and polysaccharides that are secreted locally and assembled into an organized meshwork in close association with the surface of the cells [2]. Once believed to be an inert framework for bolstering the physical conformation of tissues, it is now understood that the ECM plays a large role in different cell functions such as apoptosis, locomotion, morphogenesis and differentiation [3]. These functions are mediated by the interaction of integrins, cellsurface receptors, and ligands associated with the ECM [2]. Integrins are a large and widely studied family of cell surface receptors. Heterodimers comprised of α and β subunits, these surface proteins have been found to bind to many different ECM proteins, such as fibronectin, collagen, vitronectin, and laminin [4]. When these ECM ligands, which commonly contain the Arg-Gly-Asp (or RGD) attachment site [5], bind to integrin receptors, important operations within the cell are triggered, such as the activation of second messenger cascades [6]. Studies have shown that ligand binding of integrin receptors leads to increased lateral mobility, which allows for integrins to cluster and facilitates stronger adhesion at binding sites [6], influencing morphogenesis, apoptosis, and proliferation. Researchers have proposed configurations for artificially clustering ligands to elicit changes in values associated with cell locomotion and adhesion [7]. For example, Maheshwari et al. explored whether the presentation of integrin ligands in a clustered format affects cell adhesion and motility using a monomeric RGD peptide motif derived from the fibronectin integrin binding domain [7]. They presented the low-affinity RGD-derived ligand in a noncell-adhesive polyethylene oxide (PEO) hydrogel background interspersed with polyethylene oxide molecules configured in a star conformation. The ligand was bound to the PEO stars in clusters with an average of 1, 5, or 9 ligands per star molecule. In addition to examining the effects of different numbers of ligands bound to the star molecules, the researchers also examined five different average ligand densities for each cluster size. Not only did the cells with clusters containing the highest number of ligands exhibit increased adhesion, but the clustered presentation also enhanced cell migration speeds. One of the caveats associated with the PEO star model is the inability of PEO macromolecules to allow for control of exact numbers of RGD peptides, relying instead on averages. Additionally, it is difficult to calculate with any degree of precision the distance between RGD peptides. This problem was overcome by the utilization of Au-dot-containing micelles, which can be patterned with a high degree of precision via a substrate-patterning strategy based on self-assembly of diblock copolymer micelles. These micelles are then treated with a gas plasma, which leaves only an extended, hexagonal pattern of nanodots placed in nearly perfect regularity on a noncell-adhesive polymer background. As one nanodot can only anchor one integrin molecule, the regularity of the pattern provided the ability to calculate an optimal density of nanodots for cell adhesion and motility purposes [8]. Using osteoblasts, the researchers determined that the role of spatial distribution of single RGD peptides on cell adhesion and spreading was best observed when the nanodots are closest together, and do not appear to form focal adhesions or be affected in a clustering fashion when distances exceed 58 nm. In a similar study, the same group demonstrated that fibroblast integrin clustering was affected by the spacing of RGD-functionalized gold nanodots [9]. At higher nanodot 86
6.1
Introduction
spacings, fibroblast spreading was considerably compromised, but motility was enhanced. Another promising study by Lipski et al. involved the effect of silica nanoparticles on cells [10]. The biggest advantage of using silica particles is the versatility it affords with respect to functional group and biological moiety modification. Therefore, Lipski et al. hypothesized that silica nanoparticles could be used to show the effects of both texture and chemistry in a decoupled fashion by allowing for greater ease in manipulating nanoroughness. Bolstering Calvalcanti-Adams et al.’s research with respect to a proximity threshold [8, 9], the researchers found that surface features within 50 nm produced the greatest effect on cell functions such as proliferation. Additionally, the researchers also found an effect on focal adhesion complexes and F-actin fiber alignment that was specific to cell type, with nanoroughness decreasing endothelial cell points of contact while having the opposite effects on preosteoblasts. These new forays into the exploration of silica nanoparticle topographies hold promise in affording researchers an easier, more versatile model with which to explore the effects of substrates on cell function. One methodology that has been explored recently to manipulate cell behavior through integrin-ligand binding involves the use of functionalized magnetic nanoparticles. In the beginning of 2008, Mannix, Kumar et al. reported on the use of superparamagnetic beads of 30 nm in diameter that were surface conjugated with N1-2,4-dinitrophenyl-L-lysine:L-lysine (DNP-Lys) to target binding of cell surface IgE–Fc1RI receptor complexes [11]. Upon application of an electromagnetic field, the beads were attracted to each other, forcing the integrins to cluster in response. Furthermore, upon removal of the electromagnetic field, bead:bead attraction was eliminated, and integrin clustering was reversed. Upon this induced clustering of the integrins, an increase in calcium signaling by the cells was measured; removal of the field, and reversal of the clustering, resulted in calcium signaling to cease, demonstrating the reversibility of this technique. Aside from using ligand-conjugated nanoparticles to target cell functions such as migration, adhesion and spreading, these can also be used for targeted drug delivery. For example, in a recent study, Murphy et al. produced organic nanoparticles that were functionalized with a cyclic RGD peptide [12]. Furthermore, doxorubicin, a drug commonly used in cancer therapy, was encapsulated into the nanoparticles. The nanoparticles targeted the αv β3 integrin commonly found in tumor vasculature, and selective apoptosis was observed in the ávâ3-expressing sections of the vasculature. The treatment also demonstrated anti-metastatic activity, and no weight loss was observed as a result, indicating that functionalized nanoparticles are a viable technique for targeted drug delivery with minimal side effects. It is clear that the manipulation of the interaction of cells with ECM ligands could prove to be crucial to control cell behavior for applications in drug delivery, tissue and bioengineering. Nanotechnology could provide the materials necessary to promote the appropriate presentation of ligands and activate or accelerate a diversity of cell functions. The use of nanoparticles is of particular interest for this purpose, as they provide an efficient way to present ligands in a clustered fashion and promote integrin clustering. Many studies so far have involved the use of inorganic materials such as gold or iron oxide, which can provide interesting properties, such as electrical and magnetic conductivity and easy functionalizability. However, the main shortcoming of these materials is 87
Nanoparticles as Biodynamic Substrates for Engineering Cell Fates
that they are not biodegradable, and could have severe cytotoxic effects in vivo. For this reason, some efforts have recently revolved around the use of organic and natural materials to create functionalizable nanoparticles. Albumin nanoparticles are particularly attractive because they are not cytotoxic or antigenic, are biodegradable and can be fabricated by a variety of techniques [13–15]. Therefore, while albumin nanoparticles cannot be intrinsically electrically or magnetically manipulated, they show great potential for use in vivo due to their biocompatibility. While albumin-derived nanoparticles have shown great potential for the delivery of drugs, DNA and other macromolecules [13, 16, 17], in this work, we have concentrated on surface functionalization of the nanoparticles for the presentation of ligands to cells. In our work, we have observed enhanced migration of keratinocytes, protein assembly by fibroblasts and spatial guidance of cell attachment.
6.2 Experimental Design Cell signaling can be regulated by promoting integrin clustering through the presentation of extracellular matrix ligands. We hypothesized that the presentation of an extracellular matrix ligand on nanoparticles could be used as a tool to modify the display, conformation, and/or overall organization of the ligand and engineer ligand clustering at the nanoscale to elicit differential cellular responses. The enhanced mobility of the nanoparticles could promote ligand availability, membrane based ligand/integrin translocation, integrin mobilization and ligand internalization, activating cell signaling cascades to promote or guide cell functions. We selected albumin nanoparticles due to their biocompatibility, biodegradability, and low cytotoxicity, as well as their ability to be derivatized/encapsulated to achieve diverse biological functionality. Throughout our experiments, we used extracellular matrix ligands and proteins as positive controls, specifically, GST-FNIII9-10, the ligand we used to functionalize the nanoparticles, and whole length fibronectin, the extracellular matrix protein from which the ligand was derived. We also used unfunctionalized nanoparticles and substrates blocked with bovine serum albumin or calcein as our negative controls. Samples varied nanoparticle size and ligand concentration, and experiments were always done in triplicate and repeated three times to ensure reproducibility and repeatability.
6.3 Materials 6.3.1
Cell Culture, Fixing, Staining, and Analysis Reagents
Human fibroblasts were cultured in McCoy’s 5A medium (Invitrogen, Chicago, IL) supplemented with 10% fetal bovine serum, 1% penicillin/streptopmycin (Biowhittaker, Walkersville, MD) and 1% L-glutamine (Invitrogen, Chicago, IL). Human keratinocytes were cultured in serum-free keratinocyte growth medium (KGM) (Clonetics, San Diego, CA) containing 0.1 ng/ml epidermal growth factor (EGF), 5 μg/ml insulin, 0.5 μg/ml hydrocortisone, 50 μg/ml gentamicin, 50 ng/ml amphotericin-B, 0.15 mm calcium, and 30 μg/ml bovine pituitary extract (BPE). All cell culture reagents were maintained at 4°C until use except the L-glutamine, which was maintained at −20°C. 88
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Methods
Formaldehyde and Triton X-100 were maintained at room temperature. Rhodamine and Texas Red Phalloidin, for actin staining, were maintained at –20oC. Monoclonal antifibronectin antibody produced in mouse, clone IST-4 for fibronectin assembly staino o ing was maintained at –20 C. Bovine serum albumin for blocking was maintained at 4 C, and calcein from fat-free milk for blocking was maintained at room temperature. B-nitrophenyl N-acetyl b-D glucosaminide was maintained at –20oC and glycine/5mM EDTA was maintained at room temperature; both were used for the cell attachment assay. All these reagents were obtained from Sigma, St. Louis. MO. Secondary antibodies (fluorescein (FITC) and Texas Red AffiniPure Donkey anti-mouse (IgG) were maintained at –80oC obtained from Jackson Immunolabs, Suffolk, U.K.
6.3.2
Nanoparticle Fabrication and Functionalization
Human serum albumin for nanoparticle synthesis (30% w/v, Sigma, St. Louis, MO) was maintained at 4oC. Iodoacetamide (Sigma, St. Louis, MO) was maintained at 4oC. HCl and NaOH (Sigma, St. Louis, MO) were both maintained at room temperature. The BCA protein assay (Pierce, Rockford, IL) was maintained at room temperature, and N-succinimidyl 3-(2-pyridyldithio) propionate (Sigma, St. Louis, MO) was maintained at –20oC. The recombinant fibronectin fragment GST-FNIII9-10 was expressed in E. coli, purified, and stored at –20°C. The reagents for the alkaline phosphatase ELISA were obtained from Sigma and maintained at 4oC.
6.3.3
Microscale Plasma Initiated Patterning
A Sylgard 184 silicone elastomer kit was employed to make micropatterning stamps, and poly(DTE-co-8% PEG1K carbonate) was a courtesy of Prof. J. Kohn (Rutgers University).
6.4 Methods There are several different ways of preparing albumin nanoparticles. Techniques involving emulsification, controlled desolvation, and thermal denaturation all have their individual benefits and shortcomings. In this section, our most commonly used method for albumin nanoparticle fabrication is described. This method was used to fabricate ANPs of sub-100 nm sizes, and was modified based on the original method by Takeoka and colleagues [18, 19].
6.4.1
Albumin Nanoparticle Fabrication
Albumin-derived nanoparticles were synthesized by denaturing albumin monomers and stirring the suspension to generate nanoparticles by self-assembly processes, as diagrammed in Figure 6.1. Specifically, the albumin nanoparticles (ANPs) were synthesized by denaturing filtered (0.22 μm filter, Fisher) human serum albumin (30% w/v, Sigma, St. Louis, MO) diluted to 1% (v/v) in phosphate buffer saline (PBS) in a 250-mL glass beaker through an increase in pH to ~10.6 by the drop-wise addition of 0.1N NaOH. Subsequently, temperature was slowly increased to 80°C with the use of a hot plate. The temperature was maintained on the hot plate at 80°C for 10 minutes and the solution was then rapidly cooled to 25°C by placing the glass beaker in an ice bath. 89
Nanoparticles as Biodynamic Substrates for Engineering Cell Fates
Figure 6.1 Schematic illustrating the synthesis and functionalization process of albumin nanoparticles (ANPs). (a) ANPs are synthesized by denaturing human serum albumin through an increase in pH and temperature and aggregating the albumin into nanoparticles through a decrease in temperature and pH and a final increase in temperature and stirring. The ANPs are then reacted with SPDP for functionalization. (b) Amine-terminated ligands (in the case of this work, GST-FNIII 9-10) are reacted with SPDP and then with DTT to make the ligands reactive for functionalization. (c) The reactive nanoparticles and ligands are incubated together at room temperature for 4 to 6 hours to induce conjugation, yielding functionalized ANPs.
The glass beaker was then removed, and the temperature was maintained at 25°C for 10 minutes. The pH was decreased to ~5.9 by the drop-wise 0.1N HCl and the temperature was increased to 37°C slowly using a hot plate. Upon reaching the temperature, the solution was stirred gently using a magnetic stirrer on the hot plate in order to induce self-assembly of the denatured albumin into the nanoparticles. The solution was allowed to stir to allow for nanoparticles to aggregate and then incubated with 0.1% (w/v) iodoacetamide (Sigma, St. Louis, MO) gently shaking at room temperature for 1 hour, covered with aluminum foil to prevent deactivation of the iodoacetamide, to stop the aggregation reaction. The nanoparticle solution was placed in dialysis tubing (MWCO 100 kDa) and dialyzed at 4°C overnight to remove any unreacted monomeric albumin and filtered again (0.22 μm filter, Fisher Scientific, Pittsburgh, PA) to remove large aggregates. Nanoparticle sizes ranged from ~30–200 nm. This process exploits the use of alkaline conditions to expose the 17 pairs of disulfide bonds and one thiol group at 34 Cys [20] within albumin to the aqueous phase and convert the albumin from the more compact N-form to the B-form. By lowering the pH of the solution, the electrostatic repulsion among the negatively charged groups on the B-form albumin decreases, resulting in aggregation. Albumin was chosen to create a family of various sized nanoparticles because it is (a) easily functionalized with the ligand of interest, (b) it is biodegradable in vivo, (c) it has a high level of biocompatibility, and (d) it allows effective exposure of adhesion ligands against a relatively inert background. To ensure elimination of unaggregated species, ANP preparations were filtered to remove particulates greater than 200 nm and dialyzed to remove monomeric albumin. 90
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Methods
SDS PAGE showed ANP preparations were significantly purified from monomeric albumin following dialysis [21]. By changing the stirring time, it was also possible to produce nanoparticles of different diameters (30–200 nm), which were confirmed with dynamic light scattering (DLS) (data not shown). For DLS, samples were placed in the cuvettes recommended by the manufacturer either in stock or at dilutions of up to 1:50 in PBS and analyzed. Scanning electron microscopy (SEM) confirmed the formation of nanoparticles following the fabrication procedure (data not shown). For SEM analysis, nanoparticles were adsorbed onto an aluminum stub by incubating at 4oC and washing two times with PBS and twice with water to remove unbound nanoparticles and remove salts from the PBS that may dry and obscure imaging. The remaining solution was allowed to dry and then sputter-coated with gold-palladium to prevent charging during imaging. Using a BCA assay kit (Pierce, Rockford, IL), we estimated the protein yield of ANPs in the suspension post-dialysis and filtration (data not shown). The BCA assay was performed according to instructions in the kit; briefly, standards were prepared by diluting a 2-mg/mL stock albumin solution at 2x dilutions and loaded onto a 96 well plate in triplicate in the first three columns. Samples were loaded at different dilutions in triplicate onto the 96 well plate. Working reagent was prepared according to the instructions of the BCA assay kit and loaded into all wells. The plate was shaken gently for 30 seconds and incubated at 37oC for 30 minutes. Color changes in the plate were read using a plate reader at an absorbance of 490 nm.
6.4.2
Albumin Nanoparticle Functionalization
ANPs were functionalized with a truncated fragment of fibronectin that consists of the 9th and 10th type III domains of the protein (GST-FNIII9-10), as shown in Figure 6.1. Fibronectin, a dimeric glycoprotein, is involved in cellular processes such as adhesion, spreading and migration, and can help regulate tissue processes such as wound healing [22]. Both the 9th type III and the 10th type III domains within the selected fibronectin fragment associate with integrin cell surface receptors, and trigger intracellular signaling related to cell spreading, growth, and migration [23–25]. The GST-FNIII9-10 was produced as previously described, by cloning human fibronectin cDNA into a pGEX vector for expression as a glutathione-S-transferase fusion protein [21]. Escherichia coli cells were transformed with the expression plasmid and GST fusion proteins were separated from bacterial lysates by glutathione-sepharose affinity chromatography (GE Healthcare, Piscataway, NJ). The ANPs were functionalized with the GST-FNIII9-10 ligand using bioconjugation and peptide chemistry techniques [21, 26]. Specifically, both GST-FNIII9-10 and ANP concentrations were measured using a BCA protein assay kit (Pierce, Rockford, IL). N-succinimidyl 3-(2-pyridyldithio)propionate (SPDP, Sigma, St. Louis, MO), a heterobifunctional cross-linking agent, can react with the amine groups in the proteins to form an amide linkage at one end while the 2-pyridyldithiol group at the other end can react with sulfhydryl residues to form a disulfide bond. The GST-FNIII9-10 and ANPs were separately reacted with the SPDP for 30 minutes at room temperature at a concentration of 500 μM. The GST-FNIII9-10 was then reacted with dithiothreitol (DTT) for 30 minutes at room temperature at a concentration of 0.5 mg DTT per mg of GST-FNIII9-10 to form a free sulfhydryl group. The reacted protein and nanoparticles were then dialyzed (MWCO 91
Nanoparticles as Biodynamic Substrates for Engineering Cell Fates
o
6kDa) overnight at 4 C and the final concentration of each was again measured by the BCA protein assay. ANP-SPDP and GST-FNIII9-10-SPDP-DTT were then reacted together in a conical tube and shaken lightly for 4 to 6 hours at room temperature for functionalization and dialyzed (MWCO 100kDa) overnight at 4°C to remove any unreacted species. The efficiency of GST-FNIII9-10 conjugation to ANPs was examined using two types of enzyme-linked immunosorbent assays (ELISAs), one specific for the cell binding domain on the recombinant fibronectin fragment and one for the GST tag in the GST-FNIII9-10; additionally, ELISAs for albumin in the nanoparticles were also performed. For the albumin ELISA, albumin standards were loaded in triplicate onto a 96 well plate starting at a concentration of 100 μg/mL and diluting 10x to 0 μg/mL. Functionalized nanoparticle samples were loaded in triplicate at varying concentrations to avoid saturation. For the ligand cell binding domain ELISA, GST-FNIII9-10 standards were loaded onto another 96 well plate at a starting concentration of 10 μg/mL and diluted in 2x dilutions. Functionalized nanoparticle samples were loaded in triplicate at varying concentrations to avoid saturation. The plates were incubated at 4oC overnight and washed five times in DPBS with Ca2+ 2+ and Mg . Plates were then blocked using 13% casein (from fat free milk) for 1 hour at 37°C and washed again. Plates were incubated with primary antibody (monoclonal antialbumin produced in mouse (Sigma, St. Louis, MO)) for albumin at a 1:10,000 dilution in PBS; anti-fibronectin frag, cell attachment fragment, clone 3E3 (Millipore, Billerica, MA) for the ligand at a 1:1,000 dilution in PBS) for 1 hr at 37oC and washed. The two plates were incubated with secondary antibody (anti-mouse IgG-alkaline phosphatase antibody (Sigma, St. Louis, MO)) at a 1:20,000 dilution in PBS), incubated for 1 hour at 37°C and washed. Both plates were then incubated with alkaline phosphatase yellow liquid substrate system (Sigma, St. Louis, MO) until color developed (about 45 minutes at room temperature) and read on a plate reader at 405 nm. To stop the reaction, 3N NaOH can be added. The ELISA for the GST tag in the ligand was done also by incubating standards and samples overnight at 4°C, washing and blocking with casein. The primary antibody used was anti-glutathione-S-transferase antibody produced in rabbit (Sigma, St. Louis, MO) at a 1:2000 dilution in PBS for 1 hour at 37oC. After washing, the plate was incubated with anti-rabbit IgG (whole molecule)–peroxidase antibody produced in goat (Sigma, St. Louis, MO) at a 1:23,000 dilution in PBS for 1 hour at 37°C and washed. Plates were then incubated with Sigma-FAST Fast Red TR/Naphthol AS-MX Tablets (Sigma, St. Louis, MO) for approximately 30 minutes and read at 450 nm. The solution can be stopped with H2SO4. Increasing the amount of ligand in the conjugation reaction resulted in a proportionate increase in the levels of ligand conjugated to the surface of the nanoparticles, and nanoparticles were not saturated with ligand at lower loadings. When we examined ligand density for differentially sized nanoparticles, we found that, for a given initial mass of ligand reacted in the conjugation reaction, the extent of conjugation did not significantly differ for nanoparticles of different sizes, and nanoparticle size did not influence ligand density [27]. By establishing adsorption isotherms, we could differentially control the presentation of the ligand on the ANPs by determining the bulk concentrations of ligand and GST-FNIII9-10-ANPs required to have equivalent net concentrations. ELISAs were performed in parallel in order to confirm that equivalent albumin amounts 92
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Methods
were adsorbed to the substrate irrespective of nanoparticle size and ligand density. Also, the cell binding domain exposure of the GST-FNIII9-10 was determined and normalized by immunosorbance assays [27]. By presenting the ligand on the ANPs, instead of by adsorption directly onto the substrate, the cell binding domain exposure was found to be higher, as can be seen in Figure 6.2.
6.4.3 Albumin Nanoparticle Pattern Creation—Microscale Plasma Initiated Patterning ( PIP) Poly(DTE-co-8% PEG1K carbonate) was selected for patterning studies not only because of its biocompatibility but also because it inhibits both protein and cell attachment [28]. The polymer, in powder form, was diluted in a 98.5% v/v methylene chloride/1.5% v/v methanol solution at 1%w/v. The solutions were then spin-coated at 4,000 RPM onto clean glass coverslips to form thin films of polymer on the glass. An elastomeric poly(dimethylsiloxane) (PDMS) stamp with parallel grooves 10 to 400 μm in width and open at both ends was then utilized to selectively expose areas of the polymer surface to oxygen plasma. These sizes were specifically chosen to guide cell processes, which occur at the microscale, and confirm the functionality of the nanoparticles. The stamp was fabricated by pouring a Sylgard 184 silicone elastomer kit at a base weight to cross-linker weight ratio of 10:1 over lithographically created masters [29]. Therefore, while some of the substrate is protected by the PDMS stamp, the area under the grooves is exposed to the oxygen plasma. The polymer was treated at 50W for 60 to 120 seconds to ensure sufficient functionalization. After plasma treatment, nanoparticle solutions were incubated on the polymer surface overnight at 4oC to ensure binding and adsorption of the nanoparticles onto the substrate. Fibroblasts were seeded at 10,000-20,000 cells/cm2 on the cover slips and incubated at 37oC for 5 to 24 hours. Cells were then fixed and stained for actin as described below.
Figure 6.2 Cell binding domain exposure from GST-FNIII 9-10, measured by ELISA using mouse antifibronectin cell binding domain (Clone 3E3), which recognizes the cell binding domain in human FN. Conjugation of the ligand to ANPs increases exposure of the cell binding domain in comparison to adsorbing the ligand on a substrate directly, most likely through changes in conformation during adsorption and functionalization. Values are the average of 3 experiments performed in triplicates. Error bars represent standard error around the mean.
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6.4.4
Cell Culture
Human fibroblasts up to passage 32 were used for experiments. Fibroblasts were supplemented with serum-free media during and at least 16 hours prior to experimentation. Human keratinocyte passages 2–3 were utilized for all experimental studies. Keratinocytes were supplemented with KGM without BPE and EGF at least 16h prior to the experiment and during the experiment. For all cell experiments, nanoparticles were adsorbed on substrates at 4oC overnight or at 37oC for 1 hour. Unbound nanoparticles were then washed three times with PBS and blocked with 3% bovine serum albumin or casein at 37oC for 1 hour and washed three times with PBS. Cells were then seeded on the substrates.
6.4.5
Keratinocyte Morphology and Migration
To evaluate cytoskeletal organization and morphology, keratinocytes were seeded at a density of 8,400 cells/cm2 and fixed at 5 to 24 hours after seeding. For fixing, cells were washed three times with DPBS with Ca2+ and Mg2+ and fixed with 3.7% formaldehyde in PBS (Sigma, St. Louis, MO) at room temperature for 15 minutes and washed. Cells were then permeabilized with 0.5% Triton X-100 (Sigma, St. Louis, MO) for 15 minutes at room temperature and washed. Keratinocytes were then stained with rhodamine phalloidin (Sigma, St. Louis, MO) at a 1:200 dilution for 30 minutes at room temperature in DPBS with Ca2+ and Mg2+ for visualization of actin. Cell motility kinetics were investigated by seeding isolated keratinocytes at a concentration of 2,800 cells/cm2 on wells coated with either ligand-ANP, ANP, or ligand overnight at 4oC. Wells were then washed three times with PBS and blocked with BSA for 1 hour at 37oC. Cells were incubated in the wells and then transferred to the microscope for motility studies. Four nonoverlapping viewing fields containing single cells were identified in each of the wells and continually imaged at 20x magnification under transmitted light for a total of 10 hours at 10-minute intervals. Images were then analyzed with Image Pro Plus (Media Cybernetics, Silver Springs, MD). For each image, the x and y location of the cell centroid was noted throughout each sequence of images and the mean square displacement of the cell tracks was computed for each time interval: d 2 (t − nΔt ) =
[[x(( n + i)Δt ) − x(iΔt )] + [y(( n + i)Δt ) − y(iΔt ) ]]
N −n 1 ( N − n + 1) ∑ i=0
2
2
Cell motility was quantified by modeling the cell motility behavior as a persistent random walk in an isotropic environment [30]. Briefly, the mean-squared displacement, −t
given by d 2 = 2 S 2 P[t − P(1 − e P )], is a function of time, with two major single cell motility parameters, root mean squared cell speed, S, and directional persistence time, P. The random motility coefficient was determined by d 2 (t ) = 4 μ[t − P(1 − e − t P )]. Experimental data was used to fit the above equations and regress the best estimates for S and P.
6.4.6
Fibroblast Extracellular Matrix Assembly
For extracellular fibronectin assembly, fibroblasts were seeded at a density 35,000 cells/cm2 to ensure enough cell:cell contacts for fibroblasts to produce and assemble 94
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extracellular matrix. Cells were maintained on the nanoparticle-adsorbed substrates for 24 to 48 hours and fixed. For fixing, cells were washed three times with DPBS with Ca2+ 2+ and Mg and fixed with 1% to 2% formaldehyde in water (Sigma, St. Louis, MO) at room temperature for 9 minutes and washed. Cells were then permeabilized with 0.5% Triton X-100 (Sigma, St. Louis, MO) for 15 minutes at room temperature and washed. To stain for fibronectin in the extracellular matrix, samples were then stained with monoclonal anti-fibronectin antibody produced in mouse, clone IST-4 (Sigma, St. Louis, MO) at a 1:100 dilution at 4oC overnight and washed three times with DPBS with Ca2+ and Mg2+. Samples were then stained with Fluorescein (FITC) AffiniPure donkey anti mouse IgG (Jackson Immunolabs, Suffolk, U.K.) at a 1:200 dilution for 2 hours at room temperature and washed. Finally, cells were stained with Texas Red phalloidin (Sigma, St. Louis, MO) at a 1:200 dilution for 30 minutes at room temperature for visualization of actin.
6.4.7
Cell Attachment Assay
To test the degree of cell attachment as a function of GST-FNIII9-10 loading on the nanoparticles, 96 well nontissue culture dishes were coated overnight at 4ºC with either GST- FNIII9-10 at 2.5 to 10 mg/mL or nanoparticles conjugated with increasing levels of GST- FNIII9-10. Wells were washed three times with PBS to remove unbound ligand and blocked with 1% bovine serum albumin (Sigma, St. Louis, MO) for 1 hour at 37ºC. Substrates were washed three times with PBS and cells added at 35,000 cells/well for 90 minutes at 37ºC. Wells were washed twice with PBS and the number of cells adhered to surface determined using the hexosaminidase assay [31]. Briefly, 60 μl of substrate (composed of equal volumes of 0.5% Triton X-100 (Sigma, St. Louis, MO) and 7.5 mM b-nitrophenyl N-acetyl b-D glucosaminide (Sigma, St. Louis, MO) in 0.1 M citrate buffer, pH 5.0) was added to the cells and incubated for 90 minutes at 37ºC. After terminating the reaction by the addition of 90 μl per well of 50 mM glycine/5mM EDTA (Sigma, St. Louis, MO), pH 10.4, the absorbance was read at 405 nm on a plate reader.
6.5 Results In this section, activated response from cell interaction with functionalized albumin nanoparticles is outlined. As discussed in Section 6.3, it has been observed that cell binding domain exposure of the ligand, GST-FNIII9-10, is increased by presenting the ligand on ANPs in comparison to adsorption on a 2-D substrate. Therefore, certain cell responses important during wound healing events have been activated with the use of GST-FNIII9-10-ANPs, including keratinocyte migration, extracellular matrix assembly by fibroblasts, and spatially guided attachment of fibroblasts and human mesenchymal stem cells.
6.5.1
Enhanced Cell Migration
Keratinocyte migration occurs early after the onset of a wound in the skin, in order to close the wound and begin healing events. Previous studies showed that ligands presented on a nanoscale system could lead to integrin clustering and enhanced migration [7], while other studies showed that ligands on dynamic, internalizable submicron particles resulted in enhanced cell migration [32, 33]. Conjugating ligands on biodegradable 95
Nanoparticles as Biodynamic Substrates for Engineering Cell Fates
nanoparticles presented the opportunity to create a nanoscale interface that is dynamic and would allow cells to interact with the matrix via integrins and promoting them to actively bind, sequester, and possibly internalize the ligand-functionalized nanoparticles via specific receptor-mediated processes. Incubation of keratinocytes with GST-FNIII9-10 altered cytoskeletal organization of the cells [21]. The effect on cytoskeletal morphology due to ligand presentation was examined by staining keratinocytes for F-actin, as shown in Figure 6.3. Cells cultured on GST-FNIII9-10 adsorbed substrates appear to have well defined stress fibers, indicating strong attachment to the substrate; however, when the cells were cultured on GST-FNIII9-10-ANPs, they exhibited more filopodial extensions but fewer stress fibers, indicating a more motile phenotype [21]. Keratinocytes cultured on ANP-adsorbed substrates appeared more rounded and exhibited fewer filopodia and a less organized cytoskeletal morphology. Staining for molecular markers of cell adhesion showed an enhanced localization of phosphorylated focal adhesion kinase and paxillin, both components of the focal adhesion complex, in cells seeded on GST-FNIII9-10-adsorbed substrates. Significantly lower expression was seen in cells seeded on GST-FNIII9-10-ANPs, while minimal levels are detected on unfunctionalized ANPs. The process of attachment reflects the earliest response of cells to a surface. By varying the density of the ligand presented to keratinocytes, it was possible to determine whether the presentation of ligand via the ANPs modulated attachment of the cells. Keratinocytes were seeded on surfaces coated with the varying ligand densities (determined by ELISA) either displayed on ANPs or directly adsorbed nontissue culture polystyrene. Equal cell seeding densities were used, and cell attachment was determined by the hexosaminidase assay. While minimal cell attachment was detected for the unfunctionalized ANP conditions, at each ligand density a significant increase in cell attachment was observed when displaying the ligand on the ANPs in comparison to the ligand adsorbed to the substrate [21]. Previous reports of cell adhesion behavior on RGD-containing ligands displayed from surface configurations that induced ligand clustering [7, 34, 35] indicate enhanced cell attachment and adhesion strength. Our system differs, however, in that the cells adhered to the GST-FNIII9-10-ANPs lacked dominant stress fibers and exhibited more filopodial extensions as well as phosphorylated focal adhesion kinase and paxillin,
Figure 6.3 Fluorescent confocal microscopy images of keratinocytes incubated at 8,400 cells/cm2 on Lab-Tek chamber slides with #1 glass coverslip bottoms coated with 10 μg/ml of (a) GST-FNIII9-10, (b) ligand-conjugated ANPs, and (c) unconjugated ANPs. After 5 hours, cells were fixed and stained with fluorescein-phalloidin to visualize the actin cytoskeleton.
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which are components associated with stable focal adhesion [36, 37], whereas keratinocytes on the GST-FNII9-10 controls showed a stationary phenotype and upregulated the expression of focal adhesion proteins. This supports the observation that ligand presentation from the nanoparticles promotes availability of the ligand to cells and improves cell attachment while decreasing cell adhesion strength. The increased filopodia observed on keratinocytes seeded on GST-FNIII9-10-ANPs indicates a more motile phenotype of these cells. To evaluate cellular migration with the use of functionalized nanoparticles, wells were adsorbed with either equivalent concentrations of GST-FNIII9-10, GST-FNIII9-10-ANPs and unfunctionalized ANPs. After 10 hours, the mean squared displacement was larger for cells seeded on GST-FNIII9-10-ANPs compared to GST-FNIII9-10 alone or unfunctionalized ANPs (Figure 6.4) [21]. Other studies previously reported that keratinocyte migration can be governed by the availability of cell binding domains (i.e., type III repeat domains 9 and 10 of fibronectin) [38, 39]. In our studies, when equivalent levels of ligand were presented either conjugated to the nanoparticles or adsorbed to the substrate, increased cell binding domain availability of the ligand by presentation on the ANPs was detected via immunoabsorbance assay and shown in Figure 6.2. Due to potential differences in surface energetics of the GST-FNIII9-10 fragment at the ANP surface [40–43], it is possible that conformational changes in the ligand occurred upon functionalization to the ANPs [44].
6.5.2
Enhanced Extracellular Matrix Assembly
We also applied our ANP system to dermal fibroblast fibronectin matrix assembly. The rigidity of a substrate influences the organization of the actin cytoskeleton and changes fibroblast contractility, which has been shown to play a role in matrix assembly. In the present work, we explored the use of ANPs to alter the rigidity of the substrate at the nanoscale and regulate fibronectin matrix assembly. We hypothesized that, while the
Figure 6.4 Single cell migration was examined on substrates with ligand, nanoparticles, and 2 ligand-conjugated nanoparticles. Keratinocytes were seeded at 2,800 cells/cm for 4 hours prior to image acquisition. Images were taken over 10 hours. Cells were tracked and data was fit to models characterizing cell migration for single-cell migration experiments. Error bars represent standard error around the mean. For each experiment, n = 60. Inset: Random motility coefficients were calculated for cells on various ligand-adsorbed substrates.
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presentation of the extracellular ligand on the nanoparticles would modify the display and overall organization of the ligand, varying the size of the nanoparticles would result in different levels of cytoskeletal tension in fibroblasts, which would lead to different degrees of matrix assembly [27, 45]. To isolate the influence of the GST-FNIII9-10 presentation from ANPs on matrix assembly, experiments were conducted in a serum-free media that supported comparable levels of cell viability as examined by ethidium homodimer labeling [46] (data not shown). Serum-supplemented media may contain lysophosphatidic acids, which would promote matrix assembly by inducing contraction, and soluble fibronectin [47, 48]. In the serum-free environment, cells rely on the clustering of their integrins to the substrate to induce spreading and adhesion by activating focal adhesion kinase and other small Rho GTPases [49]. Cell attachment, adhesion, and cytoskeletal organization are necessary during extracellular matrix assembly. To evaluate the role of ligand presentation in this process, immunofluorescence analysis was performed on fibroblasts seeded on substrates with different densities of GST-FNIII9-10-ANPs [27, 45]. Using particles with the highest ligand density, we observed a correlation between increasing numbers of assembled fibronectin fibrils in the extracellular matrix and size of the nanoparticles [27]. The largest GSTFNIII9-10-ANPs supported fibril formation, which was detectable by 24 hours (Figure 6.5) with more prominent fibronectin matrix fibrils forming after 48 hours. On smaller nanoparticles, or on GST-FNIII9-10 alone adsorbed to the substrate, occasional short fibrils were detected, but most of the fibronectin staining appeared to be intracellular. At lower ligand densities, there was no detectable assembly of fibronectin matrix on GST-FNIII9-10-ANPs or GST-FNIII9-10 adsorbed on the substrate, and negligible amounts of fibronectin were assembled by fibroblasts cultured on unfunctionalized nanoparticles. Using immunochemistry techniques, it was possibly to quantifying fibronectin matrix assembly by fibroblasts after 24 hours in culture at the highest ligand loading compared to unfunctionalized nanoparticles. These results indicated a distinguishable difference in fibronectin assembled between different sized nanoparticles at the highest ligand loading (Figure 6.5). A 22% increase of assembled fibronectin matrix was observed with larger nanoparticles, and quantification of fibril densities showed greater than 10-fold higher number on 100- and 125-nm nanoparticles compared to 30- to 50-nm GST-FNIII9-10-ANPs. These results demonstrate that nanoparticle size is an important factor of fibronectin matrix assembly, and suggest that cell binding events and subsequent cell function can be modulated not just by the nanoscale presentation of the ligand and ligand density, but nanoparticle size as well. To investigate the effect of nanoscale presentation of ligand on initial cell binding and attachment events, equal numbers of fibroblasts were seeded in parallel on substrates of three different ligand densities either adsorbed on the surface or functionalized onto the ANPs. Cell attachment increases with increasing ligand density [27]. For a specific ligand density, the highest cell attachment was seen on the largest sized nanoparticles (~125 nm); significant attachment, but to a lesser degree, was also observed on the 100-nm sized nanoparticles and on the ligand-only substrate, both distinguishable from each other when analyzed by ANOVA. On 30- and 50-nm sized nanoparticles, attachment was low. Reports by our group and others affirm that cell attachment to ligands presented on substrates that promote integrin clustering also
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Figure 6.5 (a) Increased ligand concentration and ANP size promote assembly of fibronectin matrix. Human 2 foreskin fibroblasts were serum-starved overnight and seeded on substrates with ligand at 2.2 μg/cm for 24 hours (left column) or 48 hours (right column). Cells were fixed, permeabilized, and processed for immunofluorescence. Matrix fibrils were visualized using a monoclonal mouse anti-human fibronectin epitope located within domain 5 of the type III repeats, followed by FITC-conjugated secondary antibody. Cells were also stained for F-actin with Texas Red phalloidin. Increased culture time allowed cells seeded on smaller-sized nanoparticles to elongate and organize actin into filaments, yet matrix assembly did not commence, while cells on larger-sized nanoparticles not only developed a more organized cytoskeleton but also assembled more extracellular fibronectin. Images were acquired at 63x, zoom 1. (b) Extent of matrix assembly was quantified using ELISA techniques. Cells were cultured on substrates and lysed to leave behind the assembled matrix. Substrates were then blocked and incubated with anti-human fibronectin for domain 5 of the type III repeats, followed by enzyme linked secondary antibody. Values of ELISA absorbance were derived by back-calculating the concentration based on the standard curve of whole length fibronectin. The star (*) represents statistical significance via ANOVA analysis when experiments were conducted in duplicate three times (p 106 cells/s) magnetic cell separation throughput exist [40] with high levels of recovery [41]. While complexities are introduced with the use of antibodies, including binding affinities, selectivity, specificities, and cost, antibody challenges are similar no matter what separation technologies is used (i.e., FACS, affinity columns, and panning). However, as with most positive attributes of any technology, significant limitations exists, probably most notably the single parameter separation, in contrast with fluorescence-based technology in which greater than 10 simultaneous parameters can be evaluated and used to separate a cell. While it is possible to separate cells based on the number of antibody-magnetic particles conjugated to the cell [42], only one cell surface marker is targeted at a time. An alternative approach is to sequentially perform magnetic cell separations by removing the magnetic particles between separations. Troubleshooting Table Problem
Explanation
Potential Solutions
Red blood cells fail to lysis Failure to achieve high log10 depletion
Old or ineffective lysis buffer Binding affinity of magnetic reagents to antigen to low Nonspecific binding of magnetic reagents to nontargeted cells
Make up fresh buffer Use tetrameric antibody complex
Yield of no magnetically targeted cells low
Use magnetic reagents that have larger magnetic particles and use a flow-through separation system that can wash the nonspecifically bound cells out of column
7.5 Summary Points to Obtain High-Performance, Magnetic Cell Separations 1. Use magnetic reagents, which have high specificity for targeted cells and low nonspecific binding; 2. Have sufficient flow rates through the system, if it is a flow-through magnetically separator, to reduce nonspecific losses in the system; 3. Minimize the number of handling and process steps to minimize nonspecific losses.
Acknowledgments The authors wish to acknowledge the financial support of National Science Foundation (BES-0124897 to J.J.C.), the National Cancer Institute (R01 CA62349 to M.Z., R01 CA97391-01A1 to J.J.C.), and the State of Ohio Third Frontier Program (ODOD 26140000: TECH 07-001).
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CHAPTER
8 Magnetic Nanoparticles for Drug Delivery Susan P. Foy, Andrew Stine, Tapan K. Jain, and Vinod Labhasetwar* Department of Biomedical Engineering, Lerner Research Institute, Cleveland Clinic, Cleveland, OH 44195 *
Corresponding author: Vinod Labhasetwar, Ph.D., Department of Biomedical Engineering/ND-20, Cleveland Clinic, 9500 Euclid Avenue, Cleveland, OH 44195, Phone: 216-445-9364, Fax: 216-444-9198, e-mail:
[email protected] Abstract Magnetic nanoparticles (MNPs) are a multifunctional system capable of being imaged, loaded with drug, and targeted to a particular region of interest though an externally applied magnetic field (MF). The use of an oleic acid (OA) coating between the iron-oxide core and Pluronic in this method allows hydrophobic drugs to be loaded into the MNPs alone or in combination for drug delivery. With a small size of around 200 nm (hydrodynamic diameter), MNPs may diffuse easily across the cell membrane, and their uptake and drug delivery can be further increased by an external MF. All of these aspects help ensure optimal dosing, reducing toxicity to other organs, and increasing drug delivery to a targeted area.
Key terms
cellular uptake doxorubicin drug delivery iron-oxide magnetic nanoparticles water-insoluble drugs
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8.1 Introduction The magnetic properties of MNPs allow them to be imaged via magnetic resonance imaging (MRI) and targeted to a particular region by an externally applied MF. The OA-Pluronic MNPs detailed below have an iron-oxide core surrounded by OA, which encapsulates a hydrophobic drug. The hydrophobic portion (PPO) of a Pluronic coating anchors onto the OA, while the hydrophilic portion of the Pluronic (PEO) forms a corona, allowing aqueous dispersity of the MNPs. Once loaded with drug, MNPs can be targeted to a region of interest through an externally applied MF, and the drug released over a period of weeks [1]. This helps to achieve optimal dosing by reducing the systemic toxicity of the drug, and decreases the likelihood of drug resistance that would result from insufficient drug present [2]. Through MRI imaging, the biodistribution of MNPs and indirectly the drug concentration may be determined. Usually, MNPs use dextran or starch conjugated as an outer layer of the MNP to achieve aqueous dispersity in water [3]. Such methods use complex chemistry, and can only conjugate a limited amount of drugs. The conjugation methods also lead to the drug being released within a few hours [4]. The method described here takes advantage of the OA shell, with the potential to easily incorporate many hydrophobic drugs, alone or in combination. Hydrophilic drugs may also be incorporated into the MNPs after conversion to their hydrophobic form, as detailed in the example below. There are several different steps involved in the synthesis and characterization of the drug-loaded MNPs (Figure 8.1). First, the MNPs are synthesized and coated with OA and Pluronic. The size and charge are determined by a zeta potential/particle sizing system. Separately, the anticancer drug doxorubicin (DOX) used in the example is converted to its hydrophobic form, and loaded into the MNPs through stirring. If a drug is hydrophobic and can be dispersed in acetone or ethanol, this conversion is not necessary. Once the drug is incorporated in the OA shell, the MNPs are collected by magnetic separation and the unentrapped drug washed away. The amount of drug loaded is determined by extracting the drug from the MNPs in a methanol-chloroform mixture, and quantified using a suitable analytical method. The kinetics of drug release are determined using a double diffusion cell, in which one side of the cell contains the drug-loaded MNPs and the other a phosphate buffered saline (PBS)-Tween-80 (0.1%w/v) mixture acting as a sink for the drug. The drug released from the MNPs can be collected at varying time intervals over a period of weeks. A practical application of drug delivery by MNPs is demonstrated in an in vitro antiproliferative activity experiment using the breast cancer cell-line (MCF-7) as an example. In addition, a magnet can be placed below the cells to attract the MNPs into the cells faster than they would be internalized by diffusion. MNPs are used in drug delivery and targeting for a single drug in this method, but they may also be used for delivery of multiple water-insoluble agents. Certain combinations of drugs, such as doxorubicin and paclitaxel, show synergistic anticancer activity. Thus the ability to load multiple drugs in the MNPs could improve therapeutic outcomes when using MNPs as a drug delivery system.
8.2 Experimental Design One of the unique properties of the Pluronic-OA MNP formulation is its ability to achieve sustained release of a drug over a period of several weeks. A custom-designed 124
8.2
48-72 Hours Magnetic Nanoparticle Synthesis
Experimental Design
24 Hours Conversion of DOX·HCl to Hydrophobic DOX
Iron(II) + Iron(III) Ammonium Hydroxide + Oleic Acid Iron-Oxide Core + Pluronic
24 Hours Drug Incorporation and DOX-MNPs separation
Magnet Pluronic
PEO
5-8 Days 5-8 Days DOX-MNPs treatment treatment and and MTS MTS analysis analysis
PPO
Oleic Acid
PEO
Pluronic Drug MNP MCF-7 Cancer Cells
+ DOX-MNPs + MF
Uptake of DOXMNPs under MF
+ DOXMNPs - MF
DOX-MNP uptake
Figure 8.1 Schematic for the synthesis and incorporation of drug in MNPs, and delivery of the MNPs to a breast cancer cell-line.
double diffusion cell is used to determine the kinetics of drug release (Figure 8.2(a)). The double diffusion cell has two chambers, separating the drug-loaded MNPs from the released drug, which freely diffuses across a PVDF membrane. The released drug may be removed and analyzed to determine the rate of drug release (Figure 8.2(b)). An in vitro method is detailed to test the hypothesis that in the presence of a MF, more MNPs and thus drug will be taken up by a cancer cell-line than those entering by diffusion alone. Several controls are necessary in this experiment, including DOX in solution, equivalent amounts of plain MNPs to DOX-MNPs, and the presence or absence of a MF for both the plain and drug-loaded MNPs. In the presence of a MF, the treat125
Magnetic Nanoparticles for Drug Delivery Drug loaded MNPs
Receiver cell
Donor cell PVDF membrane (a)
Drug released (%)
100 80 60 40 20 0 0
5
10
15
20
25
30
Time (Day) (b)
Figure 8.2 The double diffusion cell (a) allows drug released from the MNPs to flow freely across a 0.1-μm PVDF membrane, where it can be collected and quantified to determine the drug release (b). (Figure 8.2(b) is reprinted in part with permission from [5]. Copyright 2005 American Chemical Society.)
ments at varying concentrations are delivered to the cells in a 24 well plate, and each treatment re-seeded in 6 wells in a 96 well plate. Reseeding the cells after incubating with MNPs ensures that the effects of the drug are due to actual uptake of the drug-loaded MNPs and not due to drug being released from the MNPs and then entering the cells.
8.3 Materials 8.3.1
126
Reagents
•
Ammonium hydroxide (5 M; Fisher Scientific).
•
Cancer cell-line (MCF-7 breast cancer cell-line, American Type Culture Collection ATCC, Manassas, VA).
•
Chloroform (HPLC Grade, Fisher Scientific).
•
Doxorubicin hydrochloride (DOX·HCl, Dabur Research Foundation, Ghaziabad, India). Doxorubicin (DOX) is light-sensitive; keep protected from light and store at -20ºC when not in use.
•
Fetal bovine serum (FBS, Invitrogen, Grand Island, NY).
•
Hydrochloric acid (HCl, trace metal grade, Fisher Scientific).
8.3
Materials
•
Iron(II) chloride tetrahydrate (FeCl2•4H2O, 99+%, Fisher Scientific).
•
Iron(III) chloride hexahydrate (FeCl3•6H2O, 99% pure granulated, Fisher Scientific).
•
Medium for cells (depending on cell-line).
•
MEM supplemented with 10% v/v FBS, 100 mg/mL Penicillin-streptomycin, 1% v/v minimum essential amino acids, and 1% v/v sodium pyruvate for MCF-7 cell-line.
•
Methanol (HPLC Grade, 99.9%, Acros, New Jersey).
•
MTS assay (CellTiter 96 AQueous, Promega, Madison, WI).
•
Nitrogen-purged distilled (DI) water.
•
Oleic acid (OA, Fisher Scientific).
•
Phosphate-buffered saline (PBS, pH 7.4).
•
Pluronic (F127, BASF Corporation, Mt. Olive, NJ).
•
Triethylamine (> = 99.5%, Sigma-Aldrich).
•
Trypsin.
•
Tween-80 (Sigma-Aldrich).
8.3.2
Facilities and Equipment
•
Centrifuge tubes (15 mL, 50 mL).
•
Cuvettes (Brookhaven Instruments Corporation).
•
Double diffusion cell.
•
Environ-Shaker (Max Q 4000, Barnstead|Lab-Line).
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Fine-tip transfer pipette (Samco Scientific Corporation, San Fernando, CA).
•
Fluorescence spectrophotometer (LS55, PerkinElmer, Waltham, MA).
•
Inorganic membrane syringe-driven filter (0.02 μm, Anatop 25, Whatman International Ltd, Maidstone, England).
•
Lyophilizer (FreeZone 4.5, Labconco, Kansas City, MO).
•
Magnetic block (4”× 6”; Dura Magnetics, Sylvania, OH).
•
Magnetic stir bars.
•
Magnetic stirring hot plate (PC-420D, Corning).
•
Microcentrifuge tubes (1.5 mL, Fisher Scientific).
•
Neodymium iron boron magnets (12,200 G, Edmund Scientific, Tonawanda, NY).
•
Plates (24 and 96 wells, Becton Dickinson Labware, Franklin Lakes, NJ).
•
Plate reader (BT 2000 Microkinetics Reader; BioTek Instruments, Inc., Winooski, VT).
•
PVDF membrane (0.1 μm, VVLP, Durapore Millipore, Billerica, MA).
•
Syringe (10 mL, HSW/Norm-Ject, Germany).
•
Thermometer (Quartz digi-thermo, Fisher Scientific).
•
Vials (20 mL, Sigma-Aldrich, 40 mL, Fisher Scientific).
•
Water-bath sonicator (FS-30, Fisher Scientific).
•
Zeta Potential/Particle Sizer (NICOMP 380 ZLS, Particle Sizing Systems, Santa Barbara, CA). 127
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8.4 Methods 8.4.1
Synthesis of Magnetic Nanoparticles
Bubble 1L of DI water with nitrogen for 15 minutes the day of use. Use in all the steps involved in the synthesis and formulation of MNPs. Nitrogen is used to prevent oxidation of the MNPs. 1. Prepare a 30-mL aqueous solution of 0.1 M Fe (III), and 15 mL of 0.1 M Fe (II) in water. Combine in a 150-mL beaker, add a magnetic stir bar, and cover with parafilm. Stir the solution at ~400 rpm for 5 minutes on magnetic stirring hot plate in a fume hood. 2. Add 3 mL of 5M ammonium hydroxide drop-wise over 1 minute to coprecipitate magnetite particles. Continue stirring for 20 minutes. 3. Add 100 mg OA (~10 drops with a fine tip transfer pipet), and heat to 80ºC for 30 minutes to evaporate the excess ammonia. Check the temperature of the solution every 5 minutes with a thermometer. Do not let the mixture boil. 4. Remove from heat and allow the solution to cool to room temperature. Separate the MNPs from excess OA by placing a magnet beneath the beaker until the MNPs settle. Pour off the supernatant while holding one magnet on the bottom and an additional magnet on the side just below the spout. Resuspend in 30-mL water. Repeat this wash cycle two more times, adding 45 mL water after the final wash. 5. Add 100-mg Pluronic and stir overnight, with a parafilm cover to prevent oxidation of the MNPs. 6. Remove from stirring and remove the magnetic stir bar by attracting it to the side of the beaker with a magnet on the outside. Rinse the stir bar with solution to allow the excess MNPs to fall back into the solution before removing the magnet completely. 7. Divide the MNPs into two 40-mL vials and tape two neodymium iron boron magnets with opposite polarity on either side of each vial. Allow the MNPs to separate for 4 hours at 4ºC or 7 hours at room temperature. Discard the supernatant and resuspend the MNPs in 20 mL of sterile filtered nitrogen-purged water. Repeat the wash cycle two more times. Recombine the MNPs after the final wash in a known volume of water (~10 mL), transfer to a 15-mL tube and sonicate for 5 minutes in a water-bath sonicator. (Sterile filtered nitrogen-purged water is used in the above step so that the MNPs are not contaminated by any other large particles, and to avoid contamination in cell culture experiments.) 8. Centrifuge for 10 minutes at 1,000 rpm at 4ºC and carefully transfer the supernatant into a new 15-mL tube without disturbing the pellet. The smaller MNPs will remain suspended while larger MNPs will be left behind in the pellet and may be discarded. The MNPs can be stored for 3 months at 4ºC under a nitrogen gas atmosphere. 9. Determine the nanoparticle yield by suspending the MNPs by sonication for 10 minutes, freezing a 1-mL aliquot at -70ºC in a tube of known mass, lyophilizing the sample for 2 days and weighing the dry particles. 10. Clean the beakers and stir bars by rinsing and sonication to remove loose MNPs, then swirling with a small amount of HCl in the fume hood to dissolve any MNPs that remain. After dissolving the excess MNPs, add water in excess to dilute any remaining HCl and discard in the sink.
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8.4
8.4.2
Methods
Physical Characterization of Magnetic Nanoparticles
1. Sonicate the MNPs for 1 minute and suspend a sample at 2-μg/mL in water. (A 2–5 mL suspension is required to carry out the size and zeta potential measurements of the sample.) 2. In a cuvette, sonicate the suspension for 1 minute in a water-bath sonicator. 3. Measure both the size and zeta potential of the sample.
8.4.3
Conversion of DOX HCl
8.4.3.1 Convert DOX•HCl into Water-Insoluble Doxorubicin 1. Weigh out 49 mg DOX•HCl in a small beaker, add 14 mL of 12.5% v/v methanol in chloroform, and sonicate briefly to disperse. 2. Add 60-μl triethylamine and stir for 2–3 hours. (The solution becomes clearer on addition of triethylamine.) 3. Filter the solution into a 20-mL vial (of known mass) with a 10-mL syringe and 0.02-μm filter, then filter an extra 1 mL of methanol-chloroform into the vial to wash any DOX remaining in the filter disc. 4. Cover with aluminum foil with holes in the top and leave in a fume hood to begin evaporation. (Nitrogen gas may be flushed over the surface of the DOX solution in methanol-chloroform to speed up the evaporation process if necessary. Keep the vial in a room temperature water bath if using this method to prevent the mixture from getting cold, which will slow the evaporation process.) 5. Lyophilize the sample to remove residual chloroform and determine its dry weight. Store protected from light at –20ºC for up to 1 year.
8.4.3.2 Doxorubicin in Solution 1. Prepare a concentrated hydrophilic DOX solution by dissolving DOX•HCl into a 66% v/v solution of ethanol in sterile water. 2. To prepare a 4-mg/mL solution, add 1.25 mL of 66% v/v ethanol in water to 5.0 mg of DOX•HCl and vortex until dissolved. Store protected from light at –20ºC for up to 1 year.
8.4.4
Drug Loading and Release Kinetics
8.4.4.1 Drug Loading 1. Suspend the hydrophobic DOX at 5 mg/mL in ethanol and sonicate briefly. Add 600 μL of the DOX solution while stirring to 7 mL of MNPs (4.28 mg/mL) in a 20-mL vial. Continue stirring overnight. (The drug will become incorporated into the OA shell surrounding the MNPs.) 2. Separate the MNPs from the unentrapped drug by placing magnets on either side of the vial, and pouring off the solution when they separate out. Wash the MNPs three times by resuspending the particles in water and separating them out from solution
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with the magnets. Save the first wash to analyze how much drug was not entrapped in the MNPs. Resuspend in a known volume of water (~5 mL) after the final wash.
8.4.4.2 Determine Drug Loading 1. Take a 1-mL aliquot of the DOX-loaded MNPs (DOX-MNPs) in a tube of known mass, freeze at –70ºC, lyophilize, and determine the mass of the dried sample. 2. Add 2 mL of 12.5% v/v methanol in chloroform to the dried sample and leave it to shake for 24 hours at room temperature. (This combination of solvents will extract the drug from the MNPs, with greater solubility than either solvent alone. Twenty-four hours is sufficient time for drug extraction.) 3. Divide the sample into microcentrifuge tubes, centrifuge for 10 minutes at 14,000g in an Eppendorf microcentrifuge, and collect the supernatant. 4. Make two dilutions of the supernatant, one twice as dilute as the first. For example, dilute a 100-μL aliquot of the supernatant to 5 mL in 12.5% v/v methanol-chloroform mixture, and a 100-μL sample to 2.5 mL. (Two dilutions are made to ensure that the DOX measured is in the linear portion of the calibration curve. If the samples are too concentrated, the fluorescence will be quenched and the more dilute sample will increase in fluorescence intensity.) 5. Prepare standards of DOX from 0–10 μg in 12.5% v/v methanol in chloroform. 6. Determine the drug concentration using a fluorescence spectrophotometer at λex = 485 nm and λem = 591 nm. Calculate the amount of drug loaded in the MNPs by comparing the measured value with the standard plot. (To check whether the sample is in the linear portion of the calibration curve, the sample can be diluted and the fluorescence value should decrease proportionally. If the fluorescence intensity does not decrease proportionally, quenching is occurring and the samples need to be further diluted.)
8.4.5
Kinetics of DOX Release from Magnetic Nanoparticles
1. Suspend the DOX-MNPs at 2 mg/mL in PBS buffer containing 0.1% w/v Tween-80. (Tween-80 is used to maintain sink condition so that the drug is released freely from MNPs.) 2. In a double diffusion cell with a 0.1-μm porosity PVDF membrane, fill the donor chamber with 2.5-mL DOX-MNPs and the receiver chamber with 2.5-mL PBS-Tween-80. (The drug released from MNPs will diffuse freely across the membrane but the MNPs will not.) 3. Leave the suspension to shake on rotating shaker at 110 rpm at 37ºC. 4. Completely remove the buffer from the receiving chamber at different time intervals and replace with fresh PBS-Tween-80 buffer. 5. Freeze and lyophilize the collected sample and dissolve in 12.5% v/v methanol in chloroform. 6. Prepare a standard plot (0–100-μg/mL DOX) under identical conditions by dissolving the drug in PBS-Tween-80, freezing at -70ºC, lyophilizing the sample and resuspending it in 12.5% v/v methanol in chloroform. 7. Measure the fluorescence intensity at λex = 485 nm and λem = 591 nm.
130
8.4
Methods
8.4.6 Antiproliferative Activity of Doxorubicin Loaded Magnetic Nanoparticles on MCF-7 Cells 1. Seed MCF-7 cells in a 96 well plate at 3,000 cells/well (100-μL/well) and allow the cells to attach for 24 hours. (Fill the perimeter wells in the 96 well plate with media only—cells are not seeded in these wells because the media will evaporate.) 2. Suspend the desired concentrations of DOX-MNPs, control MNPs, and DOX solution in supplemented MEM. Prepare the different concentrations with respect to the DOX content. 3. Remove media from the 96 well plates and add 100 μL of treatment to each of 6 wells, leaving some wells with plain media as a control. This is considered day 0 of the experiment for the MTS assay. 4. Replace the old media with fresh supplemented media on days 2, 4, and 5 without any additional treatment. For the MTS assay on day 5, add 20 μL of MTS reagent to each well after the media change, and incubate for 90 minutes. Place 90 μL of media from each well in a fresh 96 well plate. Measure the absorbance at 490 nm on a microplate reader. 5. Determine the effect of drug on cell-proliferation by calculating the percent difference in intensity of the treated cells compared to the untreated controls. 8.4.7 Antiproliferative Activity of Doxorubicin Loaded Magnetic Nanoparticles on MCF-7 Cells in the Presence of a Magnetic Field 1. Seed MCF-7 cells in a 24 well plate at 100,000 cells/well (1 mL/well). 2. When cells reach confluency (~2 days after seeding), suspend desired concentrations of DOX-MNPs, control MNPs, and DOX solution in supplemented MEM. 3. Remove media from the 24 well plates and add 1 mL of treatment to each well, treating some wells with plain media as a control. Stack the 24 well plate with cells on an empty 24 well plate on a 4”×6” magnet and return the plates to the incubator for 2 hours. (A 24 well plate is placed between the plate with the cells and the magnet to allow uniform attraction of the MNPs over the cells on the surface of the plate.) 4. Remove the magnet, wash the cells two times with PBS, add 50 μL of trypsin to each well and return the plate to the incubator until cells have detached (2–3 minutes). Add 1 mL of supplemented media to each well to neutralize the trypsin and transfer the contents of each well to separate 15-mL tubes. Centrifuge the cells at 1,000 rpm for 10 minutes at 4ºC and resuspend them in supplemented MEM at 30,000 cells/mL. (The cell count can be determined from one control well and the same volume of media added to all of the 15-mL tubes.) 5. In a 96 well plate, add 100 μL of the cells (30,000 cells/mL) to each of 6 wells and fill the wells in the perimeter with 100-μL media. Prepare two identical plates if running the MTS assay on days 2 and 5. Add the magnet below the 96 well plate (with 24 well plate in between) and return to incubator. This is considered day 0 for the MTS assay. (The perimeter wells will lose media due to evaporation, so cells are not seeded in these wells.) 6. On the second day after seeding the cells, aspirate off the media and add 100 μL of fresh media to each well. For the MTS assay, add 20 μL of MTS reagent to each well and incubate for 90 minutes. Place 90 μL of media from each well in a fresh 96 well plate. Measure the absorbance at 490 nm on a microplate reader. 131
Magnetic Nanoparticles for Drug Delivery
7. Determine the effect of drug on cell-proliferation by calculating the percent difference in intensity of the treated cells compared to the untreated controls. 8. For a 5-day MTS assay, remove and discard the old media from each 96 well plate and add 100 μL of fresh media on days 2 and 4. On day 5, change the media and run the MTS assay (as described above).
8.5 Data Acquisition, Anticipated Results, and Interpretation The approximate MNP yield in one batch is 90 mg. The size of the Pluronic F127 MNPs is about 200 nm as determined by the particle sizing system (Figure 8.3). After the MTS assay, the percent growth can be calculated for each treatment concentration according to the following formula: %Growth =
meanTreatedCells × 100 meanControlCells
A curve can be fit to the data to determine the IC50, or the drug concentration needed to inhibit 50% of the cell growth, using the following equation: y=
A1 − A2
1 + ( x xo )
p
+ A2
Where y = % Growth at drug concentration x, A1 = maximum % Growth, A2 = minimum % Growth, xo = inflection point of the curve, and p = largest absolute value of the slope of the curve. The IC50 for MCF-7 cells varies with the drug used. As an example, drug in solution and in MNPs for two different anticancer drugs, DOX and paclitaxel were tested in MCF-7 cells and their IC50 determined (Table 8.1).
Intensity-Wt gaussian distribution
Relative intensity
100 80 60 40 20 0 50
100
200
500
1000
Diameter (nm) Figure 8.3
132
Particle sizing system output with an average MNP size of about 200 nm.
8.6
Discussion and Commentary
Table 8.1 IC50 of DOX and Paclitaxel in MCF-7 with Drug in Solution and Loaded in MNPs Doxorubicin (ng/mL) 1
IC50
MNPs 795.5
Solution 102.9
Paclitaxel (ng/mL) 2
MNPs 10.6
Solution 9.8
1
Doxorubicin loading in MNPs: 8.2% w/w Paclitaxel loading in MNPs: 9.5% w/w IC50= Drug concentration required to kill 50% of cells
2
8.6 Discussion and Commentary The OA shell for drug loading allows multiple hydrophobic drugs to be loaded alone or in combination in the MNP formulation, targeted by MF, and released over several weeks. In the synthesis of the MNPs, prolonged exposure to an oxygen environment may cause oxidation of the MNPs and decrease their overall magnetic properties. Several small steps can be taken to decrease this risk. All water used in the synthesis, formulation, and washing of MNPs is purged with nitrogen gas to minimize the dissolved oxygen in the aqueous phase and prevent MNP oxidation. The rpm for the stir bars has been suggested, but it is most important to allow the solution to mix at the highest rpm possible without causing violent stirring, which would also introduce oxygen into the solution. In addition, when the MNPs are stored for an extended period of time, flushing nitrogen gas over the solution before covering and storing it will decrease the risk of oxidation and loss of the magnetic properties. The OA coating on MNPs will further protect the iron-oxide core from oxidation. The Pluronic coating used to disperse the MNPs in aqueous solution comes in several formulations with varying lengths in the hydrophilic (PEO) and hydrophobic (PPO) chains. The Pluronic used can alter several properties in the MNPs, including the particle size, surface characteristics like hydrophilicity and zeta potential, and the percentage of drug loading. Pluronic F127 is used in the method described, but there are several Pluronics that show increased circulation time, including Pluronic L64, Pluronic F68, and Pluronic F108. After the Pluronic has been added to the MNP formulation, improper handling of the MNPs may also lead to aggregation. For example, allowing the MNPs to remain in suspension for a long time before washing off excess Pluronic may increase their aggregation and size. Placing a magnet on the vial to attract the MNPs removes the MNPs from free Pluronic in solution and is one step that may prevent aggregation. Washing the MNPs several times removes this free Pluronic, though some may remain in solution regardless of the number of washes. This free Pluronic may also form micelles in solution, and if the critical micelle concentration (CMC) is reached, when the MNPs are loaded with drug, the free Pluronic may solubilize some of the drug and decrease the overall drug loading in the MNPs. After lyophilizing a sample of the MNPs, static charge may develop causing the apparent yield to be negative. This has been observed in particular while wearing latex gloves. Nitrile gloves produce less static charge, though handling the vial with the lyophilized sample with forceps may be the best solution in overcoming this problem. Different magnets used for targeting of the MNPs in the in vitro experiments greatly affect the uniformity of MNP uptake in the cells. The most uniform MF is achieved using a 4”×6” magnetic block from Dura Magnetics. The BioMag 96 well plate separator from 133
Magnetic Nanoparticles for Drug Delivery
Polysciences, Inc. uses 24 small square magnets imbedded in a protective case, which causes great variability in MF to each well and thus MNP uptake by cells. The 1-cm2 neodymium iron boron magnets used in MNP separation can be placed under eight well 2 chamber slides with 1-cm wells. However, this method is much more tedious and requires many chamber slides to achieve the same sample size of just one 24 well plate on a 4”×6” Dura Magnetics magnet. Troubleshooting Table Problem
Explanation
MNPs did not suspend.
MNPs may have boiled during synthesis.
Potential Solutions
During synthesis, do not allow the solution to boil. Smaller, hydrophobic particles Too much OA added during synthesis. Decrease the amount of OA added during don’t disperse with Pluronic. MNP synthesis. MNPs won’t resuspend. Final suspension may have been frozen. Do not freeze or lyophilize MNPs after synthesis. Uniform suspension lost. Excess sonication. Avoid hand held or high-powered sonication. If loaded with drug, sonicate only briefly in a water bath sonicator. Negative MNP yield after Vials used for lyophilization may have Avoid holding the lyophilized sample with lyophilization. developed static charge. nitrile or latex gloves; consider handling with forceps. The spectrophotometer reading The DOX sample measured may be too Dilute the samples until the values meafor DOX is higher when the concentrated; the spectrophotometer may sured are in the linear region (i.e., the amount of drug measured is be reading in the fluorescence quenching sample with twice as much drug will diluted. portion, with a nonlinear or negative have twice the fluorescence). slope.
8.7 Application Notes MNPs have wide application and can be used in drug loading and targeted drug delivery, as a contrast agent in MRI imaging, and to induce hyperthermia with an alternating MF.
8.8 Summary Points
134
•
MNPs with high drug-loading capacity and sustained release properties are developed [1].
•
Any hydrophobic drug or substance should be able to incorporate into the OA portion of the MNPs formulated, alone or in combination. If possible, hydrophilic drugs may be converted to a hydrophobic form and incorporated into the MNPs.
•
Drug loading into an OA shell allows sustained drug delivery over a period of weeks [1].
•
The MNPs may be targeted by a MF, increasing cellular uptake and drug delivery as compared to that achieved by diffusion.
Acknowledgments
Acknowledgments The study reported here is funded by grant R01 EB005822 (to VL) from the National Institute of Biomedical Imaging and Bioengineering of the National Institutes of Health.
References [1]
[2] [3] [4] [5]
Jain, T. K., M. A. Morales, S. K. Sahoo, D. L. Leslie-Pelecky, and V. Labhasetwar, “Iron Oxide Nanoparticles for Sustained Delivery of Anticancer Agents,” Molecular Pharmaceutics, Vol. 2, No. 3, 2005, pp. 194–205. Bezwoda, W. R., “High-Dose Chemotherapy with Hematopoietic Rescue in Breast Cancer: from Theory to Practice,” Cancer Chemotherapy and Pharmacology, Vol. 40, 1997, pp. S79–S87. LaConte, L., N. Nitin, and G. Bao, “Magnetic Nanoparticle Probes,” Materials Today, Vol. 8, No. 5, 2005, pp. 32–38. Alexiou, C., W. Arnold, R. J. Klein, and F. G Parak, et al., “Locoregional Cancer Treatment with Magnetic Drug Targeting,” Cancer Research, Vol. 60, No. 23, 2000, pp. 6641–6648. Jain, T. K., M. A. Morales, and S. K. Sahoo, et al., “Iron Oxide Nanoparticles for Sustained Delivery of Anticancer Agents,” Molecular Pharmaceutics, Vol. 2, No. 3, 2005, pp. 194–205.
135
CHAPTER
9 Imaging and Therapy of Atherosclerotic Lesions with Theranostic Nanoparticles 1
2
1
Jason R. McCarthy , Farouc A. Jaffer , and Ralph Weissleder 1
Center for Systems Biology, Harvard Medical School and Massachusetts General Hospital, 149 13th St., Rm 5406, Charlestown, MA 02129 2 Cardiovascular Research Center, Cardiology Division, Harvard Medical School and Massachusetts General Hospital, 149 13th St., 4th Floor, Charlestown, MA 02129 Corresponding author: Jason R. McCarthy, Center for Systems Biology, Harvard Medical School and Massachusetts General Hospital, 149 13th St., Rm 5406, Charlestown, MA, 02129, Phone: 617-726-5788, Fax: 617-726-5708, e-mail:
[email protected] Abstract Theranostic nanomaterials, or those bearing both therapeutic and diagnostic entities, are capable of simultaneously imaging and treating disease. In this method, we synthesize a novel atherosclerosis-targeted theranostic nanoagent based upon crosslinked iron oxide nanoparticles (CLIO) bearing fluorophores for near infrared fluorescence imaging, and near infrared light activated therapeutic (NILAT) agents for therapy. These macrophage-targeted nanoparticles are applied to the detection and localized therapy of atherosclerotic lesions in apolipoprotein E deficient mice. Intravital fluorescence microscopy enables the longitudinal examination of nanoparticle uptake before and after therapy, thus allowing for an in vivo determination of therapeutic efficacy. While theranostic nanoagents have unique strengths, including the concomitant assessment of the diagnosis and therapy of disease, the field is still in its infancy. This method provides for further study of these capabilities.
Key terms
theranostic nanoagent intravital fluorescence microscopy iron oxide nanoparticles molecular imaging light-activated therapy
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Imaging and Therapy of Atherosclerotic Lesions with Theranostic Nanoparticles
9.1 Introduction The combination of diagnostic and therapeutic entities onto one nanoscaffold enables the simultaneous diagnosis and treatment of disease. These integrated “theranostic” materials offer several potential advantages over conventional therapeutic agents, including feedback mechanisms for the determination of the localization, and therapeutic efficacy of treatments. The success of these agents in this burgeoning field is not fully realized at present, partly due to mismatches between the diagnostic and therapeutic portions, including dosing, which is often significantly higher for treatment. While this field is still in its infancy, it is clear that theranostics offer unique capabilities and their applications require further study. Atherosclerosis is a leading cause of death worldwide, and new treatments are urgently needed to limit myocardial infarction, stroke, and death. An intriguing treatment strategy is localized therapy of inflamed atherosclerotic lesions, as research over the past decade demonstrates that inflammation and the innate immune response participate critically in the initiation and progression of atherosclerosis [1–3]. In particular, macrophages contribute crucially to all stages of atherogenesis, from foam cell and fatty streak formation to the coordination of the inflammatory response leading to plaque rupture and thrombosis in advanced atherosclerotic lesions. Histopathological studies of clinical atheromata further link macrophage content, apoptosis, and macrophage-derived proteinases to rupture-prone plaques [4–8]. Macrophages thus represent an important cellular target for atherosclerosis therapies [1–3, 9–12]. In this method, we investigate the use of theranostic nanoagents in the localization and treatment of atherosclerosis, via the focal ablation of inflammatory macrophages. This is enabled by the affinity of macrophages for polysaccharide-coated iron oxide nanoparticles. Dextran-coated monocrystalline iron oxide nanoparticles (MION) have been utilized clinically to better delineate primary tumors [13], image angiogenesis [14], and detect metasteses [15, 16]. Additionally, these particles have been used to image the inflammatory cells, predominantly macrophages, of human carotid atherosclerotic lesions [17–19]. One of the greatly enabling modifications made to MION has been the crosslinking of the dextran and its amination [20]. The resulting particle, CLIO (cross-linked iron oxide) allows for facile functionalization via amide bond formation. It also offers superb stability under harsh conditions without a change in size, blood half-life, or loss of its dextran coat. Due to the similarities between dextran coated MION and CLIO, it is not surprising that it is also readily internalized by plaque inflammatory cells. In fact, 65% of the cells in experimental atherosclerotic lesions that contain CLIO are macrophages, with plaque smooth muscle and endothelial cells demonstrating modest uptake [21]. Thus, CLIO appears to be a promising scaffold for the development of theranostic nanoagents targeted to inflammatory macrophages in atherosclerosis. While any number of therapeutic moieties can be utilized to bring about a therapeutic effect, many of the options are intrinsically toxic. As these theranostic agents are to be administered systemically, complications may arise, such as extraneous tissue damage. In order to circumvent this, agents that are activate only at the site of interest such as prodrugs or photosensitizers, become attractive options. Near infrared light-activated
1 38
9.2
Experimental Design
therapeutic (NILAT) agents generate cytotoxic singlet oxygen upon illumination with the appropriate wavelength of light. Thus, the action of these agents is focal, being limited only to areas receiving laser irradiation. The combination of highly phototoxic NILAT agents with plaque-targeted optical and magnetic resonance imaging agents may yield theranostic nanoparticles capable of locating and treating inflamed atherosclerotic lesions. In this method we outline the steps necessary to synthesize a macrophage-targeted theranostic nanoparticle with the above capabilities. We begin with the synthesis of CLIO via epichlorohydrin-induced crosslinking of dextran coated MION. The particles are then made optically active by the conjugation of 5-(4-carboxyphenyl)-10,15,20-triphenyl-2,3-dihydroxychlorin (TPC) [22, 23], a potent NILAT agent, and Alexa Fluor 750 (AF750), a near infrared fluorophore, to the particle surface. A second control nanoparticle not bearing a NILAT agent is also synthesized, and is utilized as a nontherapeutic control agent in all experiments. The resulting agents are then injected into atherosclerosis laden apolipoprotein E deficient (apoE-/-) mice and the surgically exposed carotid atheromata are imaged by intravital fluorescence microscopy (IVFM). Importantly, the particles are given 24 hours to localize within the lesions, as they are long circulating, and can accumulate over time via the enhanced permeability and retention effect. Following the survival imaging session, the exposed lesions are irradiated with a 650-nm laser in order to bring about the therapeutic effect of the NILAT agent. The surgical incisions are then sutured and the mice are allowed to recover. At the designated time point, at either 1 or 3 weeks after therapy, the mice are re-injected with the respective active or control agents, which are given 24 hours to localize. Next the surgical incision is reopened, and the mice are reimaged. One of the main advantages of this procedure is that it allows for longitudinal studies of nanoparticle uptake within the atheromata. While this method primarily focuses on the application of theranostic nanomaterials to the diagnosis and therapy of inflamed atherosclerotic lesions, it can easily be applied to any number of diseases, such as cancers and autoimmune diseases. Nanoparticulate scaffolds can be targeted to many different cell or tissue types via conjugation of the appropriate targeting ligands to the particle surface. As well, the therapeutic functionality of the particles can also be chosen in order to elicit the required therapeutic effect. Most importantly, the ablation of specific cell types within the target tissues and resulting therapeutic efficacy can be readily monitored by IVFM.
9.2 Experimental Design In this method we examine the utility of theranostic nanoagents in the focal ablation of macrophages within atherosclerotic lesions. Therapeutic efficacy is determined by longitudinal examination of IVFM data. While the initial uptake and fluorescence of the synthesized theranostic nanoagent in the atherosclerotic lesions is expected to be high, reinjection and imaging of the agent localization at time points 1 and 3 weeks after therapy is expected to reveal minimal uptake of the agents due to the ablation of the inflammatory cells (predominantly macrophages) of treated lesions. The results obtained for the theranostic nanoparticle are compared with those obtained with a control nanoparticle. This control particle does not contain the therapeutic portion, but is 139
Imaging and Therapy of Atherosclerotic Lesions with Theranostic Nanoparticles
otherwise identical to the theranostic agent. While not included in this method, another possible control is the injection of saline into the atherosclerotic mice instead of the nanoparticles. As this method involves in vivo experimentation, it is important to include an adequate number of animals in each cohort to allow for inherent variability. We recommend a minimum of five animals per cohort. In addition there is inherent variability between nanoparticle preparations, thus it is important to synthesize an appropriate amount of each agent. The initial and followup injections must be from the same batch of particles, as the fluorescence of the agents is matched, and the data is obtained as a comparison of the pre- and post-treatment fluorescence intensity.
9.3 Materials 9.3.1 •
MION-47 (Center for Molecular Imaging Research, Massachusetts General Hospital);
•
5M NaOH (Fisher, cat. no. S256-500);
•
Epichlorohydrin (Fluka, cat. no. 45340);
•
30% (wt/vol) ammonium hydroxide (Aldrich, cat. no. 221228);
•
Citrate buffer: 0.025M sodium citrate pH 8 (Fisher, cat. no. S279-500);
•
6N Hydrochloric acid (Fisher, cat. no. SA56-1);
•
Hydrogen peroxide 3% (wt/vol) (Aldrich, cat. no. 323381);
•
Iron atomic spectroscopy standard concentrate, 1.00g Fe (Fluka, cat. no. 02679);
•
Phosphate buffered saline without calcium and magnesium, 10x solution (Fisher, cat. no. BP399-500);
•
Phosphate buffered saline without calcium and magnesium, 1x solution (Fisher, cat. no. BP2438-4);
•
Alexa Fluor 750 (Invitrogen, cat. no. A-20011);
•
Dimethylsulfoxide (Fisher, cat. no. D128-500);
•
Succinimidyl ester of 3-dihydroxychlorin (TPC) [23];
•
Fluorescein (Acros, cat. no. AC11924-0250 );
•
Fluorescein isothiocyanate-dextran (Sigma, cat. no. FD2000S).
9.3.2
140
Reagents
5-(4-carboxyphenyl)-10,15,20-triphenyl-2,
Facilities/Equipment
•
QuixStand Benchtop System (A/G Technology) with cartridge UFP-100-E-5A (100 kDa NMWC);
•
Amicon Ultra 15 (Fisher, cat. No. UFC9 100 08, 100 kDa NMWC);
•
Cary 50 UV-visible spectrophotometer (Varian);
•
Cary Eclipse fluorescence spectrophotometer (Varian);
•
Sephadex G-25 (Aldrich, cat no. G25150);
9.4
Methods
•
Prototypical laser scanning fluorescence microscope (Olympus Corporation, Japan);
•
650-nm diode laser, 250 mW (B&W Tech, BWF1).
9.3.3
Animal Model
All animal studies should be performed in accordance with relevant guidelines and regulations. Apolipoprotein E deficient (apoE–/–) mice are employed as a well-characterized experimental model of atherosclerosis. Female apoE–/– mice (Jackson Laboratory, Bar Harbor, Maine) are placed on an atherogenic diet (21% fat, 0.15% cholesterol, Harlan Teklad, Madison, Wisconsin) from 10 weeks until 28 weeks of age. After the initial imaging session, animals are placed on a regular chow diet to limit the formation of new lesions adjacent to those undergoing treatment.
9.3.4
Alternate Reagents and Equipment
MION-47. This reagent is available to the scientific community through our laboratory (http://cmir.mgh.harvard.edu). A number of alternate, dextran-coated iron oxide nanoparticles can be considered but have not been comparatively tested by the authors. AF750. While AF750 has been utilized by the authors for this method, a number of other fluorophores that absorb and emit in the same regime can be considered, such as Cy 7 (GE Healthcare), VivoTag 750 (VisEn Medical), and IRDye 800 (LI-COR). Fluorescent dyes, such as Cy 5.5 cannot be utilized, as they overlap with the absorption spectra of the near-infrared light activated therapeutic moieties. TPC. TPC is utilized in this method due to its availability to the authors, as it is synthesized by our laboratory, and its utility as a highly phototoxic agent. The UV-visible absorption profile is also optimized for use in tandem with longer wavelength fluorescent dyes, such as AF750, for the development of theranostic nanoagents. Other photosensitizers, such as chlorin e6 can be used, although their efficacy has not been tested by us. Prototypical laser scanning fluorescence microscope. The prototypical laser scanning fluorescence microscope used in this method was developed by Olympus. Quantitative imaging can also be done on a number of other fluorescence based systems, including those used in fluorescence molecular tomography and multiphoton microscopy.
9.4 Methods 9.4.1
Synthesis of Theranostic Nanoparticles
1. Aminated crosslinked iron oxide nanoparticles (CLIO-NH2) are synthesized by the epichlorohydrin-mediated crosslinking of dextran coated monocrystalline iron oxide nanoparticles (MION). To MION-47 is added aqueous sodium hydroxide (5M) in a ratio of 5 volumes NaOH to 3 volumes MION-47 over the course of 15 minutes while stirring at room temperature. Two volumes of epichlorohydrin are slowly
141
Imaging and Therapy of Atherosclerotic Lesions with Theranostic Nanoparticles
added and stirred vigorously for an additional 8 hours, at which time 3 volumes of 30% ammonium hydroxide are added and stirred for 10 hours. 2. Excess epichlorohydrin and ammonia are removed by diafiltration (Quixstand Benchtop System) using citrate buffer. The resulting nanomaterial is concentrated to approximately 10 mg Fe/mL using centrifugal filtration (Amicon Ultra-15, 100 kDa nominal molecular weight cutoff). CLIO-NH2 is stable and can be stored in Nalgene bottles at 4°C. 3. Determine CLIO-NH2 concentration spectrophotometrically. Mix 10 μl of CLIO-NH2 with 1-ml 6M hydrochloric acid and 10 μl of 3% hydrogen peroxide, let sit for 1 hour at room temperature, and measure optical density at 410 nm. During this time, prepare standard solutions containing 0.1–4.0 mg of Fe per ml of iron atomic spectroscopy standard concentrate in 6M HCl. 4. CLIO-NH2 is fluorescently labeled with amine reactive fluorophores. Succinimidyl esters of fluorescent dyes are preferred, although isothiocyanates demonstrate equal utility. In this instance, we are using Alexa Fluor 750 (AF750), which absorbs at 752 nm and emits at 779 nm. This dye is visualized in the Cy 7 channel of all optical imaging systems. To 20 mg CLIO-NH2 (2 mL in citrate buffer) is added 10x PBS (222 μL), followed by 1 mg of AF750 dissolved in 200 μL DMSO. The resulting solution is shaken for 4 hours at room temperature, and then purified by size exclusion chromatography (Sephadex G-25) according to the manufacturer’s instructions using PBS as the eluent to yield the magnetofluorescent nanoparticle (MFNP). This material is stable at 4°C. A portion (10 mg) of the AF750-labeled particles is set aside for use as the control (CLIO-AF750, nontherapeutic) agent. 5. The dye labeled particles are then labeled with near-infrared light activated therapeutic (NILAT) moieties. The NILAT agent used in this method, the succinimidyl ester of 5-(4-carboxyphenyl)-10,15,20-triphenyl-2,3-dihydroxychlorin (TPC), is reacted with the VT-680-labeled nanoparticle in a ratio of 1-mg NILAT to 10-mg Fe. The NILAT agent is dissolved in enough DMSO prior to addition to the nanoparticle suspension that it is 20% of the solution by volume. The reaction is allowed to proceed for 4 hours while shaking, at which time it is purified by size exclusion chromatography (Sephadex G-25) according to the manufacturer’s instructions using PBS as the eluent to yield the theranostic nanoparticle (TNP). This material is also stable at 4°C. 6. The concentration of the dye-labeled particles and chromophores are determined spectrophotometrically (Figure 9.1). The UV-visible absorption of a standard solution of CLIO-NH2 (10 μL of CLIO-NH2 in 3 mL PBS, calculated in step 3) is determined at 300 nm. Similarly, 10 µL of the MFNP is diluted to 3 mL with PBS and its absorption is also determined at 300 nm. The concentration of the particle in suspension is then calculated comparatively. The concentration of the chromophores is determined from the absorption of the dyes at their maxima in the same diluted sample and its extinction coefficient using Beer’s law. All measurements are performed in triplicate. Ideally, particle concentrations will be greater than 1-mg Fe/mL in order to decrease the volume injected into the mice in the later steps. If the concentration is found to be less than 1-mg Fe/mL, the suspension can be concentrated by centrifugal filtration, as described above. 7. The fluorescence emission of the particles is also determined by diluting 10 μL of the particle solution in 3 mL of PBS. A standard solution of AF750 in PBS is also created 142
9.4
Methods
Figure 9.1 (a) UV-vis absorption spectrum of TNP. (b) Normalized UV-vis absorption (—) and fluorescence emission spectra (- - -) of TNP. Fluorescence emission excited at 730 nm.
with its optical density (OD) matched to that of the AF750 on the nanoparticle. The solutions are then excited at 730 nm, and observed from 750 to 850 nm, and the area under the curve is integrated. Fluorescence quenching is determined by the relative integrated area.
9.4.2
Intravital Fluorescence Microscopy
1. Twenty four hours prior to imaging, mice are injected with the TNP or the control agent at a dose of 10-mg Fe/kg weight via the tail vein. This allows for nanoparticle localization and blood clearance prior to imaging. 2. On the day of imaging, the mice are anesthetized by inhalation anesthesia (2% isoflurane, 1 L/min O2) using an isoflurane vaporizer. The distal right common carotid artery is carefully exposed with removal of the periadventitial tissues and the atherosclerotic plaques are visually identified (Figure 9.2). Animals are placed on a warmed glass plate and maintained on inhalation anesthesia during the imaging session.
Figure 9.2 Field of view through a dissecting microscope following exposure of the right carotid artery. The atherosclerotic lesions is circled.
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Imaging and Therapy of Atherosclerotic Lesions with Theranostic Nanoparticles
3. Multichannel intravital fluorescence imaging is performed with a prototypical laser scanning fluorescence microscope (Olympus Corporation, Japan) after carotid artery isolation. Two excitation wavelengths, 488 and 748 nm, are used. Image acquisition is 1 second. Software (FluoView 300, Olympus) is used to control the fluorescence microscope. The images collected are 512 × 512 pixels with a pixel size of about 5.4 μm/pixel. The total image size is approximately 2.75 mm × 2.8 mm. Acquired mages are stored as 16-bit multilayer Tagged Image File Format (TIFF) files. Images in the FITC channel and Cy7 channel, with a 505- to 525-nm bandpass and a 770-nm long-pass filter, respectively, are collected simultaneously. A dry objective (4x) with a field of view of 3.25 mm and a theoretical lateral resolution of about 2.6 µm at 680 nm is used. The detectors for both visible light and near infrared signals are wide spectral response photomultiplier tubes (model R928P, Hamamatsu, Japan). 4. Once the atheroma is located within the field of view, fluorescein-labeled dextran (FITC-dextran, Sigma) is injected into the mice in order to better delineate the vasculature and luminal-encroaching plaques. FITC-dextran is a long circulating agent utilized for fluorescent angiograms, and is injected at a dose of 5 mg/kg weight at the time of imaging. Often filling defects become evident in areas of high plaque burden. These plaque-induced defects often correspond to areas of increased uptake of the TNP.
9.4.3
Light-Based Therapy
1. Following the imaging session, the atheroma is treated with wavelength-specific light in order to elicit a therapeutic effect. The exposed carotid artery is illuminated with a 650-nm diode laser (150 mW, 3 min, total fluence = 11 J/cm2) utilizing an optical fiber and collimator in order to ensure homogenous light distribution (Figure 9.3).
Figure 9.3 Illumination of the right carotid artery with a 650-nm diode laser after the initial imaging session in order to elicit a therapeutic response.
144
9.5
Data Acquisition, Anticipated Results, and Interpretation
2. After illumination, the incision is sutured and the mice are allowed to recover. 3. The mice are divided into cohorts with two different endpoints: 1 and 3 weeks. One day prior to the designated endpoint, the mice are reinjected with the respective agent, which is given 24 hours to localize, followed by exposure of the carotid artery and IVFM, as described above.
9.5 Data Acquisition, Anticipated Results, and Interpretation 9.5.1
Characterization of Theranostic Nanoparticles
Once synthesized, it is important to determine the concentrations of the particles in solution, given as mg iron/mL, and the concentrations of each of the chromophores in the nanoparticle suspension. The concentration of the TNP is determined using the value of the concentration of the CLIO-NH2 determined in Section 9.4.1, step 3. Ten microliters of CLIO-NH2 is diluted to 3 mL, and its optical density (OD) is determined at 300 nm. (Note: The OD of the particles is highly variable between particle preparations, thus it is important to determine the OD each time a new preparation of nanoparticles is synthesized.) Similar to an extinction coefficient, the ratio of concentration to OD should be constant, and can be used in the determination of the concentration of the TNP. The concentration of the TNP is determined ratiometrically after dilution of the product with PBS. For example, if the initial concentration of CLIO-NH2 was 8.43-mg Fe mL–1, and it was diluted as described (10 μL in 3 mL total volume), the final concentration would be 2.81 × 10–2 mg Fe mL–1. This solution would have an optical density of 1.67 AU at 300 nm. If 10 μL of the TNP solution is diluted to 3 mL and the OD was measured to be 0.297 AU, the concentration of the TNP would be calculated to be 1.5-mg Fe mL-1 from the equation below:
[ST]dil OD st = [TNP]dil [TNP] = [TNP]dil × D
OD TNP
Where [ST]dil is the concentration of the diluted standard. ODst is the optical density of the diluted standard, [TNP]dil is the concentration of the diluted TNP, ODTNP is the optical density of the diluted TNP, [TNP] is the concentration of TNP in the original sample, and D is the dilution factor. Thus:
(2.18 × 10
−2
mg Fe mL−1 ) 167 . ΑU = [ TNP] 0297 . ΑU
. × 10−3 mg Fe mL−1 × 3 mL−1 [TNP] = 499
1 × 10−2 mL = 150 . mg Fe mL−1
The concentration of the AF750 is calculated from the optical density of the dye at its maxima in the diluted solution and the extinction coefficient for the dye times the dilution factor using Beer’s law as follows: A = ε Cd D
145
Imaging and Therapy of Atherosclerotic Lesions with Theranostic Nanoparticles
Where A is the optical density of the diluted sample, ε is the extinction coefficient of the dye, c is the concentration of the dye in solution, and d is the pathlength of the cuvette. As before, D is the dilution factor. Thus, for the diluted solution above with an OD of 0.0848 AU at 760 nm, the concentration of AF750 is: 0.0848 AU = (2 × 105 L mol−1cm −1 )(C)(1 cm) (3 mL 1 × 10−2 mL ) C = 127 . × 10−4 M The calculation of the concentration of the TPC is complicated by its overlap at 648 nm with the broad absorption of the AF750. It is thus necessary to determine the absorption of AF750 relative to its absorption at 760 nm. This can be accomplished using the CLIO-AF750 that was synthesized for use as the control, nontherapeutic nanoparticle. Dilution of 10 μL of CLIO-AF750 to 3 mL followed by acquisition of its UV-visible absorption spectra gives ratio of the OD of AF 750 at 760 and 648 nm, which is approximately 0.45. Thus the concentration of the TPC is calculated from the following equations: ODTPC = OD648 − 0.45OD760 ODTPC = 0.0611 AU − (0.45 × 0.0848 AU) = 0.0229 AU Where OD648 and OD760 are the optical densities at the respective wavelengths in the dilute TNP solution. The concentration is then calculated from the OD of the TPC at 648, using Beer’s law and the dilution factor. 0.0229 AU = (3 × 104 L mol−1cm −1 )(C)(1 cm) (3 mL 1 × 10−2 mL ) C = 2.29 × 10−4 M All measurements should be conducted in triplicate with the average value used in all subsequent experimentation. 9.5.2
Animal Experimentation
For all animal experimentation, the minimal number of animals per cohort should be 5 to allow for a more accurate determination of the results. For the experiments detailed above, there are four total cohorts of mice, due to the number of endpoints, as well as the use of therapeutic and control nanoparticle preparations. Thus, the minimum number of animals used in this method will be 20, although more can be added in order to increase the significance of the results. 9.5.3
Intravital Fluorescence Microscopy
The fluorescent signal was determined as integrated signal intensities (SI) from manually drawn regions of interest (ROI) on areas of plaque using ImageJ software (National Institutes of Health, Bethesda, Maryland). The plaque target-to-background ratio (TBR) was calculated as follows: TBR = [SI(plaque) / SI(blood)]. SI is equal to the integrated signal density divided by the area of the ROI. For the treated plaque depicted in Figure 9.4, below, the initial TBR, before laser illumination was 4.08, which is derived from the SI of the yellow ROI divided by the SI of the green ROI (45.6 A.U./11.1 A.U.). One week after 146
9.5
Pretreatment
Data Acquisition, Anticipated Results, and Interpretation
1 week post-treatment
Figure 9.4 Carotid atheroma before and after therapy with TNP. The signal from the Cy7 channel of the IVFM (red) decreases due to the focal ablation of inflammatory macrophages (4.1 pretreatment vs. 0.6 post-treatment). TBRs are calculated as a ratio of the integrated signal intensity for a specific ROI of the plaques (yellow circles) versus an ROI for the blood (green circle). The bottom images (blue) are the angiogram acquired in the FITC channel after administration of FITC-dextran.
Pretreatment
1 week post-treatment
Figure 9.5 Carotid atheroma before and after therapy with the control agent, CLIO-AF750. The signal from the Cy7 channel of the IVFM (red) increases (5.2 pretreatment vs. 13.0 post-treatment). The bottom images (blue) are the angiogram acquired in the FITC channel after administration of FITC-dextran.
treatment the TBR decreases significantly to 0.6 (7.42 A.U./11.9 A.U., Figure 9.5). For the control nanoparticles, the TBR remains increases, with 5.2 (34.7 A.U./6.02 A.U.) before laser irradiation and 13.0 (37.1 A.U./2.8 A.U.) after. 9.5.4
Statistical Analyses
All results should be reported as mean ± standard deviation. For differences between multiple groups, a one-way ANOVA followed by a posthoc Tukey’s test for multiple comparisons should be used. A p-value of < 0.05 is considered significant.
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Imaging and Therapy of Atherosclerotic Lesions with Theranostic Nanoparticles
9.5.5
Anticipated Results
Upon completion of the experiments and relevant calculations, trends should be observed in both the treated and untreated cohorts at all time points. The TBR for all treated animals should decrease between the pre- and post-treated imaging sessions, while those animals in the control groups should show increases in TBR. This is indicative of a decrease in the number of phagocytic cells contained within the atheromata. This data can be further confirmed by correlative histology examining the localization of the agent on the microscopic level, as well as the relative macrophage content of the lesions after therapy.
9.6 Discussion and Commentary The utility of theranostic nanoagents in vivo has, thus far, received little attention. While there are a number of publications detailing the synthesis of this class of materials, methods must be developed in order to determine their efficacy. Although this method is very specific in its scope, it can be readily applied to investigate the role of focal cell ablation in any number of diseases. The synthesis of the light-activated theranostic nanoparticles is based upon a standard protocol. CLIO is formed by the epichlorohydrin induced crosslinking of the dextran coating material of MION, followed by its amination. This reaction is usually done on a large scale to limit the batch-to-batch variability inherent in the synthesis, especially the amination. Following the purification and concentration of CLIO-NH2, the AF750 and TPC are conjugated to the particle by reaction of the succinimidyl ester functionalized dyes with the amines on the nanoparticle surface, thus forming amide bonds. The control nanoparticle, bearing only AF750, is taken as an aliquot from the product formed after reaction with AF750 (Section 9.4.1, step 4). This allows for the fluorescence of the control particle to be matched to the fluorescence of the TNP. At this point, the optical properties of the products are quantified, including the concentration of the particle (in mg Fe mL-1), and the concentration of the dyes (in M). The fluorescence emission of the particle is also qualitatively examined, as compared to an equimolar solution of AF750. If too many dye molecules are present on the nanoparticle surface, dye-dye quenching can occur, resulting in a particle with minimal fluorescence emission. Unfortunately, if the fluorescence is quenched, the particles are no longer viable, and should be discarded and resynthesized using decreased amounts of the dye starting materials. The in vivo efficacy of the TNP is examined in aged atherosclerosis-laden apoE-/mice. These mice are put on a high-cholesterol diet at about 10 weeks of age, and are kept on that diet until the beginning of the study, in order to induce atherosclerosis with high levels of inflammation. One day prior to the initial imaging session, the mice are injected with the respective agents. This time allows for the maximum localization of the nanoparticles to the lesions of interest. On the day of imaging, the mice are anesthetized and the carotid artery is surgically exposed, and examined visually for the presence of atheromata. Occasionally, the artery that is exposed contains no lesions. If this does occur, the contralateral carotid artery can be used after surgical exposure. The lesions are then located in the Cy7 channel of the IVFM. At this point, FITC-dextran is injected in order to better delineate the vasculature, and Z-stack images are acquired in both the 148
9.7
Summary Points
FITC and Cy7 channels of the IVFM. Following imaging, the exposed carotid artery is illuminated with a 650-nm laser in order to elicit a therapeutic response, the surgical incision is sutured, and the mice are allowed to recover. The mice are also returned to a normal diet in order to prevent the formation of new lesions. At the requisite time point after therapy (1 or 3 weeks), the animals are reinjected with the respective agents, which are given 24 hours to localize. The surgical incision is reopened and the mice are reimaged. Following the imaging sessions, the Z-stack images are summed for each mouse, and the TBR for each mouse is determined before and after therapy using hand-drawn ROIs. The TBR is the ratio of the signal intensity from the lesion to the signal intensity from the adjacent blood. As is illustrated above, all treated mice should exhibit a significant decrease in TBR, indicative of macrophage ablation, while the control group should show no change, or a slight increase in TBR. Although unlikely, it is also possible for these mice to show a decrease in signal intensity, due to the withdrawal of the high-cholesterol diet. While this method enables the longitudinal in vivo study of theranostic nanoagent localization and therapeutic efficacy, there are several other techniques that can be utilized ex vivo, such as flow cytometry and immunohistochemistry. Digestion of carotid arteries in a collagenase cocktail, followed by fluorescent antibody labeling and flow cytometric analysis enables the identification of the cell types containing the nanoagent, as well as the relative proportion of each cell type [24]. Similarly, the carotid arteries can be embedded and sectioned for histological identification of nanoparticle localization, and the relative content of each cell type within the lesion. The main drawback of these techniques is that they require the sacrifice of the animal, and as such, can not be utilized to examine the therapeutic response over the course of the study. Troubleshooting Table Problem
Explanation
Potential Solutions
Section 9.4.1, step 7 The material is minimally or nonfluorescent.
The conjugation of the chromophores to the particle was inefficient.
Section 9.4.1, step 5 Material precipitates.
The presence of divalent cations can cause precipitation.
1. Repeat UV-vis absorption measurements. 2. Remove any impurities by dialysis. 3. Repeat reaction with chromophores. 1. Repeat UV-vis absorption measurements. 2. Material is of no utility. Start again from CLIO-NH2. Ensure that PBS does not contain calcium and magnesium.
Section 9.4.2, step 2 No lesions present.
Lesion formation is variable in the apoE-/model.
Expose contralateral carotid artery and inspect for lesions.
Section 9.4.2, step 3 No signal from lesion.
Lesion uptake of particle is poor, or nanoagent Ensure that mice have visible carotid plaques. has degraded. Repeat UV-vis and fluorescence measurements to ensure nanoparticle composition.
The conjugation of the chromophores to the particle was too efficient, causing quenching.
9.7 Summary Points Theranostic nanomaterials comprised of crosslinked dextran coated iron oxide nanoparticles, NILAT agents, and fluorophores, are capable of imaging atherosclerotic lesions.
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•
Focal macrophage ablation results in a decrease in signal from the theranostic nanoparticle upon reinjection and imaging.
•
Theranostic nanoparticles can be functionalized for use in numerous diseases, as they can be targeted to specific cell or tissue types.
•
The therapeutic portion of the theranostic nanoagent must be optimized for specific applications, with special regard paid to the intrinsic toxicity of the therapeutic ligand.
Acknowledgments We would like to thank Drs. Ethan Korngold, Jose-Luiz Figueiredo, and Rainer Kohler, and Purvish Patel for their assistance in developing this method. This work was supported in part by NIH grants U01-HL080731 (RW), U54-CA119349 (RW), and U54-CA126515 (RW).
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Saini, S., R. Sharma, and R. L. Baron, et al., “Multicentre Dose-Ranging Study on the Efficacy of USPIO Ferumoxtran-10 for Liver MR Imaging,” Clin Radiol, Vol. 55, No. 9, 2000, pp. 690–695. Kooi, M. E., V. C. Cappendijk, and K. B. Cleutjens, et al., “Accumulation of Ultrasmall Superparamagnetic Particles of Iron Oxide in Human Atherosclerotic Plaques Can Be Detected by In Vivo Magnetic Resonance Imaging,” Circulation, Vol. 107, No. 19, 2003, pp. 2453–2458. Trivedi, R. A., C. Mallawarachi, and J. M. U-King-Im, et al., “Identifying Inflamed Carotid Plaques Using In Vivo USPIO-Enhanced MR Imaging to Label Plaque Macrophages,” Arterioscler Thromb Vasc Biol, Vol. 26, No. 7, 2006, pp. 1601–1606. Trivedi, R. A., J. M. U-King-Im, and M. J. Graves, et al., “In Vivo Detection of Macrophages in Human Carotid Atheroma: Temporal Dependence of Ultrasmall Superparamagnetic Particles of Iron Oxide-Enhanced MRI,” Stroke, Vol. 35, No. 7, 2004, pp. 1631–1635. Josephson, L., C. H. Tung, A. Moore, and R. Weissleder, “High-Efficiency Intracellular Magnetic Labeling with Novel Superparamagnetic-Tat Peptide Conjugates,” Bioconjug Chem, Vol. 10, No. 2, 1999, pp. 186–191. Jaffer, F. A., M. Nahrendorf, and D. Sosnovik, et al., “Cellular imaging of inflammation in atherosclerosis using magnetofluorescent nanomaterials,” Mol Imaging, Vol. 5, No. 2, 2006, pp. 85–92. Choi, Y., J. R. McCarthy, R. Weissleder, and C. H. Tung, “Conjugation of a Photosensitizer to an Oligoarginine-Based Cell-Penetrating Peptide Increases the Efficacy of Photodynamic Therapy,” ChemMedChem, Vol. 1, No. 4, 2006, pp. 458–463. McCarthy, J. R., F. A. Jaffer, and R. Weissleder, “A Macrophage-Targeted Theranostic Nanoparticle for Biomedical Applications,” Small, Vol. 2, No. 8–9, 2006, pp. 983–987. Swirski, F. K., P. Libby, E. Aikawa, et al., “Ly-6Chi Monocytes Dominate HypercholesterolemiaAssociated Monocytosis and Give Rise to Macrophages in Atheromata,” J. Clin. Invest., Vol. 117, No. 1, 2007, pp. 195–205.
151
CHAPTER
10 Biomedical Applications of Metal Nanoshells André M. Gobin University of Louisville, Louisville, KY
Abstract This chapter details the methods associated with producing near infrared (NIR) resonant composite nanoparticles called nanoshells. Engineered nanostructures called nanoshells were first designed and fabricated at Rice University and consist of a dielectric core of silica and a metal shell, generally gold. Gold nanoshells are particularly useful for biomedical applications due to biocompatibility of gold and the ability to tune the resonance of these particles to match virtually any wavelength. This chapter addresses the methods of producing gold nanoshells, passivating the surface for in vivo studies and conjugating biomolecules to the surface followed by testing the concentration of bound antibodies on the surface. These techniques allows one to produce nanoshells with specific NIR resonance and allow targeting to a variety of cell types via antibodies or ligands or to other targets and could be used to extend the use of nanoshells beyond therapeutic applications.
Key terms
nanoshells near infrared photothermal therapy laser therapy plasmon resonance
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10.1 Introduction Nanoshells are a relatively new class of nanoparticles consisting of an ultrathin metal shell (generally gold) surrounding a dielectric core such as silica. Gold nanoparticles have low toxicity, thus gold-coated nanoshells displaying a gold surface has the same degree of biocompatibility as solid gold nanoparticles used in a variety of applications today. With their facile optical tunability, nanoshells are ideal for biological applications in the near infrared (NIR). The NIR window is defined as a region where energy of light having wavelengths between 650 to 900 nm can penetrate through tissue relatively unimpeded by hemoglobin or water. This allows one to tune the nanoshells to match a laser wavelength in this region and create a pair of nanoparticle + laser that can effectively be used for therapeutic purposes. This chapter examines the methods used in making nanoshells, conjugation of biomolecules to the surface, quantification of attached antibodies on nanoshells as well as in vitro and in vivo testing of nanoshells to determine efficacy as a therapeutic agent. Nanoshells can be designed to either strongly absorb or scatter light in the NIR based on the dimensions of the core and shell and overall size, permitting applications for heating or optical contrast as discussed in detail in this chapter. The gold surface allows for easy conjugation of proteins through the use of a PEG linker that contains a disulfide or thiol moiety. Due to their unique properties and biocompatibility, gold nanoshells have been investigated for a variety of biomedical applications. These include being used as a mechanism to provide heating for photothermally modulated drug delivery systems, fast antigen detection systems with whole blood, use in imaging applications, as an exogenous NIR absorber for tissue welding or bonding, cancer therapy by nonspecific accumulation in tumors and for targeted cell ablation using antibody targeting mechanisms. In this chapter we study the binding of an antibody to nanoshells as a method to target prostate adenocarcinoma using the prostate specific membrane antigen (PSMA). The conjugation technique to the polymer linker and to the nanoshell is highlighted in the methods section and results of measurements of antibody concentrations are shown in the results section.
10.1.1
Biomedical Applications of Metal Nanoshells
Since the development of gold nanoshells in 1997 by the Halas group, numerous potential applications have been explored. Of particular interest to this discussion is their application to biomedicine due to the inert and biocompatible nature of the gold coating, the flexibility of the chemistry that can be performed on gold surfaces, and the ease with which the optical properties of metal nanoshells can be manipulated. Gold/silica nanoshells have already proven very effective for photothermal cancer therapy in vivo by taking advantage of its ability to absorb NIR energy and create heat. In work by Hirsch and O’Neal, the nanoshells used were primarily absorbing, at about 85% absorbing efficiency and provided up to 100% regression of tumors in mice after treatment [1]; however the scattering properties of the nanoshell has also been exploited for in vitro imaging [2] as well as for combined imaging and photothermal ablation in vitro [3]. In the imaging studies it was demonstrated that the nanoshell could be used to provide adequate scattering for imaging contrast and retain NIR absorbing properties sufficient to allow photothermal ablation in vitro [3]. This chapter details the successful in vivo demonstration of the use of near infrared resonant gold nanoshells, to first increase opti154
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Introduction
cal contrast in tumors for optical coherence tomography (OCT) imaging for diagnostics and second, to subsequently treat the tumors by absorption of near infrared (NIR) light for photothermal ablation. The approach discussed in this chapter uses a single nanoparticle formulation that has been designed to have both absorption and scattering in the NIR to accomplish diagnostic imaging and therapeutic benefits simultaneously [4]. Nanotherapeutics like these can allow the development of “see and treat” applications that is expected to reduce patient care costs and allow wider delivery of treatments. Nanoshell mediated cancer therapy has many benefits compared to traditional chemotherapy or radiotherapy methods, particularly in its potential to reduce side effects. Whereas, the side effects of the drugs typically used in chemotherapy or the radiation used leaves various uncomfortable side effects, the gold nanoparticles by themselves are not known to cause any side effects. Nanoshell mediated cancer therapy begins by preferential accumulation of nanoshells into the tumors due to the leaky vasculature that is characteristic of fast growing tumors. Tumor vasculature have pore sizes that are hundreds of nanometers in diameter compared to normal vessels that have pore sizes on the order of tens of nanometers; this allows easy extravasation of nanoparticles into tumors. Permeability of particles up to 400 nm has been shown in human colon carcinoma, suggesting pore sizes up to 600 nm. Thus, tumors become laden with nanoshells while other healthy tissues with normal tight endothelial junctions in the vasculature have minimal accumulation of nanoparticles. The application of NIR light causes heating only in the nanoshell-laden tumor, leaving the healthy tissue unaffected. NIR light energy is absorbed by the nanoshell creating heat. Data shows that the heating of nanoshells upon exposure to NIR light disrupts the integrity of the cell membrane, causing death of the cells [5]. Heating of the tumor cells by this mechanism causes irreversible thermal damage thus allowing the tumor to be destroyed. It has been shown that irreversible thermal damage occurs and is evident at temperatures between 55°C to 59°C manifesting as edema, whitening and eventually tissue necrosis in the region. Since the NIR light is minimally absorbed by normal tissue components, there is minimal temperature increase in the absence of nanoshells and no detectable damage in surrounding tissue. The delivery of PEGylated nanoshells to tumors followed by therapeutic administration of NIR light showed up to 100% regression of tumors in a murine model [1].
10.1.2 Nanoshells for Combined Optical Contrast and Therapeutic Application Extinction of light on a nanoshell is due to scattering and absorbing events. The scattering property of a nanoshell can be exploited for imaging applications just as the absorbing property can be exploited for therapeutic benefit. This was demonstrated by Loo et al. in vitro using antibody targeted nanoshells to specifically bind to HER2 overexpressing tumors [3]. In their study, imaging was performed using darkfield microscopy; this allows imaging by illuminating the sample with light at an angle and collecting light scattered from the objects to create an image. Given these advantages, nanoshells can be used as contrast agents for enhanced OCT imaging based on their backscattering properties, as well as a cancer therapeutic, due to their absorbing properties.
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10.2 Experimental Design To evaluate targeting of nanoshells to cells of a particular type will depend on the needs of the experimenter and the final outcome desired, whether it is in vitro targeting studies or ultimately in vivo targeting to tumors of a particular type. In this chapter we focus on targeting nanoshells to prostate cancer by the use of antibodies specific to receptors on this cell type which is over-expressed in greater amounts (100–1000x) more than in normal cell types. This is the first fundamental issue with being able to target, one has to evaluate the availability of a target on the intended cell type and evaluate the relative abundance of that marker on the cell of interest in comparison to other cells. For antibody targeting the assay should be run in triplicate as a minimum to ensure consistency of results. For this assay it is necessary to have a negative control of nanoshells with PEG only. This allows one to determine the background amount of secondary antibodies that may become entrapped with the nanoshells during the centrifugation process. For animal studies, the minimum number of animals required is dependent on the significance levels desired for a certain percentage change in the result. To this end there are many sources including software, books, and articles for determination of these numbers. For our studies we chose 10 to 12 animals as the minimum to show significance greater than 95% between treated and untreated groups when using the formulation of nanoshells for imaging and therapeutic application.
10.3 Materials High-purity chemicals are essential for producing good nanoparticles. Except where noted, chemical were obtained from Sigma (Milwaukee, WI). The chemicals required for the many processes are grouped and listed below.
10.3.1
Nanoparticle Production
Tetraethyl orthosilicate (TEOS, 99.999%) used for producing silica cores; ammonium hydroxide, 14-15N was used as the base catalyst in silica core nanoparticle production; and (3-aminopropyl) triethoxysilane (APTES, 99%) used to provide amine groups on the surface of silica nanoparticles. Tetrakis (hydroxymethyl) phosphonium chloride (THPC, 80%) and 1M NaOH were used to produce gold colloid via the Duff process. Gold in the form of hydrogen tetrachloroaurate (III) trihydrate (chloroauric acid) 99.99% purity was purchased from Alfa Aesar (Ward Hill, MA) and used for all procedures requiring gold solutions. Polyethylene glycol–SH (PEG-SH, 5000 MW) was used for blocking and passivating nanoshells surfaces.
10.3.2
Protein Conjugation to Nanoshells Surface
Bifunctional PEG: orthopyridyl–disulfide–poly(ethylene glycol)–N– hydroxysuccinimide ester (OPSS-PEG-NHS, 2000MW) for conjugating proteins to nano- shells surfaces was obtained from Nektar (Birmingham, AL). PEG-SH (MW 5000; Nektar, Birmingham, AL) was used to block exposed gold surfaces on nanoshells to resist protein adsorption and allow better circulation in vivo. A monoclonal anti-PSMA in the form of 156
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Methods
mouse- anti-HuPSMA, clone Y/PSMA1 (M20454M) was obtained from Biodesign International (Meridian Life Sciences, Saco, ME) for PSMA targeting. Recombinant fusion proteins of mouse ephrin-A1/Fc chimera (R & D Systems, Minneapolis, MN) were obtained for EphA2 targeting.
10.3.3
Cell Culture
Media was obtained from ATCC including: Ham’s F12K, RPMI-1640, and DMEM supplemented with 4 mM l-glutamine, 1% penicillin, 1% streptomycin (GPS) and 10% fetal bovine serum (FBS).
10.3.4
In Vitro Assays
Quantification of antibody concentration on nanoshell surfaces through horseradish peroxidase (HRP) activity was measured with 3, 3’, 5, 5’–tetramethylbenzidine (TMB) assay (Sigma, Milwaukee, WI).
10.4 Methods 10.4.1
Fabrication of Gold/Silica Core Nanoshells
Gold nanoshell synthesis has been previously described by others [6]. First, silica cores were grown using the Stöber process, the basic reduction of tetraethyl orthosilicate (TEOS). Next, 45 ml of 200-proof ethanol was used with 3.0 to 5.5 ml in 0.5 ml increments of 14.8 N NH4OH to make six batches at different ammonia volumes. Then, 1.5 ml TEOS was added to each batch and allowed to react a minimum of 8 hours. Higher volume of ammonia produces larger silica nanoparticles. Silica precipitates were centrifuged and washed with 200-proof ethanol twice to remove any remaining NH4OH (200 –3500g (size-dependent), 20 ml for 20 minutes in each step). The resultant silica nanoparticles were sized using scanning electron microscopy (SEM; Philips FEI XL30). Average diameters of different batches ranged between 98 and 112 nm. Only batches with a polydispersity of less than 10% were used in subsequent steps. Reaction of the silica core nanoparticles with 200 μl of (3-aminopropyl) triethoxysilane (APTES) per batch provided amine groups on the surface of the cores to allow for adsorption of gold colloid in the subsequent step. Aminated silica cores were boiled for 2 hours with addition of 200° ethanol to maintain volume, then cooled and washed twice by centrifugation. The silica core suspensions were measured to determine the weight percent of solids and adjusted to 4 wt% for storage by addition of ethanol. For colloid production, a 1wt % gold salt solution was prepared with 99 grams 18.2 MΩ−cm H2O and 1 gram hydrogen tetrachloroaurate (III) trihydrate (chloroauric acid) 99.99% purity (HAuCl4) purchased from Alfa Aesar (Ward Hill, MA) and stored in amber bottles for use in various steps requiring gold. 400 μl of (hydroxymethyl) phosphonium chloride (THPC, 80%) was mixed with 33-ml DI water as a stock solution. To produce the colloidal gold particles the following were mixed together: 180-ml DI water, 1.2-ml 1M NaOH, 4-ml THPC stock solution, and 6.75 ml of 1 wt % gold solution. This gold colloid made through the Duff process has a size of 2 to 4 nm after aging for 2 to 3 weeks at 4°C. After aging the colloid was then concentrated ~20X through rotary evaporation and 157
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mixed with the ammine coated silica particles at a volume of 10 ml concentrated colloid: 300 μl stored silica core suspension, thus allowing small gold colloid to attach to the larger silica nanoparticle surface to act as nucleation sites in the subsequent reduction step. This resulted in the seed particles from which nanoshells are grown by reduction of additional gold using formaldehyde as the reducing agent. Finally, the gold shell was then grown by reduction of gold using 0.4 mM HAuCl4 solution (plating solution) in the presence of formaldehyde. The plating solution is made with 50-mM potassium carbonate and gold salt from the 1% solution to a final concentration of 0.4 mM HAuCl4. To produce particles with varying shell thicknesses we varied the concentration of seed particles while using the same amount of plating solution. The spectra of each set of samples were examined for optimal conditions to produce desired NIR absorbing nanoshells. NIR absorption characteristics of the nanoshells were determined using a UV-Vis spectrophotometer (Carey 5000 Varian, Walnut Creek, CA). Samples with the appropriate NIR peak resonance (~ 800 nm) were scaled up linearly to provide nanoshells for the experiment.
10.4.2
Nanoshells for Combined Imaging and Therapy In Vivo
10.4.2.1 In Vivo Model BALBc mice inoculated with 150,000 murine colon carcinoma cells (CT-26; ATCC) in 25 μl of PBS. Tumors were allowed to grow to a cross-sectional area of 20 to25 mm2 and no more that 4 to 5 mm in any one dimension before treatment. Then, 150 μl of PEGylated nanoshells at a concentration of 1.5 x 1010 nanoshells/ml were injected into the tail vein of the animals 20 hours prior to imaging and laser irradiation. A total of 36 animals were inoculated with the cancer cells. Animals were randomly divided into three groups; Group 1: Nanoshell + Laser, Group 2: PBS + Laser, and Group 3: Untreated Control.
10.4.2.2 OCT Imaging This study used a commercially available OCT imaging system, Niris Imaging System, (Imalux; 1300 nm, Cleveland, OH). The axial and transverse resolutions were approximately 10 and 15 μm, respectively. OCT images were collected for nanoshell-injected and control mice 20 hours following injection (to allow time for passive accumulation of nanoshells) and analyzed to assess the increase in contrast provided by the nanoshells in tumor tissue compared to normal tissue. OCT images of the tumor and normal tissue were taken after 20 hours of circulation. The animals were not anesthetized during the injection or circulation period, only during imaging and treatment by the NIR laser. The tumors were imaged using the Niris OCT imaging device by applying glycerol on the shaved tumor site for index matching and placing the probe in contact with the skin directly above the tumor. Images were captured at several locations on each tumor through the integrated computer and image analysis system. Normal tissue images were taken at a location at least 2 cm distant to the tumor on the same animal. For statistical analysis, images were analyzed to first quantify the contrast levels using standard thresholding for image analysis then intensity data were analyzed using an unpaired student t-test assuming equal variance with a confidence interval of 95%, p < 0.05 of the two populations of images from PBS-treated and nanoshell-treated mice. 158
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Methods
10.4.2.3 Therapeutic Laser Irradiation After imaging, the tumors were irradiated with a NIR laser. In vivo irradiation was accomplished using an Integrated Fiber Array Packet, FAP-I System, with a wavelength of 808 nm (Coherent, Santa Clara, CA) at a power density of 4 W/cm2, 5-mm diameter spot for 3 minutes. Animal survival was monitored for 7 weeks after imaging and treatment. Following treatment, survival data analysis was performed using the standard Kaplan-Meier analysis using MedCalc software to determine statistical significance after therapy. Analysis of the tumor regression was performed using the average measurements of the tumor size of the surviving populations at the times shown and compared using an unpaired student t-test assuming equal variance with a confidence interval of 95%, p< 0.05.
10.4.2.4 Nanoshell Accumulation in Tissue Three animals from each group were sacrificed following treatment to examine the tumors for the presence of nanoshells using silver enhancement staining. One half of the frozen tumors were sectioned to 8 μm, and silver staining was performed using the Sigma Silver Enhancement solutions (Sigma, Milwaukee, WI). Images of each section were taken at 64x magnification to look for the presence of nanoshells; silver staining allows the nanoshells to act as nucleation sites for deposition of silver to grow large enough to allow for visualization under light microscopy. The second half of the tumor was sent to Texas A&M University for nuclear activation analysis (NAA). Tissue samples for NAA were lyophilized and weighed; blanks and the dried tumor sample were irradiated along with precise calibration standards at the Texas A&M University’s Nuclear Science Center 1 MW TRIGA research reactor for 14 hours. The irradiation position used in this study has an average neutron flux of approximately 1 x 1013 sec-1cm-2. High-purity germanium detectors with nominal resolutions (FWHM) of 1.74 keV or better and efficiencies of 25-47 % by industry standard relative measurement were used to quantify the 412 keV gamma line from 198Au. The Canberra Industries OpenVMS alpha processorbased Genie-ESP software was used for acquisition and computation of gold concentrations.
10.4.3
Passivation of Nanoshells with PEG
Nanoshells were surface-coated with poly (ethylene glycol) PEG to enhance circulation times and reduce immune response in vivo. PEGylation was accomplished by adding 100 μl of 5-μM PEG-SH, molecular weight 5 kDa (Nektar, Huntsville, AL) to 20 ml of a nanoshell suspension with an optical density (OD) of 2.0 (~6 x 108 particles/ml) in DI water for a minimum of 8 hr at 4°C. PEG-modified nanoshells were sterilized by filtration using a 0.22-μm filter and subsequently centrifuged to increase concentration. To facilitate injection in vivo, nanoshells were resuspended in sterile phosphate-buffered saline (PBS), pH = 7.4 at physiological salt concentration, to an OD =50 (~1.5x1010 particles/ml). For concentration of the sample, a force of 1500g was used to spin down the sample to a pellet and the supernatant was removed. The sample was then diluted with sterile PBS and measured and adjusted as necessary to ensure final concentration of OD = 50. At this point the suspension of nanoshells is ready for in vivo use.
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10.4.4
Conjugation of Biomolecules to Nanoshells
A bifunctional PEG polymer, Orthopyridyl-disulfide-poly(ethylene glycol)N-hydroxysuccinimide ester (OPSS-PEG-NHS, 2000MW) was obtained from Nektar (Birmingham, AL). Protein of interest is dissolved or diluted to known concentrations with 100-mM sodium bicarbonate at pH 8.5. In the case of anti-PSMA targeted nanoshells we used a monoclonal anti-PSMA in the form of mouse-anti-HuPSMA, clone Y/PSMA1 (M20454M) obtained from Biodesign International (Meridian Life Sciences, Saco, ME). The polymer was reacted at a mole ratio of 2:1 with anti-PSMA for four hours at 4 °C. The OPSS-PEG-NHS molecule binds when the NHS group cleaves in aqueous environment leaving an activated carboxylic terminus that can bind to a free primary amine group on the antibody or other protein forming a peptide bond and covalently linking the PEG to the antibody to form OPSS-PEG-antibody. After reaction the conjugated PEG-Ab solution is incubated with nanoshells at calculated to be ~2000 Ab fragments per nanoshell particle. The mixture is allowed to incubate for 1 hour after which PEG-SH at the concentrations discussed above is added to complete passivation of the rest of the gold surface. Binding of the antibody to the surface prior to blocking allows for a higher concentration of Ab on the nanoshell surface and maximizes the use of the antibody solution. This reaction scheme is shown in Figure 10.1.
10.4.5
Quantification of Antibodies on Nanoshells
Suspensions of conjugated nanoshells prepared as described above were centrifuged at 1000g to separate unbound antibodies from the particles. Nanoshells were blocked in a 3% bovine serum albumin (BSA) for 1 hour. The washing step was repeated for a total NHS Cleaves in water O
H Step 1
R N
N O C CH 2CH2 (CH2CH 2O)n-NH-CH 2CH 2C-S-S-
+
O
H
N
O
O OPSS-PEG-N-hydroxysuccinimide
Antibody (R) with available primary amine H Step 2
R NH +
-
O C CH2CH2 (CH2CH 2O)n-NH-CH 2CH2C-S-SO
H
N
O
H2O Peptide bond forms at activated carboxylic terminus
Step 3
R N C CH2CH2 (CH2CH 2O)n-NH-CH 2CH2C-S-SH
O
O
N
-PEG -SS
Antibody (R) has PEG with a disulfide covalently attached
Figure 10.1 Representation of antibody binding to bifunctional PEG for subsequent conjugation to nanoshells. At the end of the reaction the PEG is covalently attached to the antibody and the disulfide is able to bind to the gold surface of the nanoshell after the protecting group leaves.
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Results
of two times. Suspensions were then incubated with an anti-mouse IgG antibody conjugated to horseradish peroxidase (HRP), Sigma, A-4416.Then, 450 μl of nanoshell suspension was added to 50 μl of A-4416 diluted to100 μg/ml and incubated for 1 hour. Suspensions were washed by centrifugation and resuspension twice to remove unbound secondary antibody; after a third centrifugation, supernatant and nanoshells were retained for HRP quantification. HRP standards were made up at concentrations ranging from 2 ng/ml to 100 ng/ml and nanoshells as well as supernatant were assayed for HRP using 3,3’,5,5’–tetramethylbenzidine (TMB). The reaction is generally stopped after 5-7 minutes by use of H2SO4 and read using a plate reader at 450 nm (model ELX800; BioTek Instruments, Winooski, VT). It may be useful to run the assay once to determine the reaction rate during development relative to the standards to align the assay so that the best standard curve can be obtained for the range of the concentration of antibody in the nanoshell suspension. After determination of the concentration of antibody one can determine the number of antibody molecules on the nanoshells’ surface by using the concentration of nanoshells as determined by spectroscopic measurements. It is essential to use nanoshells that contain PEG only as a control to assure the background amount of secondary antibody is accounted for during incubation step and subsequent washing.
10.5 Results 10.5.1 Gold/Silica Nanoshells Allow Both Imaging Contrast Increase and Therapeutic Benefit 10.5.1.1 OCT Image Analysis PEG-modified nanoshells were injected intravenously in tumor-bearing mice and allowed to passively accumulate in the tumor tissue due to the leakiness of the tumor vasculature. The significant accumulation of particles within the tumor tissue dramatically increased the NIR scattering within the tumor, enhancing the OCT contrast. Figure 10.2 shows representative OCT images of tumors of mice prior to irradiation with the 808-nm laser. OCT images of normal and tumor tissue of mice treated injected with saline are shown in Figure 10.2(a) and (c). Figure 10.2(b) and (d) are of mice injected with nanoshells. Note the enhanced contrast in the image (d) indicates that the gold nanoshells can be visualized with OCT system and shows higher contrast within the tissue of either the normal tissue area or the tumor treated with saline. Figure 10.3 shows the quantification of the image intensity of normal tissue (n = 3) and tumor tissue (n = 6) with PBS injection and nanoshell injections. OCT images were analyzed to quantitate the contrast and analyzed using a student’s t-test of the two populations of images from PBS treated and nanoshell (NS) treated mice. The data shows a significant increase in the contrast of tumor compared to normal tissue when nanoshells are used. No statistical difference is observed in the contrast of images of normal tissue whether nanoshells or saline are used.
10.5.1.2 Histological Analysis Histological examination of tumors using silver staining confirmed that OCT signals were the result of scattering from nanoshells within the tumor. Figure 10.4 shows the sil161
Biomedical Applications of Metal Nanoshells
(A) Normal tissue + PBS
(B) Normal tissue + Nanoshells Glass Skin
Muscle
(C) Tumor tissue + PBS
(D) Tumor tissue + Nanoshells Glass Skin
Muscle 200 μm Min Min
Max
Figure 10.2 Representative OCT images from normal skin and muscle tissue areas of mice systemically injected with nanoshells (a) or with PBS (b). Representative OCT images from tumors of mice systemically injected with nanoshells (c) or with PBS (d). Analysis of all images shows a significant increase in contrast intensity after nanoshell injection in the tumors of mice treated with nanoshells while no increase in intensity is observed in the normal tissue. The glass of the probe is 200-μm thick and shows as a dark nonscattering layer [4].
ver staining of representative areas of tumors from mice treated with nanoshells (a) or with PBS (b) showing a marked increase in darkening of the tissue in (a), indicating the presence of nanoshells within the tumor. Additionally, neutron activation analysis (NAA) verified nanoshells present in the tumor shown in Figure 10.4(a) at 12.5 ppm compared to 0 ppm for tumors of mice injected with just PBS Figure 10.4(b).
10.5.1.3 Survival Following Imaging and Therapy Tumor regression and survival of the mice were followed for 7 weeks after treatment. Figure 10.5(a) shows the tumor sizes on the day of treatment and 12 days after treatment; tumors on nanoshell-treated mice were completely regressed except for one mouse. Figure 10.5(b) shows the survival of the mice during the study period. Kaplan-Meier statistical analysis shows a median survival of 14 days for the PBS + Laser group and 10 days for the Untreated Control group. By day 21 the survival of the Nanoshell + Laser group was significantly greater than either control groups, p < 0.001. 162
10.6
*
1.00
Percent increase in contrast: Tumors versus normal tissue
Discussion of Pitfalls
0.75
0.50
0.25
0.00 PBS injected
NS injected
Figure 10.3 Quantification of OCT images shows a significant increase in intensity of images of tumors from mice with systemic nanoshell injection. For PBS-only injection there is a 16% increase in normal tissue compared to tumor tissue scattered intensity, while for nanoshell-injected mice the difference in normal compared to tumor tissue was an increase of 56% (*p 72 > 72 40
< 0.03 < 0.03 < 0.03 < 0.03 > 48 > 48 > 48 2.0
37°C. The resistance to hydrolysis exhibited by hydrazones derived from aromatic aldehydes can be attributed to the conjugation of the π bonds of –C=N- bond of the hydrazone with the π bonding benzene ring. Thus, it supports the finding that hydra187
Environmentally Responsive Multifunctional Liposomes
zones formed from aromatic aldehydes are more stable to acidic hydrolysis than those formed from aliphatic ones [71, 72]. The hydrazone hydrolysis involves the protonation of the –C=N nitrogen followed by the nucleophilic attack of water and cleavage of C-N bond of tetrahedran intermediate [73]. Any of these steps is determining and dependant on the pH. The substituents on the carbonyl reaction partner influence the rate of hydrolysis through altering the pKa of the hydrazone with electron donating substituents facilitating protonation of the –C=N nitrogen [74]. This would support the fact that PEG-HZ-PE conjugates containing hydrazone bond derived from the aliphatic aldehyde are more prone to hydrolytic degradation. Aromatic aldehyde-derived hydrazone bond is too stable for the purpose of pH-triggered drug release. Careful selection of an aldehyde and an acyl hydrazide would be necessary for the application of the hydrazone-based chemistry for the development of pH-sensitive pharmaceutical nanocarriers. As Scheme 11.9 shows, an aromatic ketone-derived hydrazone bond was introduced between PEG and PE. The presence of a methyl group (electron donating) on the carbonyl functional group would provide a sufficient lability of the hydrazone bond under mildly acidic conditions while an immediate aromatic ring (electron withdrawing) next to the hydrazone bond would offer the stability under acidic and neutral conditions. mPEG-HZ-PE conjugate, wherein the hydrazone bond is derived from an aromatic ketone, exhibited the half-lives of 2-to-3h at slightly acidic pH values, and much higher stability (up to 40 h) at the physiological pH (Table 11.3).
11.4.4
Avidin-Biotin Affinity Chromatography
To determine the pH-sensitivity of mPEG-HZ-PE conjugates, biotin-embedded micelles shielded by cleavable mPEG2000-HZ-PE, were eluted through avidin immobilized gel media columns. The control micelle formulation (incubated at pH 7.4 at 37°C for 3h) showed only a minimal biotin binding against 69% biotin binding of test micelle formulation (incubated at pH 5.0 at 37°C for 3 h), Figure 11.1. This proves shielding effect of mPEG2000-HZ-PE conjugate under physiological pH condition and deshielding after exposure to acidic environment.
11.4.5
In Vitro Cell Culture Study
To study shielding/de-shielding effect of mPEG-HZ-PE under the influence of acidic pH, internalization of Rh-labeled, TATp-bearing, mPEG-HZ-PE shielded liposomes pre-incubated at pH 7.4 and pH 5.0 was followed using H9C2 cells. As seen in Figure 11.2(a) and (b), Rh-labeled TATp-bearing, pH-sensitive liposomes incubated at pH 5.0 showed 2.5 times (ImageJ 1.34I data) more internalization than when incubated at pH 7.4 because of better accessibility of TATp for its action after detachment of pH-sensitive PEG corona from liposomal surface under the influence of acidic pH.
11.4.6
In Vivo Study
Trying to cover different physiological conditions, we attempted intratumoral injections of Rh-labeled, TATp-bearing pH-sensitive or pH-insensitive liposomes into LLC tumor bearing mice. An acidic pH at the tumor site is a well-known fact that is of interest while developing physiology-based targeted delivery systems. Under the fluorescence 188
11.4
Discussion and Commentary
80
Percent Biotin bound
70 60 50 40 30 20 10 0 pH 5.0
pH 7.4
Figure 11.1 Binding of pH-sensitive biotin-micelles to NeutrAvidin columns after incubation at room temperature at pH 5.0 and 7.4
(a)
(b)
Figure 11.2 Fluorescence microscopy showing internalization of Rh-PE-labeled/TATp/pH-sensitive liposomes by H9C2 cells after incubation at pH 7.4 (12.2a) and pH 5.0 (12.2b)
microscope with TRITC filter, samples prepared 6 hours post-injection from tumors injected with TATp-bearing, Rh-labeled, pH-sensitive liposomes demonstrated intensive and bright red fluorescence which was four times (as per ImageJ 1.34I data) more than that observed in the samples obtained from the tumors injected with TATp-bearing, Rh-labeled, pH-insensitive liposomes (Figure 11.3(a) and (b)).
11.4.7
In Vivo pGFP Transfection Experiment
We attempted a localized transfection of tumor cells by the direct intratumoral administration of sterically shielded with pH-sensitive (containing mPEG-HZ-PE, 25) or pH-insensitive (containing mPEG-DSPE) conjugates TATp-liposome-pGFP complexes into the tumor tissue by the intratumoral injections. Histologically, hematoxylin/eosin-stained tumor slices in animals injected with both preparations 189
Environmentally Responsive Multifunctional Liposomes
(a)
(b)
Figure 11.3 TRITC image of frozen tissue section treated with intratumoral injection of Rh-labeled/TAT/pH-nonsensitive liposome (a) or Rh-labeled/TATp/pH-sensitive liposome (b) into LLC tumor bearing mice.
showed the identical typical pattern of poorly differentiated carcinoma (polymorphic cells with basophilic nuclei forming nests and sheets and containing multiple sites of neoangiogenesis; Figure 11.4(a) and (b)). However, under the fluorescence microscope with FITC filter, samples prepared 72 hours postinjection from tumors injected with pH-sensitive PEG-TATp-liposome-pGFP complexes demonstrated intensive and bright green fluorescence compared to only minimal GFP fluorescence observed in the samples obtained from the tumors injected with pH-insensitive PEG-TATp-liposome-pGFP complexes (Figure 11.5(a) and (b)). The enhanced pGFP transfection by using pH-sensitive PEG-TATp-liposome-pGFP complexes is an ultimate result of the removal of mPEG-HZ-PE coat under the decreased pH of the tumor tissue, and better accessibility of deshielded TATp moieties in TATp-liposome-pGFP complexes for internalization by the cancer cells allowing for the increased interactions of pGFP with cancer cell nuclei. Owing to their physicochemical properties, the long-circulating (PEGylated) liposomal carriers have the ability to accumulate inside the tumor tissue via the EPR effect, without further escape into undesired nontarget sites. The pH at tumor sites is acidic [12, 13]. Therefore, when TATp-pGFP-liposomes with an additional pH-sensitive
(a)
(b)
Figure 11.4 Histology of tumor tissue after the hematoxylin/eosin staining under bright-field light microscopy. Untreated tumor (a), and treated tumor (b).
190
11.5
(a)
Conclusion
(b)
Figure 11.5 Fluorescence microscopy images of the LLC tumor sections fom the tumors injected with pGFP-loaded TATp-bearing liposomes with the pH-cleavable PEG coat (a) and with the pH-nonclevable PEG coat (b).
PEG coating accumulate in the tumor tissue, the lowered pH-mediated removal of the protective PEG coat takes place, and TATp moieties become exposed and accessible for the interaction with cells. This leads to rapid pGFP pay-load delivery into the cancer cells as result of the extensive TATp-mediated internalization of liposomes, and thereby enhanced transfection. The ImageJ analysis indicated a three times less transfection in the case of PEG-TATp-pGFP-pH-insensitive liposomes as non-detachable PEG coat interferes and sterically hinders the interactions between TATp and target cancer cells.
11.5 Conclusion pH-sensitive mPEG-HZ-PE conjugates based on hydrazone bond chemistry were synthesized. The pH-dependant hydrolytic kinetics could be tuned using appropriate aldehyde or ketone and acyl hydrazide. These conjugates have immense applications in targeted drug delivery systems (e.g., the development of the targeted drug carriers carrying a temporarily hidden function such as cell penetrating peptide, TATp), and a detachable PEG-HZ-PE, which, in addition to prolonging circulation half-life of carriers, can expose TATp function only under the action of certain local stimuli (such as lowered pH), represent a significant step on the way toward “smart” multifunctional pharmaceutical nanocarriers for target accumulation by EPR effect and intracellular penetration in a controlled fashion.
191
Environmentally Responsive Multifunctional Liposomes
Troubleshooting Table Problem
Explanation
Potential Solutions
Presence of impurities in the final Impurities of unreacted starting mate- Optimize the mobile phase compoproduct while separation on silica gel rials or byproducts show up in the nents, and composition taking into column. final product due to many reasons. account sample loading and dimensions of the column. Use specific visualizing agents such as Difficulty in identification of PEG or PEG or PEG-lipid components show Dragendorff for PEG and PEG-lipid conjugates on TLC plates. similar Rf values. Phosphomolybdnum spray reagent for lipids. Difficulty in growing tumors in some Some animals show delayed growth Wait until tumor grows to desired size mice injected with tumor cells by s.c. of tumors after s.c. injection of tumor (allow some more time). route. cell.
11.6 Summary Points 1. Hydrazone-based pH-sensitive linkages were introduced between polyethylene glycol and lipid moieties to synthesize pH-sensitive PEG-PE conjugates. The hydrolytic kinetics of such linkages was monitored using size exclusion chromatographic method. 2. The pH-dependant hydrolytic stability of hydrazone-based linkages is influenced by nature of carbonyl function and substitutions on acyl hydrazide and carbonyl part of the linkage. 3. The in vitro biotin-avidin binding, internalization of fluorescently labeled nanocarriers in the in vitro cell culture using H9C2 cells clearly indicated pH sensitivity of designed environmentally sensitive nanocarriers. 4. A cell penetrating peptide, TATp, was successfully anchored on the surface of environmentally sensitive nanocarriers. 5. Rh-labeled or pGFP complexed, TATp bearing pH-sensitive nanocarriers showed increased accumulation or enhanced transfection, respectively, in tumor bearing mice after intratumoral injections of these prototypes compared to pH-nonsensitive counterpart.
Acknowledgments This work was supported by the NIH grants RO1 HL55519 and RO1 CA121838 to VPT.
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Gümüsderelioglu, M., and Topal, I. U. “Vinyl ether/acrylic acid terpolymer hydrogels synthesized by [gamma]-radiation: characterization, thermosensitivity and pH-sensitivity.” Radiation Physics and Chemistry 73, 2005, 272–279. Hurwitz, E., Wilchek, M., and Pitha, J. “Soluble macromolecules as carriers for daunorubicin.” J. Appl. Biochem. 2, 1980, 25–35. Laguzza, B. C., Nichols, C. L., Briggs, S. L., Cullinan, G. J., Johnson, D. A., Starling, J. J., Baker, A. L., Bumol, T. F., and Corvalan, J. R. “New antitumor monoclonal antibody-vinca conjugates LY203725 and related compounds: design, preparation, and representative in vivo activity.” J Med Chem 32, 1989, 548–555. Beyer, U., Roth, T., Schumacher, P., Maier, G., Unold, A., Frahm, A. W., Fiebig, H. H., Unger, C., and Kratz, F. “Synthesis and in vitro efficacy of transferrin conjugates of the anticancer drug chlorambucil.” J Med Chem 41, 1998, 2701–2708. Kratz, F., Beyer, U., Roth, T., Schutte, M. T., Unold, A., Fiebig, H. H., and Unger, C. “Albumin conjugates of the anticancer drug chlorambucil: synthesis, characterization, and in vitro efficacy.” Arch Pharm (Weinheim) 331, 1998, 47–53. Oishi, M., Nagasaki, Y., Itaka, K., Nishiyama, N., and Kataoka, K. “Lactosylated poly(ethylene glycol)-siRNA conjugate through acid-labile beta-thiopropionate linkage to construct pH-sensitive polyion complex micelles achieving enhanced gene silencing in hepatoma cells.” J Am Chem Soc 127, 2005, 1624–1625. Kataoka, K., Itaka, K., Nishiyama, N., Yamasaki, Y., Oishi, M., and Nagasaki, Y. “Smart polymeric micelles as nanocarriers for oligonucleotides and siRNA delivery.” Nucleic Acids Symp Ser (Oxf), 49, 2005, 17–18. Kong, S. D., Luong, A., Manorek, G., Howell, S. B., and Yang, J. “Acidic hydrolysis of N-Ethoxybenzylimidazoles (NEBIs): Potential applications as pH-sensitive linkers for drug delivery.” Bioconjug Chem 18, 2007, 293–296. Sawant, R. M., Hurley, J.P., Salmaso S., Kale, A. A., Tolcheva, E., Levchenko, T. and Torchilin, V. P. “ ‘Smart’ Drug Delivery Systems: Double-targeted pH-responsive pharmaceutical nanocarriers.” Bioconjugate Chem. 17, 2006, 943–949. Kale, A. A., and Torchilin, V. P. “Design, synthesis, and characterization of pH-sensitive PEG-PE conjugates for stimuli-sensitive pharmaceutical nanocarriers: the effect of substitutes at the hydrazone linkage on the ph stability of PEG-PE conjugates.” Bioconjug Chem 18, 2007, 363–370. Jeffs, L. B., Palmer, L. R., Ambegia, E. G., Giesbrecht, C., Ewanick, S., and MacLachlan, I. “A scalable, extrusion-free method for efficient liposomal encapsulation of plasmid DNA.” Pharm Res 22, 2005, 362–372. Torchilin, V. P., Levchenko, T. S., Rammohan, R., Volodina, N., Papahadjopoulos-Sternberg, B., and D’Souza Gerard, G. M. “Cell transfection in vitro and in vivo with nontoxic TAT peptide-liposome-DNA complexes.” Proc. Natl. Acad. Sci. U. S. A. 100, 2003, 1972–1977. Kale, A. A., and Torchilin, V. P. “Enhanced transfection of tumor cells in vivo using “Smart” pH-sensitive TAT-modified pegylated liposomes.” J Drug Target 15, 2007, 538–545. Sawant, R. M., Hurley, J. P., Salmaso, S., Kale, A. A., Tolcheva, E., Levchenko, T., and Torchilin, V. P.“ ‘Smart’ Drug Delivery Systems: Double-targeted pH-responsive pharmaceutical nanocarriers.” Bioconjug Chem. 17, 2006, 943–949. Torchilin, V. P., Levchenko, T. S., Rammohan, R., Volodina, N., Papahadjopoulos-Sternberg, B. and D’Souza, G. G. M. “Cell transfection in vitro and in vivo with nontoxic TAT peptide-liposome-DNA complexes.” Proceedings of the National Academy of Sciences of the United States of America 100, 2003, 1972–1977. Rideout, D. “Self-assembling drugs: a new approach to biochemical modulation in cancer chemotherapy.” Cancer Invest. 12, 1994, 189-202; discussion 268–269. Lukyanov, A. N., Gao, Z. and Torchilin, V. P. “Micelles from polyethylene glycol/ phosphatidylethanolamine conjugates for tumor drug delivery.” Journal of Controlled Release 91, 2003, 97–102. Apelgren, L. D., Bailey, D. L., Briggs, S. L., et al. “Chemoimmunoconjugate development for ovarian carcinoma therapy: preclinical studies with vinca alkaloid-monoclonal antibody constructs.” Bioconjugate Chem 4, 1993, 121–126. Baker, M. A., Gray, B. D., Ohlsson-Wilhelm, B. M., Carpenter, D. C., and Muirhead, K. A. “Zyn-Linked colchicines: Controlled-release lipophilic prodrugs with enhanced antitumor efficacy.” Journal of Controlled Release 40, 1996, 89–100. Cordes, E. H., and Jencks, W.P. “The Mechanism of hydrolysis of schiff’s bases derived from aliphatic amines.” J. Am. Chem. Soc. 85, 1963, 2843–2848. Harnsberger, H. F., Cochran, E.L., and Szmant, H.H. “The basicity of hydrazones.” J. Am. Chem. Soc. 77, 1955, 5048–5050.
195
CHAPTER
12 Biodegradable, Targeted Polymeric Nanoparticle Drug Delivery Formulation for Cancer Therapy 1,2
Eric M. Pridgen, Frank Alexis,
2,3,4
1,2,4
Robert S. Langer,
and Omid C. Farohkzad
2,3*
1
Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139 MIT-Harvard Center for Cancer Nanotechnology Excellence, Cambridge, MA 02139 3 Labortatory of Nanomedicine and Biomaterials, Departments of Anesthesiology, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA 02115 4 Harvard-MIT Division of Health Sciences and Technology, Cambridge, MA 02139 2
*Corresponding author: Omid C. Farokhzad, M.D., Assistant Professor of Anesthesiology, Harvard Medical School, Department of Anesthesiology, Brigham and Women’s Hospital, 75 Francis Street, Boston, MA 02115, e-mail:
[email protected], Phone: 617-732-6093, Fax: 617-730-2801
Abstract Polymeric nanoparticle delivery systems have the potential to significantly impact the treatment of cancer. Nanoparticles offer the ability to design a delivery vehicle that maximizes the therapeutic index of a drug by encapsulating the drug, targeting it to cancerous tissue, and releasing it in a controlled manner for optimal dosing. This chapter describes the complete technique for the preparation and characterization of a polymeric nanoparticle delivery system. The preparation of the delivery system includes descriptions for the synthesis of the polymers, formation of nanoparticles that encapsulate chemotherapeutic drugs, and surface functionalization with ligands for targeting to cancerous tissue. The characterization of nanoparticle physicochemical properties is described along with the evaluation of the delivery system in a cell-based model for binding, uptake, and cytotoxicity. A discussion of methods to optimize the delivery system is included to provide a guide for the engineering of a delivery system for specific applications.
Key terms
polymeric nanoparticles, biodegradable polymers, cancer therapy, surface functionalization, chemotherapeutic drugs, controlled drug release, aptamers, targeted nanoparticles, drug delivery
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12.1 Introduction Although research efforts over the past 30 years have led to improvements in patient survival, cancer is currently the second-leading cause of death in the United States. One potential way to achieve dramatic improvements in the treatment of cancer is through the use of new technologies. Nanotechnology is an emerging field that the National Cancer Institute (NCI) has recognized as having the potential to make paradigm-changing impacts on the detection, treatment, and prevention of cancer [1]. Nanoparticle delivery systems have the potential to become a key technology in the treatment of cancer. Nanoparticles have several advantages as delivery vehicles that make them useful for cancer therapy. They are typically on the order of 100 nm, comparable in size to many viruses, although these systems can be fabricated over a wide size range [2]. The small size allows nanoparticles to overcome many biological barriers, access tumor tissue through porous vasculature [3, 4], and achieve cellular uptake (Figure 12.1) [5]. The surface of nanoparticles can be engineered to increase blood circulation time and influence biodistribution [6], while targeting ligands attached to the surface can result in enhanced uptake by target tissues [7]. Encapsulation of chemotherapeutic drugs inside nanoparticles can increase the therapeutic index by delivering an elevated dose directly to a tumor while limiting systemic toxicity [8]. Drug release from nanoparticles can either be controlled over a period of time or triggered based on an environmental stimulus specific to the tumor tissue such as pH or temperature [9, 10]. Furthermore, the solubility and stability of chemotherapeutic drugs can be improved through encapsulation, providing an opportunity to reevaluate potential drugs that were previously ignored based on poor pharmacokinetics or high toxicity
Uptake Uptake Binding Binding
Drug DrugRelease Release
Nucleus Nucleus
Extravasation
Targeted, drug-loaded nanoparticles Malignant cells Normal cells Endothelial cells
Figure 12.1 The small size of nanoparticles allows them to extravasate into malignant tissue through leaky tumor vasculature. Targeting ligands on the surface of nanoparticles are able to bind to receptors on malignant cells, causing uptake through receptor-mediated endocytosis. Encapsulated drug can then be released from the nanoparticles in a controlled manner for a therapeutic response.
198
12.1
Introduction
[11]. In addition to chemotherapeutic drugs, imaging agents can also be encapsulated within or conjugated to the surface of nanoparticles to improve tumor detection [12, 13]. Finally, nanoparticles can be engineered to be multifunctional with the ability to target cancerous tissue, carry imaging agents for detection, and deliver a chemotherapeutic payload [14]. The flexibility in design of nanoparticle delivery systems offers an opportunity to develop novel approaches to deliver drugs that may result in alternative or complementary therapeutic options for patients with cancer. Polymeric nanoparticle delivery systems consist of several components that can be engineered based on the desired application. These components are the core, corona, targeting ligand, and payload (Figure 12.2). Considerations of each component are necessary when designing a delivery system because each component affects the overall performance of the system. The core region affects drug encapsulation and release profiles. The corona region influences particle size, blood circulation half-life, and particle stability. Targeting ligands are used to enhance cellular uptake after accumulation in tumor tissue through binding and endocytosis. The payload used is based on the application, but could consist of a chemotherapeutic drug for therapy or imaging agents for detection and monitoring of a tumor. The design criteria for a nanoparticle drug delivery system to treat cancer include the following specifications [15]: 1. Small size (preferably between 10 and 200 nm); 2. High drug loading and encapsulation efficiency; 3. Low rate of aggregation (particle stability); 4. Optimized pharmacokinetics and biodistribution properties.
Corona
Targeting Ligand
· Affects particle size and stability · Influences biodistribution and circulation half-life
Payload · Includes chemotherapeutic drugs and imaging agents · Properties affect encapsulation and release · Loaded by physical entrapment or chemical conjugation
· Increases cellular uptake after accumulation in tumor tissue through binding and endocytosis · Ligands include peptides, antibodies, nucleic acids, carbohyrates, small molecules, and surface morphology
Core · Consists of biodegradable polymer · Properties affect drug encapsulation and release
Figure 12.2 Components of a nanoparticle delivery system and the effects of each component on the properties of the system.
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Biodegradable, Targeted Polymeric Nanoparticle Drug Delivery Formulation for Cancer Therapy
In this chapter, the formulation of targeted, biodegradable polymeric nanoparticle drug delivery systems for cancer therapy will be described. Several different methods will be discussed in order to provide the reader with the flexibility to design a nanoparticle delivery system for a desired application. The materials comprising the core and corona will be biodegradable and biocompatible polymers approved by the U.S. Food and Drug Administration (FDA) for clinical use. The use of approved biomaterials will facilitate the translation of the delivery system into clinical practice. The core will consist of a polyester such as poly(D,L-lactic acid) (PLA) or poly(D,L-lactide-co-glycolic acid) (PLGA) [16]. The safety of these polymers in clinical use is well established, first as a biomaterial in Vicryl sutures [17] and later as excipients for sustained release of parenteral drugs [18]. The surface will be modified with poly(ethylene glycol) (PEG), a hydrophilic polymer that significantly reduces nonspecific interactions with proteins, resulting in increased blood circulation times [19–21]. Targeting ligands such as aptamers and antibodies will be conjugated to the PEG corona through several different chemistries that are common for bioconjugation. Chemotherapeutic drugs will be encapsulated during nanoparticle formation using several different synthesis methods. In addition to a detailed protocol for the formulation of a nanoparticle delivery system, this chapter will also describe how to characterize the physicochemical properties of the delivery system and evaluate the system’s performance in vitro using a cell model.
12.2 Materials 12.2.1
Polymer Synthesis of PLA-PEG and PLGA-PEG
12.2.1.1 Materials for Conjugation via carbodiimide Chemistry •
PLGA-COOH or PLA-COOH (Store under nitrogen at –20°C.)
•
NH2-PEG-X, where X = –CH3, –OH, –MAL, or –COOH (Store at –20°C.)
•
EDC [1-ethyl-3-(3-dimethylaminopropyl)-carbodiimide hydrochloride] (Prepare fresh before use.)
•
Anhydrous dichloromethane (DCM) (Safety note: Avoid contact. Use proper gloves when handling and use in a hood only.)
•
Cold methanol (Safety note: Avoid contact. Highly Flammable. Use proper gloves when handling and use in a hood only with sources of ignition removed.)
12.2.1.2 Materials for Conjugation via Ring Opening Polymerization •
200
-Lactide (Store under nitrogen at –20°C)
D,L
•
HO-PEG-X (where X = –COOH or –MAL) (Store at –20°C)
•
Anhydrous toluene (Safety note: Avoid contact. Highly Flammable. Use proper gloves when handling and use in a hood only with sources of ignition removed.)
•
Tin(II) 2-ethylhexanoate (Store under dry conditions.)
•
Sodium sulfate
•
Cold methanol (Safety note: Avoid contact. Highly Flammable. Use proper gloves when handling and use in a hood only with sources of ignition removed.)
12.2
Materials
•
Acetonitrile (Safety note: Avoid contact. Highly Flammable. Use proper gloves when handling and use in a hood only with sources of ignition removed.)
•
Chloroform (Safety note: Avoid contact. Use proper gloves when handling and use in a hood only.)
•
Magnesium sulfate
•
47 mm PTFE filter membrane, 0.45 μm
12.2.2
Nanoparticle Formation
12.2.2.1 Materials •
PLGA-PEG or PLA-PEG polymer (from Section 12.3.1)
•
Drug of interest
•
Acetonitrile (Safety note: Avoid contact. Highly Flammable. Use proper gloves when handling and use in a hood only with sources of ignition removed.)
•
Ultrapure water
•
Millipore Amicon Ultra-4 or Ultra-15 centrifugal filter units (NMWL – 100 kDa)
•
Dichloromethane (DCM) (Safety note: Avoid contact. Use proper gloves when handling and use in a hood only.)
•
Polyvinyl alcohol (PVA) (Molecular weight – 30 kDa). Prepare 1% (w/v) or 0.3% (w/v) PVA in water solution.
12.2.2.2 Facilities/Equipment •
Probe sonicator
12.2.3
Ligand Conjugation
12.2.3.1 Materials •
PLA-PEG-COOH or PLGA-PEG-COOH (carbodiimide chemistry)
•
PLA-PEG-MAL or PLGA-PEG-MAL (maleimide-thiol chemistry)
•
Ligand of interest
•
Phosphate-buffered saline (PBS), pH 7.4
•
Ultrapure water (RNase/DNase free depending on the targeting ligand)
•
EDC [1-ethyl-3-(3-dimethylaminopropyl)-carbodiimide hydrochloride] (Prepare fresh before use.)
•
NHS (N-hydroxysuccinimide) (Prepare fresh before use.)
•
Millipore Amicon Ultra-4 or Ultra-15 centrifugal filter units (NMWL – 100 kDa)
•
2-Iminothiolane-HCl (Traut’s Reagent) or other reagent for introducing thiol groups (Prepare fresh before use.)
12.2.4
Quantification of Drug Encapsulation
12.2.4.1 Materials •
Drug-encapsulating nanoparticles 201
Biodegradable, Targeted Polymeric Nanoparticle Drug Delivery Formulation for Cancer Therapy
•
Water (HPLC grade)
•
Acetonitrile (HPLC grade)
12.2.4.2 Facilities/Equipment •
HPLC system with UV detector
•
Reversed-phase column (column specifications will be specific to drug used)
12.2.5
Release Experiments
12.2.5.1 Materials •
Drug-encapsulating nanoparticles
•
Phosphate-buffered saline (PBS), pH 7.4
•
Dialysis units (Molecular weight cutoff will be dependent on drug molecular weight)
•
Acetonitrile (Safety note: Avoid contact. Highly Flammable. Use proper gloves when handling and use in a hood only with sources of ignition removed.)
12.2.5.2 Facilities/Equipment •
HPLC system with UV detector
•
Reversed-phase column (column specifications will be specific to drug used)
12.2.6
Postformulation Treatment
12.2.6.1 Materials •
Millipore Amicon Ultra-4 or Ultra-15 centrifugal filter units (NMWL – 100 kDa)
•
10% (w/v) sucrose in water solution
12.2.7
Cell Binding and Uptake Experiments
12.2.7.1 Materials
202
•
8-well microscope chamber slides
•
6-well tissue culture plates
•
Cell growth medium
•
Opti-MEM reduced-serum medium
•
Fluorescent nanoparticles
•
Phosphate-buffered saline (PBS), pH 7.4
•
4% (v/v) formaldehyde in ultrapure water
•
0.1% (v/v) Triton-X in PBS
•
Rhodamine phalloidin (available from Invitrogen) (Dilute 20 μL of dye in 1 mL PBS)
•
Mounting medium with or without DAPI
•
Trypsin, 0.25% (1×) with EDTA
12.3
Methods
12.2.7.2 Facilities/Equipment •
Fluorescence microscope or confocal fluorescence microscope
•
Flow cytometer with appropriate lasers and detectors
12.2.8
Cytotoxicity Experiments
12.2.8.1 Materials •
48-well tissue culture plates
•
Cell growth medium
•
Opti-MEM reduced-serum medium
•
Drug-encapsulating nanoparticles
•
CellTiter 96 AQueous One Solution Cell Proliferation Assay (MTS) (available from Promega)
•
Phosphate-buffered saline (PBS), pH 7.4
12.2.8.2 Facilities/Equipment Plate reader
•
12.3 Methods A complete description of the techniques used for the formulation and characterization of a nanoparticle delivery system for cancer therapy is provided in this section. A summary of the steps and characterization parameters is provided in Figure 12.3. Section 12.3.1 describes the synthesis of poly(D,L-lactic acid)-block-poly(ethylene glycol) (PLA-PEG) and poly(D,L-lactide-co-glycolic acid)-block-poly(ethylene glycol) (PLGA-PEG) diblock copolymers using carbodiimide chemistry or ring opening polymerization (ROP). The resulting copolymers are then formed into nanoparticles in Section 12.3.2 using several different methods, which also allow the encapsulation of chemotherapeutic drugs. Section 12.3.3 details the conjugation of targeting ligands to
PLGA +
PLGA-PEG
PEG
Drug
Ligand
+
Polymer Synthesis
Nanoparticle Formation
Ligand Conjugation
In vitro Evaluation
· Molecular weight · Chemical structure
· Particle size and shape · Surface charge · Drug encapsulation · Drug release
· Ligand density · Ligand orientation
· Cell binding and uptake · Cytotoxicity
Figure 12.3 Overall procedure for formulation and characterization of polymeric, targeted nanoparticles. The major steps are in bold and key characterization parameters are listed beneath each step.
203
Biodegradable, Targeted Polymeric Nanoparticle Drug Delivery Formulation for Cancer Therapy
the nanoparticle surface to complete the delivery system. In Sections 12.3.1 through 12.3.3, relevant characterization tools are discussed. Further characterization of the encapsulation and release of the drug component are described in Sections 12.3.4 and 12.3.5. Long-term storage of nanoparticles is detailed in Section 12.3.6. Finally, in vitro experiments are described in Sections 12.3.7 and 12.3.8 to evaluate the delivery system in the context of a cell-based disease model.
12.3.1
Polymer Synthesis of PLA-PEG and PLGA-PEG
In this section, the synthesis of PLGA-b-PEG and PLA-b-PEG is described using two different methods. The first method is the conjugation of commercially available PLGA or PLA to PEG using carbodiimide chemistry. Carbodiimides are zero-length cross-linkers used to aid in the formation of amide linkages between carboxylate (–COOH) and amine (–NH2) functional groups. EDC [1-ethyl-3-(3-dimethylaminopropyl)-carbodiimide hydrochloride] is a popular carbodiimide. It can react with carboxylic acids to form a highly reactive O-acylisourea intermediate, which can then react with a nucleophile such as a primary amine to form an amide bond [22]. Other potential reactions can occur between the active intermediate and thiol groups or oxygen atoms such as those in water. Using EDC, PLGA-COOH or PLA-COOH is conjugated to heterobifunctional NH2-PEG-X, where X represents possible functional groups such as methyl (–CH3), hydroxyl (–OH), maleimide (–MAL), or carboxylate (–COOH) (Figure 12.4(a)). In the case of a carboxylic acid on the PEG, EDC is used to activate the carboxylic acid on PLGA or PLA and then separated from the polymer before addition of the NH2-PEG-COOH (Figure 12.4(b)). The only functional group that X cannot be is an amine because the PLGA or PLA could then conjugate to both ends of the PEG unless it is desired to use PLA-PEG-PLA or PLGA-PEG-PLGA to form nanoparticles. The choice of functional group on the PEG depends on the active functional group of the targeting ligand that will be conjugated or the surface properties desired for the nanoparticle. The second method for synthesizing PLA-b-PEG is to use ring opening polymerization (ROP) (Figure 12.4(c)). In this method, the PEG must have a hydroxyl functional group (HO-PEG-X, where X = –COOH or –MAL) from which D,L-lactide can polymerize through ROP using tin(II) 2-ethylhexanoate as a catalyst. Similarly, PLyGzA copolymers are synthesized using a mixture of D,L-lactide (y molar) and D,L-glycolide (z molar). Reactivity of D,L-glycolide is higher than D,L-lactide, so control of the random copolymerization content should be optimized using different molar ratios of the two monomers. Following synthesis, the polymers should be characterized with nuclear magnetic resonance (NMR) for chemical structure and conjugation efficiency as well as gel permeation chromatography (GPC) for polymer molecular weight.
12.3.1.1 Protocol for Conjugation via Carbodiimide Chemistry (if X = –CH3, –OH, or –MAL) 1. Dissolve PLGA-COOH or PLA-COOH in anhydrous DCM at a concentration of 10 mg/mL. 2. Dissolve NH2-PEG-X in anhydrous DCM at a concentration of 10 mg/mL in a separate vial.
204
12.3
Methods
EDC
PL x G y A-COOH
PL x G y A-PEG-X
H 2N-PEG-X (a)
PL x G y A-COOH
PL x G y A-PEG-COOH
EDC
H 2N-PEG-COOH
Remove excess EDC Activated PL x G y A
Activated PL x G y A
+
EDC (b)
Sn(Oct) 2 catalyst
HO-PEG-X
PLA-PEG-COOH Lactide monomer (c)
Figure 12.4 Schematic diagrams of diblock polymer synthesis via carbodiimide chemistry conjugation using NH2-PEG-X with (a) X = –CH3, –OH, –MAL, and (b) X = –COOH or via (c) ring opening polymerization using tin(II) 2-ethylhexanoate (Sn(Oct)2) as a catalyst.
3. Dissolve EDC in anhydrous DCM at a concentration of 10 mg/mL in a separate vial. 4. Add EDC to PLGA or PLA using a 5× molar excess of EDC and vortex. 5. Add PEG to the PLGA/EDC or PLA/EDC solution using a 2× molar excess of PEG. 6. React overnight for 15–20 hours at room temperature while stirring. Cover the solution to protect from light. 7. Precipitate the polymer in cold water or cold methanol. 8. Centrifuge the resulting solution for 30 minutes at 2,500 rpm. 9. Discard the supernatant and dry resulting pellet under vacuum until solvent is removed. 10. Store polymer under nitrogen at –20°C. 205
Biodegradable, Targeted Polymeric Nanoparticle Drug Delivery Formulation for Cancer Therapy
12.3.1.2 Protocol for Conjugation via Carbodiimide Chemistry (if X = –COOH) 1. Dissolve PLGA-COOH or PLA-COOH in anhydrous DCM at a concentration of 10 mg/mL. 2. Dissolve NH2-PEG-X in anhydrous DCM at a concentration of 10 mg/mL in a separate vial. 3. Dissolve EDC in anhydrous DCM at a concentration of 10 mg/mL in a separate vial. 4. Mix EDC with PLGA or PLA using a 5× molar excess of EDC and allow reaction to occur for 2 hours at room temperature while stirring. Cover the solution to protect from light. 5. Precipitate the polymer in cold water or cold methanol. 6. Centrifuge the resulting solution for 30 minutes at 2,500 rpm. 7. Discard the supernatant. Repeat twice to remove all EDC. 8. After the third wash, dry the resulting pellet under vacuum until solvent is removed. 9. Dissolve the activated PLGA or PLA in anhydrous DCM at a concentration of 10 mg/mL. 10. Add PEG to the PLGA solution using a 2× molar excess of PEG. 11. React overnight for 15–20 hours at room temperature while stirring. Cover the solution to protect from light. 12. Precipitate the polymer in cold water or cold methanol. 13. Centrifuge the resulting solution for 30 minutes at 2,500 rpm. 14. Discard the supernatant and dry resulting pellet under vacuum until solvent is removed. 15. Store polymer under nitrogen at –20°C.
12.3.1.3 Protocol for Conjugation via Ring Opening Polymerization 1. Dissolve vacuum-dried D,L-Lactide (1.6 g, 11.1 mmol) and HO-PEG-X (0.289 g, 0.085 mmol) in anhydrous toluene (10 mL) containing anhydrous Na2SO4 (200 mg, 1.4 mmol) in a round-bottom flask (see Figure 12.5 for experimental setup). 2. Heat to a reflux temperature of 120°C. 3. Add tin (II) 2-ethylhexanoate (20 mg, 0.05 mmol) to initiate the polymerization. Stir for 15 minutes to remove all water. 4. Stir for 12 hours with reflux. 5. Cool solution to room temperature. 6. Add cold water (10 mL) and stir vigorously at room temperature for 30 minutes to hydrolyze unreacted lactide monomer. 7. Transfer resulting mixture to a separation funnel containing chloroform (50 mL) and water (30 mL). 8. After layer separation, collect the organic layer (bottom layer) and dry by adding anhydrous magnesium sulfate (200 mg). 9. Filter the solution using 0.45 μm PTFE filter membrane and concentrate under vacuum. 10. Dissolve the dried material in acetonitrile. 11. Pour solution in cold methanol for precipitation. 12. Centrifuge resulting solution for 10 minutes at 4,000 rpm. 206
12.3
Methods
Oil bath temperature control
Condenser
Screw caps
Water flow at room temperature
Round-bottom flask with stir bar
Stir plate
Oil bath
Figure 12.5
Experimental setup for ring opening polymerization reaction.
13. Remove the supernatant and dry resulting pellet under vacuum until solvent is removed. 14. Store polymer under nitrogen at –20°C.
12.3.2
Nanoparticle Formation
This section describes three methods for the formation of nanoparticles using the polymers synthesized in Section 12.3.1. The three methods are nanoprecipitation [23], oil-in-water (o/w) emulsification-solvent evaporation (single emulsion) [24, 25], and water-in-oil-in-water (w/o/w) emulsification-solvent evaporation (double emulsion) [6] (Figure 12.6). The choice of method is usually dependent on the drug physicochemical properties along with the requirements for encapsulation and particle size. Nanoprecipitation and single emulsion are methods typically used to encapsulate lipophilic drugs. The nanoprecipitation technique requires the drug to be soluble in a water-miscible organic solvent. Nanoparticles are formed instantaneously upon addition of the organic phase to the aqueous phase due to rapid solvent displacement, resulting in a reduced particle size without the need for sonication or homogenization [26]. The single emulsion technique requires the drug to be soluble in a water-immiscible organic solvent. Oil-in-water emulsions are formed with the addition of surfactants after sonication or homogenization. Solvent evaporation results in polymer precipitation into nanoparticles. The third method, double emulsion, is used to encapsulate hydrophilic drugs. In this technique, the drug is dissolved in the aqueous phase and emulsified with a surfactant in a water-immiscible organic solvent containing the polymer. This first emulsion is then added to a second aqueous phase with or without surfactant to form the second emulsion, where polymer precipitation into nanoparticles occurs due to solvent evaporation. 207
Biodegradable, Targeted Polymeric Nanoparticle Drug Delivery Formulation for Cancer Therapy
Nanoprecipitation
Single Emulsion (o/w)
Double Emulsion (w/o/w)
Drug/polymer solution added dropwise to aqueous solution
Incubate to allow solvent displacement
Sonicate or homogenize
Incubate to allow solvent evaporation
Sonicate or homogenize
Form primary emulsion (w/o)
Add aqueous phase Sonicate or homogenize Surfactant Polymer Drug Organic Phase Aqueous Phase
Form secondary emulsion (w/o/w) Incubate to allow solvent evaporation
Figure 12.6 Nanoparticle formation using the nanoprecipitation, single emulsion, or double emulsion method.
The double emulsion technique typically yields nanoparticles with larger sizes than in the other two methods [27]. Once formed, the particle size and surface charge of the nanoparticles should be characterized. Light scattering techniques can be used to determine particle size and population uniformity, while electron microscopy (TEM) can be used to image the size, shape, and uniformity of the nanoparticle population. Light scattering instruments can also be used to measure the zeta potential of the nanoparticles, providing a measurement of the surface charge of the nanoparticles. Encapsulation of the drug in the nanoparticles must also be characterized, and this process is described in Section 12.3.4.
12.3.2.1 Protocol for Nanoprecipitation Method 1. Dissolve 1 mg of polymer in 100 μL of acetonitrile and 100 μg of drug in 100 μL of acetonitrile. 2. Add the polymer/drug solution (200 μL total volume) dropwise to 400 μL of ultrapure water under stirring. 3. Mix the resulting solution for at least 2 hours. 4. Wash the nanoparticle solution at least twice with ultrapure water using 100 kDa Amicon filters. The nanoparticles should be centrifuged at 3,000 rpm or less. 5. Resuspend the nanoparticles in the desired buffer.
12.3.2.2 Protocol for Single Emulsion Method 1. Dissolve 20 mg of polymer and 0.5 mg of drug in 1 mL of dichloromethane (DCM). 2. Add the resulting solution to 2 mL of PVA (1% w/v) in ultrapure water. 208
12.3
Methods
3. Sonicate with a probe sonicator for 15–30 seconds at 10W. 4. Stir moderately overnight in a hood to evaporate the solvent. 5. Wash the nanoparticle solution at least twice with ultrapure water using 100 kDa Amicon filters. The nanoparticles should be centrifuged at 3,000 rpm or less. 6. Resuspend the nanoparticles in the desired buffer.
12.3.2.3 Protocol for Double Emulsion Method 1. Dissolve 1 mg of the drug in 2 mL of ultrapure water to prepare a drug stock solution. 2. Dissolve 50 mg of the polymer in 1 mL of DCM to prepare a polymer stock solution. 3. Add 50 μL of drug solution to 1 mL of the polymer solution and emulsify the mixture using a probe sonicator at 10W for 15–30 seconds. 4. Add 2 mL of 1% w/v PVA in water to the emulsion and sonicate for 15 seconds at 10W using a probe sonicator. 5. Pour the resulting solution into 50 mL of aqueous PVA (0.3% w/v) with gentle stirring. 6. Stir the solution overnight to allow evaporation of the solvent. 7. Wash the nanoparticle solution at least twice with ultrapure water using 100 kDa Amicon filters. The nanoparticles should be centrifuged at 3,000 rpm or less. 8. Resuspend the nanoparticles in the desired buffer.
12.3.3
Conjugation of Targeting Ligand
This section describes two different chemistries for the conjugation of targeting ligands to the nanoparticle surface: carbodiimide and maleimide-thiol chemistry (Figure 12.7). Conjugation occurs through functional groups on the ligand and the end of the PEG corona. Carbodiimide chemistry, which was used for polymer conjugation, forms a stable amide linkage between carboxylate and amine functional groups. Maleimide-thiol chemistry forms a stable thioester linkage between maleimide and thiol (–SH) functional groups. Both chemistries result in covalent linkages that are favored over noncovalent linkages for stability in the physiological environments (pH, high salt concentrations) of the body. The choice of conjugation chemistry depends on the targeting ligand and the desired surface properties of the delivery system. For instance, nucleic acid ligands can be modified with thiol, carboxylate, or amine end groups for conjugation. If a negative surface charge is desired, which has been shown to minimize interactions with proteins in the blood [28], a carboxylate functional group at the end of the PEG would be favored, leading to the use of amine-modified nucleic acid ligands for conjugation. In addition, the negative charge may be used to prevent electrostatic interactions between the surface and the negatively charged nucleic acids, resulting in less physical adsorption of the ligand. This is described later in the protocol for ligand conjugation using carbodiimide chemistry. In the case of peptide ligands, the addition of a cysteine amino acid with a free thiol group favors the use of maleimide-thiol chemistry for the conjugation [29]. For protein ligands such as antibodies, the maleimide-thiol conjugation chemistry is also commonly used [30–32]. The frequency of free thiol groups in proteins is usually low compared with groups such as carboxylates and amines [22]. Conjugation using these groups will therefore restrict the thioester linkage to a limited number of sites within the 209
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NP
NHS
NH2 -modified aptamer NP
NP
EDC (a)
NP
2-Iminothiolane HCI Antibody
NP Thiol-modified antibody (b)
Figure 12.7 Schematic diagrams of ligand conjugation chemistries. (a) Conjugation of an amine-modified aptamer with a stable amide bond using carbodiimide chemistry. (b) Conjugation of an antibody with a stable thioester bond using maleimide-thiol chemistry.
protein targeting ligand. Free thiol groups can also be introduced into a protein ligand using reagents that modify amine groups. One commercially available reagent is 2-iminothiolane (Traut’s Reagent) (Pierce). A description of this is included later in the protocol for ligand conjugation using maleimide-thiol chemistry. Other reagents such as N-succinimidyl S-acetylthioacetate (SATA) (Pierce) have spacers between the amine linkage and the free thiol generated so that the thiol group is away from the surface of the protein, potentially improving the conjugation efficiency. One disadvantage of using the thiol chemistry is the potential for disulfide formation between thiol groups on different proteins, leading to cross-linking of the targeting ligand. This can be mitigated through the addition of chelating agents such as ethylenediaminetetraacetic acid (EDTA) or slightly acidic pH. After conjugation of the targeting ligand to the nanoparticle delivery system, the conjugation should be confirmed and quantified. By attaching a fluorescent probe to the ligand, the presence of the ligand on the nanoparticles can be qualitatively confirmed using fluorescence microscopy, flow cytometry, or a fluorescence plate reader. In addition, gel electrophoresis can be used to separate nanoparticles with ligand from free ligand to confirm conjugation. For quantitative assessment of conjugation, assay kits such as Picogreen (DNA), Ribogreen (RNA), or BCA (protein) can be used to measure the amount of ligand on the surface of the nanoparticles.
12.3.3.1 Protocol for Ligand Conjugation via Carbodiimide Chemistry 1. Suspend PLA-PEG-COOH or PLGA-PEG-COOH nanoparticles in phosphate-buffered saline (PBS), pH 7.4, at 10 mg/mL. 210
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2. Add a 5× molar excess of EDC and a 10× molar excess of NHS to the nanoparticle solution and incubate for 20 minutes at room temperature. 3. Rinse the nanoparticle solution three times with PBS using 100 kDa Amicon filters to remove excess EDC. 4. Add 1 mg/mL amine-modified nucleic acid ligand in a 1:1 molar ratio (ligand:polymer ratio) and incubate 1 hour at 37°C with gentle agitation or 4 hours on ice. 5. Rinse nanoparticle solution twice with ultrapure water using 100 kDa Amicon filters to remove unconjugated ligand and suspend nanoparticles in desired buffer. The nanoparticles should be centrifuged at 3,000 rpm or less. The targeted nanoparticle delivery system is now ready for characterization or use.
12.3.3.2 Protocol for Ligand Conjugation via Maleimide-Thiol Chemistry 1. Dissolve protein ligand in PBS, pH 7.4, at a concentration of 10 mg/mL. If using protein or peptide ligand with free thiol groups, skip to step 4. 2. Dissolve 2-iminothiolane-HCl (Traut’s Reagent) in PBS at a concentration of 5 mg/mL. 3. Mix protein solution with Traut’s Reagent solution with a 40× (10×–50×) molar ratio of Traut’s Reagent to modify protein with a free thiol group and incubate for 1 hour at room temperature. 4. After incubation, add the resulting solution to 10 mg/mL PLA-PEG-MAL or PLGAPEG-MAL nanoparticles in PBS. The ligand should be added in a 5% (1%–50%) molar ratio (protein:polymer). 5. Incubate the resulting solution overnight at 4°C with gentle agitation. 6. Rinse nanoparticle solution twice with ultrapure water using 100 kDa Amicon filters to remove unconjugated ligand and suspend nanoparticles in desired buffer. The nanoparticles should be centrifuged at 3,000 rpm or less. The targeted nanoparticle delivery system is now ready for characterization or use. When using aptamer ligands, it is necessary to heat the aptamers prior to conjugation to expose the functional group. Aptamers can be heated at 90°C for 5 minutes or 60°C for 15 minutes, and then incubated with the nanoparticles. Nanoparticles can also be heated, although heating will increase the drug release rate.
12.3.4
Quantification of Drug Encapsulation
This section describes the quantification of drug encapsulation within nanoparticles. Drug encapsulation is measured using either a direct or indirect method. For the direct method, nanoparticles are dissolved in an organic solvent that the polymer is soluble in to extract the drug. The extracted drug is then quantified using a convenient assay. For many drugs, reversed-phase high performance liquid chromatography (RP-HPLC) with UV detection is used for quantification based on a calibration curve. In this case, a convenient solvent to use for extraction is acetonitrile because it is present in the RP-HPLC mobile phase. The following protocol describes quantification using the direct method. For the indirect method, the drug present in the aqueous phase after encapsulation is measured to determine the amount of drug that was not encapsulated in the 211
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nanoparticles. The flow-through during the nanoparticle wash steps must be collected and the drug present is quantified using a convenient assay. From the quantification of the drug, the drug encapsulation efficiency and the drug loading can be calculated according to the following equations. Encapsulation Efficiency ( %) = Drug loading ( %) =
Mass of encapsulated drug ∗100 Mass of initial drug
Mass of encapsulated drug ∗100 Mass of polymer used for encapsulation
(12.1)
(12.2)
12.3.4.1 Protocol for Quantification of Drug Encapsulation 1. Collect 500 μg of drug-encapsulating nanoparticles in 200 μL of water or PBS solution. 2. Add 200 μL of acetonitrile to the nanoparticle solution, mix vigorously, and incubate for 24 hours. If the incubation lasts longer than 24 hours, store sample at 4°C to minimize the evaporation of the acetonitrile. 3. Quantify the drug in the resulting solution using RP-HPLC.
12.3.5
Drug Release Studies
This section describes the measurement of drug release profiles for nanoparticle delivery systems. There are two methods used to measure release rate, with the choice dependent on the solubility of the drug in the release medium. With either method, drug-encapsulating nanoparticles are contained within a dialysis unit that is incubated in release medium. The dialysis membrane must have a molecular weight cutoff that allows the drug to diffuse through while retaining the nanoparticles. The release medium and conditions should mimic the physiological conditions under which the drug will be released in the body. For instance, many release studies are conducted with nanoparticles incubated at 37°C in PBS, as described later in the release protocols. For drugs with low solubility in the release medium, a large reservoir of the release medium should be used to maintain the condition of infinite sink for the drug. Nanoparticles are collected from the dialysis unit at specified time points for measurement of the drug remaining in the nanoparticles using the direct method described in Section 12.3.4. For drugs with higher solubility in the release medium, the same method can be used. However, an alternative method is to use a small reservoir of release medium. Samples can be collected from the release medium to quantify the drug released using the indirect method described in Section 12.3.4. This method reduces the amount of material required for the study. The frequency of sampling in each method will depend on the release rate, with more time points required during the faster release periods.
12.3.5.1 Protocol for Release Experiment with a Low-Solubility Drug 1. Prepare 15 mg of drug-encapsulating nanoparticles and resuspend in PBS, pH 7.4, with a final concentration of 2.5 mg nanoparticles/mL; 15 mg will be enough
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material for 10 time points using 500 μg per sample and triplicate samples at each time point. 2. Split nanoparticles equally into 27 dialysis units (500 μg nanoparticles per unit, 200 μL of sample). The remaining three 500-μg samples should be used to measure the drug mass at t = 0 minutes. 3. Incubate the dialysis units in 4L of PBS buffer at 37°C with gentle stirring. 4. At each time point, collect the nanoparticle samples from three dialysis units and keep separate for triplicate measurements of the drug release. To evaluate possible burst release from nanoparticles, early time points should be analyzed (15, 30, and 60 minutes). 5. Add 200 μL of acetonitrile to each 200-μL nanoparticle sample, mix vigorously, and incubate for a minimum of 24 hours. 6. Quantify the drug mass. The release medium should be changed frequently, such as every hour or at every time point, to ensure that the infinite sink condition remains throughout the release study. The drug release can be calculated using the drug mass (MD) measured at t = 0 minutes and at a specified time point n as shown in (12.3). Drug Release (t = n,%) =
M D (t = 0) − M D (t = n) ∗100% M D (t = 0)
(12.3)
12.3.5.2 Protocol for Release Experiment with High-Solubility Drug 1. Prepare 1.5 mg of drug-encapsulating nanoparticles and resuspend in PBS, pH 7.4, with a final concentration of 2.5 mg nanoparticles/mL. 2. Split nanoparticles equally into three dialysis units (500 μg nanoparticles per unit, 200 μL sample). 3. Incubate the dialysis units in 1 mL of PBS buffer at 37°C. 4. At each time point, collect 100 μL of dialysate from each of the three samples and replace it with 100 μL of fresh PBS buffer. 5. Quantify the drug mass. The drug release can be calculated using the drug mass (MD) measured at t = 0 minutes and the drug mass measured in the release medium at a specified time point n. However, the mass of drug removed (MD,R) from the release medium for sampling at each time point must be accounted for when calculating the total mass of drug released at each time point. Drug Release (t = n,%) =
12.3.6
M D (t = n) + M D, R ∗100% M D (t = 0)
(12.4)
Postformulation Treatment
This section describes the treatment of nanoparticles post-formulation to improve stability if the nanoparticles will be stored instead of immediately used. Lyophilization or freezing at –20°C are two methods used for the long-term storage of nanoparticles. In both methods, a lyoprotectant (or cryoprotectant) needs to be added to prevent 213
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aggregation such as sucrose or trehalose [33]. To ensure that the nanoparticles recovered after storage are the same, they should be tested for changes in particle size, drug encapsulation, and drug activity.
12.3.6.1 Method for Particle Storage with Sucrose Lyoprotection 1. Prepare drug-encapsulating nanoparticles. 2. After washing the nanoparticles with water using a 100-kDa filter, resuspend nanoparticles in 10% (w/v) sucrose with a 4:1 mass ratio of sucrose to nanoparticles. The final nanoparticle concentration should be 2 mg/mL. 3. Store nanoparticles either by freezing at –20°C or by lyophilization. 4. When ready for use, resuspend nanoparticles in desired medium and wash three times with a 100-kDa Amicon filter to remove all sucrose from the sample.
12.3.7
In Vitro Experiments: Cell Binding and Uptake Studies
This section describes in vitro experiments aimed at studying the cell binding and uptake of the nanoparticle delivery system. If using nontargeted nanoparticles, the purpose of the experiment is to demonstrate that the delivery system is taken up by the cells of interest. For targeted nanoparticles, the purpose is to show that the targeted delivery system has enhanced selective binding and uptake by cells expressing the targeted receptor. To do these types of studies, a cell line is required that expresses the targeted receptor as well as a control cell line that does not express the receptor. Fluorescence is a convenient tool for cell uptake experiments using fluorescent nanoparticles, although radioactivity is also used for these types of experiments. There are two ways to prepare fluorescent nanoparticles. One method is to encapsulate a hydrophobic fluorescent dye such as NBD cholesterol (22-(N-(7-nitrobenz-2-oxa-1, 3-diazol-4-yl)amino)-23,24-bisnor-5-cholen-3β-ol) (Invitrogen) within nanoparticles using the same protocol used to encapsulate hydrophobic drugs [34]. While this method is simple, the disadvantage is that the dye can escape from the nanoparticle during incubation with cells. A second method which avoids this issue is to conjugate a dye such as AlexaFluor (Invitrogen) to PLA or PLGA through an amine functional group using the carbodiimide chemistry described earlier. This approach will slow the release of dye during the incubation. Using either approach, fluorescent nanoparticles with and without the targeting ligand can be formulated and tested for uptake and specificity. Binding and uptake by cells can be observed qualitatively using fluorescence microscopy or quantitatively using flow cytometry. Microscopy allows the determination of whether nanoparticles are bound to the surface of a cell or internalized within the cell [35]. Colocalization studies can also be conducted to determine whether the nanoparticles end up in endosomes, lysosomes, or escape into the cytoplasm of the cell [36]. Flow cytometry can only quantify nanoparticle internalization by cells since the following method uses trypsin to collect the cells.
12.3.7.1 Protocol for Fluorescence Microscopy Imaging 1. Grow adherent cells on 8-well microscope chamber slides in appropriate cell growth medium until the cells are 70% confluent.
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2. On the day of the experiment, remove cell growth medium and incubate the cells with Opti-MEM medium prewarmed to 37°C for 30 minutes. 3. Fluorescent nanoparticles should be prepared in PBS and concentrated to 4 mg/mL. 4. Add 50 μg of fluorescent nanoparticles to the cells and incubate for 2 hours at 37°C. 5. Remove nanoparticles and gently wash the cells twice with 500 μL PBS. 6. Add 250 μL of 4% formaldehyde to fix the cells and incubate for 20 minutes. 7. Wash cells twice with 500 μL PBS. 8. Add 250 μL of 0.1% Triton-X and incubate for 3 minutes. 9. Wash cells twice with 500 μL PBS. 10. Add 250 μL of rhodamine phalloidin dye and incubate for 20 minutes. (This step is only necessary if interested in staining the cytoskeleton.) 11. Wash cells twice with 500 μL PBS. 12. Remove all liquid and mount cells using mounting medium with DAPI if staining the nucleus. Otherwise, use the mounting medium without DAPI. 13. Image cells using a fluorescence microscope or confocal fluorescence microscope.
12.3.7.2 Protocol for Quantification of Internalization by Flow Cytometry 1. Grow adherent cells on 6-well plates in appropriate cell medium until the cells are 70% confluent. 2. On the day of the experiment, remove cell growth medium and incubate the cells with Opti-MEM media prewarmed to 37°C for 30 minutes. 3. Fluorescent nanoparticles should be prepared in PBS and concentrated to 4 mg/mL. 4. Add 100 μg of fluorescent nanoparticles to the cells and incubate for 2 hours at 37°C. 5. Remove nanoparticles and gently wash the cells twice with PBS. 6. Add 500 μL of trypsin and incubate until cells release from the plate surface. 7. Add 3 mL of media to the cells. 8. Collect cells and centrifuge for 1 minute at 1,000 ×g to recover the cells. 9. Remove the media and resuspend in a buffer such as PBS for flow cytometry analysis. 10. Analyze cells using flow cytometry.
12.3.8
In Vitro Experiments: Cytotoxicity Studies
This section describes in vitro experiments aimed at studying the cytotoxicity of the nanoparticle delivery system. The purpose of these experiments is to demonstrate the enhanced toxicity of the nanoparticles in cells expressing the targeted receptor compared with cells that do not express the receptor due to enhanced uptake. These experiments are similar to the uptake studies described in the previous section except that the nanoparticles contain a drug instead of a fluorescent dye. Toxicity is evaluated using a cell proliferation assay such as the MTS assay (Promega).
12.3.8.1 Protocol for Cytotoxicity Study 1. Grow adherent cells on 48-well plates in appropriate cell growth medium until the cells are 70% confluent.
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2. On the day of the experiment, remove cell growth medium and incubate the cells with Opti-MEM medium prewarmed to 37°C for 30 minutes. 3. Drug-encapsulating nanoparticles should be prepared in PBS, washed just prior to use to remove any free drug, and concentrated to 4 mg/mL. 4. Add varying amounts of nanoparticles to the cells and incubate for 1 hour at 37°C. 5. Remove nanoparticles and gently wash the cells twice with PBS. 6. Incubate the cells for 72 hours in cell growth medium without changing the medium to allow cells to proliferate. 7. Add MTS reagent to cells and quantify cell proliferation using a plate reader. When seeding cells, the outer wells of the plates show greater variability, so only the inner wells should be used for more consistent results. The optimal nanoparticle concentration and incubation times will need to be determined experimentally for each delivery system and each cell model. The incubation times should be kept short since longer times allow the drug to escape from the nanoparticles. The free drug could then be taken up by the cells and contribute to the toxicity for both targeted and nontargeted nanoparticles.
12.4 Data Acquisition, Results, and Interpretation In the methods described in the previous section, data was generated for three main goals: characterization of the synthesized polymers, characterization of the nanoparticle delivery system, and evaluation of performance in an in vitro cell model. This section will discuss the acquisition and interpretation of that data.
12.4.1
Polymer Characterization
Before using a polymer for nanoparticle formation, the polymer needs to be characterized thoroughly since it has a significant influence on the properties of the nanoparticle. The critical parameters include the averaged molecular weight and polydispersity, which are characterized using gel permeation chromatography (GPC). The polymer should also be chemically characterized using nuclear magnetic resonance (NMR). GPC is a technique used to separate polymers based on size. A set of standards with known molecular weights are used to generate a standard curve of retention time versus molecular weight from which the polymer analyzed can be compared. A molecular weight distribution can then be generated for the unknown polymer. Polystyrene or PEG is usually used as the standard. One limitation of this technique is that the correlation between molecular weight and hydrodynamic radius for the standard may be different than the polymer analyzed, leading to an error in the absolute value of the molecular weight for the analyzed polymer. If the polymers are purchased from a vendor, then molecular weight information would be provided. However, if using ring opening polymerization, then the molecular weight would need to be determined. For the ring opening polymerization protocol described in Section 12.3.1.3, the average molecular weight should be ~10.5 kDa. An alternative method to GPC is the use of viscosimetry, which can be used to determine molecular weight based on the concept that larger molecules will be a greater impediment to flow and result in higher solution viscosities. 216
12.4
Data Acquisition, Results, and Interpretation
NMR is a spectroscopic technique that allows different chemical groups in a molecule to be identified based on their chemical shifts. If the polymer structures are known, which is the case for PLA-PEG and PLGA-PEG, the conjugation efficiency can be estimated. By dissolving a polymer in a deuterated solvent such as deuterated chloroform or 1 deuterated dimethyl sulfoxide (DMSO), H NMR can be used to identify the different 1 chemical groups. An example H NMR spectrum for PLA-PEG is shown in Figure 12.8. In the figure, the different peaks correspond to the –CH, –CH2, and –CH3 groups in the polymer. Since the –CH and –CH3 groups are only present in PLA monomer, the peak area ratio of –CH3 to –CH should be approximately 3 to correspond with the ratio of hydrogen atoms. The PEG polymer has two –CH2 groups per monomer. If the signal from –CH in PLA is compared with the signal from –CH2 divided by 4 to account for the two methylene groups per PEG and two hydrogen atoms per methylene group, the ratio can be compared with the ratio of the expected molecular weights to determine whether there is free PLA or free PEG remaining. If nonconjugated PEG is remaining, another separation can be performed to remove the remaining unreacted PEG using precipitation in cold water.
12.4.2
Nanoparticle Characterization
The nanoparticle delivery system requires significant characterization because the physicochemical properties of the system determine its performance. The critical parameters include particle size, surface charge, drug encapsulation, drug release, and ligand conjugation. Several analytical tools are available to analyze each parameter.
O O
H C
H2 C N yH
O C H2
DMSO solvent peak z
CH 3
PLA-PEG -CH 2 peak
-CH 3 peak
-CH peak
5.5
5.0
4.5
4.0
3.5
3.0
2.5
2.0
1.5
1.0
0.5
ppm
1
Figure 12.8 H NMR characterization of PLA-PEG dissolved in deuterated DMSO synthesized using ring opening polymerization.
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Particle size and polydispersity can be estimated in solution using light scattering or treated for analysis using scanning electron microscopy (SEM) or transmission electron microscopy (TEM) (Figure 12.9). Light scattering is used to quantify the hydrodynamic radius and polydispersity of a nanoparticle population and is slightly affected by the solution. TEM and SEM analyze dry particles, providing images of the nanoparticles that can be used to qualitatively observe particle size, shape, and polydispersity. The two analytical tools are complementary and should both be used to fully characterize the nanoparticle size. However, because the samples are treated differently, the particle size measurements will not correspond exactly with each other. The particle size will vary greatly depending on the polymer used and nanoparticle formation conditions. In addition to particle size, light scattering instruments can be used to measure the zeta potential, which is an estimate of the surface charge of the nanoparticles. For zeta potential, the measurement is very sensitive to the ionic environment. Therefore, the measurement will be most accurate in a solution that mimics physiological conditions, such as PBS. The zeta potential will vary depending on the functional end groups and the presence of targeting ligands. For example, carboxyl end groups result in a negative charge of approximately –50 mV [35]. Drug encapsulation can be quantified using the protocols described in Section 12.3.4. Regardless of the assay used to quantify the drug, it is important to determine whether the polymer or solution interferes with the drug quantification. Controls using nanoparticles without the drug should be prepared and treated the same as the drug-encapsulating nanoparticles to determine interference. Encapsulation efficiencies
100
100 80
Frequency
Frequency
80 60 40 20
60 40 20
0 40
49
60
74
92
113 139 172 211
Hydrodynamic diameter (nm)
(a)
(c)
0 40
50 51 54
67
83 102 125 155 190
Hydrodynamic diameter (nm)
(b)
100 nm
Figure 12.9 Particle size distributions of PLA-PEG nanoparticles prepared using nanoprecipitation (a) after washing (mean hydrodynamic diameter = 83 nm; polydispersity = 0.348) and (b) after filtration using a 0.1 μm filter (mean hydrodynamic diameter = 56 nm; polydispersity = 0.053). (c) Transmission electron micrograph (TEM) of PLA-PEG nanoparticles negatively stained with uranyl acetate solution.
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and drug loads vary based on the physicochemical properties of the drug and polymer, formulation conditions, and initial drug load. For hydrophobic drugs such as docetaxel encapsulated in PLGA-PEG nanoparticles, a typical drug load is 1% (w/w) and encapsulation efficiency is approximately 10% [34]. Drug release rates will also vary depending on several parameters discussed in Section 12.5.5 (Figure 12.10). For docetaxel encapsulated in PLGA-PEG nanoparticles with a PLGA molecular weight of approximately 10.5 kDa, half of the drug is released in 12 hours [36]. The release profile is usually biphasic, with an initial burst release followed by a slower release over a few days. Ligand conjugation can be quantified using two different methods. The first is the direct method, where the ligand attached to the surface is measured after washing away the unconjugated material. The second is the indirect method, where the wash containing the unconjugated ligand is collected and analyzed. By comparing this with the initial amount of ligand used, the conjugated ligand can be determined. There are many different analytical tools available to quantify ligand conjugation. One tool is colorimetric assay kits such as the BCA assay for proteins, Picogreen for DNA, or Ribogreen for RNA. X-ray photoelectron spectroscopy (XPS) can be used to analyze the surface chemistry of a nanoparticle and detect the presence of a ligand based on its chemical signature. Ultraviolet (UV) absorbance is a simple technique that is used to quantify small molecules. Ligands can also be labeled with a fluorophore, assuming it does not interfere with the conjugation, and quantified using flow cytometry or a fluorescence plate reader. When quantifying ligand conjugation, it is necessary to account for interference in the assay by the polymer and ligand on the surface due to noncovalent interactions. For polymer interference, nanoparticles without ligand can be prepared in the exact same conditions as the nanoparticles with ligand to correct for the nanoparticle signal in the assay. For noncovalent interactions, nanoparticles can be
Mass fraction of docetaxel released
1
0.75
0.5 PLGA0.17PEG3400 PLGA0.17PEG12000 PLGA0.19PEG3400
0.25
PLGA0.19PEG12000 PLGA0.45PEG3400 PLGA0.67PEG12000 PLGA0.67PEG3400
0 0
24
48 Time (hours)
72
96
Figure 12.10 Drug release profiles for docetaxel encapsulated in PLGA-PEG nanoparticles with varying PLGA and PEG molecular weights. For each of the formulations, a biphasic release profile is observed where an initial burst release occurs followed by a slower release rate. (Reproduced with permission from [36]. Copyright 2008 National Academy of Sciences, U.S.A.)
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incubated with ligands but without the conjugation chemistry reagents. For example, if using thiol-maleimide chemistry to conjugate a thiol-modified protein to the nanoparticle, the three samples to analyze would be nanoparticles without protein incubation, nanoparticles incubated with unmodified protein, and nanoparticles incubated with thiol-modified protein. Ligand conjugation is calculated using conjugation efficiency and ligand weight fraction on the nanoparticle surface. Both are calculated using (12.1) and (12.2) but with the ligand masses used instead of drug masses.
12.4.3
In Vitro Experiments
The effectiveness of the nanoparticle delivery system needs to be evaluated in a cell model once the system is fully characterized. The critical parameters are binding selectivity and uptake by targeted cancer cells as well as cytotoxicity in both targeted and nontargeted cells. Binding and uptake experiments are designed to demonstrate that targeted nanoparticles selectively enhance interactions with targeted cells (receptor-positive cells) compared with nontargeted nanoparticles. When conducting these experiments, nanoparticles binding to the cell surface and nanoparticles taken up by targeted cells through endocytosis need to be distinguished. For these experiments, the incubation time and amount of nanoparticles should be optimized to emphasize the differences between targeted and nontargeted nanoparticles. The incubation time should be approximately 1 to 2 hours, with shorter incubation times preferred for several reasons. First, it reduces background signal by minimizing dye leakage from the nanoparticles. Second, it minimizes nonspecific uptake of cells through fluid-phase endocytosis. Surface-bound and endocytosed nanoparticles can be distinguished through confocal fluorescence microscopy. Using 3-D reconstruction of imaged cells, nanoparticles on the surface can be distinguished from those inside the cell (Figure 12.11). Nanoparticle position can be further elucidated using colocalization analysis, in which the position of the nanoparticles is compared with a dye that localizes to specific cellular compartments, such as endosomes or lysosomes [36]. Specificity of targeted nanoparticles can be evaluated using colocalization analysis as well. Using a fluorescent ligand such as a fluorescently labeled antibody specific to the targeted receptor, imaging can be used to show the association of the targeted nanoparticles with the receptor on the cell surface or in cellular compartments if endocytosed. Specificity can also be demonstrated using a competitive binding study, where targeted nanoparticles and free ligand are incubated with receptor-positive cells. The free ligand is usually in 10–100× molar excess, allowing it to bind to the receptor and block binding of the nanoparticles, demonstrating the nanoparticles’ specificity for that receptor. Fluorescent nanoparticles allow quantification of binding and uptake using several different analytical tools. Using flow cytometry, the uptake of nanoparticles can be quantified, but surface-bound nanoparticles are not included because the cells are trysinized [37]. The trypsin cleaves surface proteins, which should detach surface-bound nanoparticles. However, this tool allows comparison of uptake under different experimental conditions as well as analysis of the uptake kinetics. Another analytical technique is to grow the cells on 96-well plates and use a fluorescence plate reader for quantification. With this technique, both surface-bound and endocytosed nanoparticles can be quantified but not distinguished. An alternative to fluorescence is 220
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Data Acquisition, Results, and Interpretation
16 hrs
2 hrs
LNCaP
NP
NP-Apt
PC3
NP
NP-Apt
(a)
A
B
LNCaP C
D
E
NP-Apt
F
(b)
Figure 12.11 (a) Binding study of aptamer-targeted PLA-PEG nanoparticles incubated with LNCaP (receptor-positive prostate cancer cells) and PC3 cells (receptor-negative prostate cancer cells) with incubation times of 2 and 16 hours. A rhodamine-dextran dye (red) is encapsulated within the nanoparticles, the cell nuclei is stained with 4’,6’-diamidino-2-phenylindole (blue), and the actin cytoskeleton is stained with Alexa-Fluor Phalloidin (green). The samples were imaged using fluorescence microscopy. (b) 3-D reconstruction of the cell using confocal microscopy rotated along the z-axis through the mid z-axis point of the cell to demonstrate that nanoparticles are present inside the cell. (Reproduced with permission from [35]. Copyright 2004 American Association for Cancer Research.)
the use of radioactive polymers, radioactive molecules conjugated to polymers, or radioactive molecules encapsulated in nanoparticles to quantify surface-bound and endocytosed nanoparticles using a scintillation counter [36]. Regardless of the method used, targeted nanoparticles should be compared with nontargeted nanoparticles in both receptor-positive and receptor-negative cells to fully evaluate the specificity and
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Biodegradable, Targeted Polymeric Nanoparticle Drug Delivery Formulation for Cancer Therapy
enhancement in interactions between nanoparticles and cells due to the targeting ligand. Cytotoxicity studies are the other key in vitro experiment to demonstrate the effectiveness of the delivery system. The key parameters are the drug concentration and incubation time. The incubation time used for the binding and uptake experiments can also be used for these experiments since binding or uptake has been demonstrated under those conditions. Incubation times should be kept as short as possible since longer times allow more of the drug to be released in the solution before nanoparticles are taken up by the cells. The free drug in the solution can then contribute to the toxicity. Shorter times limit this effect and make the toxicity data more representative. The drug concentration should then be varied by preparing drug-loaded nanoparticles and incubating varying amounts of the nanoparticles with the cells. For cytotoxicity studies, targeted and nontargeted nanoparticles loaded with the drug should be compared in receptorpositive and receptor-negative cells to demonstrate the enhanced toxicity of targeted nanoparticles in receptor-positive cells. Further controls include targeted and nontargeted nanoparticles without the drug in both cell types to evaluate whether the nanoparticles themselves are toxic to the cells. Results from cytotoxicty experiments can be presented in two ways (Figure 12.12). The first way is to show the toxicity at specific conditions which may be most representative of in vivo conditions, such as a specific incubation time and drug concentration [23, 34]. The other way is to present the entire dose-response curve, showing the toxicity as a function of the drug concentration [38, 39]. As part of the dose-response curve, the IC-50, which represents the drug concentration where 50% of the cells are killed, can be calculated and compared across different conditions.
12.5 Discussion and Commentary This section will focus on the optimization of the nanoparticle delivery system. Important physicochemical properties to consider when designing a delivery system include the particle size, particle shape, surface chemistry, drug loading, drug release, and targeting. The parameters available for manipulation of the system include the nanoparticle formulation parameters as well as the components of the system, which can be changed independently due to the modular design of the nanoparticles. Both allow significant control over the physicochemical properties of the nanoparticles and provide flexibility in the design of the system. By understanding how the components and formulation parameters influence nanoparticle properties, the delivery system can be tailored to the design criteria for the application of interest.
12.5.1
Particle Size
Nanoparticle size is a key property of the delivery system that influences biodistribution and blood circulation half-life. An important advantage of using nanoparticles for the treatment of cancer is that the small size (1018)
10–100 nm
N/A
n-type (1018)
50 nm–10 mm
Yes
*
Illumination refers to the ability to irradiate the front side of the wafer with light during the anodic etch process.
245
Porous Silicon Particles for Multistage Delivery
Electrode spacer Tank ring Pt mesh electrode
Pt mesh electrode Tank ring Electrode spacer Seal O-ring Patterned substrate Al electrode Tank base Seal O-ring Al electrode
Screw
Screw
Tank base
(a)
(b)
Figure 13.7 View of the anodic etch tank. (a) Disassembled view of the tank components. (b) Schematics of the assembly of the components.
•
The ability to provide adequate backside electrical contact for the Si wafer. The backside contact must not be exposed to the etch solution. The backside contact is typically provided through thin aluminum foil, shaped as the wafer. Thus, the tank must provide enough mechanical stability to guarantee a uniform contact between the aluminum foil and the backside of the Si wafer.
•
The ability to immerse a mesh electrode, facing the wafer and parallel to the wafer, at a fixed, replicable distance. The mesh electrode is usually constituted of Pt, an HF-resistant metal with sufficiently good electrical properties.
•
The ability to expose a majority of the front side surface of the Si wafer to the etch solution, in order to maximize the yield of each etch process.
•
The ability to resist acid attack by HF; Teflon and aluminum oxide are the materials of choice for the realization of the tank.
•
The ability to allow for the escape of gaseous species formed during the etch process. The tank must have an opening from which the gas can escape, and if the Pt mesh is positioned horizontally, the gas bubbles must be able to escape between the grid.
A constant current power supply capable of currents up to 8A is required. Chemicals:
246
•
AZ-5209 photoresist, or equivalent positive, thin photoresist is required for photolitography.
•
Isopropanol is required for the conservation of the PSPs following their release.
•
49% HF is required for the etch solution.
•
Anhydrous 200-proof EtOH is required for the etch solution.
•
Acetone, methanol, and isopropanol are required to clean the substrates.
13.2
Fabrication of PSPs
Characterization: •
Scanning electron microscope with 0.5-nm resolution;
•
Nitrogen absorption analysis tool (Quantasorb 3 from Quantachrome).
13.2.2
Methods
13.2.2.1 Thin Film Deposition The thin film deposition provides a masking layer to the Si wafers, necessary for the patterned anodic etch. 1. A 100-nm Si wafer (substrate) is stripped of eventual organic contaminants in 2:1 H2SO4:H2O2 piranha solution in an acid hood. 2. The wafer is rinsed for 5 minutes under flowing deionized water, spin dried. 3. The wafer is transferred to a carrier boat for oxide growth. The boat is placed at the center of an open furnace tube. Dry air is flowed into the furnace; the temperature is raised to 1,000°C and left there for 40 minutes, growing 50 nm of gate oxide. The exact thickness of the oxide layer is measured and recorded in a white light ellipsometer. 4. The substrate is transferred to the LPCVD furnace; the wafer is placed in the center of the loading boat and two dummy wafers are disposed on each side to guarantee uniformity of the resulting thin film. Si3N4 is deposited to reach the thickness of 80 nm, usually requiring 25 minutes of deposition. The exact thickness of the nitride layer is measured and recorded in a white light ellipsometer. Knowledge of the nitride thickness is necessary to properly time the dry etch step. Guidelines The uniformity of the thin film layer is the most important aspect of this step. Layers of uniform thickness (within a 5% maximum variation) are necessary for the success of the protocol, although 1% uniformity is generally preferred. To ensure the best possible uniformity, the substrate must always be carefully placed on the boat, in the center of the furnace, where the temperature is most uniform. The substrate must face away from the gas source and be surrounded by as many dummy wafers as possible.
13.2.2.2 Photolitography The photolitography transfers the desired 2-μm holes pattern on the photoresist layer on top of the substrate. The patterned photoresist acts as masking layer for the dry etch. 1. The substrate is coated with HMDS to improve photoresist adhesion in an HMDS oven for 5 minutes. 2. AZ-5209 positive photoresist is spun on the substrate using: 500 RPM speed, 1,000 RPMS acceleration for 5 seconds, followed by 5,000 RPM/4,000 RPMS/30 seconds, resulting in a resist thickness of approximately 700 nm. 3. The photoresist is soft baked for 8 minutes in an oven at 90°C. 4. The 2-μm pattern is transferred from the photomask to the photoresist using a Karl Suss MA6 Mask Aligner, 70J exposure (approximately 3 seconds) using soft vacuum contact. 247
Porous Silicon Particles for Multistage Delivery
5. The transferred pattern is developed in an MIF 726 developer for 20 seconds, and then inspected for uniformity under a 100× optical microscope. 6. If the pattern is sufficiently uniform, the substrate is hard baked for 8 minutes in an oven at 120°C, to completely crosslink the photoresist. 7. If the pattern is not sufficiently uniform, the photoresist can be removed with acetone under sonication. The acetone residues can then be cleaned by subsequent rinses in methanol and isopropanol. The protocol can then be resumed from point 2. Guidelines The uniformity of the pattern is the most important aspect of this step. Even if a small portion of the substrate is not properly exposed, the substrate should be reprocessed. To obtain the best possible uniformity, it is critical to know the UV light source power for the aligner, which would allow for one to calculate the exposure time necessary to obtain the correct exposure. Since the source power cannot be measured, the best practice is to initially calibrate the exposure/development times on several dummy Si wafers, spun with photoresist, and use the best obtained parameters to pattern the substrate.
13.2.2.3 Dry Etch The dry etch transfers the desired micrometric pattern from the photoresist to the silicon. This allows the patterned anodic etch to take place. 1. The substrate is transferred in a plasma etch tool, with the patterned side exposed to the plasma, where it undergoes the dry etch processes necessary to form a 200-nm trench into the Si by means of a 4-minute CF4 etch (25 sccm, 200 mTorr, 250W in a Plasmatherm RIE). 2. The substrate is flipped to expose the backside (unpatterned) to the plasma. A 4-minute CF4 etch (25 sccm, 200 mTorr, 250W in a Plasmatherm RIE) is employed to expose the bare silicon on the backside and ensure electrical contact for the successive anodic etch. Guidelines The timing and chemistry of the dry etch to obtain the desired trench depth and profile is the crucial aspect of this step. Each tool and etch chemistry will have their specific etch rate for Si3N4 and Si. Using the previously calculated thickness of the Si3N4 layer, it is possible to estimate the correct etch time (in seconds) necessary to form the 200-nm trench into the silicon, simply employing: t = RSi 3 N 4 ⋅ hSi 3 N 4 + RSi ⋅ 200
(13.1)
where RSi 3 N 4 is the etch rate for Si3N4 in nanometers per second, hSi 3 N 4 is the thickness of the Si3N4 sacrificial layer in nanometers as measured by ellipsometry, and RSi is the etch rate for Si in nanometers per second.
13.2.2 Anodic Etch The anodic etch selectively porosifies the substrate where the silicon is directly exposed to the HF solution, forming PSPs. 248
13.2
Fabrication of PSPs
1. The patterned substrate is stripped of photoresist and organic contaminants in 2:1 H2SO4:H2O2 piranha solution for 8 minutes. 2. A 200-nm thin film of sputtered aluminum is deposited on the nonpatterned backside of the substrate to improve electrical contact. The sputtering is performed for 12 minutes in a 16-wafer holding Varian sputter. 3. The etch tank is assembled as follows (Figure 13.8): i.
The tank ring is placed upside down, and the wafer is placed on top of the tank ring, sitting on the seal o-ring, with the patterned side facing inside the ring, where the solution will be poured. ii. The aluminum-covered backside of the substrate is placed in conformal contact with an aluminum foil shaped like a table tennis racket.
ii.
The base of the tank is screwed to the tank ring, ensuring sealing of the tank and providing the pressure necessary to guarantee the electrical contact between the substrate and the aluminum foil. The handle of the aluminum foil racket is now outside the tank and provides the contact spot to connect to the power supply.
iv. The tank is flipped back in the upright position and the platinum mesh is inserted at a distance of approximately 2.5 cm. An annular Teflon spacer positioned between the wafer and the platinum mesh determines the distance. v.
The etch solution, specified in Table 13.2, is poured in the etch tank.
vi. The anode (positive lead) of the power supply is connected to the aluminum electrode. vii. The cathode (negative lead) of the power supply is connected to the platinum mesh electrode.
1
2
3
4
5
6
7
8
Figure 13.8 Assembly of the etch tank. (1) Upside-down view of the etch ring with seal o-ring mounted. (2) The substrate is placed on top of the o-ring seal with the nonpatterned backside facing outside the ring. (3) The aluminum electrode is placed on top of the substrate backside to provide electric contact. (4) The etch tank bottom, placed on top of the aluminum electrode, is screwed together with the tank ring to ensure electric contact and seal the tank. (5) The tank is flipped upright and the mesh electrode spacer is inserted in the etch ring. (6) The Pt mesh electrode is inserted in the etch ring. (7) The HF:EtOH solution is poured in the etch tank. (8) The anode is connected to the aluminum electrode and the cathode to the platinum mesh electrode.
249
Porous Silicon Particles for Multistage Delivery
4. The current is started with the porosification current density and time specified in Table 13.2, to produce PSPs with size, pore size, and porosity specified therein. The current density is then raised to the release current density value and time specified in Table 13.2, forming the release layer. Guidelines This is the most important step of all, where the PSP and the release layer are formed. The choice of the correct current density will produce PSPs with the desired pore size and porosity. Additionally, another critical factor is the current density of the release layer. If set too high, the elements will release in the etch solution and be lost; if set too low, the elements will not release from the substrate and be unusable.
13.2.2.5 Release of pSi Elements to Obtain PSPs of Desired Shape/Size/Pores 1. The etch tank is emptied of the etch solution. 2. The tank is rinsed three times with deionized water to reduce HF concentration. 3. The tank is disassembled and the substrate removed. 4. The substrate is rinsed for 5 minutes under running deionized water to completely remove any HF residues. 5. The substrate is spin dried. 6. The substrate is then inspected visually under a 100× optical microscope. A goldenyellow color of the substrate indicates the successful formation of the porous elements. Observing yellow/purple circles of the appropriate diameter (2 μm) surrounded by a yellow colored corona under the optical microscope is also an indication of the successful formation of the porous elements. 7. The substrate is soaked for 30 minutes in HF to strip the SiO2 and Si3N4 layers. Incomplete removal of these layers will prevent the release of the elements from the substrate and/or cause shattering of the elements. 8. The substrate is rinsed for 5 minutes under running deionized water to completely remove HF residues. 9. The substrate is spin dried. 10. The substrate is inspected visually. A dull yellow-grayish tint is a positive predictor for the element release from the substrate. A yellow-golden tint as in the previous inspection is a negative predictor for the element release. Under a 100× optical microscope a grey/purple tint is a positive predictor for the element release, while a yellow/purple tint is a negative predictor for the element release. Table 13.2 Anodic Etch Parameters Used to Obtain Desired Pore Size Target Pore Size
Etch Solution Etch Current (HF:EtOH) Density (A/cm2)
Etch Current Time (Seconds)
Release Solution (HF:EtOH)
Release Current Density (A/cm2)
Release Current Time (Seconds)
6 nm
1:1 1:3
0.0129 0.0390
110 90
2:5 1:3
0.779
15 nm
0.620
6 6
26 nm
1:3
0.0900
45
1:3
0.620
6
Current densities are measured on the effective area of Si exposed to the etch solution.
250
13.2
Fabrication of PSPs
11. The substrate is transferred to a crystallization dish filled with 40 ml of isopropanol. 12. The crystallization dish is placed in a sonication bath until the release of the elements, typically 1 minute. The occurred release can be visually determined by a subtle change in tint of the substrate, from grayish/green to shiny gray. 13. After release, the substrate is again inspected under a 100× optical microscope to determine release efficiency. The presence of dull gray disks of approximately twice the diameter of the original lithographic pattern indicates a released element. The presence of gray/purple or yellow/purple disks indicates nonreleased PSPs. 14. The isopropanol suspension rich in PSPs is then transferred in a 50-ml centrifuge tube and stored at 4°C.
13.2.2.6 Scanning Electron Microscopy (SEM) Characterization A small aliquot of the PSP-rich suspension is spotted on a 17-mm SEM stage. The isopropanol is allowed to dry and the sample is analyzed in a scanning electron microscope. Cross-sectional views of the PSPs can be obtained, cleaving the substrate before releasing the PSPs and mounting the substrate piece on a 45° or 90° SEM stage.
13.2.2.7 Nitrogen Absorption/Desorption Characterization A suspension containing 10 mg of PSPs (corresponding approximately to the product of 10 substrates) is centrifuged until the PSPs form a pellet at the bottom of the centrifuge tube, and all but 10 ml of the supernatant is removed; the PSPs are resuspended. The suspension is transferred to a nitrogen absorption analysis cuvette and dried completely. The cuvette containing the PSP powder is mounted on a nitrogen absorption analyzer and the absorption/desorption curves are collected. Using the provided software, the average pore size, the pore distribution, and the porosity for the analyzed PSPs are obtained by means of the Barret-Joyner-Halenda (BJH) model.
13.2.3
Characterization
The PSPs resulting from the described protocol are shown in Figure 13.9. The PSPs are analyzed by SEM to inspect their overall features and when using the standard. The 2-μm photolithographic pattern will result in quasi-hemispherical PSPs of 3.2-μm diameter and 1-μm height. The top side of the PSP, from where the porosification began, is characterized by a circular nucleation site, surrounded by an external corona. Pores run perpendicular to the nucleation site surface and parallel to the external corona surface (Figure 13.9). The nucleation layer, which extends 10–20 nm below the nucleation site, is constituted of pores with 2–3 nm in diameter. Right below the nucleation layer, the pore size rapidly increases to the one determined by the anodic etch parameters. The bottom side of the PSP is bowl-shaped and the pores are normal to the surface and have the characteristic size imparted by the anodic etch.
251
Porous Silicon Particles for Multistage Delivery Backside
Front side
Cross section
Pores (back side) Pores (cross section)
(a)
(b)
Figure 13.9 SEM images of PSPs. (a) Fabricated according to the parameters in the last row of Table 13.2. (b) Fabricated according to the parameters in the first row of Table 13.2. The front side shows the circular nucleation site surrounded by the external corona. The cross section shows the pores directionality from the nucleation site to the particle back side. (Reproduced with permission from [29] courtesy of Nature Publishing Group.)
13.3 Oxidation and Surface Modification with APTES of PSPs 13.3.1
Reagents
•
Deionized (DI) water;
•
IPA;
•
Hydrogen peroxide (H2O2);
•
Concentrated (95–98%) sulfuric acid (H2SO4);
•
3-aminopropyltriethoxysilane (APTES).
13.3.2
Methods
13.3.2 Wet Oxidation of PSPs 1. PSPs in isopropyl alcohol (IPA) (or any other organic media in which the PSPs are suspended) are dried in a glass beaker, on a hot plate (80°C–90°C) in a fume hood. The smallest amount of liquid is desirable for this step, as this reduces the drying time for the process. 2. A piranha solution consisting of 1 volume of H2O2 and 2 volumes of H2SO4 is used for the wet oxidation of the PSPs. H2O2 is added to the dried PSPs and sonicated. Owing to the hydrophobicity of the silicon, the PSPs normally tend to float. Concentrated (95%–98%) H2SO4 is then added slowly to this solution. 3. The PSP suspension is then heated to 100°C–110°C for 2 hours with intermittent sonication in a bath sonicator to disperse the PSPs. Utmost precautions should be taken during these steps, and the process should be carried out in a fume hood. Sonication helps not only in dispersing the PSPs but also in dislodging any air pockets within the pores of the PSPs. 4. The particulate suspension is then transferred to centrifuge tubes, and the PSPs are spun down at ~3,000g. The supernatant is discarded and the PSPs are resuspended in deionized (DI) water and transferred to microcentrifuge tubes and spun down again. 252
13.3
Oxidation and Surface Modification with APTES of PSPs
This process is referred to as washing the PSPs and is critical for the proper removal of any unreacted substrates. In this way the PSPs are washed five to six times in DI water until the pH of the suspension is approximately around 5 to 6. PSPs may then be transferred to an appropriate buffer (if used immediately) or sorted in IPA or DI water and refrigerated at 4°C until further use.
13.3.2.2 Surface Modification of PSPs with APTES 1. PSPs that are oxidized by the piranha method are washed thoroughly in water and then washed in IPA three to four times. After the washings, PSPs are resuspended in IPA. 2. PSPs are then transferred to a solution of IPA containing 0.5% (v/v) of APTES for 45 minutes to 2 hours, at room temperature. The PSPs are sonicated intermittently in a bath sonicator and placed on a tabletop shaker for the duration of modification. 3. The chemical modification is usually performed in a microcentrifuge tube. The reaction volumes used are below 0.8 ml. The lower volumes are ideal for the modification of micron-sized PSPs, as this consumes lower reagents during the modification and subsequent washing steps. 4. The PSPs are washed with IPA four to six times as described earlier and stored at 4°C. Alternatively, aliquots can be taken, dried, and stored under vacuum and desiccant until further use. Figure 13.10 shows the schematic of the surface modification by the APTES Useful Tip: It is difficult to spin down PSPs in aqueous media completely; most PSPs tend to stick to the walls of the tube or remain in suspension. This leads to huge losses of PSPs, especially if they undergo several cleaning steps to remove suspending media or reactants. In order to recover the maximum amount of PSPs during the centrifugation step, adding a small amount of detergent (like TritonX-100) assists in the formation of a nice PSP pellet. Typically 1–2 μl of 1% TritonX-100 in 300–600 μl of aqueous media should do the trick. Make sure that the Triton is removed before proceeding to any further work with the PSPs. This is normally done by removing the supernatant after the PSPs have been spun down and then adding media to the pellet slowly, attempting not to disturb the PSPs. The PSPs are spun down again, the supernatant is discarded, and fresh media is added again. This can be done two to three times, depending on the discretion of the researcher.
Hydroxylated silicon surface
APTES
OH
OH 2 CH 3 C
OH
OH 2 CH 3 C
OH
+
OH 2 CH 3 C
APTES modified surface O
Si(CH 2 ) 3 NH2
O
Si(CH 2 ) 3 NH2
O
Figure 13.10 Schematic showing the modification of silicon surface with 3-aminopropyl triethoxy silane (APTES).
253
Porous Silicon Particles for Multistage Delivery
13.4 Fluorescent Dye Conjugation of PSPs PSPs modified with APTES can be conjugated with any commercially available fluorescent dyes that have a hydroxy-succinimidyl ester (NHS) conjugated to them. The NHS ester readily reacts with primary amines. NHS conjugated dyes are commonly used to tag proteins and antibodies and can be purchased from Invitrogen and Pierce.
13.4.1
Reagents
•
10 mM Phosphate buffer (PB);
•
pH~ 7.3 (for the conjugation);
•
1% Triton X-100 (for washing unconjugated dye).
13.4.2
Methodology
1. The APTES-modified PSPs are washed and suspended in the conjugation buffer. 2. The fluorescent dye to be conjugated is dissolved in the buffer and mixed with the PSP suspension. 3. The mix is sonicated and reacted for up to 1 hour. (The conjugation protocol provided by the supplier can also be followed.) 4. After reaction the PSPs are washed three times in 1% TritonX-100 followed by five to six washes in PB.
13.5 Zeta Potential Measurement 13.5.1 •
Equipment
ZetaPals Zeta Potential Analyzer (Brookhaven Instruments Corp., Southborough, Massachusetts)
13.5.2
Reagents
•
10 mM Phosphate buffer (PB);
•
pH~ 7.3 (for suspending the PSPs for performing the Zeta potential measurements).
13.5.3
Methodology
1. The application window for the zeta potential measurement (ZetaPals) is opened in order to power on the laser. After 15 minutes (for laser warm-up), the zeta potential measurements can be done. 2. The cuvette for holding the particulate suspension is rinsed with filtered (0.2 μm filter) buffer. The cuvette is filled with 1.5–2 ml of buffer. A small amount of PSPs is suspended in the buffer and well mixed either using a pipette or a brief sonication. 3. Make sure the electrodes are cleaned and rinsed with the buffer in which the measurements are performed. 4. After the electrodes are placed in the cuvette, the measurement for the zeta potential is started. 254
13.6
Count and Size Analysis of PSPs
5. Typically, three runs of 25 cycles per run are performed, but for more consistent results during each run, the number of cycles can be increased based on the discretion of the user. 6. The counts per second (cps) during data acquisition should be above 20 Kilo counts per second (Kcps), and below 700 Kcps. The instrument will automatically register if the quality of the sample for measurement is good or bad. 7. A detailed explanation of operating the equipment can be found in the manual of the instrument or from a training session with a Brookhaven Instruments scientist. 13.5.4
Results
After oxidation the PSPs charge is negative. The negative charges depend on the number of hydroxyl groups that are formed on the surface of the PSPs. After modification with APTES, the PSPs become less negative due to the surface coverage by the silane. A complete multilayer APTES coverage leads to PSS with higher positive Z-potential. Table 13.3 shows the typical results of zeta potential measurement on PSPs.
13.6 Count and Size Analysis of PSPs 13.6.1
Materials
13.6.1.1 Reagents 1. ISOTON II Diluent (Beckman Coulter); 2. Accuvettes (Beckman Coulter); 3. Standard cuvette (VWR); 4. 10-mL syringe (BD); 5. Single-use 0.20 mm syringe filter (Sartorius Stedim Biotech).
13.6.1.2 Facilities/Equipment 1. Z2 COULTER COUNTER Cell and Particle Counter (Beckman Coulter); 2. PC Computer with AccuComp Software (Beckman Coulter); 3. 50 μm Ampoule Aperture Tube (Beckman Coulter); 4. Sonicator (Branson).
13.6.2
Methods
1. 20 mL of ISOTON diluent into a CLEAN Accuvette. 2. Aliquot filter the ISOTON. 3. Clean a cuvette using 1 mL of the filtered ISOTON to remove dust/debris inside of the cuvette. 4. Aliquot 2 mL of filtered ISOTON into the clean cuvette. Table 13.3 Zeta Potential of PSPs as Measured in 10 mM PB PSP Sample
Zeta Potential (mV)
Oxidized PSPs APTES PSPs
From –29 to –34 From +5 to +11 255
Porous Silicon Particles for Multistage Delivery
5. Using a concentrated sample of PSPs, aliquot a small volume (between 0.5 to 4 μL) into the cuvette (concentrated samples are usually in the range of 2 × 108 particles/mL). 6. Sonicate cuvette to ensure homogeneity within sample. 7. Touch SETUP on the control panel of the Z2. 8. Place sample into machine for measurement. Do not allow the probe to go all the way to the bottom of the cuvette. The cuvette is placed onto an Accuvette cap so that the probe can reach the sample. 9. Adjust and examine the “Aperture Viewer” so that during the experiment one can observe any possible blocking of the aperture. 10. Input the upper and lower size limits: i.
For 3.2 mm PSPs (seen as 2 μm): 1.1–2.8 μm.
11. Touch SETUP again, scroll down to Optimize Settings, and move cursor to say YES. 12. Touch START/STOP and review settings. 13. Touch START/STOP again, and the Z2 should begin the measurement. 14. Observe the Concentration on the control panel of Z2; if it is too high, consider diluting the sample. 15. When the measurement is finished, import the run into PC using AccuComp software. 16. Inspect the graph for one central peak; then using the software, calculate the number of PSPs measured/counted. 17. Remove the sample, sonicate briefly, and repeat steps 7–16 four more times. 18. Average the counts and find the standard deviation. 19. To get to your overall count, multiply the number/mL of the measured sample by the dilution factors used when the sample was prepared. Note: Each measurement can be further analyzed to give size distribution, overlaying runs, and averaging multiple runs into one file/graph. Furthermore, Beckman Coulter has recently released new counters called “Multisizers” that have aperture sizes that range from 20 to 2,000 mm and thus can count particles as small as 400 nm.
13.6.3
Data Acquisition, Anticipated Results, and Interpretation
When the sample is measured at the Z2 Analyzer, the resulting signal is calculated into the volume of diluent displaced per event. This gives an idea of the change in morphology of the particles over time and of the total number of readable particles present at each time point. Figure 13.11 is a sample Z2-generated graph using the AccuComp software. In this figure, we have the cell/particle diameter versus the number of particles counted per milliliter. One can move the cursor to whatever location to display the number of particles per milliliter at that particular volume; furthermore, one can select a whole area between two volumes to find the number of particles counted in that section. The six menu options at the top left-hand corner of the figure allows for the manipulation of items within the figure. Under the “Run File” menu, users can save graphs generated, add overlays, and export critical data to Excel. In “Graph,” users can customize the options displayed on the graphs from a pull-down menu with a list of possible x and y values. The “Analyze” menu allows the user several options to interpret the data
256
13.7
Using Inductively Coupled Plasma–Atomic Emission Spectroscopy (ICP-AES)
Differential number
(a)
10000 9500 9000 8500 8000 7500 7000 6500 6000 5500 5000 4500 4000 3500 3000 2500 2000 1500 1000 500 0
0
0.2
0.4
0.6
0.9
1
1.2
1.4
1.6
1.9
2
2.2
2.4
2.6
2.8
3
1.9
2
2.2
2.4
2.6
2.8
3
Cell diameter (μm) Differential number
(b)
1300 1200 1100 1000 900 800 700 600 500 400 300 200 100 0
0
0.2
0.4
0.6
0.9
1
1.2
1.4
1.6
Cell diameter (μm)
Figure 13.11 Graphs produced by AccuComp displaying the size distribution of a PSP. (a) Typical profile for vectors with extra large pores (60–80 nm). (b) Profile for vectors that have been broken; notice the typical peak at ~2 mm.
and determine the total number of particles counted and measured in that particular file.
13.7 Using Inductively Coupled Plasma–Atomic Emission Spectroscopy (ICP-AES) to Determine the Amount of Degraded Silicon in Solution 13.7.1
Materials
13.7.1.1 Reagents 1. 0.45 μm Nylon Filter Tubes (VWR); 2. 15 and 50 mL Polypropylene Conical Tubes (BD FALCON); 3. ISOTON II Diluent (Beckman Coulter); 4. Distilled H2O; 5. Yttrium; 257
Porous Silicon Particles for Multistage Delivery
6. Silicon.
13.7.1.2 Facilities/Equipment 1. ICP-AES/OES (Varian); 2. Autosampler (Varian); 3. Argon Saturator Accessory (Varian, suggested).
13.7.2
Methods
1. Collect sample (i.e., 100 μL). Highly recommend collecting sample in triplicate. 2. Place sample into nylon filter tube. 3. Centrifuge sample at 4,200 rpm for 10 minutes. 4. Remove filter and collect the solution that flowed through. 5. Prepare a diluted sample to be analyzed by ICP. i.
For each individual sample, aliquot 5 mL of a solution that contains distilled water and 1 ppm of Yttrium into a 15-mL conical tube.
ii.
Aliquot a known amount of sample from the flow through into the conical tube. Keep this amount consistent (i.e., 50 μL from the ~100 μL).
6. Prepare known concentrations of silicon “standards” with 1 ppm of Yttrium. Suggest preparing 0, 25, 50, 100, 250, 500, and 1,000 ppm solutions of silicon. 7. Briefly shake sample and silicon standards. 8. Load samples and standards in autosampler*. 9. Set up template for acquisition. ICP starts each run by running the known concentrations and finishes by running a calibration off one of the standards. Suggest using 50 ppm of silicon as the control calibration and rerun the known concentrations after 15–20 samples have been analyzed. 10. When all the samples have been measured, examine the data of each sample and, if necessary, mask any run that may have extremely high standard deviations. 11. Export data to Excel or any other spreadsheet application. 12. Analyze data to determine silicon concentration of samples. *Note: It is highly advisable that the operation and measurement of samples using the ICP-AES/OES machine be done by an operator that is highly proficient in running the machine.
13.7.3
Data Acquisition, Anticipated Results, and Interpretation
13.7.3.1 Data Acquisition The data received by the user will be in spreadsheet format, with the first row showing the data labels, as shown in Table 13.4. Table 13.4 Tube
Data Labels for ICP Data Sample Labels
Si 250.690
Si 251.432
Si 251.611
Si 288.158
Y 360.074
Y 371.029
The first column, “Tube,” designates in what rack and position (Rack:Position) within the autosampler the machine is measuring from. In this particular setup there are 258
13.7
Using Inductively Coupled Plasma–Atomic Emission Spectroscopy (ICP-AES)
two racks each with 60 positions available to hold the samples (racks 1 and 2) and a third rack that holds the known concentrations. The next column is the “Sample Label” that is used at the start of the experiment to designate what sample is being measured. The next four columns that start with “Si” correspond to the four wavelengths used to measure the concentration of Silicon within the sample. The last two columns are used to show the measurement of Yttrium within the sample. Yttrium measurement is important to assess the stability of measurements in time. The tool normalizes the Yttrium reading to 1.00 for the first sample in both wavelengths and then uses this value to calibrate for the decay in concentration found in the subsequent samples. Calibration to the Yttrium standard is crucial, since, depending on the number of samples, measurements can take up to several hours and may need to run overnight (60 samples take about 3–4 hours), and thus measurements would need to be adjusted for any decay in the readings. Each sample will have its own row, including the known concentrations given in μg/L (Table 13.5). The known concentrations are set to the actual value and are used to build a standard curve/line. The sample’s values are then extrapolated from the curve obtaining a numerical value. At the end of a cycle, the machine runs the calibration concentration, labeled as “Cont. Calib. Verif.,” to verify proper calibration. In this particular
Table 13.5 Actual Values from ICP-AES Analysis (in μg/L) Tube
Sample Labels
Si 250.690
Si 251.432
Si 251.611
Si 288.158
Y 360.074
Y 371.029
3:1 3:2 3:3 3:4 3:5 3:6 2:1 2:2 2:3 2:4 2:5 2:6 2:7 2:8 2:9 2:10 2:11 2:12 2:13 2:14 2:15 2:16 2:17 2:18 2:19 2:20 3:7
Blank Si Standard A Si Standard B Si Standard C Si Standard D Si Standard E 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 Cont. Calib. Verif.
0 25 100 250 500 1,000 56.8 28.9 28.2 65.2 55.5 55.1 42.2 51.9 30.4 73.3 55.2 24.4 4.0 2.7 7.0 22.0 11.7 38.2 30.6 37.3 49.5
0 25.0000e 100 250 500 1,000 70.9 68.5 60.1 uv 64.6 77.6 50.1 42.6 60.0 63.4 84.4 75.5 28.1 uv 47.9 39.6 uv 67.8 27.3 uv 49.3 50.9 40.4 56.0 72.1 Q
0 25 100 250 500 1,000 40.0 36.1 36.3 69.7 49.4 50.7 55.9 37.7 43.6 72.3 57.8 7.9 9.7 15.6 29.6 18.3 24.0 34.6 36.6 31.3 52.1
0 25 100 250 500 1,000 45.6 32.0 31.2 62.3 53.2 45.1 50.5 44.9 33.8 68.2 53.4 9.1 11.9 12.6 29.3 17.2 17.9 36.9 37.1 26.2 45.8
0.8 0.8 0.8 0.8 0.8 0.8 0.7 0.7 0.7 0.7 0.7 0.7 0.7 0.7 0.7 0.7 0.7 0.7 0.7 0.7 0.7 0.7 0.7 0.7 0.7 0.7 0.8
0.9 0.9 0.9 0.9 0.9 0.9 0.8 0.8 0.8 0.8 0.8 0.8 0.8 0.8 0.8 0.7 0.7 0.8 0.8 0.8 0.7 0.8 0.8 0.8 0.8 0.8 0.8
Values from acceptable wavelengths are in red.
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Porous Silicon Particles for Multistage Delivery
example the reference sample is set to 50 ppm or 50 μg/L. Upon inspection of the sample data, some values are observed to have additional letters or even negative sign, such as Q, uv, and e. Q and uv are used to designate values that are found to be under detection limits. The operator designates the letter e to a particular value that had to be edited. This is preformed only when a value has an extremely high internal standard deviation, in an attempt to mitigate the effect of any stray data within the run of that particular sample.
13.7.3.2 Analyzing data Only data from wavelengths for which the reference sample was measured within 10% of the actual value should be used for analysis. Inspecting the output file, it can be observed that a “Q” is placed next to any value in the control calibration row that does not fall within 10% of the known value of 50 μg/L. Thus, only wavelengths with a reference sample value between 45 and 55 μg/L are used. The wavelength’s control concentration values are then rescaled such that the reference sample concentration measurement is set equal to the expected concentration of 50 μg/L. For example, if the control calibration sample value for a given wavelength were to be 48, then every value in the column would be multiplied by 50/48. Then using the resulting values that have been rescaled or normalized to a value of 50 from each sample and averaging with the other samples (since each sample was run in triplicate) will result in the final concentration of silicon of that particular sample in μg/L (Table 13.6). However, one still needs to account for the dilution factors involved in the preparation of the sample. Thus, the true concentration of silicon in the sample can be found multiplying by the dilution factor, as seen in (13.2): Vsol ×2 Vsample
(13.2)
where Vsol = volume of solution; Vsample = volume of the sample. Multiply by two, since we only used half of the sample to be measured. This simple calculation provides the amount of silicon in your sample in μg/L. Depending on the preference of the user, there are two alternative methods to interpret the results. The most beneficial, for most applications, is displaying the amount of silicon released into solution as a percentage of the total amount of silicon that can be released per PSP (Figure 13.12(a)). This type of interpretation facilitates the display of minor degradation rate changes within the different PSPs. Using this interpretation, PSPs that degrade quicker would show a steeper slant during their “linear” degradation. The other interpretation displays the total amount of silicon that is in solution (Figure 13.12(b)). This analysis would be useful in showing the different amounts of silicon contained in the different PSP types. Ideally, it would show that once degraded, PSPs with larger pores would release a lower amount of silicon in solution.
260
13.7 Using Inductively Coupled Plasma–Atomic Emission Spectroscopy (ICP-AES) to Determine the Amount of Degraded Silicon in Solution Table 13.6 Normalized Values from ICP-AES Analysis Tube
Sample Labels
Si 250.690
Si 251.432
Si 251.611
Si 288.158
Y 360.074
Y 371.029
3:1 3:2 3:3 3:4 3:5 3:6 2:1 2:2 2:3 2:4 2:5 2:6 2:7 2:8 2:9 2:10 2:11 2:12 2:13 2:14 2:15 2:16 2:17 2:18 2:19 2:20 3:7
Blank Si Standard A Si Standard B Si Standard C Si Standard D Si Standard E 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 Cont. Calib. Verif.
0 25 100 250 500 1,000 57.3 29.1 28.5 65.8 56.1 55.6 42.7 52.4 30.7 74.0 55.7 24.6 4.1 2.8 7.1 22.2 11.8 38.6 30.9 37.7 50.0
0 25.0000e 100 250 500 1,000 70.9 68.5 60.1 uv 64.6 77.6 50.1 42.6 60.0 63.4 84.4 75.5 28.1 uv 47.9 39.6 uv 67.8 27.3 uv 49.3 50.9 40.4 56.0 72.1 Q
0 25 100 250 500 1,000 38.4 34.6 34.8 66.9 47.4 48.6 53.6 36.2 41.9 69.4 55.5 7.6 9.3 15.0 28.4 17.5 23.0 33.2 35.1 30.1 50.0
0 25 100 250 500 1,000 49.8 35.0 34.1 68.0 58.1 49.3 55.1 49.1 36.9 74.5 58.4 10.0 13.0 13.7 32.0 18.8 19.5 40.3 40.5 28.6 50.0
0.8 0.8 0.8 0.8 0.8 0.8 0.7 0.7 0.7 0.7 0.7 0.7 0.7 0.7 0.7 0.7 0.7 0.7 0.7 0.7 0.7 0.7 0.7 0.7 0.7 0.7 0.8
0.9 0.9 0.9 0.9 0.9 0.9 0.8 0.8 0.8 0.8 0.8 0.8 0.8 0.8 0.8 0.7 0.7 0.8 0.8 0.8 0.7 0.8 0.8 0.8 0.8 0.8 0.8
Values that have been normalized are in blue.
MP1
MP2 350
120
300
100
250
Si (μg)
Su in sal n (% of total)
MP1 140
80 60
MP2
200 150
40
100
20
50 0
0 0
12
24
36 48 Time (hrs) (a)
60
72
0
12
24
36 48 Time (hrs)
60
72
(b)
Figure 13.12 ICP graphs produced in Excel showing multiple ways to display the amount of silicon dissolved into the solution. MP1 and MP2 refer to a PSP of medium-sized pores with 10 and 15 nm, respectively. (a) Displaying the amount of silicon in solution by using the percentage of total possible silicon in solution. (b) Displaying the amount of silicon by showing the amount, by mass, of silicon.
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Porous Silicon Particles for Multistage Delivery
13.8 Flow Cytometry to Characterize PSP Shape, Size, and Fluorescence Intensity 13.8.1 Materials 13.8.1.1 Reagents 1. Sodium chloride; 2. Distilled water; 3. Phosphate buffered saline, PBS pH 7.2 (Gibco); 4. 5-mL polystyrene round bottom tubes (BD Falcon). 13.8.1.2 Facilities/Equipment 1. FACSCalibur (Becton Dickinson, BD); 2. Computer running CellQuest software (Becton Dickinson, BD). 13.8.2 Methods 1. Start system. Power on Calibur, then computer. i.
Allow 15 minutes for machine to warm up in “STNDBY.”
2. Mix 9g of NaCl into 100 mL of distilled water, thus 9% NaCl. 3. Make a 1:10 dilution of 9% NaCl in water (sheath fluid). 4. Load sheath fluid into proper compartment and empty out the waste, if necessary. 5. Set up acquisition parameters: i.
Parameters include detectors/amps, instrument settings, file names, location of saved file, compensation, and threshold.
ii.
For particles only, see Table 13.7 for reference settings.
6. Prepare sample. Aliquot ~5 × 105 PSPs into 500 mL of PBS into a polystyrene tube. We suggest to run the samples in triplicate, by either reading same sample three times or running three different samples each with at least 1 × 105 PSPs. 7. Briefly vortex sample. 8. Load polystyrene tube, press RUN, and select a flow rate: i.
LOW: 12 μL/min (information obtained from the BD Web site on January 21, 2009);
ii.
MID: 35 μL/min (suggested to start here if using same concentration as above);
iii. HI: 60 μL/min. 9. After each sample, briefly run some distilled water through the machine until there are no events recorded in the acquisition plots. 10. Repeat steps 7–9 for each sample. 11. Analyze samples using CellQuest software. Table 13.7
Recommended Instrument Settings for Particle Measurement
Detector
Voltage (V)
Mode
FSC SSC FL1 FL2 Threshold
E-1 475 800 750 Primary: FSC
LOG LOG LOG LOG 30 (for other applications, leave at 52) 52
Secondary: SSC
262
13.8
13.8.3
Flow Cytometry to Characterize PSP Shape, Size, and Fluorescence Intensity
Data Acquisition, Anticipated Results, and Interpretation
The CellQuest software allows the user to customize the type of data that can be collected and displayed. Certain graphs can only be used for acquisition or analysis or for acquiring and analyzing (Acquisition → Analysis) the data simultaneously. The software is able to display data in five basic graphs: histogram, density, dot, contour, and 3-D plot. The first three can be set to Acquisition, Analysis or Acquisition → Analysis, while the last two, contour and 3-D plot, can only be used for the Analysis display and data should be acquired through other graphs (Figure 13.13). Histograms may also be overlaid, thus allowing users to compare several curves on the same plot. Quantitative results from CellQuest can be obtained for histograms and regions (available in square, polygon, or circle) or gates of interest (both are manually drawn by user). Statistics are selected by choosing the appropriate type from the “Stats” pull-down menu located in the toolbar at the top of the screen. This results in an embedded box that can be resized or moved with the user-selected statistics inside. This box contains
(a)
(b)
(c)
(d)
Figure 13.13 Using flow cytometry to study the PSPs’ size and fluorescence. (a) Depicts the relative size (FSC) and shape (SSC) of the PSPs thorough a contour plot, Region R1 represents the gating region. (b) 3-D plot showing the distribution of PSPs gated in (a), where the z-axis represents the total number of counts/particles. (c) Histogram showing the background fluorescence of unloaded PSPs and is used to set up M1 so that at least 99% of events are captured here and M2 captures the rest, and (d) the increase in fluorescence distribution after loading the particles, keeping M1 and M2 regions the same as in part (c). (Reproduced with permission from [29] courtesy of Nature Publishing Group.)
263
Porous Silicon Particles for Multistage Delivery
several parameters including (but not limited to) mean, median, C.V., standard deviation, peak, total event, and events gated. For the analysis of PSP shape and size, a bivariate plot (dot, contour, or density can be used) graphing forward scatter (FSC) versus side scatter (SSC) is used. This type of analysis can also be used to exclude events by defining a polygonal region of interest around the population of interest and analyzing the statistics within that region to obtain values for the geometric mean in both the X and Y parameters. In addition, the FACSCalibur has the capabilities for fluorescence analysis. In relation to this procedure, only two colors will be described: FL1 (green) and FL2 (red). The green fluorescence (FL1) can detect FITC and QDot 525, as an example, using a 530/30 bandpass filter. The red fluorescence (FL2) can detect QDot 565 using a 575/26 bandpass filter. If single color detection is needed, color compensation can be set to zero. However, when detecting dual green-red color, FL1 compensation is set to 25% FL2, and FL2 compensation is set to 35% FL1 using the Compensation palette under the Cytometer pull-down menu in the CellQuest window. This type of fluorescent setup allows users to characterize and quantify the amount of second-stage NPs loaded into the PSPs. To accomplish this, first the region of interest is located within a dot plot of FSC versus SSC. Then a histogram displaying the detector (FL1 or FL2) is created. This plot selectively displays the events within the defined region that correspond to the second-stage NP used.
13.9 Loading and Release of Second-Stage NPs from PSPs 13.9.1
Loading of NP into PSPs
13.9.1.1 Materials Reagents 1. Nanoparticles (i.e., QDots, SWNT); 2. 1.5-mL low-binding polypropylene centrifuge tubes (VWR International); 3. DI water; 4. Tris(hydroxymethyl) aminomethane (Tris-HCl). Facilities/Equipment 1. Thermo Scientific Barnstead LabQuake Tube Rotators (Thermo Scientific).
13.9.1.2 Methods 1. Put 3.0 × 105 PSPs in low-binding polypropylene tubes in 3 mL of DI water. 2. Adjust Tris-HCl to a pH of 7.3. 3. Add NPs and adjust the final solution to 20 mL using Tris-HCl (i.e., 2 mM QDots: 5 μL Qdots + 3 μL H2O + 12 μL Tris-HCl, or 20 ng/μL PEG-FITC-SWNTs: 9 μL SWNTs + 3 μL H2O + 8 μL Tris-HCl). 4. Incubate samples on tube rotator (~20 r.p.m.) for 15 minutes at room temperature. 5. Dilute samples with Tris-HCl to final volume of 150 μL and measure fluorescence intensity using flow cytometry. 264
13.9
13.9.2
Loading and Release of Second-Stage NPs from PSPs
Release of NPs from PSPs
13.9.2.1 Materials Reagents 1. Nanoparticles (i.e., QDots, SWNT); 2. 1.5 mL low-binding polypropylene centrifuge tubes (VWR International); 3. DI water; 4. Tris(hydroxymethyl) aminomethane (Tris-HCl); 5. Sodium chloride. Facilities/Equipment 1. Thermo Scientific Barnstead LabQuake Tube Rotators (Thermo Scientific).
13.9.2.2 Methods 1. Combine 2.1 × 106 PSPs at pH 7.3 with a final solution of 140 mL: i.
2 μM QDots in 200 mM Tris-HCl;
ii.
20 ng μL PEG-FITC-SWNT in 20 mM Tris-HCl; –1
iii. 1 μM QDots + 10 ng μL PEG-FITC-SWNT in 50 mM Tris-HCl. –1
2. Incubate samples on tube rotator (~20 r.p.m.) for 15 minutes at room temperature. 3. Wash samples in 1.4 mL of DI water. 4. Centrifuge for 5 minutes at 4,200 r.p.m. 5. Remove supernatant and resuspend in 70 μL DI water. Use 10 μL from each sample to assess fluorescence intensity using flow cytometry. Record intensity at time 0 and then over several time points (i.e., 30, 60, 90, 180, 360, 1,200 minutes). 6. Dilute residual 60 μL to 3 mL using 20 mM Tris-HCl 0.9% NaCl (release buffer). 7. Incubate at 37°C on tube rotator (~20 r.p.m.) for your defined amount of time. 8. After each time period has expired, centrifuge the sample for 5 minutes at 4,200 r.p.m. and measure fluorescence using flow cytometry.
13.9.3
Data Acquisition, Anticipated Results, and Interpretation
Determining the amount of agent that is loaded or released is critical for any delivery system. Proper characterization of the second-stage NPs is necessary for the optimal loading into the first stage vector. The knowledge of the second stage’s surface charge, size, and concentration will greatly impact the choice of the pore size and surface charge of the first stage to be used to optimize the loading and release of these second-stage NPs (Figure 13.14). To characterize the first stage PSPs after they have been loaded and to determine the kinetics of second-stage NPs release both flow cytometry and confocal microscopy can be used. These are extremely useful tools when the loaded NPs are fluorescently tagged. Flow cytometry can characterize the amount of loaded NPs based on fluorescence intensity (Figure 13.15). To properly evaluate the amount of loaded NPs, it is necessary to compare two samples: PSPs with pores whose size will not allow the loading of the NPs (pore size too small), and PSPs with an adequate pore size to properly load the NPs of choice. This type of analysis allows for the discrimination of the amount
265
Porous Silicon Particles for Multistage Delivery
(a)
(b)
(c)
(d)
(e)
(f)
Figure 13.14 Models representing the three major mechanisms responsible for the optimal loading and release of second-stage NPs from PSPs. Size, dose, and charge are critical factors that govern the amount of NPs that can be loaded within the PSPs. Size dependency and the size of the pores determine the types of NPs that can be preferentially loaded in PSPs. (a) NPs that are too big remain outside. (b) NPs that are smaller than the size of the pores are loaded into the PSPs. Dose dependency: (c) a lower concentration of NPs in the loading solution results in reduced loading into the pores while (d) an increased concentration will result in increased number of NPs loaded within the pores. Charge dependency: (e) NPs with a surface charge opposite to PSPs are strongly attracted into the pores, while (f) NPs with a similar charge to that of the PSPs will result in NPs being partially or completely repelled from loading into the pores. (Reproduced with permission from [29] courtesy of Nature Publishing Group.)
of fluorescence that can be attributed to NPs adhering to the surface of the PSPs, and the fluorescence due to the NPs loaded inside the pores. Confocal microscopy is useful in determining the distribution of the second-stage NPs within the first-stage PSPs (Figure 13.16) and in quantifying the amount of fluorescence attributed to the embedded NPs. However, a large sample population would be needed to get a statistically significant average intensity and thus flow cytometry would be more appropriate. The distribution of the NPs within the PSPs can be detected by simply zooming in on the PSP and then defining a ROI around that PSP. The next step is to draw an intensity profile/line covering the diameter of the PSPs, a graph that displays the intensity versus the length of the line is produced and showing the fluorescence intensity of the NPs distributed in the PSP. For example, when PSPs are loaded simultaneously with two types of NPs, it was concluded that the larger NPs were exclusively found in the central area of the PSP (associated with the larger pores), while smaller NPs were found throughout the entire vector but with a primary accumulation on the border of the PSP (associated with the smaller pores) (Figure 13.16). The release of the NPs from within the pores of the PSPs can also be characterized using flow cytometry (Figure 13.15). This is achieved by indirectly measuring the residual fluorescence of PSPs after they have released the second stage NPs. Carefully choosing time points and displaying the data as percentage released of the optimal loading can give crucial data regarding the release kinetics from within the pores and thus assist in on the best choice of PSP characteristics needed for optimal delivery.
266
13.10
SP oxidized
LP oxidized 200
Mean fluorescence
Mean fluorescence
2000 1600 1200 800 400
160 120 80 40 0
0 0
15
Carboxl Q-dots
30 45 Time (minutes) Amino Q-dots
0
60
15
LP APTERS
Amino Q-dots
60 PEG-FITC-SWNTs
SP APTES 200
Mean fluorescence
Mean fluorescence
45
(b)
2000 1600 1200 800 400 0
160 120 80 40 0
0
15
Carboxl Q-dots
30 45 Time (minutes) Amino Q-dots
60
0
PEG-FITC-SWNTs
15
Carboxl Q-dots
(c)
30 Time (minutes) Amino Q-dots
45
60
PEG-FITC-SWNTs
(d)
LP oxidized
LP oxidized
100
100
Released payload (%)
Released payload (%)
30 Time (minutes)
Carboxl Q-dots
PEG-FITC-SWNTs
(a)
80 60 40 20
80 60 40 20 0
0 0.5
1
1.5 3 Time (minutes)
Carboxl Q-dots
6
20
0.5
1
1.5 3 Time (minutes)
Carboxl Q-dots
PEG-FITC-SWNTs
(e)
6
20
PEG-FITC-SWNTs
(f)
SP APTES
LP APTES 100
Released payload (%)
100
Released payload (%)
Discussion and Commentary
80 60 40 20
80 60 40 20 0
0 0.5
1
1.5 3 Time (minutes)
Carboxl Q-dots
6
20
PEG-FITC-SWNTs
0.5
1
1.5 3 Time (minutes)
Carboxl Q-dots
(g)
6
20
PEG-FITC-SWNTs
(h)
Figure 13.15 Loading and release of second-stage NPs from PSPs. (a–d) Four different types of PSPs were loaded with different second-stage NPs and their mean fluorescence measured by flow cytometry over time were measured: (a) LP oxidized, (b) SP oxidized, (c) LP APTES, and (d) SP APTES. (e) Release of Q-dots and PEG-FITC-SWNTs from LP oxidized, (f) SP oxidized, (g) LP APTES, and (h) SP APTES was measured over time and expressed as a percentage of the total amount of second-stage NP payload released from the PSPs for every time interval, after optimal loading. (Reproduced with permission from [29] courtesy of Nature Publishing Group.)
13.10 Discussion and Commentary This chapter describes a novel multistage delivery system (MDS) based on PSPs capable of sequentially negotiating biobarriers and improve targeted delivery of imaging and 267
Porous Silicon Particles for Multistage Delivery
(a)
(c)
(b)
(d)
(f)
(e)
(h)
(i)
(g)
(j)
Figure 13.16 Simultaneous loading and release of Q-dots and PEG-FITC-SWNT. (a) FACS histogram-overlay of unloaded PSPs; PSPs loaded with PEG-FITC-SWNTs (+SWNTs), with Q-dots (+Q-dots) and with both Q-dots and SWNTs (+Q-dots +SWNTs). Flow cytometry analysis of (b) simultaneous loading and (c) release of second-stage NPs. (d–g) Confocal microscopy images show the localization of PEG-FITC-SWNTs (green) and Q-dots (red) in a single PSP: (d) bright-field, (e) green and red (f) fluorescence, and (g) overlay are shown. (h, i) Fluorescence intensity profiles of each channel along the orange dashed lines in (e) (PEG-FITC-SWNTs) and (f) (Q-dots) are shown, respectively. (j) The green and red arrows incorporated into the SEM image confirm the spatial distribution of fluorescence in the PSP. White scale bars in (d–g) are 3 mm. (Reproduced with permission from [29] courtesy of Nature Publishing Group.)
therapeutics. The versatility and ease of modification of the MDS are one of its major advantages over competing multistage delivery technologies. The methods previously outlined constitute the core of the MDS technology, but few crucial guidelines must always be kept in mind when attempting to implement this system:
268
•
Each step of the PSP fabrication process must be thoroughly controlled and validated to obtain satisfactory results and replicability. As with any silicon manufacturing process, good manufacturing practice is the key to a high-throughput, high-yield process producing functional devices according to specifications.
•
The surface modification process of the PSPs can lead to a significant loss of PSPs during the several steps required. To minimize this loss, it is highly recommended that a small amount of detergent (i.e., Triton X-100) is added to pellet down the PSPs. In a volume of 300–600 μL, 1–2 μL of Triton can help recovering millions of
13.10
Discussion and Commentary
PSPs that may have otherwise been discarded with the supernatant. However, at the user’s earliest convenience, this detergent should be removed from the surface of the PSP since it may inhibit the further modifications required. •
The loading and release kinetics of second-stage NPs can be controlled by tailoring the first-stage PSP’s features. Confocal microscopy can be used to confirm optimal loading conditions, determine the distribution of multiple second-stage NPs, and ensure homogeneity within each first-stage PSP analyzed.
•
Procedures described include using flow cytometry to determine shape, size, and intensity, Z2 Coulter Counter to analyze the concentration and size distribution, and ICP-AES to quantify the amount of silicon in solution. These methods provide details regarding the status of the PSPs and therefore need to be calibrated using control samples of known and defined nature prior to each analysis.
The quantification of trace amounts of silicon must be performed meticulously to obtain an accurate quantification. When preparing samples, it is essential that no tool/material comes into contact with glass. In the construction of the standard curve, the selection of the correct concentrations (standards have to adequately represent the range of expected values) greatly increases the accuracy of the measurements. After successfully replicating the methods outlined earlier, the reader can modify and expand them in order to better suit its specific application. The versatility of the MDS technology allows users to easily build upon the core methods and to adapt them to a variety of different drug delivery scenarios. In particular: •
The design of the PSP size and shape can be optimized to enhance the PSP function using proprietary mathematical algorithms developed in our laboratory [45, 46]. While other NPs follow the laminar flow through the center of the capillary, the PSP tumbles along the wall of the capillary and eventually binds to markers on the tumor associated endothelial capillary wall.
•
The PSPs can be surface modified with peptide sequences used to target tumor cells incorporated into the tumor vasculature. The PSP can use humanized monoclonal antibodies or peptide sequences and specific aptamers in order to avoid antibody targeting limitations and increase system stability.
•
The possibility to maximize drug/second-stage NP loading and release through the modulation of PSP external and internal surface charges.
•
Controlling the details of the pore structure, the PSP can be engineered to deliver drug or secondary NPs only in the direction of the endothelium. This minimizes the amount of NPs swept away in the bloodstream immediately after their release. It is also possible to obtain a PSP where only the external corona is functionalized with targeting moieties, the nucleation layer has been removed, and a small pore layer has been formed on the opposite side of the PSP. Such a PSP will attach to the endothelium with the nucleation layer facing the endothelial cells, and the release of NPs will occur only in one direction: from the nucleation layer towards the vessel wall.
•
PSPs can be engineered to deliver drug and/or second-stage NPs upon endothelial binding or at a tuned delivery rate. Enzymatically degradable cross-linking peptides or pH-responsive polymers could be dispersed within the porous matrix of the PSP alongside NPs for environmentally triggered release. 269
Porous Silicon Particles for Multistage Delivery
•
Functionalization with permeation enhancers will enable the PSPs to open tight junctions of the endothelial lining, through which NPs can pass to augment and/or create appropriate EPR conditions.
•
The MDS is capable of codelivering drug cocktails. Many chemotherapy protocols involve a combination of drugs given together or in sequence. The PSP payload volume is large enough to carry a cocktail of free drugs and/or drugs containing NPs, together with thermal ablation agents and imaging NPs.
•
The MDS enables In Silico Delivery Design to create a personalized therapy for each drug/disease combination. As the pharmaceutical industry has utilized large combinatorial compound libraries to identify new drug candidates, similarly, the MDS can be assembled in a combinatorial way optimizing shape, size, chemistry, surface targeting modalities, and charge modifications of the PSP, for the wide choice of available NPs.
Troubleshooting Table
270
Problem
Explanation
Potential Solutions
Si3N4 film is not uniform.
Nonuniform gas distribution during LPCVD. Nonuniform temperature during LPCVD.
Litographic pattern is: 1) too small or absent. 2) too large or photoresist is absent. 3) nonuniform.
The pattern is: 1) underexposed or under-developed. 2) overexposed or overdeveloped. 3) improperly exposed or developed. OR The mask or substrate is contaminated with dust.
PSP is: 1) too flat. 2) too rounded. 3) too thin. 4) too thick. 5) cracked. 6) is not released. 7) released ahead of time. Pore size is: 1) too big. 2) too small.
1) Dry etch is too shallow. 2) Dry etch is too deep. 3) Porosification time is too short. 4) Porosification time is too long. 5) Porosification or release current density is too high. 6) Release current density is too low. 7) Release current density is too high.
Add more dummy wafers. Move the relative position of the substrate to the gas source. Flip the substrate facing the direction with respect to the gas source. Change the position of the substrate within LPCVD tube. Wait longer for temperature stabilization before gas insertion. Improve temperature uniformity in the tube tuning the Si3N4 deposition recipe. 1) Increase exposure or development time. 2) Decrease exposure or development time. 3) Vary exposure or development time. OR Clean the mask/substrate: acetone-methanol-isopropanol or piranha. 1) Increase dry etch time. 2) Decrease dry etch time. 3) Increase porosification time. 4) Decrease porosification time. 5) Reduce release current density or porosification current density. 6) Increase release current density. 7) Reduce release current density.
Porosification current density is: 1) too high. 2) too low.
1) Reduce porosification current density. 2) Increase porosification current density.
Z2’s aperture is blocked.
Dirty cuvette or ISOTON.
Hit “UNBLOCK” on control panel; OR Remove sample, wash aperture, load Accuvette with filtered ISOTON, hit “FUNCTION” → “FLUSH APERTURE”
Acknowledgments
Problem
Explanation
Potential Solutions
Z2’s software shows more than one central peak [Figure 13.11(b)].
Small second peak: Large number of PSPs sticking together; OR Large second (or more) peak(s): PSPs breaking up within sample being measured (seen with PSPs with high porosity). The machine cannot build up enough pressure to create the proper flow rate to introduce PSPs into the system.
Briefly sonicate the sample longer. Check sonicator water level; the water needs to be set at the marked operating level.
The sensitivity of the machine is set such that it can detect extremely small particles/events, which inherently results in the detection of any dust or dirt that may be present in the system due to previous samples or poor cleaning.
Allow the machine to aspirate distilled water, and measure the tube with water and observe the number of events; if large, repeat. If after several cycles of aspirating water does not work, check sheath fluid level and replenish if necessary.
“RUN” button on FACSCalibur is not green after pushed or the Status is Standby. FACSCalibur’s software shows a high noise or background acquisition.
If tube does not fit properly and you hear pressure/gas leaving the top of tube, try a new tube or replace the o-ring.
Acknowledgments The authors would like to recognize M. Landry for excellent graphical support, Dr. D.L. Haviland for his superior expertise and experience with flow cytometry, Dr. Glen Snyder for his technical support at ICP-AES, Dr. Kaushal Rege for his continual support and useful commentary when compiling this chapter, and all present and past members of The Division of NanoMedicine for useful discussion and assistance.
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273
CHAPTER
14 Mathematical Modeling of Nanoparticle Targeting 1, 2
Elena V. Rosca
1, 2*
and Michael R. Caplan
1
2
Harrington Department of Bioengineering, Arizona State University, Center for Interventional Biomaterials, Arizona State University *Corresponding Author: Michael R. Caplan, Harrington Department of Bioengineering, Arizona State University, P.O. Box 879709, Tempe, AZ 85287-9709, Phone: 480-965-5144, Fax: 480-727-7624, e-mail:
[email protected].
Abstract Mathematical models based on the principle of conservation of mass can greatly enhance understanding of the behavior of and lead to design principles for nanoparticles used for drug or image contrast agent targeting. Implementing such models can be performed at the molecular scale, tissue scale, and organism scale, or at combinations of these scales. Molecular scale modeling is focused on changes in concentrations of bound and unbound nanoparticles with respect to time using chemical kinetics. Tissue scale modeling adds convection and diffusion within tissues along with reaction terms as in molecular scale modeling. Organism scale modeling uses compartmental models with rates of mass exchange between compartments. Once the model is capable of generating accurate predictions of the system’s behavior under conditions not yet studied, the equations on which the model is based most likely incorporate the physical phenomena important to the behavior of the nanoparticles.
Key terms
mathematical modeling nanoparticles drug delivery mass transport protein binding ligands cell surface receptors
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Mathematical Modeling of Nanoparticle Targeting
14.1 Introduction All branches of science and engineering rely on some type of modeling to analyze, interpret, or explain data. Therefore, models serve diverse functions from aiding scientists in organizing data to deciding what data mean and developing an understanding of complex phenomena [1]. For example, understanding a complex event from empirical experimentation might prove to be a difficult and daunting task involving multiple trials to uncover the complex interplay of the principles involved. A theoretical model cannot only be helpful but sometimes critical to understanding the complex interplay of important factors affecting a system’s behavior. Mathematical models are a class of models that involve the use of mathematics to describe a set of physical phenomena quantitatively. Such models allow a researcher to simulate one possible set of relationships among the components that he or she deems important. Comparison of the simulation results to experimental data can indicate that the factors that the scientist deemed important are indeed working the way modeled if the model and data produce similar results. If a large disparity between predicted and experimental data is observed, the model is perhaps too simplistic (omitting major underlying phenomena) or the interactions may be modeled incorrectly. If the discrepancy is relatively small, perhaps some parameters are estimated inaccurately. This can be thought of as using a model as a hypothesis generator. The model is in fact a statement of the hypothesis: that the physical components of the system relate as described in the mathematics. The model is then used to simulate what would happen under various sets of conditions to find a set of theoretical results that, if found to exist in reality, would lend credence to the relationships being as they are described in the model. The experiments are then performed, and the experimental results are compared to the theoretical predictions as described above. If there is a good fit between prediction and data, it is possible that the phenomena are accurately described in the model. However, the normal caveats about experimental validation of hypotheses apply, namely, that one test of a hypothesis does not prove the hypothesis to be true. Additionally, as we will discuss later in this chapter, there is the added caveat that a large number of fitted parameters can make a model fit many sets of data even if the model is not an accurate description of the physical phenomena. Targeting with nanoparticles is a complex problem that encompasses multiple phenomena: interaction of the particle with the target cells, delivery throughout the tissue of interest, stability of targeting moieties (ligands), clearance by various organs, and others. Modeling these factors can assist in the rational development of more effective targeting particles. In particular, modeling can help researchers deal with tradeoffs inherent to the design process such as those between dose of particles and specificity [2]. Here we describe methods for modeling at three different length scales: (1) interactions of the particles with the target at the molecular/cellular scale, (2) delivery and diffusion/convection through tissue at the tissue scale, and (3) systemic delivery, clearance, and biodistribution at the organism scale. At each of these scales, this chapter discusses the available modeling techniques applicable to that length scale, provides in-depth discussion of how to apply those techniques, and indicates how these techniques can be or have been applied to advance targeting of nanoparticles. Molecular/cellular scale modeling is mainly concerned with interaction between the nanoparticles and cell surface receptors. Typically nanoparticles are carriers of specific molecules (ligands) able to interact with cell surface receptors effectively creating 276
14.2
Molecular/Cellular Scale
multivalent constructs [3–8]. Modeling at this level has been focused on understanding the effects of multivalent interactions. These studies suggest that multivalent interactions exhibit increased avidity (overall increased binding of the constructs of higher valency), which is predicted to result in greater targeting specificity [2]. Chemical kinetics are used to describe the interactions between such multivalent particles and cells. Thermodynamics can be used to better estimate parameters for these biophysical models [8, 9]. Tissue scale modeling adds diffusion and/or convection of the particles through the tissue in which the target cells reside. One method of delivery to tumor can be via passive transport from the blood due to high permeability and multiple fenestrations in tumor vasculature [10–12]. Mass transport in these cases is a function of diffusion, interstitial pressure, and tumor pressure. Tissue heterogeneity and anisotropy are also factors that affect fluid distribution. A different approach to delivery consists of local delivery followed by diffusion and/or perfusion [13, 14]. Models in this case are concerned with bulk fluid flow velocities, tissue permeability, filtration of nanoparticles, and other parameters that can influence nanoparticle distribution within the tissue. Last, modeling at the organism scale involves a much broader view of the issue at hand. This scale typically seeks to address biostability, biodistribution, and clearance rates of the nanoparticles. Some parameters important in organism scale modeling are the size of the particles, injection volume and location, dose frequency, and concentration [15]. Such issues are often studied using compartmental models in which the organs or tissues encountered by the particle are modeled as compartments that are interconnected through rates of transfer from one compartment to another.
14.2 Molecular/Cellular Scale 14.2.1
Methods
In molecular/cellular scale modeling of nanoparticles, the model describes binding of the particle with the target cell via the cells’ surface receptors or other surface-bound markers. The most widely used method to study biophysics at this scale is chemical kinetics. Also known as receptor-ligand modeling, this approach was first adapted to study binding of molecules to cell surface receptors by Perleson [16] and has since been extensively reviewed by Lauffenburger and Linderman [17]. The foundation for this type of model is a single binding event between a cell surface receptor and a soluble ligand (such as a growth factor) forming a bound complex. This event can be described and simulated mathematically using the principle of conservation of mass with the following set of equations: dL = − kf LR + kr C dt
(14.1)
dC = kf RL − kr C dt
(14.2)
R = R0 − C
(14.3)
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where L is the concentration of the ligand, R is the concentration of the unbound receptor, C is the concentration of the receptor-ligand complex, kf is the association rate, kr is the dissociation rate, and R0 is the total density of receptors. Equations such as these can be written using the following procedure: 1. Determine the number of terms in each equation. Equations (14.1) and (14.2) have two terms each because each species (L for (14.1) and C for (14.2)) participates in two reactions (association L + R → C and dissociation C → L + R). 2. Determine the species variables (concentrations) that must be in each term. The reactants always determine kinetic order (note: these must be mechanistic reactions, not overall stoicheometry). The first term, describing association, is written with second-order kinetics since two freely moving molecules must collide for association to occur. The second term, describing dissociation, is written with first-order kinetics because only the presence of receptor-ligand complexes (no collision) is necessary for these events to occur. The appropriate rate constant is then added to each term. 3. Determine the sign of each term. The signs of each term are written to describe whether association or dissociation adds (+) or removes (−) ligand or complexes from the system. It can be seen by adding (14.1) and (14.2) that the overall change in mass of the system with time is zero; thus, mass is conserved. Mass must be conserved for the overall system. These equations are inserted into a program which can solve ordinary differential equations such as MATLAB (Mathworks) as follows: 1. Enter each parameter value by naming them p.name with the syntax “p.kf = 1e6;” for the example of setting the association rate to 1 × 106. Also enter initial conditions and the time at which the simulation will end (p.tf) using the same syntax. Note that the user must make sure units (e.g., meters, seconds, and so forth) are consistent. 2. Enter “[t y] = ode15s(@equationfile, [0 p.tf], y0, options, p);” where “ode15s” is the ordinary differential equation solver chosen, “equationfile” is the name of the function where the equations are defined, “y0” is a row vector containing the initial conditions, options are defined as in MATLAB help, and “p” calls the parameter values defined above. 3. Output variables can be calculated. For example the number of unbound receptors could be calculated by “R = p.R0 − y(:,2);” where “y(:,2)” denotes y values in the second column of the [t y] matrix as a function of time. Figures can be plotted based on these calculated values or on the raw data as desired. 4. The equations are defined in a file beginning with “function yp = equationfile (t, y, p);” where “equationfile” must match the name supplied in step 2 exactly. The variables are defined as “L=y(1);” and “C=y(2)”. Immediately after this, any variables calculated with algebraic equations should be calculated, in this case R(t) is defined as “R = p.R0 – C;”. 5. Finally the ordinary differential equations are defined as “yp(1) = -p.kf * L * R + p.k2 * C;” and “yp(2) = p.kf * L * R – p.kr * C;”. Perleson and DiLisi extended this model by applying it to receptor clustering and binding of multivalent ligands (such as antibodies) to oligomeric receptors of B cells [16]. Since nanoparticles have many ligands bound to their surfaces, they likely behave similarly to these multivalent molecules. Converting Perleson and DiLisi’s model into 278
14.2
Molecular/Cellular Scale
the notation used in this chapter, L0 represents the total concentration of divalent molecules, C1 represents the concentration of divalent molecules bound by one ligand to the cell, and C2 represents the concentration of divalent molecules bound to the cell by two ligands, which also corresponds to the concentration of cross-links. dL = −2 kf R(t )L(t ) + kr C1 (t ) dt
(14.4)
dC1 = 2 kf R(t )L(t ) − kr C1 (t ) − kxC1 (t )R(t ) + 2 k− xC2 (t ) dt
(14.5)
dC2 = kxC1 (t )R(t ) − 2 k− xC2 (t ) dt
(14.6)
R0 = R(t ) + C1 (t ) + C2 (t )
(14.7)
where kx and k-x are the association and dissociation rate constants of the second ligand to bind (thus forming the crosslink). These equations are generated in the same manner as described for the single ligand, but the coefficient 2 is necessary in (14.4) and (14.5) to adjust the probability of collision since there are two ligands on the divalent molecule and in (14.5) and (14.6) because a C2 species occupies two receptors either of which can dissociate. The rate constants, kx and k-x, differ from kf and kr by a factor accounting for the increased effective concentration of the ligand when it is tethered to the cell surface by the first receptor-ligand bond. Shewmake et al. [18] defined a factor, VR, which accounts for the increased effective concentration. This binding enhancement factor corrects the association rate constant of secondary binding events in relation to the first binding event. Shewmake’s work is based on work by Krishnamurthy, Whitesides, and coworkers [8], who modeled an inhibitor tethered to the enzyme which it inhibits. Their model calculates Ceff, which is similar to VR*C1 in (14.11), as a function of the root-mean-squared distance between the ends of the polymeric linker, Rg = 〈r2〉1/2, and the distance between tether site and binding site, a. Shewmake et al. applied this to multivalent targeting for several cases, including a random-coil model for linkers between ligands resulting in: VR = ϕIh I =
(3)1 2 (2 π)1 2 R g
(14.8) ⎛ 3a 2 ⎞ ⎟ exp ⎜⎜ − 2 ⎟ ⎝ 2 Rg ⎠
(14.9)
where ϕ is a scalar accounting for excluded volume and h is the ratio of interstitial fluid and the cell surface area. Caplan and Rosca applied this model to multivalent targeting by allowing for two or more different cell types that differ only in the number of receptors expressed (cell types with different R0 values). Using this model they investigated the binding of targeting molecules with various valence (monovalent, divalent, trivalent, and tetravalent), of which various concentrations were applied to cells, and for constructs targeting one receptor type (homovalent) or two receptor types (heterovalent). For the homo,bivalent
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model, (14.4) is modified to allow binding of the unbound construct to two different cell types, M and N: dL = kr (C1 M + C1 N ) − 2 kf ( RM + RN ) dt
(14.10)
Two sets of (14.5) to (14.7) are created, one set for constructs bound to cell type M and another set for constructs bound to cell type N. An additional difference from (14.4) to (14.7) is the introduction of the parameter VR, the binding enhancement factor. The introduction of this parameter allows the replacement of kx and k-x with kf and kr because VR encompasses the effects of secondary binding events of the multivalent constructs so, for instance, the equation for C1,M (C1 binding to cell type M) becomes: dC1, M = 2 kf LRM − kr C1, M − kf VRC1, M RM + kr C2 , M dt
(14.11)
in which the association rate between C1,M and an additional receptor is multiplied by VR. This model can be used to test the dominant premise of targeting, that more drug or imaging molecules will be bound to the target cell, by taking the ratio of constructs bound to the target cell (C1,M + C2,M) versus the number of constructs bound to nontarget cells (C1,N + C2,N). This ratio, defined as specificity, provides a quantitative description of how effective the targeting would be under such conditions. 14.2.2
Data Acquisition, Anticipated Results, and Interpretation
The equations in and of themselves are the mathematical representation of the physical phenomena, but most often they are a means to an end rather than the ultimate goal. In this case, Caplan and Rosca sought to use the mathematical model to elucidate principles for rational design of such multivalent constructs. By considering the possible ways in which the constructs could be designed or employed, several points of control became apparent by which designers can modify constructs. In this system these include the affinity of the receptor-ligand bond, the number of receptors on the target cell, the ratio of the receptors between target and nontarget cells, the concentration of the construct (dose), number of ligands on the construct (valence), and the properties of the linker between ligands. These correspond to parameters in the equations or initial conditions KD (kr/kf ), R0,M, R0,M/R0,N, L0, n (as in Cn), and VR, respectively. Caplan and Rosca varied the receptor number on the target cell (R0,M), construct concentration (L0), and valence (n) while keeping the other parameters constant. They developed sets of equations for homo,trivalent, homo,tetravalent, hetero,divalent (two ligands of each type), and hetero,trivalent (three ligands of each type) constructs similar to those shown above. Results from these models, shown in Figure 14.1, depict the simulated binding specificities of multivalent constructs when the initial construct concentration (Figure 14.1(a)) and the number of receptors on the target cells (Figure 14.1(b)) are varied. 14.2.3
Discussion and Commentary
Illustrating the purpose of modeling at this scale, it is instructive to note several things. First, Caplan and Rosca were able to narrow the scope of their experimental study from 280
14.2 3
Binding specificity
Binding specificity
3.5 3 2.5 2 1.5 1 −11
Molecular/Cellular Scale
−9 −5 −7 Log of construct concentration (M) (a)
−3
2.5 2 1.5 1 10
100 1000 10000 100000 Receptors on target cell (#/cell) (b)
Figure 14.1 Binding specificity of different constructs (monovalent is depicted by a thick dashed line, divalent is depicted by a thick solid line, trivalent is depicted by a thin dashed line, and tetravalent is depicted by a thin solid line) as (a) construct concentration and (b) receptor numbers are varied. (From: [2]. © 2005 Reproduced with permission from Elsevier.)
all possible variations in the design of these constructs to those aspects of the design that had direct correlations to parameters in the mathematical equations. Likewise, the need to quantify a measure of output, in this case specificity, highlighted the need to study binding to two different cell types. In vitro characterization of targeted constructs predating this work studied binding of constructs to the target cell type and control experiments were typically constructs with a nonfunctional ligand. Modeling showed that multivalency could achieve increased avidity for the target cell type without necessarily increasing specificity for the target cell type. Thus, merely writing the equations and choosing how to quantify the output of the model provided an advance to the field in the form of clarifying this metric. Second, the model yielded insights that would not be available by intuition alone or, if intuition could have achieved them, were not intuited prior to the application of this model. Modeling provides a formalism for breaking very complex problems down into manageable pieces which can then be assembled into the mathematical models described. The molecular scale models shown here are broken down into equations for each species of interest (e.g., L, C1, C2, and so forth), and each of these equations is further broken down into a summation of terms which each represent an association or dissociation event. When these pieces were reassembled and parameters varied, the results produced provided insights that were not initially obvious. For example, when the concentration of construct was varied (Figure 14.1(a)), the specificities of multivalent constructs at high concentration were no different than those for monovalent. At low concentration, however, the expected trend for which specificity increases as valence increases was predicted. Since the model keeps track of the various individual species (C1, C2, and so forth), Caplan and Rosca were able to determine that this was due to the prevalence of C1 species at high concentration, which in effect made all binding monovalent due to saturation of the available receptors even when the constructs were multivalent. In a similar manner, when receptor density on the target cell was varied, a biphasic trend was observed with specificity increasing at lower receptor number and decreasing at higher receptor number. Again, availability of information on the individual species revealed that specificity is mostly a function of the percentage of constructs bound by most or all of the ligands (C2 for divalent, C3 for trivalent, and so forth). At lower receptor numbers, the percentage of these species 281
Mathematical Modeling of Nanoparticle Targeting
increases more rapidly on target cells than on nontarget cells; however, at higher receptor numbers, the percentage on target cells approaches 100%, so the percentage increases more rapidly on nontarget cells. Thus, although the avidity monotonically increases with increasing receptor numbers, specificity is predicted to be biphasic. These insights provide general design principles that can be used to increase the likelihood of successful application of nanoparticle targeting. The results shown in Figure 14.1 indicate that the receptor-ligand binding affinity should be two to three orders of magnitude weaker than the required dose if multivalency is to achieve specificity in excess of the ratio of receptors. Additionally, a receptor target which expresses a mid-range number of receptors must be chosen even though a receptor at very low or very high copy number might have a greater ratio of expression between target and nontarget. Experiments must then be performed to validate such design principles, but modeling can provide the initial impetus to perform such experiments and indicate how one should carry out the experiment to see the predicted result.
14.3 Tissue Scale 14.3.1
Methods
Tissue scale modeling can be used to address spatial variations in tissues. An example of this level of modeling is diffusion/convection modeling of nanoparticles delivered directly to tissue containing a tumor. Models at the tissue scale can also account for spatial variations in tissue or construct that arise either due to tissue architecture, such as the growth of a tumor in the tissue, or through delivery of the construct in a particular way (e.g., systemically through the blood or injected directly into the tissue). The application of the principle of conservation of mass to such convection/diffusion problems has a very long history, but recently these principles have been applied directly to nanoparticle targeting. Morrison et al. [19] developed a model describing the injection of macromolecules into brain tissue in which the macromolecules can convect with fluid flow, diffuse, be driven across a capillary wall into the blood stream, or be inactivated by metabolism. Rd
[
∂C = De ∇ ⋅ ( φ∇C) − ∇ ⋅ ( φvC) − Lp s(1 − σ)( pe − pi ) ∂t
(e
Pemv
]
− 1) C − kirr C
(14.12)
where Rd accounts for the distribution of the macromolecule between the intracellular and extracellular space, De is the effective diffusion coefficient, φ is the volume fraction not filled by cells or extracellular matrix, Lp is the vascular hydraulic conductivity, s is the capillary surface area per volume of tissue, pi and pe are the interstitial and Starling pressures, Pemv is the microvascular Peclet number, and kirr is the rate constant for degradation of the macromolecule. This equation accounts for accumulation of the biomacromolecule with time (left side), diffusion (first term, right), convection (second term, right), loss to the blood stream (third term, right), or deactivation (fourth term, right). Solving (14.12) requires one initial condition (in this case C = 0 at t = 0) and two boundary conditions. One boundary condition at the injection site (r = 0) is set so that the concentration of the macromolecule in the injection is held constant (C = C0). A typical second boundary condition used as r → ∞ is that the concentration remains 282
14.3
Tissue Scale
unchanged (C = 0 at r = ∞). Morrison et al. use a simplified version of this equation, in which the third term is omitted, to model convection-enhanced delivery to the brain of a therapeutic molecule which cannot cross the blood-brain barrier. Similar models can be applied to delivery of nanoparticles to tissue if parameter values are known for De and the retardation coefficient (σ) of the particles in the tissue. Rosca et al. [20] have best fit these values for targeted polymers and quantum dots and found that De values of 6 × 10−6 and 1 × 10 and filtration coefficients (1 − σ) of 1 and 0.25, respectively, describe the diffusion/convection of these particles in an agarose mock of brain tissue. Stukel et al. [21] have incorporated the molecular scale binding interactions discussed above into Morrison et al.’s model of convection-enhanced delivery. In the study, brain tissue was modeled using a nodal network with a region of healthy cells and a subdomain of tumor cells. The method presented here is the finite difference scheme used by Stukel et al.; however, it is possible to perform similar modeling using COMSOL Multiphysics which is a finite element simulation. The method described for molecular scale modeling is modified as follows: −6
1. Equations are derived as in molecular scale modeling; however, there are additional terms for diffusion, D∇2L, and convection, –v∇L, which can be modeled in Cartesian coordinates with Taylor series expansions: ⎡∂2 L ∂2 L ⎤ ⎡ L + Li −1 − 2 Li Lj +1 + Lj −1 − 2Lj ⎤ D∇ 2 L = D ⎢ 2 + = D ⎢ i +1 + 2 ⎥ ⎥ k2 h2 ∂y ⎦ ⎣ ⎦ ⎣∂ x ⎡
(1 − σ)v∇L = (1 − σ)⎢v x ⎣
⎛ Lj − Lj −1 ⎞ ⎤ ⎡ ⎛ L − Li − 1 ⎞ ∂L ∂L⎤ + vy = (1 − σ)⎢ v x ⎜ i ⎟⎥ ⎟ + vy ⎜ ⎥ ⎠ ⎝ ⎠⎦ ⎝ ∂x ∂y⎦ h k ⎣
(14.13)
(14.14)
where h and k are the distance between nodes in the x and y coordinates respectively, Li is the construct concentration at x-position i, and Lj is the construct concentration at y-position j. 2. These equations are now nested in a loop structure which varies i and j from 1 to n and 1 to m, respectively, where nh and mk are the dimensions of the tissue. The tumor is defined as several i,j pairs and distinguished by a greater p.R0 value. 3. Boundary conditions are set at i = 0, i = n + 1, j = 0, and j = m + 1. Concentration boundary conditions are set as L0,j = 1e-9, for the example of a constant concentration boundary condition at i = 0. No flux boundary conditions can be set by declaring L0,j = L1,j since there will be no flux at this boundary because there can be no concentration gradient. Stukel et al.’s model is intended to represent a catheter placed within brain tissue through which a solution of drug-targeting construct is injected and the fluid velocity is oriented radially outward from the source. Equations (14.15) and (14.16) describe the transport of drug-targeting constructs including convection. Equation (14.15) describes the equation in Cartesian coordinates, while (14.16) shows the equation in spherical coordinates for which the Cartesian equation is a 2-D simplification. $ $ ∂ L$ ⎡∂2 L$ ∂2 L$ ⎤ $ $ + C$ − ⎡v ∂ L + v ∂ L ⎤ =⎢ 2 + − 3αRL ⎥ ⎥ ⎢ 1 x y 2 ∂ y$ ⎦ ∂ y$ ⎦ ∂ t$ ⎣ ∂ x$ ⎣ ∂ x$
(14.15)
283
Mathematical Modeling of Nanoparticle Targeting
$ ∂ L$ 1 ∂ ⎛ 2 ∂ L$ ⎞ $ $ + C$ − β ∂ L ⎜r$ ⎟ − 3αRL = 2 1 2 ⎜ ⎟ r$ ∂ r$ ∂ t$ r$ ∂ r$ ⎝ ∂ r$ ⎠
(14.16)
where L$ is the dimensionless concentration of unbound concentration (concentration scale is R ), R$ is the dimensionless unbound receptor density, C$ is the dimensionless den1
0
sity of complexes with one ligand bound, v$ x and v$ y are the dimensionless Cartesian components of vr = Q/4πr2 for which the flow rate, Q, is held constant at 3 μL min-1 (β = $ y, $ and r$ are dimensionless coordinates ( D/k is the (Q kr )/(4πD 3 /2 ) = 6,906.59), and x, length scale). α is a dimensionless parameter (α = R0/KD) describing the relationship between receptor density and receptor-ligand affinity; β is a dimensionless parameter dependent on the radial velocity, from which the x and y velocity components, vx and vy, are calculated for each time and matrix location. The equations for C1, C2, C3, and R are calculated for each node at each time point, and these equations remain the same as in the model discussed in the molecular scale section. Boundary conditions are set at the catheter edge (r = 0.64 cm) to be L$ = L$ for 0 < t < t , and L$ = 0 for t = t < t where t is injectate
c
c
f
c
43,200 seconds. This simulates the injection of nanoparticles for some duration tc and then injecting an artificial cerebrospinal fluid afterwards. The external boundary (edges far from catheter tip) is set to no-flux for all times. The source was placed in the center of the matrix. Initial condition for the tissue is L$ = 0 at t = 0.
14.3.2
Data Acquisition, Anticipated Results, and Interpretation
Results from this diffusion/convection model of nanoparticle targeting are tracked as total constructs (L + C1 + C2 + C3) at each node because imaging and/or therapy would depend on the total amount of construct—not just the amount bound to the cells. Figure 14.2 illustrates that enhancement of contrast occurs only when unbound construct is washed away from the tissue. Diffusion alone can accomplish this, but the time required is impractically long. This model predicts that convection-enhanced delivery can dramatically decrease the time required to achieve desirable levels of contrast between target and nontarget tissue. Figure 14.2 shows these results as well as demonstrates the volume of tissue that can be effectively probed using this approach.
14.3.3
Discussion and Commentary
The results from this diffusion/convection model reveal several points about nanoparticle targeting of cancer, particularly in the brain. First, even when tumor location is unknown, the model predicts that it is possible to achieve contrast in excess of 10:1 for tumor tissue versus surrounding tissue. Second, the time required for constructs to be convected to the tumor and then for unbound construct to be convected away from the tumor is large relative to typical imaging procedures but is reasonable for a clinical procedure. Combined with the third prediction, that concentration must be less than the receptor-ligand affinity to achieve high contrast, this severely limits the choice of contrast agents that can be used. Typical magnetic resonance imaging (MRI) contrast agents are long-lived but require high concentration. Conversely typical positron emission tomography (PET) contrast agents can be used at low concentration but are very short-lived (minutes). The convection-enhanced delivery model shown here quantifies 284
14.4
Organism Scale
Figure 14.2 Concentration of targeting constructs achieved via convection-enhanced delivery at different time points and locations. Panels represent total construct concentration (z axis, molecules/cell) at each position (x and y axes, cm) at: (a) 12,000 seconds, (b) 43,000 seconds, (c) 86,000 seconds, (d) 172,000 seconds, (e) 432,000 seconds, and (f) 864,000 seconds. Contrast is visible at (d) t = 172,000 seconds and reaches maximum at (f) 864,000 seconds. (From: [21]. © 2008 Reprinted with permission from Elsevier.)
the problems of applying multivalent targeting to cancer imaging, but it also provides a means to study possible solutions to these issues.
14.4 Organism Scale 14.4.1
Methods
Models at the organism scale also make use of the principle of conservation of mass; however, using the approach discussed in the tissue scale modeling section would be impractical. This is due to several reasons including that the number of nodes required to accurately reflect whether tissue/organ architecture would be very large, architecture would require having regions in which diffusion dominates and regions in which convection dominates, and several other problems. Instead, when one needs to model nanoparticle targeting on the scale of the whole organism, compartmental models are typically used in which each compartment is modeled using one of the techniques discussed above and the connections between the compartments are typically modeled using mass transfer rates between compartments. One recent example is the work of Davis et al., who investigated the efficacy of targeting and delivering siRNA to tumors using transferrin-targeted nanoparticles [22]. The model is comprised of three interconnected compartments: plasma, tumor interstitial volume, and tumor intracellular volume. Concentrations (mol/L) of siRNA in each of these spaces are defined as C1 (plasma), C2 (interstitial tumor), and C3 (intracellular tumor). The equations governing the concentrations of the nanoparticles in these compartments are:
285
Mathematical Modeling of Nanoparticle Targeting
dC1 V = k21C2 2 − ( k12 + kelim )C1 dt V1
(14.17)
dC2 V V = k12 C1 1 + k32 C3 3 − ( k21 + k23 )C2 dt V2 V2
(14.18)
dC3 V = k23C2 2 − k32 C3 dt V3
(14.19)
These equations are derived similarly to the method described for molecular scale modeling with the difference that, instead of mechanistic reactions, the terms in each equation describe rates of transfer from one compartment to another and are typically written as first-order events. As can be seen in the schematic depiction of this model (Figure 14.3(a)), this is a relatively simple model in which the terms multiplied by k12 represent transfer from the blood to the tumor interstitial space, k21 the opposite, k23 represents uptake into tumor cells, k32 the opposite. The only additional term is the elimination of particles from the blood (kelim). V1, V2, and V3 are the volumes corresponding to each compartment, and the ratio of these volumes must be accounted for because the transfer between compartments is in mass per time; however, the variables being calculated are in concentration units.
14.4.2
Data Acquisition, Anticipated Results, and Interpretation
This model was validated against in vivo data by fitting the extravasion rate (k12) and setting the rate of return to blood (k21) and tumor uptake (k23) to zero. As can be seen in Figure 14.3(b), the model result for nanoparticles in the tumor fits the experimental data very well if a dilution rate of 25 min−1 is included in the definition of total particles in the
(a)
(b)
Figure 14.3 Compartmental modeling of tumor-specific targeting. (a) The three-compartment model that was used to derive the equations describing tumor targeting. (b) A comparison of model predictions to experimental data collected. (From: [22]. © 2007 Reprinted with permission from PNAS.)
286
14.5
Model Validation and Application
tumor. It should be noted that the dilution effect would have been more accurately handled by adding convection terms in (14.17) accounting for particles being injected in (none in the dilutant, so this term is zero) and washed out (−Q C1/Vtv), where Q is the volumetric flow rate of the dilutant. If Q is set to a nonzero value for t < 25 minutes and to zero for t > 25 minutes, this would more accurately reflect the mass transport of the experiment performed by Davis et al.
14.4.3
Discussion and Commentary
The strength of organism scale modeling is that it gives a description of the behavior of the targeting construct within the overall study system, the organism. However, the limitation that is usually found in this type of model is the lack of mechanistic description of the physical meaning of the parameters. In this case, the model yields general information about the relative importance of the various transfer terms. For example, since the data can be fit by setting k21 and k23 to zero, we can reasonably conclude that uptake into the cells does not affect the concentration of particles in the tumor and that, once the particles enter the tumor, their rate of transfer back into the blood is negligible. We also see that the data can be fit reasonably well with a first-order rate of transfer from the blood to the tumor tissue. However, this sort of compartmental model does not provide any information as to why, mechanistically, the transfer from blood to tumor is first-order. This limitation can potentially be overcome by combining a compartmental model with the molecular or tissue scale models discussed in the previous sections. For example, a model in which three compartments represent: (1) blood, (2) nontarget cells, and (3) tumor cells could be used. The rate of transfer from the blood to compartments 2 or 3 could be modeled using a term similar to the third term of the right side of (14.18) or using the terms representing binding of unbound constructs to cells as in (14.4). Once in either compartment 2 or 3, the equations describing the biophysics of multivalent interactions could track unbound (L) and the various bound constructs (C1, C2, and so forth). The only constructs which could be exchanged with the blood would be unbound constructs. Such a multiple-scale model could perhaps provide both the overall description of nanoparticle performance while also providing mechanistic detail that is necessary to use modeling as a design tool.
14.5 Model Validation and Application 14.5.1
Statistical Guidelines
Mathematical models of physiological systems or processes are approximations and estimations of the real system. The process of creating the model can generate error due to either under-parameterization or over-parameterization. An under-parameterized model, a too simplistic representation of the system, will give inaccurate predictions due to having made simplifying assumptions that are not quite true; thus, the predictions will be inaccurate if important phenomena were omitted due to such simplifications. An over-parameterized model, complex relative to the prior knowledge that the modeler has about the system, contains many parameters for which there is little to no prior information upon which to estimate those parameters. These parameters need to be fit 287
Mathematical Modeling of Nanoparticle Targeting
to data, and in many cases it is possible to fit experimental data even if the underlying equations are not accurate descriptions of the physical behavior of the system. This is probably the most common mistake because it is usually hidden under the impression that the equations provide a very good fit of the system’s behavior [23]. To avoid the risk of over-parameterization, two general rules to follow are: (1) the number of data points should considerably exceed the number of parameters to be fitted, and (2) the technical behavior of the optimization process will improve as the ratio of data to parameters increases [24]. Fitting a model to data entails the adjustment of model parameters to achieve a concordance between the model prediction and the actual data. However, parameter estimation can be accomplished independently of fitting from previously existing data, and, if this estimated value is not adjusted in the fitting process, model validation is more meaningful [25]. Model fitting is often used to indicate the predictive value of the model; however, there is a clear distinction between the two. Model fitting takes a model that is missing several key parameter values and then trains the model by finding those parameter values that allow the model to best describe the data. As discussed earlier, if the model includes the phenomena important to the function of the system, it should be able to match the data closely. It is possible, particularly if the model is over-parameterized (fitting too many parameters), to match the data closely despite the fact that the model does not accurately describe the underlying phenomena. In such a case, if the model were to be used to predict what would happen if the conditions were changed and the experiment run again, it would predict poorly. The procedure for best fitting parameters is as follows: 1. Create either a spreadsheet or a matrix with experimental data in one column and the model value for conditions identical to each experimental point in another column. 2. Subtract the model result from the data or vice versa. 3. Square the difference. This is the square of the error 4. Sum the “squares of the error.” 5. Vary parameter values either manually or through an automated method (some software will have a feature that does this, but to do this in MATLAB requires writing a simple code to vary the parameters). Find the parameter set that minimizes the sum of squares of the error. These are the best-fit parameter values. The true test of whether the model accurately reflects the phenomena important to the function of the system is to use the model to make a prediction under conditions not used to fit the parameters in the model. This process of predictive validation is closely related to hypothesis testing of an experimental hypothesis. 1. Use the model to make a prediction of what data will result under certain, previously unmeasured, conditions. 2. Perform experiments under those conditions to measure data. Perform sufficient replicates so that 95% confidence intervals are of reasonable size (this will depend on the level of accuracy desired in the model and variance in the experimental system).
288
14.6
Summary Points
3. For each condition predicted/measured, compute the t-statistic between the average x −x where of the data (x) and the model predicted value (x) using the equation t = ( σ/ n ) σ is the standard deviation of the data and n is the number of replicates. 4. Compare the value of the t-statistic with the established t-value corresponding to the desired level of significance and degrees of freedom. If no statistical differences are found, the hypothesis that the model prediction was different from the data was not found to be valid, which is one indication that the model may be valid. Determination of statistical significance by the method in step 3 is mathematically identical to plotting the experimental data with their confidence intervals (i.e., 95%, 99% confidence intervals) and the model prediction on the same plot and then visually inspecting to determine if the model predictions do or do not lie within the confidence intervals (this will only work with confidence intervals—not standard deviations or standard error of the mean). It is important to note that this approach will never reject the alternative hypothesis, and “not rejection” of the null hypothesis does not necessary mean that the null hypothesis is true—only that there is not sufficient evidence against it. Also rejecting the null hypothesis does not mean that the alternative hypothesis is true—only that it is more accurate given the data. Similarly, with this approach one can never prove that the model is true—only that the conditions used to test the model did not demonstrate a flaw in the model. Troubleshooting Table Problem
Potential Solution
Code will not run.
Check syntax (i.e., parenthesis, operator, variable names). Function name/call do not match (also dashes or numbers in the name may cause this error). Make equations dimensionless so that variables are on the same order of magnitude (~1). Try a different ordinary differential equation solver. Adjust tolerances. Check equations. Check loop structure (for finite differences). Check for sign error in equations. Check order of reaction. Check the predictions of the model against a case for which an analytical solution is known. Check the values of the parameters.
Suspension on time steps.
Concentrations are negative.
Results do not seem correct.
14.6 Summary Points 1. Mathematical models based on the principle of conservation of mass can greatly enhance understanding of the behavior of and lead to principles for design of nanoparticles used for targeting. 2. Implementing such models can be performed at the molecular scale, tissue scale, and organism scale, or at combinations of these scales. 3. Molecular scale modeling is focused on changes in concentrations of species with respect to time using chemical kinetics. 4. Tissue scale modeling adds convection and diffusion within tissues along with reaction terms as in molecular scale modeling. 289
Mathematical Modeling of Nanoparticle Targeting
5. Organism scale modeling uses compartmental modeling with rates of mass exchange between compartments. 6. Once the model is capable of generating accurate predictions of the system’s behavior under conditions not yet studied, the equations on which the model is based most likely incorporate the physical phenomena important to the behavior of the nanoparticles.
Acknowledgments The authors thank our funding sources: NIH (R21 NS051310, K22 DE014386) and Arizona Biomedical Research Commission Grant (#0707).
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Shewmake, T., F. Solis, and M. R. Caplan, “Effects of linker properties on multivalent targeting,” Biomacromolecules, Vol. 9, No. 11, 2008, pp. 3057–3064. Morrison, P. F., D. W. Laske, H. Bobo, E. H. Oldfield, and R. L. Dedrick, “High-flow microinfusion: tissue penetration and pharmacodynamics,” Am. J. Physiol., Vol. 266, No. 1, Pt. 2, 1994, pp. R292–R305. Rosca, E. V., J. M. Stukel, R. J. Gillies, J. Vagner, and M. R. Caplan, “Specificity and mobility of biomacromolecular, multivalent constructs for cellular targeting,” Biomacromolecules, Vol. 8, No. 12, 2007, pp. 3830–3835. Stukel, J. M., J. J. Heys, and M. R. Caplan, “Optimizing delivery of multivalent targeting constructs for detection of secondary tumors,” Ann. Biomed. Eng., Vol. 36, No. 7, 2008, pp. 1291–1304. Bartlett, D. W., H. Su, I. J. Hildebrandt, W. A. Weber, and M. E. Davis, “Impact of tumor-specific targeting on the biodistribution and efficacy of siRNA nanoparticles measured by multimodality in vivo imaging,” Proc. Natl. Acad. Sci. USA, Vol. 104, No. 39, 2007, pp. 15549–15554. Lemmon, A. R., and E. C. Moriarty, “The importance of proper model assumption in bayesian phylogenetics,” Syst. Biol., Vol. 53, No. 2, 2004, pp. 265–277. Garfinkel, D., and K.A. Fegley, “Fitting physiological models to data,” Am. J. Physiol., Vol. 246, No. 5, Pt. 2, 1984, pp. R641–R650. Landaw, E. M., and J. J. DiStefano, 3rd, “Multiexponential, multicompartmental, and noncompartmental modeling. II. Data analysis and statistical considerations,” Am. J. Physiol., Vol. 246, No. 5, Pt. 2, 1984, pp. R665–R677.
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CHAPTER
15 Techniques for the Characterization of Nanoparticle-Bioconjugates 1
2
3
Benita J. Dair, Katherine Tyner, and Kim E. Sapsford * 1
Division of Chemistry and Materials Science, Office of Science and Engineering, Center for Devices and Radiological Health, U.S. Food and Drug Administration. 2Division of Applied Pharmacology Research, Office of Testing and Research, Office of Pharmaceutical Science, Center for Drug Evaluation and Research, U.S. Food and Drug Administration. 3Division of Biology, Office of Science and Engineering, Center for Devices and Radiological Health, U.S. Food and Drug Administration. 10903 New Hampshire Avenue, Silver Spring, MD 20993, U.S.A. *Contact Author:
[email protected] Abstract There are a variety of well-developed analytical tools that have been successfully applied to unmodified/native nanoparticle (NP) characterization. The question addressed here is whether these same technologies can be used for the analysis of NP-bioconjugates, given the added complexity of their composite structure, and if they can provide the additional information sought by the user. The short answer is, of course, yes, but as found with unmodified NP analysis, it is fair to say that no one technique can provide a complete characterization of engineered NP-bioconjugates. Rather, a combination of techniques must be used to characterize the many metrics associated with the NP scaffold itself and also the overall NP-bioconjugate assembly. The aim of this chapter is to provide the reader with an overview of the general principles and potential information available from each technology, along with some pertinent examples which highlight both the potential advantages and/or drawbacks of each particular technique. Key terms
review nanoparticle biomolecule bioconjugation separation microscopy spectroscopic mass spectroscopy thermal
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15.1 Introduction Nanotechnology is a rapidly expanding, multidisciplinary field of research with the potential to revolutionize many fundamental and applied aspects of science. In particular, nanoparticles (NPs) modified with biological molecules are emerging in areas as varied as biomedical therapeutic and diagnostic research [1–4], the study of fundamental biological processes/interactions [1], in vivo and in vitro biosensors for clinical, food, and biodefense applications [1, 2, 5–7], bioelectronics [5, 6], nanodelivery systems used in the food industry [8], and novel functional bioassembled architectures/macrostructures [9, 10]. Essential to reliably predicting the function of these novel hybrid nanomaterials is intimate knowledge, and hence extensive characterization, of both the NP and the biomolecular layer [12–15]. A schematic highlighting some of the components that make up a typical NP-bioconjugate is shown in Figure 15.1, and descriptions are provided in Table 15.1. The exact nature of the NP-bioconjugate is highly dependent on the particular system under investigation. For example, the biomolecule can be inside the particle, rather than outside, and in some instances the biomolecule is larger than the NP. Generally, the NP-bioconjugate will be comprised of: (1) the nanoparticle scaffold, with or without an additional shell layer, which may have either an active or passive role in the desired application, (2) various ligands added to make the nanoparticle soluble in an aqueous environment, biocompatible (especially polyethylene glycol -PEG species), and/or reactive to aid in bioconjugation (such as -NH2, -COOH, or -SH), and (3) the biological molecule, such as antibodies, peptides, DNA, and carbohydrates, used to sense/target/treat can either bind directly to the NP surface, via an intermediate linker, or be sequestered
(d) (a)
(c)
(b)
(e)
(f)
(g)
(h)
Figure 15.1 Schematic of the various potential NP-bioconjugate components and configurations (not to scale). (a) Biomolecule interacting with NP core. (b) Biomolecule interacting with NP core via intermediate ligands. (c) Biomolecule interacting with NP shell layer that surrounds the NP core. (d) Biomolecule interacting with NP shell layer—NP core via intermediate ligands. (e) Porous NP core containing entrapped biomolecules. (f) Porous or hollow NP core containing entrapped biomolecules surrounded by a NP shell layer. (g) NP core (or NP core/NP shell structures) particles smaller in size than the much larger biomolecule. (h) NP core (or NP core/NP shell structures) particles smaller in size than the much larger biomolecule attached via intermediate ligands.
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Table 15.1 Nanoparticle Components NP Core
NP Shell
Can be a solid, porous or hollow environment.
A shell layer surrounding a solid or hollow core.
Examples Solid: metallic NPs, semiconductor NPs, QDs Porous: polymer, dendrimers Hollow: Carbon NPs, gold nanoshell NPs, viral NPs*, liposomes
Surface Ligands
Typically bifunctional, interacting with both the NP core/shell surface and its surrounding environment. The terminal moiety can be stabilizing, provide aqueous solubility and/or reactive, allowing subsequent bioconjugation. Examples Functional Groups Gold nanoshell NPs, Silica Carboxylic acids (-COOH), amines nanoshell NPs (hollow or (-NH2), thiols (SH), -PEG, hydroxyls solid—magnetic or Au cores (-OH) common), Semiconductor QD Reactive Chemistries: “click chemcore/shell NPs, Carbon NPs, istry,” affinity-based (biotin-avidin, viral NPs, mixed metallic nickel NTA-poly-histidene, core/shell NPs (Ag/Au struc- succinimidyl esters, maleimides tures), liposomes
Targeting Biomolecules Interacts either directly with the NP core/shell or reacts with surface ligands. Is responsible for the unique specificity of the NP-bioconjugate. Examples Antibodies, peptides, proteins, carbohydrates, aptamers, nucleic acids (DNA/RNA), enzymes, simple molecules (biotin, small toxins, drugs), biomimics, receptors, cofactors, substrates
*Viral NP typically refers to the coat protein cage that surrounds and protects the viral genes of a number of different viruses.
inside the core of the NP. The specifics of NP modification and bioconjugation have been the subject of a number of excellent reviews and book chapters [7, 16–18]. There are a variety of NPs and NP-bioconjugate physicochemical metrics that are important to address. These include NP size and size distribution, shape, topology, molecular weight, aggregation state, purity, chemical composition, surface characteristics, functionality, Zeta potential (overall charge), stability, and solubility [11, 19, 20]. Bioconjugation of NPs typically occurs via stochastic synthesis, resulting in a distribution of NPs functionalized with different populations of biomolecules. This can be of particular concern when single biomolecule labeling of the NP is desired, as is the case for many bioassembly-based applications [1]. Bioconjugation to the NP surfaces therefore pose additional questions and metrics that need to be addressed, including: (1) confirmation of biomolecule attachment, (2) average ratio of NP-to-biomolecule and ratio distribution, (3) hydrodynamic radius, (4) structure and orientation of the biomolecule upon attachment, and (5) stability of bioconjugation to NP environment for the intended application. Structure, orientation, and stability of the biomolecule are of particular interest as these govern how well the NP-bioconjugate functions in its intended application. Correct orientational control of the biomolecule, such as antibodies, for example, will prevent blockage of the active site and prevent mixed avidity that can occur if random orientations are present [21]. There are a variety of well-developed tools that have successfully been applied to characterization of the NP themselves and NP-bioconjugates with the exact choice somewhat dependent on the physical properties of the species under investigation [8, 19, 22, 23]. The Nanotechnology Characterization Laboratory (NCL) [24], in particular, has developed a variety of standardized analytical tests, termed the assay cascade, used to characterize not only the physicochemical characteristics, but also the in vitro and in vivo properties of NP materials used in cancer research [25]. While the ultimate test of successful NP-bioconjugation is, of course, functionality in the desired application, where activity infers the presence and activity of the biomolecule on the NP surface, this may not provide specific details of the NP-bioconjugate architecture. The aim of this chapter is to provide the reader an 295
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informed review of the characterization methods available, expressly focused on NP-bioconjugates, along with some pertinent examples which highlight both the potential advantages and/or drawbacks of each particular technique. The techniques discussed have been grouped under six main categories based upon the intrinsic type of analysis performed: separation-based, scattering, microscopy, spectroscopic, mass spectroscopy, and thermal.
15.2 Methods 15.2.1 Separation-Based Techniques Separation-based techniques such as chromatography, electrophoresis, and centrifugation are routinely used to purify NP-bioconjugates. However, in many cases they can also provide approximate hydrodynamic radius, purity of product, NP-to- biomolecule ratio, and stability (e.g., postproduction degradation). Chromatography is a separation technique that relies upon differing affinities of the multiple sample components for the chosen chromatographic mobile and stationary/solid phase. There are many types of chromatographic techniques and likewise numerous detectors available for measuring the eluting fractions including: UV-visible absorbance, light scattering, fluorescence, refractive index measurements, and mass spectroscopy [26]. Column-based liquid chromatography techniques, in particular high performance liquid chromatography (HPLC), have been used extensively for NPbioconjugate separations [26, 27]. HPLC is often preferred over classical (gravity or low pressure) chromatography due to improved peak resolving power [26, 27]. The ability of size exclusion-based HPLC to explore the size and shape polydispersity of various quantum dot (QD) materials was recently demonstrated [28]. Of the many varieties of chromatography columns available, reverse-phase [29, 30], ion-exchange [31], and size exclusion (SEC) [13, 26, 27, 32, 33] are the most common for NP-bioconjugate studies. In most cases chromatography techniques are capable of purifying NP-bioconjugates both from unmodified NP and free biomolecules, as demonstrated for amine-modified gold NP-cytochrome c conjugates [29] and polymer-coated QD-antibody complexes [27]. Care should be taken to limit nonspecific interactions with the solid phase matrix which can be problematic. In some instances optimized HPLC has demonstrated the exquisite ability to resolve NP-bioconjugates with different NP-to-biomolecule ratios, providing both the distribution and overall average ratio of NP-to-biomolecule per sample [30, 31]. Reverse-phase HPLC has been used to determine the distribution of ligands per dendrimer for (3-(4-(prop-2-ynyloxy) phenyl) propanoic acid) conjugated to the primary surface amines of dendrimer NPs [30]. Anion exchange HPLC was used to investigate DNA-gold NP conjugates and demonstrated the superior resolving power of HPLC over gel electrophoresis for separating 5-nm gold NPs labeled with 1, 2, or 3 PolyT DNA (see Figure 15.2(a)) [31]. In contrast, agarose gel electrophoresis achieved higher resolving capabilities compared to SEC-HPLC for PEG functionalized QDs [13], highlighting the need to tailor techniques for each particular NP-bioconjugate system under investigation. Chromatography is likewise a powerful tool for investigating NP-bioconjugate stability postproduction, as demonstrated for nanohydrogel materials used for drug delivery [34].
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Figure 15.2 Separation techniques. (a) Comparison of (i) agarose gel electrophoresis and (iii) anion exchange high-performance liquid chromatography (AE-HPLC) purification of polyT DNA conjugated to 5-nm gold NPs. (i) Agarose gel electrophoretic separation of gold NP-DNA bioconjugates functionalized with 0, 1, 2, and 3 DHA strands. (ii) Optical density analysis of the agarose gel electrophoresis bands in (i) demonstrating band overlap and limited resolution. (iii) AE-HPLC purification of the same gold-NP bioconjugates illustrating the superior resolving power, especially at the peak base, of the technique. Images kindly provided by Dr. Claridge (Berkeley). Reprinted with permission from [31], Copyright 2008 American Chemical Society. (b) Agarose gel separation of different DNA-conjugated gold NPs at various modification ratios. Note, at the lower DNA-to-gold NP ratios multiple distinct narrow bands are observed in the gel representing modification ratios of 1, 2, 3, and so forth. However, at higher ratios of DNA-to-gold NP, broader bands, which move increasingly slower in the gel, were observed, reflecting the increase in DNA loading and concurrently larger overall hydrodynamic size of the DNA-gold NP. Images kindly supplied by Dr. Parak (Lugwig-Maximilians-Universität). Reprinted with permission from [49], Copyright 2003 American Chemical Society. (c) Agarose gel characterization of maltose binding protein (MBP)-QD bioconjugates. The gel image clearly shows the separation of QD conjugates with different numbers of MBP protein-per-QD. Due to the Poisson distribution, smaller ratios demonstrate several mobility bands which merge into a single band as the ratio increases, suggesting a more homogeneous product. Images kindly provided by Dr. Mattoussi (U.S. Naval Research Laboratory). Reprinted with permission from [51], Copyright 2006 American Chemical Society.
Hydrodynamic chromatography (HDC) uses a nonporous stationary phase and a pressure-driven mobile phase to fractionate mixtures in a channel [35]. Larger particles reside in the faster-moving central region of the parabolic flow profile, while smaller species readily diffuse to the slower-moving regions near the channel walls, resulting in efficient separation. This technique has demonstrated characterization of lipid nanocapsules [35] and fluorescently labeled polystyrene NPs [36], but has yet to be applied to NP-bioconjugates, where its resolving capabilities maybe limited to separating unconjugated biomolecules from NP-bioconjugate. 297
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Field flow fractionation (FFF) encompasses a continually evolving family of analytical separation techniques. The sample is introduced into a pressure-driven mobile phase contained within an open channel (no stationary phase), comprising a parabolic flow profile, and a field is applied perpendicular to the direction of flow [37, 38]. Through a combination of complex effects, the sample components separate into different laminae regions of the parabolic flow above an accumulation wall and hence separate due to differing transport velocities. The main applied fields are crossflow (flow, including asymmetric flow), centrifugal (sedimentation), electrical fields (electrical) and thermal/temperature gradients (thermal), although magnetic and dielectrophoric fields have also been used. Detection of eluted fractions is typically achieved by coupling the FFF channel outlet to a UV-visible detector or multiangle light scattering (MALS) measurements. Both sedimentary and flow FFF, which separate based on effective mass and diffusion coefficients, respectively, have predictable retention times that depend on various physical parameters of the particle constituents, including effective mass, hydrodynamic diameter, density, and/or volume [37]. The resulting fractograms can provide both size (peak height) and size distribution (peak width) information. Precalibration with known “size” NP standards is often desired; however, in the case of flow, FFF is not always necessary if all geometric dimensions of the fractionation channel are accurately known [39]. Thermal FFF has also demonstrated the ability to separate according to both size and surface potential, as demonstrated using silica NPs [40]. To date, the FFF family has found limited use for NP-bioconjugate analysis, with applications mainly concerning polymer NPs modified with targeting peptides [41], biodegradable polymer NPs for drug delivery [39, 42–44], and QD-DNA conjugates [45]. Magnetic FFF has been used to characterize dextran-coated magnetic NPs [46]. As with any separation technique, nonspecific binding can occur at the FFF accumulation wall and hence optimization is required to obtain the desired separation [39]. Optimization can include varying the buffer type and ionic strength as well as the choice of membrane used as the accumulation wall, including the membrane molecular weight cutoff (MWCO) and material [47]. FFF has not yet demonstrated the ability to resolve NPs functionalized with varying numbers of biomolecules, but this may in part be due to its current limited application in this field, as opposed to a fundamental lack of ability. Slab gel- and capillary-electrophoresis are the two main types of electrophoretic techniques successfully applied to the characterization of NPs. Slab gel electrophoresis measures the electrophoretic mobility of charged species in a gel matrix when an electric field is applied. For NPs both the overall size and charge density will influence the direction and distance moved in the gel. In many, but not all, cases bioconjugation has limited influence on the overall surface charge and therefore the electrophoretic mobility is dominated by the hydrodynamic size. Gel electrophoresis, when combined with appropriate controls, is a powerful tool for demonstrating biomolecule attachment to the NP scaffold through sensitive changes in mobility [13, 48, 49–55]. As the NP-bioconjugates become larger in size they tend to migrate at slower rates in the gel matrix, as illustrated in Figure 15.2 [31, 49, 51]. Gel electrophoresis has demonstrated exquisite resolution under optimal conditions and is able to separate NPs labeled with 1, 2, 3, and so forth biomolecules (see Figure 15.2) [31, 49, 51, 53, 54]. On the small scale the technique is routinely used to separate and purify NPbioconjugates, and extracted particles can be further characterized using techniques 298
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such as AFM and mass spectrometry [13]. Gel electrophoresis has not only demonstrated the ability to separate NPs based on size and shape [56], but also revealed unanticipated NP-biomolecule nonspecific binding, which blocked the biomolecule active site [57]. Agarose and polyacrylamide gel electrophoresis (PAGE: SDS and native) represent the main gel matrices used for NP-bioconjugates’ characterization. Colored NPs (gold, carbon nanotubes, silver) can be visualized by eye, while fluorescent NPs (quantum dots or fluorescently labeled) are detected using an appropriate excitation source and detector (e.g., CCD camera). Proteins and DNA present on the NP surface can be detected via staining, using Coomassie Blue for proteins and ethidium bromide (or SYBR dyes) for DNA, which is typically performed after NP measurement to demonstrate comobility with the NP. The use of gel electrophoresis for determining absolute hydrodynamic diameters was investigated in a comprehensive study by Parak and coworkers characterizing gold-DNA conjugates [49, 54]. While extremely sensitive to the extent of NP-bioconjugation, gel electrophoresis suffered several limitations with respect to absolute effective diameters derived using either a calibration curve or Ferguson plots [49–51, 54, 58]. Both methods use gold NPs of increasing “known” size to calibrate the mobility-diameter relationship resulting in calibrations based on rigid size increases and not the flexible “soft” increases more likely to occur from DNA attachment to a NP surface [49, 54]. Although not simple to design or prepare, more appropriate calibration materials may eliminate some inherent limitations. Capillary electrophoresis (CE) measures the electrophoretic mobility of charged species in an open capillary (no solid matrix) filled with a liquid electrolyte, when an electric field is applied. Through a combination of electrophoresis of the sample components and the electro-osmotic flow (EOF) of the electrolyte buffer, the sample components are transported from the positive anode to the cathode with separation based on the species size-to-charge ratio [59]. EOF of the electrolyte buffer is observed when the capillary wall is charged and NP studies to date use fused-silica capillaries rendered negatively charged through ionization via exposure to a basic solution. Under these conditions, positively charged, species generally elute the fastest, and in the case of NPs, smaller diameters elute first, as demonstrated for gold and gold/silver NPs [60–62]. If separation based on pure electrophoresis of the sample components is desired, the capillary walls can be coated with a neutral polymer which suppresses EOF and likewise any interaction of the sample components with the interior wall [110]. UV-visible absorbance and, where appropriate, fluorescence (specifically, laser induced fluorescence-LIF) represent common methods for species detection in CE, with mass spectroscopy occasionally used. Extensive optimization of the electrolyte components, including surfactants and pH, is required for effective CE [62, 64]. Variations on the traditional CE theme exist, including capillary gel electrophoresis, where the capillary is filled with gel matrix, and micellar and microemulsion electrokinetic chromatographies, which aid in the separation of neutral species [65]. CE has been applied to the study of a number of NP-bioconjugates, including QD-BSA [64], silicon NP-streptavidin [66], and iron oxide NP-protein/antibody conjugates [67]. CE has also been used to study the drug loading abilities of poly(lactic acid) NPs [65], plasma protein absorption to PEGylated polymer NPs [68], and IgM interactions with QD-anti-IgM bioconjugates [69]. However, to date, CE has mainly been used in a qualitative sense to demonstrate bioconjugation through the differing mobilities of 299
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the NP-bioconjugates compared to the free NPs, and its quantitative capabilities remain unproven but of high potential as the technology evolves. Analytical ultracentrifugation (AUC) is a separation method that is often used to determine sample purity and average molecular weight in liquid-based dispersions without the need for special solvents, such as those found commonly in sucrose/glycerol gradient centrifugation. AUC consists of a high-speed centrifuge rotor with cell compartments and an optical system (usually UV) used to measure concentration gradients of the sample when centrifugal force is applied [70]. Two main modes are used in AUC— sedimentation velocity and sedimentation equilibrium—and they can be used sequentially to provide information about the individual NPs and NP interactions. Size, size distribution, and shape of NPs can be calculated with AUC, with no assumption about the dimensions of the particle needed, as opposed to light scattering techniques (see Section 15.2.2). In addition to the basic structure of a NP material, AUC can provide both structural and conformational information about conjugated biomolecules. AUC theory and basic techniques have been reviewed in the literature [71, 72]. The main formulism for AUC analysis involves determining the sedimentation coefficient, s, which contains information about the particle’s physical properties, described by
[M (1 − νρ) Nf ] = u ω r = s 2
(15.1)
where M is the molar weight of the solute (in g/mol), N is Avogadro’s number, ν is the partial specific volume of the particle (in mL), ρ is the density of the solvent (g/mL), ω is the angular velocity (in radians per second), r is the distance of the particle from the axis of rotation, f is the frictional coefficient, and u is the particle velocity. AUC can elucidate the binding of small molecules to NPs, NP self-association or aggregation (as demonstrated for human serum albumin NPs [73]), and interactions between heterogeneous NPs as each noninteractive species is separated into a unique boundary [73–76]. In addition, the size of the individual portions of the NP-bioconjugate and the overall size of the complex may be determined. Good agreement between sizes determined from the AUC sedimentation coefficients and those observed in transmission electron microscopy (TEM) (see Section 15.2.3) for unconjugated gold NPs have been found and the average stoichiometry upon protein ligand (lactose repressor) bioconjugation to the gold NP determined without the need to first remove the unconjugated protein [74]. Stoichiometry of the NP-to-ligand may also be determined with this method as demonstrated for bovine serum albumin (BSA) modified QDs [75]. Researchers have also used AUC to size CdSe/ZnS core/shell QDs as well as study their bioconjugation with dihydrolipoic acid (DHLA) and poly(ethyelene glycol) [75] and to study protein interactions with silica NP cores [76]. Benefits to the AUC method include small sample sizes (20 μL) and a wide range of usable concentrations. In addition, AUC is nondestructive and the sample may be recovered for subsequent analysis, thus allowing for detailed testing of post-formulated products.
15.2.2
Scattering Techniques
Scattering techniques, as the name implies, measure the scattering of radiation (e.g., light or particles) through its interaction with a sample, and information about the NP structure, morphology, hydrodynamic size, and aggregation state, as well as the biomolecule conformation and the NP-bioconjugate stability, can be obtained. 300
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Dynamic light scattering (DLS), also known as photon correlation spectroscopy (PCS) or quasi-elastic light scattering (QRLS), is a nondestructive technique used to size particles in solution from the nanometer-to-micron size scale [77–81]. When a sample area is subjected to incident light, the total light that reaches the detector located a specific distance and angle away from the sample is the sum of the scattered waves from all of the illuminated particles. Small particles in solution undergo Brownian motion or thermal fluctuations in which they continuously vibrate, move, rotate, and collide with one another. This motion causes the distances between the scattering particles to change, resulting in constructive and destructive interference of the scattered light over time and intensity fluctuations in the detected signal. The time dependence of the fluctuations, and notably the decay rate, Γ, can be fitted to give a diffusion coefficient, D, for the particles, as described by Γ = Dq2
where q = (4πn0 λ 0 ) sin( θ 2)
(15.2)
where n0 is the index of refraction of the solvent, λ0 = wavelength of incident light, and θ = angle of measurement. Using the Stokes-Einstein relationship, the hydrodynamic radius of the molecules, Rh, can be calculated from the diffusion coefficient D using Rh = kT 6πη o D
(15.3)
where T is the temperature in Kelvin, k is the Boltzmann constant, and ηo is the viscosity of the solvent. A monodisperse sample gives rise to a single decay rate, which is rather straightforward to analyze for particle size. However, polydisperse samples give rise to a series of exponential decays, which are analyzed for size distributions by fitting to assumed distribution functions, which may or may not represent the actual particle distribution [77]. DLS measurements are very common in characterizing NP solutions and NPbioconjugates [51, 82–84]. For instance, Figure 15.3(a, b) presents DLS data of luciferase (Luc8)-conjugated quantum dots (QDs), along with corresponding TEMs, to compare the size of the conjugated particles before and after modification [82]. Pons and coworkers used DLS to investigate the hydrodynamic dimension variations of QDs capped with various surface ligands, including various PEG-polymer ligands, and maltose binding protein (MBP) [51]. One current drawback of DLS is that the hydrodynamic radius reported assumes a spherical particle; therefore, hydrodynamic radii reported for nonspherical shapes may not reflect the true size of the particles. There are, however, models being developed in the DLS literature for nonspherical shapes, such as rods [85]. DLS also has problems distinguishing between two species close in hydrodynamic radius, and given that the scattering intensity is proportional to the sixth power of the particle radius, care must be taken when interpreting data from samples containing a wide range of size distributions, since the scattering signal will be heavily weighted to small numbers of larger particles [86]. Sample preparation is extremely important and care must be taken to remove large contaminating particles, such as dust particles, which are highly scattering. Filtration prior to analysis, or the use of prefiltered solvents, is commonly employed to reduce this issue. The benefits of DLS are numerous and include rapid sample analysis, taking a few minutes at most, inexpensive technology compared to other characterization techniques, and many benchtop models are commercially available. Sample preparation is 301
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Figure 15.3 Dynamic light scattering (DLS) and Zeta potential. (a, b) The DLS and transmission electron microscope (TEM) characterization (inserts) of QD-luc8 NP-bioconjugates, pre- and post- polymeric encapsulation, respectively. Polymeric encapsulation cross-linked two to three QD-luc8 NPs resulting in an overall increase in the diameter, as observed in the DLS analysis. (Images kindly provided by Dr. Xing and Dr. Rao (Stanford University). Reprinted from Biochemical and Biophysical Research Communications, [82], Copyright 2008, with permission from Elsevier.) (c) Zeta potential characterization. Schematic of a charged particle and its associated potentials, including zeta potential. (Reprinted with permission granted by Malvern Instruments Ltd. UK [97].) (d) Simultaneous measurement of both the Zeta potential (squares) and the dynamic light scattering (DLS) determined particle size (triangles) of streptavidin modified silica NPs during a pH titration in water. The streptavidin modified silica NPs are found to be unstable above pH 7.0 where clearly the mean particle size rapidly increases (suggesting agglomerate formation) and the Zeta potential drops below 20 mV. (Images reprinted with permission from the Hindawi Publishing Corporation (DOI#10.1155/2008/712514) [98].)
relatively simple and measurements can be made in any media or solvent of interest. DLS is very sensitive, capable of measuring very dilute solutions (~0.01% w/v), and the technology is improving to allow measurement of concentrated samples, thereby reducing the requirement for dilution. This technique is also particularly useful for monitoring stability of particles or formulations postproduction providing valuable information regarding shelf life. Fluorescence correlation spectroscopy (FCS) is similar to DLS in that it measures fluctuations due to diffusion, aggregation, and interactions, but rather than scattering, FCS measures fluorescence. The technique commonly uses optical microscope instrumentation, in particular confocal microscopy, to excite (using single- or multiphoton excitation) and measure fluorescence within a confined optical volume. Data is essentially derived from monitoring sample transit times in a known confined excitation volume. Note that confocal microscopy can be used in the backscatter mode to study nonfluorescent particles, as demonstrated for gold and latex particles [87]. While FCS is best used for rapidly diffusing fluorescence molecules (such as organic dyes), it has been 302
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successfully used to accurately size the hydrodynamic radius of slower diffusing NPs such as QDs and fluorescent beads [13, 87–91] and characterize the photophysical properties of QDs [92, 93]. FCS has also been used to determine binding kinetics between 100-nm unilamellar vesicles and fluorescently labeled peptides [94]. Fluorescence lifetime correlation spectroscopy has been used to monitor the metal enhanced fluorescence (MEF) resulting from Cy5-labeled DNA hybridizing to DNA-modified silver NPs [95]. Electrokinetic potential, or zeta (ζ) potential, characterizes the surface charge of a particle, which can influence its stability, dispersability, and agglomeration [96]. A charged particle in solution will have a layer of opposite charges around its surface called the double diffuse layer, consisting of an inner core layer of tightly bound charges (the Stern layer), and a more diffuse outer layer of charges, within which is a boundary referred to as the slipping plane (see Figure 15.3(c)) [97]. Within the slipping plane, the particle and those associated diffuse ions can be considered to move as a single entity. The potential difference between this point and the bulk solvent is the Zeta potential. Zeta potential is determined by applying an electric field across a sample and measuring the velocity at which charged species move towards the electrode. The velocity, which is proportional to the zeta potential, is measured as a phase or frequency shift in the incident light using the technique of laser Doppler velocimetry (LDV) [51]. The resulting electrophoretic mobility, μE, is calculated using (15.4), where v is the velocity and E is the applied field [96, 97]: μE = v E
(15.4)
From the electrophoretic mobility, the Zeta potential (ζ) is determined using the Henry equation [96]: ζ = 3ημE 2 εf ( Ka)
(15.5)
where η is the viscosity, ε is the dielectric constant and f(Ka) is the Henry function, for which one of two common approximations are generally assumed: (1) 1.5 for aqueous media containing particles larger than 200 nm dispersed in an electrolyte media (the Smoluchowski approximation), or (2) 1.0 for particles in a low dielectric constant media (the Huckel approximation) [96, 97]. Zeta potential can be used as a measure of particle stability, with a value of +/− 25 mV often selected as an arbitrary delineation of stability. Absolute values larger than 25 mV indicate stability and represent highly charged particles that repel one another, while values 100), and therefore many TEM images, must be surveyed followed by averaging. Alternatively, a complementary method should be employed in conjunction with TEM to provide an average size measurement for the sample. Third, because samples are placed into the vacuum column for imaging, they must be dry. Typical TEM samples are made by drop-drying a solution onto a holey/lacey C film Cu-grid and either allowing the solution to dry or actively wicking away excess solution with absorbent film. However, drying out the NP-bioconjugates may cause the biologic or polymeric molecules to collapse on themselves and the particles to aggregate. Thus, the resulting TEM image of the NP-bioconjugates may not be a true reflection of its native state or size, and care must be taken to interpret the images. Complementary methods are therefore commonly employed to get a true indication of the morphology of the NP-bioconjugates in their intended media. Cryogenic-TEM is a more recent advancement in the technology and has been applied to the study of transient nanostructures such as lipid micelles, vesicles, and bilayers [166], and may present a truer reflection of a NP-bioconjugate native state. Scanning electron microscopy (SEM) can also be used to image NP shapes and sizes. The technique uses a high energy electron beam, ranging from a few hundred eV up to ~100 keV, which is rastered across the surface energy secondary electrons, and/or X-rays are generated at each point, and each provides different types of information about the sample. The intensities of the secondary electrons are a function of both the sample composition and the topographic geometry of the sample. Only the low energy secondary electrons generated near the surface are able to escape the sample and be measured, and hence SEM is primarily a topographical technique. Magnification of SEM can range from ~25× to ~250 k×, and features down to ~0.5–5 nm can be resolved depending on the spot size of the beam and its interactions with the sample. In addition to topography details, the high energy backscattered electrons (BEI mode) also give information about the composition, with contrast arising from differences in atomic number. The SEM (and TEM) can also be combined with energy dispersive X-ray spectroscopy analysis (EDX, EDS, or EDAX) which characterizes the generated X-rays to provide elemental composition. For a more in-depth description of the SEM technique and instrumentation, the reader is referred to [132, 167, 168]. There are several limitations to the SEM technique. First, there is a need to coat nonconductive samples with a conductive layer (e.g., gold), such that the sample does not build up charge from the electron beam, so care must be taken when interpreting SEM images from coated samples. If the gold coating is thick, for example, the details of the sample surface would be that of the gold and its coating process rather than the NP sample. Like TEM, traditional SEM requires dry samples that do not outgas and, if in powder form, adhere to the sample mount. Environmental SEM (ESEM) does allows 311
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sample imaging under low pressure, fairly high humidity and without the requirement for a conducting overcoat and has been particularly useful for imaging biological samples [169, 170] but has not been applied to NP-bioconjugates to date. Traditional SEM techniques have been used to characterize NP shapes and sizes prior to and after bioconjugation [171–173], but like TEM these are more commonly used to characterize the NP core as opposed to the conjugated biomolecules. SEM has the added advantage of a larger imaging field of view than the TEM [174]. Figure 15.6(d) shows an SEM of protein-conjugated polystyrene NPs with a perforated microstructure [175]. The combination of SEM and the elemental analysis of EDX has been used to characterize a fungal protease-gold NP bioconjugate [172] and the growth of hydroxyapatite crystals of physiologically clotted fibrin modified gold NPs [121]. Traditional transmission optical light microscopy involves sample illumination from below coupled with detection/observation from above and typically measures the reflection or absorption of the light. However, unlike electron microscopy, traditional light microscopy cannot typically resolve nanoscale features 5
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Figure 15.7 Spectroscopic techniques for NP-bioconjugate characterization. (a) (i) Wide-field fluorescence microscopy images of lanthanide-ion doped oxide-NPs labeled with an Alex488-labeled protein. The left image NP emission and right image shows Alex488 emission. Stepwise photo-bleaching of the Alex488 is observed for individual NP-protein bioconjugates and the number of bleaching steps counted to precisely measure the number of proteins-per-NP. The data is summarized in (ii), which shows the distribution of Alex488-proteins-per-NP. (iii) Pie graphs summarizing the distribution of Alex488-per-NP, Alex488-per-protein and Alex488-proteins-per-NP. (Images kindly supplied by Dr. Casanova (Ecole Polytechnique). Reprinted with permission from [183], Copyright 2008 American Chemical Society.) (b) Successive FT-IR spectra of the functionalization of detonated nanodiamond samples with biotin. (i) FT-IR of pristine nanodiamond (arrow shows the carbonyl band of the nanodiamond material which disappears upon reduction), (ii) hydroxylated nanodiamond, produced through reaction of (i) with borane, (iii) silanization of (ii) with (3-aminopropyl) trimethoxysilane, followed by biotinylation to produce biotinylated nanodiamond (iv). The presence of characteristic bonds confirms successful modification at each stage of the synthesis. (Image kindly provided by Dr. Krueger (Christian-Albrechts-Universität zu Kiel). Reprinted with permission from [224], Copyright 2008 American Chemical Society.)
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214]. Suffice to say that the FRET phenomenon is highly dependent on a number of factors, most importantly the extent of donor/acceptor spectral overlap and the distance between the two. The underlying process has been likened to a molecular ruler with sep6 aration sensitivities for donor/acceptor distances proportional to r and usually falls in the 1–10-nm range. Medintz and coworkers have demonstrated the unique abilities of semiconductor QDs as donors in a variety of FRET formats [215–218]. In an elegant FRET study, they used six mutants of maltose binding protein (MBP), each labeled at a different unique site with a rhodamine dye, to determine FRET distances for each mutant and establish the orientation of MBP immobilized to a QD NP [215]. FRET has also been used to monitor the binding of fluorescently labeled proteins or peptides to the surface of QDs [55, 219], but is more commonly used as a signal transduction mechanism in functional assays. A number of researchers have used gold NP acceptors as quenchers in energy transfer studies with fluorescent donor species and the resulting surface energy transfer (SET) process has been shown to have a nontraditional r4 distance dependency, essentially extending the reach of the molecular ruler [220–222]. Sen and coworkers recently used Trp-gold SET to probe conformational changes that occur when BSA binds gold NPs [222]. Infrared (IR) spectroscopy measures the absorption of IR radiation by a sample resulting from vibrational stretching and bending modes within the molecule. Technical advances in IR spectroscopy, notably Fourier transform-IR (FT-IR), have resulted in its now-routine use in the characterization of protein structures [223]. Many researchers have used FT-IR spectroscopy to demonstrate NP bioconjugation through the appearance of characteristic spectral bands, including biotin to diamond NPs (see Figure 15.7(b)) [224], dextran or albumin to gold NPs [206, 225], hemoproteins on gold and silver NPs [104, 193], streptavidin to silicon [66], and β-lactoglobulin adsorbed on silica NPs [196]. In the case of globular proteins, careful interpretation of the stretching and bending vibrations in the amide regions can provide secondary structural information regarding α-helical, β-sheet, turns, and “other” (also referred to as unordered or random coil) strands [196, 200, 206, 223]. The amide I band (1,600–1,700 cm–1) region, in particular, is found to show substantial changes related to α-helical → β-sheet structural conformational changes. Samples are prepared by either depositing within solid KBr pellets [104, 200, 224] or dissolving in an aqueous solvent [197, 206, 225]. Liquid samples are measured using either special IR optical cuvettes [225] or an attenuated total reflection (ATR) attachment [196, 206]. As with many of the techniques discussed, appropriate background spectra must be acquired prior to sample analysis and subsequently subtracted from the sample spectra. This is particularly important for aqueous samples where water is found to be strongly absorbing in the IR region [223]. Some researchers perform H2O → D2O exchange prior to the measurement, although this can result in band frequency shifts and incomplete exchange which can complicate spectral interpretation [223]. The rate of H2O → D2O exchange monitored in the amide II region (1,510–1,580 cm−1), resulting from increased exposure of internal protein peptides to the external aqueous environment, was used to infer tertiary conformational changes of β-lactoglobulin adsorbed on silica NPs [196]. Nuclear magnetic resonance (NMR) spectroscopy and nuclear magnetic imaging (MRI) measure the intrinsic magnetic moment of certain nuclei in the presence of an applied magnetic field [226]. While a number of atomic nuclei comprise odd numbers of neutrons or protons, and hence intrinsic magnetic moments, hydrogen-1 (1H), carbon-13 316
15.2
13
Methods
15
( C), and occasionally nitrogen-15 ( N) isotopes represent the most commonly used in NMR studies. Application of an external magnetic field causes splitting of nuclear spin state energy levels within these magnetic nuclei and, assuming a Boltzmann distribution between the states, absorption (typically in the radio frequency range) of electromagnetic radiation can occur and effect transition between these magnetically split energy states. Through these unique chemical shifts, measured in ppm versus an internal standard (commonly tetramethylsilane-TMS), and the peak splitting (Zeeman effect) that occur, NMR spectroscopy can provide physical, chemical, and structural/environmental information about the species under study. MRI measures relaxation rates referred to as either T1 or T2 values, which correspond to different relaxation mechanisms—spin-lattice and spin-spin, respectively. Superparamagnetic materials such as iron oxide NPs and NPs doped or labeled with gadolinium (Gd) are commonly used in MRI as contrast agents for biomedical applications [227, 228]. NMR spectroscopy is nondestructive and has been used to determine the structure and dynamic interactions of many biological molecules, including proteins and nucleic acids [226]. NMR spectroscopy has also been used to characterize PEG-stabilized lipid NPs [229] and gold NPs [230] as well as QD cap exchange reactions [51] and dendrimer NP-surfactant interactions [231]. High-resolution 2-D 1H-15N NMR (see [226] for an overview of 2-D NMR) was used to compare the interactions of human carbonic anhydrase (HCA) I and II with silica NPs, demonstrating enzyme-dependent conformational changes upon interaction with the NP [232]. By looking at the environmentally sensitive NMR peak shifts, splitting, and/or relaxation rates (T1 and T2), researchers are able to elicit information concerning the mechanism of interaction, highlighting NMR as a technique capable of providing dynamic and structural NP-bioconjugate conformation information. Sample preparation is often key, as NMR is relatively insensitive and requires relatively high concentrations of pure materials to obtain quality spectra, with deuterated solvents preferred. That said, given the different NMR techniques available, such as high-resolution 2-D NMR, Nuclear Overhauser Effect (NOE) NMR, and transverse relaxation optimized spectroscopy (TROSY), NMR represents a powerful and underutilized tool available to researchers for NP-bioconjugate characterization [226].
15.2.5
Mass Spectroscopy
Mass spectroscopy (MS) comprises a family of analytical technologies that analyze samples based on their mass-to-charge ratio. The basic instrument arrangement includes an ionizing source, the mass analyzer, and the detector, with the various types of MS referring to different ionizing and/or mass analyzer technologies. MS has been used to study protein structure by measuring either the intact protein, denatured protein, or enzyme digested protein, and in particular demonstrates its utility in proteomics [233]. When dealing with protein-containing samples, electrospray ionization (ESI) and matrix-assisted laser desorption/ionization (MALDI) represent the MS techniques of choice [233]. MS has been used to characterize NP-bioconjugates and has found particular utility in the analysis of protein based NPs, such as viral NPs, where mass increases in the viral coat proteins due to the addition of biotin or fluorophore species was successfully monitored using MALDI–time of flight (TOF)-MS [234–236]. Through the measured mass increase, the stochiometry of the additional species added per virus NP could readily be determined. MALDI-TOF MS has also been used to qualitatively demonstrate 317
Techniques for the Characterization of Nanoparticle-Bioconjugates
hemoprotein binding to gold and silver NPs [104]. Inductively coupled plasma (ICP)-MS was used to determine TiO2 NPs binding a dopamine ligand used to complex a gadolinium MRI contrast agent [227]. The application of MS techniques is currently fairly limited for NP-bioconjugate characterization; this may in part be due to the relative cost of the instrumentation and the required level of expertise needed to run analyses.
15.2.6
Thermal Techniques
Thermal gravimetric analysis (TGA) is a method that utilizes a high-precision balance to determine changes in the weight of a bulk sample relative to changes in temperature. By modifying the temperature and the rate of heating, information can be obtained about the sample. Such information includes determining the relative amounts of inorganic versus organic components or the amount of adsorbed water or other solvents present within the material. In terms of NP-bioconjugates, TGA may aid in determining the amount of conjugate biomolecules as well as their thermal stability as shown in Figure 15.8(a) for magnetic NPs functionalized with PEG-based polymers [237]. The amount of dendrons attached to gold NPs and subsequent surface reactions have also been monitored with TGA [238], as well as the amount of an active therapeutic within a NP-bioconjugate, as demonstrated for paclitaxel bound to gold NPs [239]. Further calculations can reveal information about the average number of ligands attached per NP and the extent of surface functionalization as demonstrated for PEG/lactose ligands on gold NPs [240].
Figure 15.8 Thermal analysis of NP-bioconjugates. (a) Thermogravimetric analysis (TGA) used to determine the thermal stability and organic component of inorganic/organic NP-bioconjugates. Iron oxide magnetic NPs were functionalized with poly(ethylene glycol) monomthacrylate (PEGMA) via a silane initiator, [4-(chloromethyl)phenyl]triclorosilane (CTS), by applying a copper-mediated atom transfer radical polymerization (ATRP). The distinct TGA curves of (i) as prepared magnetic NPs, (ii) CTS- magnetic NPs, and (iii, iv, v) polymerized-(PEGMA)-magnetic NPs after 1-, 2-, and 4-hour polymerization times, respectively, provide indications of the amount of CTS and polymerized-(PEGMA) present on the magnetic NPs. (Images kindly provided by Dr. Neoh (National University of Singapore). Reprinted with permission from [237], Copyright 2008 American Chemical Society.) (b) Differential 2+ scanning calorimetry (DSC) of different components of bovine serum albumin (BSA)-Zn NPs formed in polyethylene glycol (PEG) solutions. DSC thermograms of (i) zinc acetate control, (ii) PEG control, 2+ (iii) BSA alone, and (iv) BSA-Zn NPs are shown. (Images kindly supplied by Dr. Yuan (Shanghai Jiaotong University School of Pharmacy) and reprinted by permission from IOP Publishing Ltd: Nanotechnology [244], copyright 2007.)
318
15.3
Summary Points
As with TGA, differential scanning calorimetry (DSC) and isothermal titration calorimetry (ITC) are thermal methods that can provide bulk information about the NPbioconjugate. The basic measurement monitors the difference in the amount of heat required to increase/decrease the temperature of a sample versus a reference material. DSC is used to study various transitions including melting, crystallization, glass transition, and decomposition. Subsequent analysis can indicate the state of the NP-bioconjugate such as the stability of the biomolecule, structural information of both the NP and biomolecule including crystallinity, and how the different components are interacting with each other. Researchers have used DSC to elucidate the structure and stability of surface coatings of NP-bioconjugates as well as the state of therapeutic payloads. For example, DSC has been used to determine the state (order versus disordered, interdigitated, and so forth) of dodecylamine and cetyltrimethylammonium bromide (CTAB) ligands bound to gold NPs [241] and the stability of solid, lipid NP-insulin complexes [242], and to investigate the physical state of paclitaxel incapsulated inside poly(lactic-coglycolic acid) NPs [243]. DSC has also been used to study how individual components of a NP-bioconjugate system (Zn nanoparticles and bovine serum albumin) interact with each other [244] (see Figure 15.8(b)). ITC has the potential to investigate the stoichiometry, affinity, and enthalpy of the NP-biomolecule interaction, as demonstrated by various polymeric NPs binding proteins, but as an analytical technique still remains vastly underutilized [14]. Thermophoresis or thermodiffusion involves local heating of a sample and monitoring the resulting motion of the particles due to the temperature gradient [13, 245, 246]. Similar to thermal FFF (Section 15.2.1), the direction that particles move in a temperature gradient is found to be dependent both on the overall size and on the surface potential of the particle. The effective diameter of the particle can be estimated by conversion of the measured diffusion coefficients. Thermophoresis has found limited application for NP-bioconjugate characterization to date, demonstrating the ability to determine the size of various PEG-functionalized QDs [13] and colloidal suspensions [246]. That said, it is very much an evolving technology and has demonstrated potential.
15.3 Summary Points As the design of NP-bioconjugates and their subsequent applications become more diverse and complex, it is essential that researchers have at hand techniques capable of intimately characterizing these specialized hybrid materials. This chapter has endeavored to highlight some of the major characterization techniques currently available to NP-bioconjugate researchers. All these techniques have associated advantages and disadvantages including relative cost, ease of use, resolution capabilities, sample preparation, ease of data interpretation, versatility, and bulk versus single particle analysis. Probably the most important issues from a NP-bioconjugate characterization viewpoint are: (1) confirmation of biomolecule attachment to the NP, (2) determination of the average ratio of NP-to-biomolecule, which includes the individual NP-to-biomolecule ratio and resulting ratio distribution, (3) the NP-bioconjugate hydrodynamic radius and aggregation state, and finally (4) the activity of the biomolecule upon NP attachment (as related to its orientation, structure, and stability). Clearly there are a number of techniques available to the researcher that can, at least to some extent, address some of these 319
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questions. For example, chromatography and electrophoresis techniques are relatively cheap and widely available, can readily confirm biomolecule attachment to the NP surface, and can provide purification and characterization (hydrodynamic size) of the NP-bioconjugate product, and in some instances even provide the ability to resolve NPs with different ratios of biomolecules attached. DLS and Zeta potential characterization of NP-bioconjugates are relatively cheap and simple to perform providing hydrodynamic radius, aggregation state, and surface potential information. The electron microscope techniques, SEM and TEM, are mainly used for characterization of the NP core (not so much the biomolecule to date) and, while relatively expensive equipment- and maintenance-wise, characterize the size and shape of the NP on an individual particle basis. AFM, in contrast, can divulge a range of information about both the NP and the biomolecule again on a single-particle basis. Many of the spectroscopic techniques provide bulk analysis of the NP-bioconjugate, with NMR and IR spectroscopy demonstrating the ability to characterize biomolecule conformational states. Also desirable are techniques with the ability to characterize the NP-bioconjugate under physiological environments as well as their stability postproduction. Characterizing the NPbioconjugate stability to sterilization will likewise become increasingly important for in vivo applications. Many of the techniques described require dried samples or samples suspended in ultrapure liquids, which may result in nonphysiological states and result in perturbed properties of the NP-bioconjugate, so interpretation should be erred on the side of caution. It is apparent that while there are many techniques, no one technique can address all questions or apply to all types of NP-bioconjugates. Full characterization will require a combination of techniques and the exact choice and the extent of tailoring required will depend on the particular NP-bioconjugates under characterization. Such characterization may require collaboration between researchers, since a number of the techniques discussed require specialized facilities and/or training. As mentioned previously, the NCL [24] offers to perform extensive characterization of nanoparticle materials to researchers involved in the areas of cancer therapy and diagnostics. Alternatively, there are a limited number of commercial companies that offer nanoparticle characterization services, such as nanoComposix [247]. Many of the technologies described are continually evolving to meet the demands of nanoscale characterization, and while bulk analysis will continue to play a significant role, additional focus should be placed on techniques capable of purifying and characterizing individual NP-bioconjugate populations. Many of the technologies described here will play a pivotal role in the future development of these novel and increasingly complex NP-bioconjugates and are indispensable to the future of this field.
Acknowledgments The authors would like to thank CDRH/OSEL/Division of Biology and Division of Chemistry and Materials Science and CDER/OPS/OTR/Division of Applied Pharmacology Research. The mention of commercial products, their sources, or their use in connection with material reported herein is not to be construed as either an actual or implied endorsement of such products by the Department of Health and Human Services.
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About the Editors Kaushal Rege, Ph.D. is an assistant professor of chemical engineering at Arizona State University in Tempe, AZ. He received his Ph.D. in chemical engineering from Rensselaer Polytechnic Institute in Troy, NY and did his postdoctoral research at the Center for Engineering in Medicine at Massachusetts General Hospital and Harvard Medical School in Boston, MA. Dr. Rege works in the areas of cancer nanotechnology, synergistic cancer therapeutics, and molecular engineering. Igor L. Medintz, Ph.D. is a research biologist in the Center for Bio/Molecular Science and Engineering at the U.S. Naval Research Laboratory in Washington D.C. He received his Ph.D. in molecular, cellular, and developmental biology from the Graduate School and University Center of the City University of New York in 1999. Dr. Medintz’s research interests lie in the development of methods to bridge the inorganic/organic molecular interface in the pursuit of nanosensors and other active nanomaterials.
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List of Contributors
List of Contributors Frank Alexis MIT-Harvard Center for Cancer Nanotechnology Excellence and Harvard-MIT Division of Health Sciences and Technology Cambridge, MA 02139 Labortatory of Nanomedicine and Biomaterials, Departments of Anesthesiology, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA 02115
Benita J. Dair Division of Chemistry and Materials Science Office of Science and Engineering Center for Devices and Radiological Health U.S. Food and Drug Administration 10903 New Hampshire Avenue Silver Spring, MD 20993
Ardalan Ardeshiri Department of Biomedical Engineering Oregon Health and Science University Portland, OR 97239
Jonathan S. Dordick Department of Chemical and Biological Engineering Rensselaer Polytechnic Institute 110 8th Street Troy, NY 12180 E-mail:
[email protected] Prashanth Asuri Department of Chemical and Biological Engineering Rensselaer Polytechnic Institute 110 8th Street Troy, NY 12180 Shyam Sundhar Bale Department of Chemical and Biological Engineering Rensselaer Polytechnic Institute 110 8th Street Troy, NY 12180 Akhilesh Banerjee Department of Chemical and Biological Engineering Rensselaer Polytechnic Institute 110 8th Street Troy, NY 12180 Rohan Bhavane The Division of Nanomedicine Department of Biomedical Engineering The University of Texas Health Science Center at Houston Houston, TX 77030 Michael R. Caplan Harrington Department of Bioengineering Center for Interventional Biomaterials Arizona State University PO Box 879709 Tempe, AZ 85287-9709 E-mail:
[email protected] Jeffrey J. Chalmers Department of Chemical and Biomolecular Engineering Director, University Cell Analysis and Sorting Core The Ohio State University 125 Koffolt Laboratories 140 West 19th Avenue Columbus, OH 43210 Telephone: (216) 292-2727 Fax: (216) 292-3769 E-mail:
[email protected] Ciro Chiappini Department of Biomedical Engineering The University of Texas Austin, TX77030 Aaron R. Clapp Department of Chemical and Biological Engineering Iowa State University Ames, IA 50014 E-mail:
[email protected] 334
Omid C. Farokhzad Assistant Professor of Anesthesiology Harvard Medical School Department of Anesthesiology Brigham and Women’s Hospital 75 Francis Street Boston, MA 02115 E-mail:
[email protected] Mauro Ferrari The Division of Nanomedicine, Department of Biomedical Engineering, The University of Texas Health Science Center at Houston, Houston, TX 77030 Department of Biomedical Engineering, The University of Texas, Austin, TX 77030 Department of Experimental Therapeutics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030 Department of Bioengineering, Rice University, Houston, TX 77005 E-mail:
[email protected] Katye, M. Fichter Department of Biomedical Engineering Oregon Health and Science University Portland, OR 97239 André M Gobin Assistant Professor - Bioengineering University of Louisville Louisville, KY 40292 E-mail:
[email protected] Tapan K. Jain Department of Biomedical Engineering Lerner Research Institute Cleveland Clinic, Cleveland, OH 44195 Farouc A. Jaffer Cardiovascular Research Center, Cardiology Division Harvard Medical School and Massachusetts General Hospital 149 13th St., 4th Floor Charlestown, MA 02129 Amit Joshi Department of Chemical and Biological Engineering Rensselaer Polytechnic Institute 110 8th Street, Troy, NY 12180
List of Contributors
Ravi S. Kane Department of Chemical and Biological Engineering Rensselaer Polytechnic Institute 110 8th Street Troy, NY 12180 E-mail:
[email protected] Amit A. Kale Center for Pharmaceutical Biotechnology and Nanomedicine Northeastern University 312 Mugar Hall 360 Huntington Avenue Boston, MA 02125 Vinod Labhasetwar Department of Biomedical Engineering Lerner Research Institute 9500 Euclid Avenue Cleveland Clinic, Cleveland, OH 44195 E-mail:
[email protected] Dominik J. Naczynski Department of Chemical and Biochemical Engineering Rutgers University Piscataway, NJ 08854 Ravindra C. Pangule Department of Chemical and Biological Engineering Rensselaer Polytechnic Institute 110 8th Street Troy, NY 12180 Emily Pawelsk Department of Biomedical Engineering Rutgers University Piscataway, NJ 08854 Eric M. Pridgen Department of Chemical Engineering MIT-Harvard Center for Cancer Nanotechnology Excellence Massachusetts Institute of Technology Cambridge, MA 02139
Robert S. Langer Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139 MIT-Harvard Center for Cancer Nanotechnology Excellence Cambridge, MA 02139 Harvard-MIT Division of Health Sciences and Technology Cambridge, MA, 02139
Elena V. Rosca Harrington Department of Bioengineering Center for Interventional Biomaterials Arizona State University PO Box 879709 Tempe, AZ 85287-9709
Jonathan Martinez The Division of Nanomedicine Department of Biomedical Engineering The University of Texas Health Science Center at Houston Houston, TX 77030
María Pía Rossi New Jersey Center for Biomaterials Department of Chemical and Biochemical Engineering Rutgers University Piscataway, NJ 08854
Hedi Mattoussi U.S. Naval Research Laboratory Center for Bio/Molecular Science and Engineering Code 6900 4555 Overlook Avenue, SW Washington, D.C. 20375
Kim E. Sapsford Division of Biology Office of Science and Engineering Center for Devices and Radiological Health U.S. Food and Drug Administration 10903 New Hampshire Avenue Silver Spring, MD 20993, U.S.A. E-mail:
[email protected] Jason R. McCarthy Center for Molecular Imaging Research Harvard Medical School and Massachusetts General Hospital 149 13th St., Rm 5406 Charlestown, MA 02129, USA E-mail:
[email protected] Igor L. Medintz U.S. Naval Research Laboratory Optical Sciences Division, Code 5611 4555 Overlook Avenue, SW Washington, D.C. 20375 Prabhas V. Moghe Department of Chemical and Biochemical Engineering Department of Biomedical Engineering Rutgers University Piscataway, NJ 08854 E-mail:
[email protected] Rajesh R. Naik Nanostructured and Biological Materials Branch Materials and Manufacturing Directorate Air Force Research Laboratory Wright-Patterson AFB, OH 45433-7750 E-mail:
[email protected] Dhiral A. Shah Department of Chemical and Biological Engineering Rensselaer Polytechnic Institute 110 8th Street Troy, NY 12180 Mei Shao Department of Chemical and Biomolecular Engineering The Ohio State University 125 Koffolt Laboratories 140 West 19th Avenue Columbus, OH 43210 Ram I. Sharma Department of Chemical and Biochemical Engineering Rutgers University Piscataway, NJ 08854 Joseph M. Slocik Nanostructured and Biological Materials Branch Materials and Manufacturing Directorate Air Force Research Laboratory Wright-Patterson AFB, OH 45433-7750
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About the Editors
Andrew Stine Department of Biomedical Engineering Lerner Research Institute Cleveland Clinic, Cleveland, OH 44195 Ennio Tasciotti The Division of Nanomedicine Department of Biomedical Engineering The University of Texas Health Science Center at Houston Houston, TX 77030 Xiaodong Tong Biotechnology Institute and Department of Bioproducts and Biosystems Engineering University of Minnesota St Paul, MN 55108 Vladimir P. Torchilin Center for Pharmaceutical Biotechnology and Nanomedicine Northeastern University 312 Mugar Hall 360 Huntington Avenue Boston, MA 02125 E-mail:
[email protected] Katherine Tyner Division of Applied Pharmacology Research Office of Testing and Research Office of Pharmaceutical Science Center for Drug Evaluation and Research U.S. Food and Drug Administration 10903 New Hampshire Avenue Silver Spring, MD 20993 David Vance Department of Chemical and Biological Engineering Rensselaer Polytechnic Institute 110 8th Street, Troy, NY 12180
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Tania, Q. Vu Department of Biomedical Engineering Oregon Health and Science University Portland, OR 97239 E-mail:
[email protected] Ralph Weissleder Center for Molecular Imaging Research Harvard Medical School and Massachusetts General Hospital 149 13th St., Rm 5406 Charlestown, MA 02129 Susan Westerfield Department of Biomedical Engineering Lerner Research Institute Cleveland Clinic, Cleveland, OH 44195 Ping Wang Biotechnology Institute and Department of Bioproducts and Biosystems Engineering University of Minnesota St Paul, MN 55108 E-mail:
[email protected] Songtao Wu Biotechnology Institute and Department of Bioproducts and Biosystems Engineering University of Minnesota St Paul, MN 55108 Ying Xiong Department of Chemical and Biomolecular Engineering The Ohio State University 125 Koffolt Laboratories 140 West 19th Avenue Columbus, OH 43210 Maciej Zborowski Department of Biomedical Engineering Cleveland Clinic 9500 Euclid Avenue Cleveland, OH 44195
Index A Acetal linkage, 172 Active targeting, 228–30 ability evaluation, 231 challenge, 228–29 concepts, 229 ligands for, 228–30, 231 Albumin nanoparticles (ANPs), 85–103 benefits of, 88 cell attachment assay, 95 cell culture, 94 creation, 93 displaying ligand on, 96 enhanced cell migration, 95–97 enhanced ECM assembly, 97–99 experimental design, 88 fabrication, 89–91 fibroblast extracellular matrix assembly, 94–95 functionalization, 89, 91–93 introduction to, 86–88 keratinocyte morphology and migration, 94 materials, 88–89 methods, 89–95 microscale plasma initiated patterning, 89 pattern creation, 93 pitfalls, 100–102 preparations, 90 results, 95–99 summary points, 102–3 three-dimensional presentation, 101–2 unfunctionalized, 97 Analytical ultracentrifugation (AUC), 300 Anodic etch, 248–50 defined, 248–50 guidelines, 250 nitrogen absorption/desorption characterization, 251 parameters, 250 SEM characterization, 251 tank, 246 tank assembly, 249
See also Porous silicon particles (PSPs) Antibodies binding, 112 concentration evaluation, 163 epitopes, 33–34 quantification on nanoshells, 160–61 Atherosclerosis, 138 Atomic force microscopy (AFM), 35, 308–9 cantilever, 309 defined, 308 information provided by, 308 with QD labeling, 309 sensitivity/versatility, 309 See also Microscopy techniques Au-phage networks, 243 Avidin-biotin affinity chromatography, 184, 188 B Bacteria, 116 Bacterial magnetic particles -PEI (BMP-PEI), 241 Batch magnetic separators, 110 Bicinchroninic acid (BCA), 8 Binding enhancement factor, 279 Biocatalysts, 48 Biomolecule conjugation, 61–65 dye-labeled, 64 protocol, 64–65 Boltzmann constant, 48 Buffers, 28 Butyl vinyl ether (BVE), 173 C Capillary electrophoresis (CE), 298, 299–300 application, 299 defined, 299 species detection, 299 Carbon nanotubes (CNTs), 2 acid oxidation of, 19 adsorption of proteins onto, 2 biofunctionalization of, 18 337
Index
Carbon nanotubes (CNTs) (continued) covalent attachment of proteins onto, 5–7 covalently attached protein conjugates, characterization, 13–18 functionalized, 2 multiwalled (MWNTs), 3 physical adsorption proteins, 3–4 physical adsorption proteins, characterization, 7–11 protein assisted solubilization, 4–5 protein-assisted solubilization, characterization, 11–13 single-walled (SWNTs), 2 solubilizing, 18 uniform dispersion of, 18 See also CNT-protein conjugates Carotid atheroma, 147 Cell attachment assay, 95 Cell-penetrating peptide (CPP), 169 Cells antibody-conjugate binding, 112 binding and uptake studies, 214–15 double diffusion, 126 patterning, with human fibroblasts, 100 variability, 114 CellSearch system, 108 Chromatography, 296–97 high performance liquid (HPLC), 296 hydrodynamic, 297 Circular dichroism (CD), 1, 313–14 defined, 313 protein structure determination with, 14–15 spectra changes, 313 use of, 35 vibrational (VCD), 314 Cis-aconityl linkage, 171 CNT-protein conjugates, 1–21 anticipated results, 7–18 application notes, 19–20 data acquisition, 7–18 discussion and commentary, 18–19 interpretation of data, 7–18 introduction to, 2–3 materials, 3 methods, 3–7 summary points, 21 troubleshooting table, 19 Coil-coil peptide-NP assembly, 28–31 disassembly, 30–31 gold NP, 28–29 gold-QD heterostructures, 29–30 Colocation analysis, 220
338
COMSOL, 283 Confocal microscopy, 266 Core-shell QDs, 57 Covalently attached CNT-protein conjugates, 5–7 characterization, 13–18 characterization with tryptophan fluorescence, 15–16 determination with CD spectroscopy, 14–15 Hammett analysis, 13–14 operational and storage stability, 17–18 thermostabilization, 17 Critical micelle concentration (CMC), 133 Crosslinked iron oxide nanoparticles (CLIO), 137 concentration, 148 purification, 148 synthesis, 141 See also Theranostic nanoparticles Cytospin, 119 Cytotoxicity studies, 215–16, 222 D Differential scanning calorimetry (DSC), 319 Dihydrolipoic acid (DHLA), 59, 60 capped QDs, 61 preparation, 60 thiols and, 62 Double diffusion cells, 126 Double emulsion method, 207, 209 Doxorubicin (DOX), 124 Drug encapsulation, 211–12 efficiency, 211 of hydrophilic drugs, 225 of hydrophobic drugs, 225 physiochemical properties, 225 Drug-loaded MNPs, 124 anticipated results, 132–33 antiproliferative activity, 131–32 application notes, 134 characterization, 129 data acquisition, 132–33 discussion and commentary, 133–34 DOX*HCI conversion, 129 experimental design, 124–26 facilities and equipment, 127 interpretation, 132–33 kinetics of DOX release, 130 materials, 126–27 methods, 128–32 multiple drugs, 124 reagents, 126
Index
summary points, 134 synthesis, 128–32 synthesis schematic, 125 troubleshooting table, 134 See also Magnetic nanoparticles (MNPs) Drug loading, 129–30, 212 determining, 130 protocol for quantification, 212 Drug release, 226–28 control, 226, 231 parameters, 226 stimuli, 227 Drug release studies, 212–13 drug mass and, 213 with high-solubility drug, 213 with low-solubility drug, 212–13 See also Polymeric nanoparticle delivery systems Dry etch, 248 Dynamic light scattering (DLS), 35, 91, 301–2 defined, 301 drawbacks, 301–2 illustrated, 302 measurements, 301 E Electronic cell counting system, 114 Electrophoresis, 298–300 capillary (CE), 299–300 slab gel, 298–99 types of, 298 Electrophoretic mobility, 303 Electrospray ionization (ESI), 317 Electrospun scaffolds, 102 Enhanced fluorescent proteins (EFPs), 76 Enhanced permeability and retention (EPR), 169 Enrichment process, 116–17 flow, 118 illustrated, 117 performance, 118 Enzyme-attached polystyrene nanoparticles, 41 Enzymes attachment, porous silica coating for, 42 in biocatalyst preparation, 49 entrapment of, 41–42 immobilized, efficiency, 40 loading and activity assay, 42–44 as proteins, 40 Ethylene glycol vinyl ether (EGVE), 173 Extracellular matrix (ECM), 86 fibroblast assembly, 94–95 ligands, 86, 87 proteins, 86
F Ferrite oxide particles, 240 Fibroblasts cell patterning with, 100 ECM assembly, 94–95 use of, 94 Field flow fractionation (FFF), 298 Flow cytometry, 215 anticipated results, 263–64 data acquisition, 263–64 illustrated, 263 interpretation, 263–64 materials, 262 methods, 262 See also Porous silicon particles (PSPs) Fluorescein, 76 Fluorescence correlation spectroscopy (FCS), 302–3 Fluorescence energy transfer. See Förster resonance energy transfer (FRET) Fluorescence measurements, 65–66 continuous excitation, 65 spectra acquisition protocol, 65–66 time-resolved mode, 65 Fluorescence microscopy, 214–15, 312 Fluorescent dye conjugation, 254 Fluorescent spectroscopy, 314–15 Focal macrophage ablation, 150 Förster resonance energy transfer (FRET), 54, 312, 315–16 defined, 315 efficiency, 67, 68, 71 measurements, 55 as nonradiative process, 315 phenomenon, 316 See also QD-based FRET Forward scatter (FSC), 264 Fourier transform infrared (FT-IR), 1, 10–11 G Gel electrophoresis, 298–99 Gel permeation chromatography (GPC), 204, 216 Gold nanoparticle assembly, 28–29 Gold-quantum dot heterostructures, 29–30, 34 Gold/silica core nanoshells, 157–58 histological analysis, 161–62 OCT image analysis, 161 results, 161–63 survival following imaging/therapy, 162 H Hammett analysis, 13–14 339
Index
High performance liquid chromatography (HPLC), 296 Hydrazide-activated phospholipids, 176 Hydrazone-based mPEG-HZ-PE conjugates synthesis, 176–84 aliphatic aldehyde-derived, 176–77 application, 185–86 aromatic aldehyde-derived, 177–80 aromatic ketone-derived, 180–83 half-lives, 187 pH sensitivity, 188 success, 185 time required, 187 Hydrazone linkages, 173 hydrolytic kinetics, 192 hydrolytic stability, 192 See also PH-sensitive linkages Hydrodynamic chromatography (HDC), 297 Hydrogels, 46 I Immunomagnetic labeling, 117 Inductively coupled plasma-atomic emission spectroscopy (ICP-AES), 257–60 data acquisition, 258–60 data analysis, 260 graphs, 261 materials, 257–58 methods, 258 normalized values from analysis, 261 See also Porous silicon particles (PSPs) Infrared (IR) spectroscopy, 316 Integrins, 86 Intravital fluorescence microscopy (IVFM), 139, 143–44, 148, 149 In vitro cell culture study, 184, 188 Isothermal titration calorimetry (ITC), 319 K Keratinocytes incubation of, 96 migration, 94 morphology, 94 L Ligand conjugation, 95, 228–30 active targeting and, 228–30 approaches, 229 covalent, 229 Ligands for active targeting, 228–30, 231 densities, 98 displaying on ANPs, 96 340
ECM, 87 nanoscale presentation effect on, 98 presentation on cytoskeletal organization, 99 Light-based therapy, 144–45 Light microscopy, 312 Light scattering techniques, 208 Loading NPs into PSPs, 264, 267 Lower critical solution temperature (LCST), 227 Low pressure chemical vapor deposition (LPCVD), 245 Lyophilization, 213 M Macrophages, 138 Magnetic cell separation, 107–20 in bacteria, 116 batch, 110 CellSearch system, 108 data acquisition, 117–19 delivery to breast cancer cell-line, 125 discussion and commentary, 120 earliest reports, 108 enrichment process, 116–17 examples, 115–16 immunomagnetic labeling, 117 interpretation, 117–19 introduction to, 108–16 materials and methods, 116–17 partial flow-through, 110 principle, 110–15 quantification of performance, 114–15 in rare cancer cell detection, 115–16 red cell lysis step, 117 results, 117–19 in stem cell isolation, 115 step, 117 summary points, 120 in T cell depletion, 115 Magnetic field (MF), 123, 131–32 Magnetic forces, 110–11 on cells without labeling, 111 on labeled cells, 111 Magnetic nanoparticles (MNPs), 42, 123–34 characterization, 124, 129 conjugated antibodies, 109 defined, 123 diffusion, 123 dispersion, 133 dividing, 128 DOX*HCI conversion, 129 drug-loaded, 123–34 for enzyme attachment, 42, 46–47 enzyme immobilization, 46–47
Index
interaction between, 111–14 kinetics of DOX release, 130 outer layer, 124 results, 46–47 static charge, 133 synthesis, 124, 128, 133 targeting of, 124, 133 TEM image, 47 yield, 132 See also Nanoparticle-enzyme hybrids; Nanoparticles Magnetic resonance imaging (MRI), 124, 284 Magnetotgactic bacteria, 116 Maltose binding protein (MBP), 63 Mass spectroscopy (MS), 317–18 defined, 317 inductively-coupled plasma (ICP), 318 Mathematical models, 275–90 best fitting parameters, 288 as hypothesis generators, 276 introduction to, 276–77 molecular/cellular scale, 276–82 organism scale, 275, 285–87 statistical guidelines, 287–89 summary points, 289–90 tissue scale, 277, 282–85 troubleshooting table, 289 validation and application, 287–89 MATLAB, 278 Matrix-assisted laser desorption/ionization (MALDI), 317 Maximum tolerated dose (MTD), 230, 231 Membrane molecular weight cutoff (MWCO), 298 Mercaptoundecanoic acid (MUA), 59 Metal ion-peptide recognition, 32–33 Metal nanoshells. See Nanoshells Microbots, 241 Microscale plasma initiated patterning, 89, 93 schematic, 101 spatial guidance, 100–101 Microscopy techniques, 308–12 atomic force microscopy (AFM), 35, 308–9 fluorescence microscopy, 312 illustrated, 310 light microscopy, 312 scanning electron microscopy (SEM), 49, 91, 218, 251, 311–12 transmission electron microscopy (TEM), 29, 35, 49, 218, 300, 308, 309–11 See also NP-bioconjugates Molecular/cellular scale modeling, 276, 277–82 anticipated results, 280
data acquisition, 280 discussion and commentary, 280–82 interpretation, 280 methods, 277–80 model description, 277 summary points, 289 See also Mathematical models Monocrystalline iron oxide nanoparticles (MION), 138 MPEG-HZ-PE conjugates, 176–77 aromatic aldehyde-derived hydrazone-based, 177–80 aromatic ketone-derived hydrazone-based, 180–83 in vitro pH-dependent degradation of, 184 Multidrug resistance (MDR), 228 Multifunctional peptides, 31–32 Multiphysics, 283 Multistage delivery system (MDS), 237 classes, 241–42 defined, 239 gold/bacteriophage nanoparticle network, 242 PSPs in, 237–71 schematic, 240 silicon-based, 241 success, 242–43 transport of therapeutic agents, 242 versatility and ease of modification, 268 Multiwalled carbon nanotubes (MWNTs), 3 acid-treated, 20 long oxidized, 17 operational and storage stability, 18 sonication, 6 Murine monoclonal antibodies (MoAb), 171 MWNT-DNAzyme conjugates, 20 N Nanocrystals, 72 Nanomaterials, 2 Nanoparticle-enzyme hybrids, 39–49 application notes, 49 discussion and commentary, 47–49 enzyme-attached polystyrene nanoparticles, 41, 44–45 enzyme loading, 42–44 fluorescent quantum dot, 77 introduction to, 40 ligand-conjugated, 87 magnetic nanoparticles, 42, 46–47 materials, 40 methods, 41–44 polyacrylamide hydrogel nanoparticles, 41–42, 45–46 341
Index
Nanoparticle-enzyme hybrids (continued) results, 44–47 summary points, 49 troubleshooting, 49 Nanoparticle formation, 201, 207–9 double emulsion protocol, 209 nanoprecipitation protocol, 208 single emulsion protocol, 208–9 See also Polymeric nanoparticle delivery systems Nanoparticles, 27 albumin, 85–103 biocatalysts, 48 biochemical cues, 102 components, 295 enzyme-attached polystyrene, 41 extravasation into malignant tissue, 198 fluorescent, 220–22 gold assembly, 28–29 magnetic, 42, 123–34 palladium decorated gold, 32 physiochemical metrics, 295 physiochemical properties, 197 polyacrylamide hydrogel, 41–42 polymeric, 197–231 production, 156 shape, 224 size, 222–24 surface chemistry, 224–25 synthesis precursors, 27 theranostic, 137–50 toxicity, 102 unmodified/native characterization, 293 See also NP-bioconjugates Nanoprecipitation, 207 protocol for, 208 requirement, 207 Nanoshells accumulation in tissue, 159 biomedical applications of, 154–55 cell culture, 157 for combined imaging and therapy in vivo, 158–59 for combined optical contrast/therapeutic application, 155 conjugation of biomolecules to, 160 defined, 153 experimental design, 156 gold/silica core, 157–58, 161–63 introduction to, 154–55 materials, 156–57 mediated cancer therapy, 155 metal, 153–66 methods, 157–61 nanoparticle production, 156 342
OCT scanning, 158 passivation with PEG, 159 pitfalls, 163–65 protein conjugation to surface, 156–57 quantification of antibodies on, 160–61 results, 161–63 statistical analysis, 165–66 therapeutic laser irradiation, 159 troubleshooting table, 166 in vitro assays, 157 in vivo model, 158 Nanotechnology, 294 Nanotechnology Characterization Laboratory (NCL), 295 Near infrared light-activated therapeutic (NILAT) agents, 139, 142 Near infrared (NIR) light gold nanoparticles and, 163 heating and, 155 in imaging large body sections, 165 scattering, 161 Near infrared (NIR) resonant composite nanoparticles. See Nanoshells NP-bioconjugates, 293–320 architecture, 295 characterization of, 293 introduction to, 294–96 mass spectroscopy (MS), 317–18 methods, 296–319 microscopy techniques, 308–12 physiochemical metrics, 295 potential, schematic, 294 scattering techniques, 300–308 separating unconjugated biomolecules from, 297 separation-based techniques, 296–300 spectroscopic techniques, 312–17 summary points, 319–20 thermal techniques, 318–19 Nuclear magnetic resonance (NMR), 49, 204, 216, 316–17 defined, 316 environmentally sensitive peak shifts, 317 as nondestructive, 317 O OCT images, 158 analysis, 161 intensity quantification, 166 quantification of, 163 representative, 162 scanning, 158 statistical analysis, 165–66 Oleic acid (OA) coating, 123 Optical density (OD), 145
Index
Organic fluorescence dyes, 76 Organism scale modeling, 276, 285–87 anticipated results, 286–87 data acquisition, 286–87 discussion and commentary, 287 interpretation, 286–87 methods, 285–86 summary points, 290 tumor-specific targeting, 286 See also Mathematical models P Paclitaxel (Taxol), 230 Partial flow-through separators, 110 Particle size analysis, 35 PEG, 239 bifunctional polymer, 160 chains, 230 linker, 154 passivation of nanoshells with, 159 PE-PEG1000-TATp conjugate synthesis, 183–84, 186 Peptide-nanoparticle assemblies, 25–36 antibody epitopes, 33–34 anticipated results, 34–35 application notes, 36 coil-coil mediated assembly, 28–31 data acquisition, 34–35 discussion and commentary, 35 interpretation, 34–35 introduction to, 26–27 materials, 27–28 mediated by metal ion-peptide recognition, 32–33 methods, 28–32 summary points, 36 synthesis of hybrid structures, 31–32 troubleshooting table, 36 Peptides, 27 as antibody epitopes for nanoparticle assembly, 33–34 coil-coil NP assembly, 28–31 enzymatically degradable cross-linking peptides, 269 multifunctional, 31–32 RGD, 86 Photobleaching, 76 Photolitography, 247–48 Photon correlation spectroscopy (PCS). See Dynamic light scattering (DLS) PH-sensitive linkages, 169–92 acetal, 172 approaches, 170 avidin-biotin affinity chromatography, 184, 188
chemicals, 174–75 cis-aconityl, 171 conclusion, 191 discussion and commentary, 185–91 in drug release, 171 hydrazone, 173 hydrazone-based mPEG-HZ-PE conjugates synthesis, 176–84, 183–86 introduction to, 170–74 materials, 174–75 methods, 176–85 in new function appearance, 171 PEG-TATp-liposome-pGFP complexes, 190 PE-PEG1000-TATp conjugate synthesis, 183–84, 186 pGFP complexed liposomal formulations, 175 polyketal, 172 poly(ortho-esters), 173 in protective “coat” removal, 171 rhodamine-labeled liposomal formulations, 175 summary points, 192 syntheses, 175 TATp-bearing, 175 thiopropionates, 173–74 trityl, 172 troubleshooting table, 192 vinyl ether, 172–73 in vitro cell culture study, 184, 188 in vitro pH-dependent degradation of PEG-HZ-PE conjugates, 184, 186–88 in vivo study, 185, 188–89 in vivo transfection with pGFP, 185, 189–91 Physical adsorption of proteins, 3–4 characterization of, 7–11 determination, 9–10 determination with FT-IR, 10–11 harsh conditions, 10 loading by BCA assay, 8 retention of activity, 8–10 PLA-PEG copolymers, 203 conjugation efficiency, 217 synthesis of, 204–7 PLGA-PEG copolymers, 203 conjugation efficiency, 217 synthesis of, 204–7 Pluronic coating, 133 Polyacrylamide hydrogel nanoparticles, 41–42 entrapped enzymes, 45–46 hydrogels, 46 results, 45–46 Polyethylene oxide (PEO), 86 343
Index
Polyketal linkage, 172 Polymer characterization, 216–17 Polymeric nanoparticle delivery systems, 197–231 active targeting, 228–30 application notes, 230–31 cell binding and update experiments, 202–3 cell binding and uptake studies, 214–15 components, 199 cytotoxicity studies, 203, 215–16 data acquisition, 216–22 design criteria, 199 discussion and commentary, 222–30 drug encapsulation, 211–12 drug loading, 225–26 drug release, 226–28 drug release studies, 212–13 illustrated components, 199 interpretation, 216–22 ligand conjugation, 201, 228–30 materials, 200–203 methods, 203–16 nanoparticle characterization, 217–20 nanoparticle formation, 201, 207–9 overall procedure, 203 particle shape, 224 particle size, 222–23 PLA-PEG and PLGA-PEG synthesis, 204–7 polymer characterization, 216–17 polymer synthesis, 200–201 post-formulation treatment, 202, 213–14 quantification of drug encapsulation, 201–2 release experiments, 202 results, 216–22 summary points, 231 surface chemistry, 224–25 targeting ligand conjugation, 209–11 troubleshooting table, 230 in vitro experiments, 220–22 Poly(ortho-esters), 173 Polystyrene-enzyme hybrid nanoparticles, 41 results, 44–45 SEM image, 45 synthetic route, 44 See also nanoparticle-enzyme hybrids Porous silicon particles (PSPs), 237–71 anodic etch, 248–50 characterization, 251 chemo-physical properties, 239 count and size analysis, 255–57 defined, 239 discussion and commentary, 267–71
344
dry etch, 248 fabrication, 245–51 fabrication steps, 268 first-stage, 239 flow cytometry for, 260–64 fluorescent dye conjugation, 254 homogeneity within, 243 inductively coupled plasma-atomic emission spectroscopy (ICP-AES), 257–60 introduction to, 238–45 in isopropyl alcohol (IPA), 252 loading kinetics, 244 loading of NPs into, 264, 267 materials, 245–47 methods, 247–51 for multistage delivery, 237–71 oxidation and surface modification with, 252–53 photolitography, 247–48 release kinetics, 244 release of NPs from, 265 SEM micrographs of, 244, 252 surface modification, 243, 268–69 surface modification with peptide sequences, 269 thin film deposition, 247 troubleshooting table, 270–71 zeta potential measurement, 254–55 Positron emission tomography (PET) contrast agents, 284 Post-formulation treatment, 202, 213–14 Prostate specific membrane antigen (PSMA), 154 Proteins covalent attachment of, 5–7, 13–18 ECM, 86 enzymes as, 40 physical adsorption on carbon nanotubes, 3–4, 7–11 solubilization of carbon nanotubes, 4–5, 11–13 thermostabilization of, 17 Protein-solubilized CNTs, 4–5 characterization of, 11–13 Raman spectroscopy for, 12–13 with UV-Vis spectroscopy, 11–12 Prototypical laser scanning fluorescence microscope, 141 Q QD-based FRET, 53–72 biomolecule conjugation, 61–65 conclusions, 72
Index
data analysis and interpretation, 66–71 donor-acceptor distances, 68–70 fluorescence measurements, 65–66 interaction with dye pairs and, 56 materials, 56 methods, 56–66 organic dyes and, 63 quantum dot synthesis, 56–58 reaction rates of surface-bound substrates, 70–71 summary points, 72 surface ligand exchange, 58–61 Quantum dot bioconjugates, 70 core-shell, 55 forming, 79 treating cells with, 79 Quantum dots automated tracking program, 80 best fit, 67 in biological applications, 77 BSA-modified, 300 composite dye signal, 67 conjugated, 301 control spectrum, 66 core-only, 59 core-shell, 57, 59 DHLA-capped, 61 disperse dried, 60 as fluorescent tags, 54 for FRET-based applications, 53–72 functionalization of, 29 gold heterostructures, 29–30, 34 materials, 296 molecular dynamics, 81 nanoparticles, 77 photophysical properties, 303 real-time dynamics, 80 for stability, 72 synthesis, 56–58 trajectory, 80 use limitations, 78 Quasi-elastic light scattering (QRLS). See Dynamic light scattering (DLS) R Raman spectroscopy, 12–13, 303–5 Rare cancer cell depletion, 115–16 Receptor-ligand modeling, 277 Red cell lysis step, 117 Releasing NPs from PSPs, 265, 267 Resonance Raman (RR), 304 RGD-functionalized gold nanodots, 86 Rhodamine, 76 Ring opening polymerization (ROP), 204
S Scanning electron microscopy (SEM), 49, 91, 218, 311–12 characterization, 251 defined, 311 limitations, 311–12 See also microscopy techniques Scattering techniques, 300–308 defined, 300 dynamic light scattering (DLS), 301–2 fluorescence correlation spectroscopy (FCS), 302–3 illustrated, 304 Raman spectroscopy, 303–5 small angle X-ray scattering, 306–7 X-ray diffraction (XRD), 305–6 See also NP-bioconjugates Scherrer equation, 305 Separation-based techniques, 296–300 analytical ultracentrifugation (AUC), 300 chromatography, 296–97 electrophoresis, 298–300 field flow fractionation (FFF), 298 illustrated, 297 types of, 296 See also NP-bioconjugates Side scatter (SSC), 264 Signal intensities (SI), 146 Single emulsion method, 208–9 Single-walled carbon nanotubes (SWNTs), 2, 313 aqueous dispersion of, 4 disperse purified, 5 functionalization of, 2 protein adsorbed, 5 purified HIPCO, 3 SBP adsorbed onto, 10 UV-Vis spectrum for, 11 Slab gel electrophoresis, 298–99 Slipping plane, 303 Small angle X-ray scattering, 306–7 defined, 306 measurements, 306 schematic, 307 uses, 306–7 Spectral deconvolution algorithms, 72 Spectroscopic techniques, 312–17 circular dichroism (CD), 313–14 defined, 312 fluorescent spectroscopy, 314–15 Förster resonance energy transfer (FRET), 315–16 illustrated, 315 infrared (IR) spectroscopy, 316
345
Index
Spectroscopic techniques (continued) nuclear magnetic resonance (NMR), 316–17 UV-visible spectroscopy, 312–13 See also NP-bioconjugates Squamous cell carcinoma of the head and neck (SCCHN), 116 Static light scattering (SLS), 308 Stem cell isolation, 115 Sucrose lyoprotection, 214 Surface-enhanced Raman scattering (SERS), 304 Surface-enhanced resonance Raman scattering (SERRS), 304 Surface modification (PSPs), 252–53 Surface plasmon resonance (SPR), 313 T Targeting ligand conjugation, 209–11 chemistry selection, 209 protocol via carbodimide chemistry, 210–11 via maleimide-thiol chemistry, 211 Target-to-background ratio (TBR), 146, 149 T cell depletion, 115 Tetrameric antibody complex (TAC), 117 Theranostic nanoparticles, 137–50 alternative reagents and equipment, 141 animal experimentation, 146 animal model, 141 anticipated results, 148 characterization of, 145–46 data acquisition, 145–47 experimental design, 139–40 facilities/equipment, 140–41 functionalization, 150 intravital fluorescence microscopy, 143–44, 146–47 introduction to, 138–39 light-based therapy, 144–45 materials, 140–41 optimization for application, 150 reagents, 140 statistical analyses, 147 summary points, 149–50 synthesis of, 141–43, 148 troubleshooting table, 149 See also Nanoparticles Therapeutic laser irradiation, 159 Therapeutic (NILAT) agents for therapy, 137 Thermal gravimetric analysis (TGA), 318 Thermal techniques, 318–19 DSC, 319 illustrated, 318 ITC, 319 346
TGA, 318 thermophoresis, 319 See also NP-bioconjugates Thermodiffusion, 319 Thermophoresis, 319 Thermostabilization, of proteins, 17 Thin film deposition, 247 Thiopropionates, 173–74 Tissue scale modeling, 277, 282–85 anticipated results, 284 data acquisition, 284 discussion and commentary, 284–85 interpretation, 284 methods, 282–84 summary points, 289 uses, 282 See also Mathematical models Tracking single biomolecules, 75–82 discussion and commentary, 81 introduction to, 76–78 materials, 78–79 methods, 79 troubleshooting table, 82 Transesterification activity, 43, 44 Transmission electron microscopy (TEM), 29, 35, 49, 218, 300, 308, 309–11 defined, 309 limitations, 311 at low accelerating voltages, 310 uses, 310 See also Microscopy techniques Trityl linkage, 172 Troubleshooting tables CNT-protein conjugates, 19 drug-loaded MNPs, 134 mathematical models, 289 nanoshells, 166 peptide-nanoparticle assemblies, 36 pH-sensitive linkages, 192 polymeric nanoparticle delivery systems, 230 porous silicon particles (PSPs), 270–71 theranostic nanoparticles, 149 tracking single biomolecules, 82 Tryptophan fluorescence, 15–16 Two-step labeling, 111, 112 U UV-visible spectroscopy, 35, 312–13 V Vibrational CD (VCD), 314 Vinyl ether linkage, 172–73 Vitro assays, 157
Index
X X-ray diffraction (XRD), 305–6 Z Zeta potential, 295 defined, 303 determining, 303 measurement, 254–55, 303 uses, 303
347
