Cell-Based Biosensors: Principles and Applications (Engineering in Medicine & Biology)

Cell-Based Biosensors Principles and Applications

Artech House Series Bioinformatics & Biomedical Imaging Series Editors Stephen T. C. Wong, The Methodist Hospital and Weill Cornell Medical College Guang-Zhong Yang, Imperial College Advances in Diagnostic and Therapeutic Ultrasound Imaging, Jasjit S. Suri, Chirinjeev Kathuria, Ruey-Feng Chang, Filippo Molinari, and Aaron Fenster, editors Biological Database Modeling, Jake Chen and Amandeep S. Sidhu, editors Biomedical Informatics in Translational Research, Hai Hu, Michael Liebman, and Richard Mural Cell-Based Biosensors: Principles and Applications, Ping Wang and Qinjun Liu, editors Data Mining in Biomedicine Using Ontologies, Mihail Popescu and Dong Xu, editors Genome Sequencing Technology and Algorithms, Sun Kim, Haixu Tang, and Elaine R. Mardis, editors High-Throughput Image Reconstruction and Analysis, A. Ravishankar Rao and Guillermo A. Cecchi, editors Life Science Automation Fundamentals and Applications, Mingjun Zhang, Bradley Nelson, and Robin Felder, editors Microscopic Image Analysis for Life Science Applications, Jens Rittscher, Stephen T. C. Wong, and Raghu Machiraju, editors Next Generation Artificial Vision Systems: Reverse Engineering the Human Visual System, Maria Petrou and Anil Bharath, editors Systems Bioinformatics: An Engineering Case-Based Approach, Gil Alterovitz and Marco F. Ramoni, editors Text Mining for Biology and Biomedicine, Sophia Ananiadou and John McNaught, editors Translational Multimodality Optical Imaging, Fred S. Azar and Xavier Intes, editors

Cell-Based Biosensors Principles and Applications Ping Wang Qingjun Liu Editors

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 catalog record for this book is available from the British Library.

ISBN-13: 978-1-59693-439-9 Cover design by Pilar Colleran © 2010 Artech House 685 Canton Street Norwood, MA 02062 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 Foreword

xi

Preface

xiii

Acknowledgments

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CHAPTER 1 Introduction 1.1 1.2 1.3 1.4

Definition of Cell-Based Biosensors Characteristics of Cell-Based Biosensors Types of Cell-Based Biosensors Summary References

CHAPTER 2 Cell Culture on Chips 2.1 2.2

Introduction Cell Immobilization Factors 2.2.1 Physical Factors 2.2.2 Chemical Factors 2.2.3 Biological Factors 2.3 Basic Surface Modification Rules 2.3.1 Hydrophilicity Improving 2.3.2 Roughness Changing 2.3.3 Chemical Coating 2.4 Typical Methods 2.4.1 Special Physical Structure 2.4.2 Microcontact Printing 2.4.3 Fast Ink-Jet Printing 2.4.4 Perforated Microelectrode 2.4.5 Self-Assembled Monolayer 2.4.6 Microfluidic Technology 2.5 Summary References

1 1 3 4 10 11

13 13 14 14 15 15 16 17 18 18 20 22 24 26 27 29 30 33 33

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Contents

CHAPTER 3 Mechanisms of Cell-Based Biosensors 3.1 3.2

37

Introduction Metabolic Measurements 3.2.1 Cell Metabolism 3.2.2 Extracellular pH Monitoring 3.2.3 Other Extracellular Metabolite Sensing 3.2.4 Secondary Transducers Action Potential Measurements 3.3.1 Action Potential 3.3.2 The Solid-Electrolyte Interface 3.3.3 Cell-Electrode Interface Model 3.3.4 Cell-Silicon Interface Model 3.3.5 Secondary Transducers Impedance Measurements 3.4.1 Membrane Impedance 3.4.2 Impedance Model of Single Cells 3.4.3 Impedance Model of Populations of Cells 3.4.4 Secondary Transducers Noise Sources 3.5.1 Electrode Noise 3.5.2 Electromagnetic Interference 3.5.3 Biological Noise Summary References

37 38 38 40 43 44 44 45 47 52 54 55 56 56 57 59 61 62 62 63 63 64 64

CHAPTER 4 Microelectrode Arrays (MEA) as Cell-Based Biosensors

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3.3

3.4

3.5

3.6

4.1 4.2 4.3

Introduction Principle Fabrication and Design of MEA System 4.3.1 Fabrication 4.3.2 Different MEA Chips 4.3.3 Measurement Setup 4.4 Theoretical Analysis of Signal Process in MEA Systems 4.4.1 Equivalent Circuit Model of Signal Process 4.4.2 Impedance Properties Analysis of MEA 4.4.3 Analysis of Extracellular Signal 4.5 Application of MEA 4.5.1 Dissociated Neural Network on MEA 4.5.2 Slice on MEA 4.5.3 Retina on MEA 4.5.4 Pharmacological Application 4.6 Development Trends 4.6.1 Lab on a Chip 4.6.2 Portable MEA System

65 68 69 69 74 77 79 79 80 82 84 84 86 88 89 92 92 92

Contents

4.7

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4.6.3 Other Developmental Trends Summary References

CHAPTER HAPTER 5 Field Effect Transistor (FET) as Cell-Based Biosensors 5.1 5.2 5.3

92 93 93

97

Introduction Principle Device and System 5.3.1 Fabrication of FET-Based Biosensor 5.3.2 FET Sensor System Theoretical Analysis 5.4.1 Area-Contact Model 5.4.2 Point-Contact Model Application 5.5.1 Electrophysiological Recording of Neuronal Activity 5.5.2 Two-Way Communication Between Silicon Chip and Neuron 5.5.3 Neuronal Network Study 5.5.4 Cell Microenvironment Monitoring Development Trends Summary References

97 98 100 100 102 103 104 105 106 106 108 109 112 114 115 116

CHAPTER HAPTER 6 Light Addressable Potentiometric Sensor (LAPS) as Cell-Based Biosensors

119

5.4

5.5

5.6 5.7

6.1 6.2

6.3

6.4

6.5

6.6

Introduction Principle 6.2.1 Fundamental 6.2.2 Numerical Analysis Device and System 6.3.1 Device 6.3.2 Microphysiometer System 6.3.3 Detecting System of Cell-Semiconductor Hybrid LAPS Application 6.4.1 Signaling Mechanism Study 6.4.2 Functional Characterization of Ligand/Receptor Binding 6.4.3 Identification of Ligand/Receptor 6.4.4 Drug Analysis Developing Trend 6.5.1 LAPS Array System for Parallel Detecting 6.5.2 Multifunctional LAPS System Summary References

119 121 121 122 124 124 126 129 132 133 134 136 137 143 144 145 146 146

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CHAPTER HAPTER 7 Electric Cell-Substrate Impedance Sensor (ECIS) as Cell-Based Biosensors 7.1 7.2

7.3

7.4

7.5

7.6

7.7

Introduction Principle 7.2.1 Electrochemical Impedance 7.2.2 Cell-Substrate Impedance 7.2.3 AC Frequency and Sensitivity Characteristics of Interdigitated Electrodes Device and System 7.3.1 Device Fabrication 7.3.2 Bioimpedance Measurement System Theoretical Analysis 7.4.1 Lumped Model 7.4.2 Analytical Model 7.4.3 Data Calculation and Presentation Applications 7.5.1 Monitoring of Cell Adhesion, Spreading, Morphology, and Proliferation 7.5.2 Monitoring of Cell Migration and Invasion 7.5.3 Monitoring of Cellular Ligand-Receptor Interactions 7.5.4 Cytotoxicity Assays Development Trends 7.6.1 High-Throughput Screening 7.6.2 Integrated Chip Summary References

CHAPTER 8 Patch Clamp Chip as Cell-Based Biosensors 8.1 8.2

8.3

8.4

8.5 8.6

Introduction Theory 8.2.1 Conventional Patch Clamp 8.2.2 Patch Clamp Chip Sensor Device and System 8.3.1 Patch Clamp Chip Device 8.3.2 Patch Clamp Chip System 8.3.3 Cells Preparation Biomedical Application 8.4.1 Ionic Channels Research 8.4.2 Drug Discovery 8.4.3 Drug Safety Development Trends Summary References

151 151 152 152 154 156 160 160 161 164 164 165 165 167 167 169 170 172 173 173 175 175 176

179 179 179 179 181 182 182 188 193 194 194 199 200 202 203 203

Contents

CHAPTER 9 Other Cell-Based Biosensors 9.1

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Quartz Crystal Microbalance (QCM) as Cell-Based Biosensors 9.1.1 Introduction 9.1.2 Principle of QCM 9.1.3 QCM Sensors and Measurement System 9.1.4 Biomedical Application 9.2 Surface Plasmon Resonance (SPR) as Cell-Based Biosensors 9.2.1 Introduction 9.2.2 The Principle of SPR 9.2.3 SPR Sensors and Measurement System 9.2.4 Biomedical Application 9.3 Immune Cell-Based Biosensors 9.3.1 Introduction 9.3.2 Mast Cell–Based Biosensors 9.3.3 Dendritic Cell–Based Biosensors 9.3.4 B Cell–Based Biosensors 9.4 Summary References

207 207 208 210 211 217 217 219 220 221 225 225 226 227 229 229 230

CHAPTER 10 1 Developments of Cell-Based Biosensors

233

10.1 10.2

Introduction Cell-Based Biosensors with Integrated Chips 10.2.1 Integration Chip of the Same or Similar Functional Sensors 10.2.2 Multisensors Involve Sensing Elements with Different Functions 10.2.3 Multifunctional Chip Monitoring Different Parameters 10.3 Cell-Based Biosensors Using Nanotechnology 10.3.1 Nano-Micropatterned Cell Cultures 10.3.2 Nanoporous-Based Biosensor 10.3.3 Nanoprobes to Intracellular Nanosensors 10.4 Cell-Based Biosensors with Microfluidic Chips 10.4.1 Microfluidic Flow 10.4.2 Soft Lithography 10.4.3 Dielectrophoresis 10.5 Biomimetic Olfactory and Gustatory Cell-Based Biosensors 10.5.1 Bioelectronic Nose and Bioelectronic Tongue 10.5.2 Olfactory and Gustatory Biosensors with Special Receptors 10.5.3 Olfactory and Gustatory Cell-Based Biosensors References

233 233 234 235 236 237 238 239 240 241 242 243 245 246 247 247 248 250

Glossary

255

About the Editors

261

List of Contributors Index

262 263

Foreword The field of biosensors and bioelectronics has enveloped many new areas such as molecularly sensitive receptors, biomimetic sensors, nanotechnology, and more. One of the most enduring topics, however, is that of cell-based biosensors, which are able to detect directly biological effects and to convert them, via the living cell, to electrical signals. Hence, the cell-based biosensor serves as the archetypal interface between a biological and an electronic system. Interdisciplinary skills are essential for the development of cell-based biosensors and the knowledge of engineers, physicists, chemists, and biologists must be combined to ensure success. This book fulfills this demand, describing in detail the fundamentals, design, technology, and applications of cell-based biosensors along with concrete realizations of the art. This volume systematically deals with the concepts, technology, and development of cell-based biosensors. It combines descriptions of microelectronics and information technology with biological fundamentals to introduce the basic principles and applications of cell-based biosensors. It provides a topical description of research progress in cell-based biosensor models, sensing techniques, and novel microstructure biosensor developments in the field over the past 10 years. In addition, many innovative applications of cell-based biosensors in areas such as biomedicine are detailed. As one who has had the duty and privilege of tutoring graduates and researchers in the elements of biosensors and bioelectronics in both university and commercial environments over the past 30 years, I am pleased to see this monograph on biosensors and am delighted to write this foreword. The authors’ 10 years of their own research in the area have furnished them with sufficient material and confidence to contribute a series of original results and observations to the field of cell-based biosensor throughout this book. The authors rightly aspire to stimulate the invention of new technologies for both the study and the application of biological science. Cell-based biosensors have a long pedigree, but remain a hot subject today. They attract a lot of attention from research groups in various fields, such as microelectronics, cell biology, electrochemistry, and mathematical modeling. A fundamental attribute of the area is to provide in-depth studies of cellular structure and activity. This theme of scientific interest features strongly in this book. This book covers surface-coating materials, surface-cell interface models, sensor design and fabrication rules, and systems and applications from a professional view. It describes principles, developments, applications, and promising aspects

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Foreword

of cell-based microelectrode arrays (MEAs), field-effect transistors (FETs), lightaddressable potentiometric sensors (LAPS), and electrical cell impedance sensing (ECIS). Cells are not only considered as sensing elements, but also as rich information sources accessed using techniques such as cell impedance spectra, microenvironment evaluation, and electrophysiological signals, which are surveyed in multifunctional integrated chips. These are complemented with other novel technologies that could be employed for cell measurements. This book is timely since the subject of cell-based biosensors has begun to coalesce into a definable subdiscipline of biosensors with its own principles and features. Ping Wang and Qingjun Liu deliver good interdisciplinary knowledge and research backgrounds in both engineering and biology as editors of this book. They have taken on the task of surveying the broad field of cell-based biosensors from a perspective that stresses the underlying principles. They construct an outline of the field that includes principles and applications from living cells through biological to electronic interfaces and methods from characterization through synthesis to technological application. At the same time, they provide a reasonably comprehensive description of the particular classes of cell-based biosensors that have become important in biosensors and bioelectronics. I believe this monograph is one of the first to have taken this interdisciplinary approach to the broad subject of biosensors and bioelectronics. It is targeted toward graduate students, researchers, and lecturers in the field of biosensors and bioelectronics, who will find it a very useful text and reference. Professor Anthony P. F. Turner Editor-in-chief of Biosensors & Bioelectronics Cranfield University Cranfield, United Kingdom October 2009

Preface In the second half of the twentieth century, interest in the development of biosensors, which conventionally are electric sensing devices, has increased considerably. Scientific research was followed by an emerging demand from various application fields. In the 1960s, the technique of chemical sensors grew rapidly and resulted in the possibility for direct detection of various ion and molecular types with certain selectivity limits. The research and development of conventional macrosensors soon turned in the direction of microsensors and even nanosensors as a result of the miniaturization in biosensors and expanding applications, including biology and medicine. In a decade, cell-based biosensors have become a research hotspot in biosensors and bioelectronics fields because they can detect the functional information of biological living cells. They are characterized by their high sensitivity, excellent selectivity, and rapid response and have been applied in many fields, such as biomedicine, environmental monitoring, and pharmaceutical screening. Currently, cell-cultured technology, silicon micromachining technology, and genetic technology have dramatically promoted the exploration of cell-based biosensors. Although the practical application of cell-based biosensors has been developing rapidly, the theoretical background of their operation is clarified only partly or not at all in many cases. There are debates about the signal excitation mechanisms, the signal conditioning methods, and the interpretation of practically measurable and theoretically expected results. Developing the cell-based biosensors means conducting considerable basic research at the same time. This is one of the main and common commercialization barriers of cell-based biosensors. This book provides a survey of this field from a systems engineering perspective. The structure of this book is simple and builds upon basic concepts. There are 10 chapters, each reviewing a fundamental block in our survey of cell-based biosensors. In each chapter, it provides details relevant to the section. Chapter 1 briefly introduces the development history and basic concept and knowledge of cell-based biosensors, including the definition, characteristics, and main types of cell-based biosensors. Chapter 2 provides some basic knowledge about cell culture on chips. This is one of the essentials that could impact the whole cell-based biosensors work pro-

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Preface

cedure, and it is quite different from traditional cell culture. Here, a lot of work is needed to improve the surface characteristics. Chapter 3 deals with the mechanism and models of cell-based biosensors. Served as a transducer and an interface in cell-based biosensors, it is also very essential in how a cell couples to transducers and how it receives and responds to outside stimuli. Chapter 4 describes the microelectrode array (MEA) as a cell-based biosensor that is designed for transferring and recording cellular action potential including frequency, amplitude, wave shape, velocity, and so on. Due to simple fabrication, good biocompatibility, and convenience for observing in a microscope as well as detecting cellular multiparameters, MEA has been extensively applied on cell-based biosensors. Chapter 5 describes the cell-based field-effect transistor (FET) sensor fabricated by semiconductor technology. This type of sensor signifies another sensing effect used to record extracellular signals. As a highly organized living microstructure, the cell itself expresses much information according to the changing environment outside the entity. Chapter 6 proposes a light addressable potentiometric sensor (LAPS). As a novel kind of semiconductor device, LAPS can detect extracellular ions concentration and indicate the metabolism information of cells, instead of conventional electrodes. The microphysiometer is a typical application of LAPS in biology. On the sensor, living cells are cultured and confined in the microenvironment. By detecting its response, the variation of certain molecular concentrations can be monitored. Chapter 7 mentions the electric cell substrate impedance sensing (ECIS). Bioimpedance technology has been developing for a long time, covering the electric currents associated with the life processes and their biopotentials. Based on the bioimpedance technology, ECIS probes the electrochemical processes in the cell, and the tissue thus owns the capability of monitoring physiology changes, which have great differences among various cells and tissues. Chapter 8 introduces the patch clamp chip. Patch clamp technology is now a golden standard in the research of electrophysiology. However, it has certain inevitable limitations and can hardly be applied to drug screening and cellular communication in neural networks. The emergence of a planar patch clamp chip makes it possible to record the electrophysiological process of ionic channels in a highly parallel and automatic way. Chapter 9 proposes other new cell-based biosensors: quartz crystal microbalance (QCM), surface plasmon resonance (SPR), and immune cell-based biosensors. At the same time, those cell array-based biosensors can be used in fundamental studies of multicellular interactions in the immune system and other areas of cell biology. Chapter 10 discusses the developing trends of cell-based biosensors combining with up-to-date technologies in science and engineering, such as microelectronics, nanotechnology, and molecular biology to fabricate the integrated, multifunctional, intelligent, or smart cell-based biosensors chips.

Preface

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We are indebted to our coauthors for their personal knowledge and expertise and generous contribution to their chapters. Each chapter provides a gateway to the field it represents. As previously mentioned, many sources have been used to complete the information presented here including papers, books, and trade literature containing relevant, up-to-date materials. These sources are referred to and listed in the references sections at the end of each chapter. Ping Wang, Ph.D. Editor Professor at Zhejiang University, China Deputy Director of Biosensors National Special Lab Zhejiang, Hangzhou, China October 2009

Acknowledgments We would like to acknowledge all of the contributors for our book: Wei Zhang for Chapter 2; Hua Cai for Chapter 3; Lidan Xiao and Qingmei Chen for Chapter 4; Jun Zhou for Chapter 5; Hui Yu for Chapter 6; Zhaoying Hu for Chapter 7; Peihua Chen for Chapter 8; and Chunsheng Wu for Chapter 9. We also give many thanks to Jun Wang, Liping Du, and Chengxiong Wu, Gong Cheng, Weiwei Ye, Ning Hu, Shuai Zhen, and Zhiyuan Liu for their help in compiling and correcting notes. We thank Artech House Publishers for their willingness to give prompt assistance during the proposal and manuscript stage. We also thank Mark Walsh and Christine Daniele, who encouraged us with their tireless support of this book. Our hope is that books like this provide a continuous stream of knowledge for students, researchers, and engineers who are working in the field. We would be remiss if we did not acknowledge those institutes and foundations that have financially supported us overall with teaching and research work on cell-based biosensors topics through the past over 10 years: the National Natural Science Foundation of China and National Distinguished Young Scholars Fund (Grant No. 30627002, No. 60725102, No. 30700167), the State Key Laboratory of Transducer Technology of China (Grant No. SKT0702), the Zhejiang Provincial Natural Science Foundation of China (Grant No. Y2080673), the National Basic Research Program of China (973 Program, Grant No. 2009CB320303), and the National High Technology Research Program of China (863 Program, Grant No. 2007AA09210106). Ping Wang and Qingjun Liu Editors Zhejiang University Hangzhou, China October 2009

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CHAPTER 1

Introduction Ping Wang

1.1

Definition of Cell-Based Biosensors Cell-based biosensors are special devices that employ immobilized living cells as sensing elements, combined with sensors or transducers to detect the intracellular and extracellular microenvironment condition, physiological parameters, and produce responses through the interaction between stimulus and cells. The main feature of cell-based biosensors is that they employ living cells as receptors in contrast to the other types of biosensors that contain only materials extracted from living things. They consist of two main parts: one is from living cells, which is the primary transducer used in the first sensing element receiving and producing signals; the other belongs to the secondary transducers used in converting the physiological signals to electrical signals. Picking up, isolating, and immobilizing the living cell on the surface of transducers, and designing and fabricating the special sensor chips to assure good coupling and get accurate signals from cells are the main work in cell-based biosensors research. In a decade, cell-based biosensors have become a research hotspot in biosensors and bioelectronics fields because they can detect the functional information of biological active analytes. They are characterized by their high sensitivity, excellent selectivity, and rapid response, and they have been applied in many fields, such as biomedicine, environmental monitoring, and pharmaceutical screening. Currently, cell-cultured technology, silicon micromachining technology, and genetic technology have promoted exploration of cell-based biosensors dramatically. The basic schematic diagram of cell-based biosensors is shown in Figure 1.1. It mainly consists of two parts: one is living cells or a neural network cultured on the surface of a transducer, and the other is a transducer including potential sensing and chemical sensing, sometimes also with stimulus elements. The living cell serves as the sensing element or primary transducer to respond to external stimuli, such as electric and chemical stimuli, antiviral drugs, and various receptor ligands. Then it will produce corresponding outputs or changes, such as extracellular changes of molecules or ions, action potential and impedance change induced by the cellular metabolism, and so on. Transducers or secondary transducers such as silicon field-

1

2

Introduction

Figure 1.1 Basic schematic diagram of cell-based biosensors.

effect devices can detect these responses and convert them into electrical signals. These make up the whole cell-based biosensors. Generally, when external stimulation, such as drugs, chemicals, and electric stimuli, are added into cell-based biosensors, cells cultured on a chip would produce action potential and ionic or molecular changes that can be detected by devices underneath a thin layer of electrolyte. The potential and current changes couple with the transducers, so that the extracellular environmental changes can be monitored by the cell-based biosensors. These sensors can be applied to measure the extracellular action potential, impedance, and transmission path of ionic channels, and they can detect the transmission velocity of biological signals along the layer of neurons. Culturing living cells on devices is one of the essentials that could determine the performance of the whole cell-based biosensor. What we do is quite different from common cases, in which cells are mostly cultured directly on a petri dish, slide, or other glass or polymeric plastic culture dishes. Due to good surface hydrophilicity and negatively charged of its material, most cells could locate and spread well on the culture dish. Besides, many conventional operation manuals and protocols are available for different types of cells, including cardiac myocytes, neurons, glial cells, epithelium, embryonic cells, and so on. It is a big challenge to culture cells on chips comprised of silicon or other conductive metals and to gain good contact between the substrate and cell membrane. However, the main problem is that the material itself is not attractive to cells in roughness, hydrophilicity, surface functional groups, and viscosity. Hence, more work is needed to improve the surface characteristics of transducers. To assure good coupling and get accurate signals from living cells, study on the mechanism and models of coupling models between cells and transducers is necessary to design and fabricate transducer chips better. When cells serve as transducers, it is very important to know how cells sense the stimulating signals, how cells response to external stimulation, and how cells couple to the transducers. Besides, developing new transducer chips means conducting considerable basic research on the various detection techniques to complete cell metabolic measurements, action potential measurements, impedance measurements, and so on.

1.2 Characteristics of Cell-Based Biosensors

3

Cell-based biosensors have the potential of providing rapid, sensitive, low-cost measurement technology for monitoring analyte concentrations. Cells are equipped with a host of receptors that can transduce chemical and biological signals into electrical ones. If efficiently coupled to an electronic readout device, cells could function as versatile biosensors in a variety of applications [1]. By using living cells as sensitive elements, cell-based biosensors are able to respond to many kinds of chemical and biological analytes and detect functional information. It has been implied that the on-off of cellular receptors and ionic channels induces the migration of electriferous clusters and ions on both sides of cellular membrane, which will couple with microelectronic devices. When detection system is improved, cells can become sensitive units of biosensors for environment detection or drug discovery [2]. Besides, some researchers have demonstrated two-way, noninvasive communication between external electronics and cells cultured on the chip [3]. Changes of extracellular metabolism products, such as ions and large biomolecules, are induced by the transformation of intracellular physiological status. Thus, we can deduce the intracellular physiological state by detecting the metabolism products.

1.2 Characteristics of Cell-Based Biosensors A common special feature of cell-based biosensors is that they employ living cells as receptors, in contrast to other types of biosensors that contain only materials extracted from living things. Unique combinations of enzymes or highly sensitive physiological receptor mechanisms become available that are present in intact cells but may be impossible to duplicate using isolate enzymes in the biosensor. Another advantage should be that the materials can fulfill their biological functions within their natural biological media. In these circumstances, bioactive compounds may have the best activity and lifetime, and they can even be regenerated or resynthesized by the living cells. Thus, a better stability of biosensors may be expected. If the living cells perish, abrupt observable changes will occur in the sensor’s behavior, instead of a slow drift due to the receptor dissolution that is characteristic of other types. The common problems of living cell–based biosensors can be summarized as follows [4]: • The natural environmental conditions, in which the cell can stay alive for long period, must be maintained continuously, and this requires severe control of physical and chemical parameters of the environment. • The metabolism of the cells must be maintained and they must be fed continuously. • The living cells must be immobilized around or on the surface of the transducer without limiting their biological functions. The supramolecular organic chemistry and cellular mimicry should be exploited in the future. • The lifetime of sensors is mainly determined by the lifetime of the cells. The particular advantages of using cell-based biosensors are as follows:

4

Introduction

• They are less sensitive to inhibition by solutes and are more tolerant of suboptimal pH and temperature values than enzyme electrodes, though they must not exceed the narrow range in case of the cells dying. • A longer lifetime can be expected than with the enzymatic sensors. • They are much cheaper because active cells do not need to be isolated. Because cell-based biosensors have lots of advantages (e.g., long-term recording in noninvasive ways, fast response, and easy fabrication), they have extensive applications, including pharmaceutical screening, cellular physiological analysis, toxin detecting, peripheral nerve regeneration, and environment monitoring, as well as in vivo recordings; thus, they are also promising in fields of neuronal prostheses and reconstruction of damaged sense organs. Although the practical applications of cell-based biosensors have been developing rapidly, the theoretical background of their operation hasn’t been clarified completely in many cases. There are debates about the signal excitation mechanisms, the signal conditioning methods, and the interpretation of practically measurable and theoretically expected results. Developing cell-based biosensors means conducting considerable basic research at the same time. This is one of the main commercialization barriers of cell-based biosensors. Despite recent successful research results, cell-based biosensors also have some disadvantages beyond these general problems. They are as follows: • Some types of cell-based biosensors may have a longer response time than enzymatic sensors. • They need more time to return to the baseline level after using. • Cells contain many enzymes, and care must be taken to ensure selectivity.

1.3 Types of Cell-Based Biosensors At present, the secondary transducers used in cell-based biosensors mainly include the microelectrodes array (MEA), field-effect transistor (FET), light addressable potentiometric sensor (LAPS), electric cell-substrate impedance sensor (ECIS), patch clamp chip, quartz crystal microbalance (QCM), surface plasmon resonance (SPR), and so on. MEA, which is designed for transferring and recording cellular action potential including frequency, amplitude, wave shape, and velocity, is shown in Figure 1.2. Using micromachining technology, MEA is fabricated by depositing Au, Ir, Pt, or other metals on glass or silicon substrate to form electrodes, connecting leads, depositing the passivation layer, and exposing the electrode sites where the cells or tissues contact. Usually MEA is composed of microelectrodes arranged in a matrix, connecting leads and welding pads. Due to simple fabrication, good biocompatibility, and convenience for observing in microscope as well as detecting cellular multiparameters, MEA has been extensively applied in cell-based biosensors. Being array sensor, MEA is predominant in long-term, real-time, noninvasive measure-

1.3 Types of Cell-Based Biosensors

5

Figure 1.2 Schematic diagram of MEA for cell-based biosensors.

ment for signal coupling and transferring between cells compared with the patchclamp technology [5]. Some key technologies of MEA for cell-based biosensors are still underway. Many problems need to be solved. For example, substrate surface is easily eroded when dipped in the solution for a long time. The gap between cells and electrodes is difficult to control by cellular swimming or moving when adhered to the MEA. These will affect the veracity of measurement. MEA is very effective, not only for monitoring the cellular electrophysiological activity, but also for screening and evaluating pharmacology and toxicology. It can be used for observing signal transfer of cardiac myocytes and filtering drugs for the heart. It can also record extracellular action potentials and neuronal responses to different substances, which is very important in pharmacological research. For example, some researchers cultured a spinal neuron network of a mouse on a 64-channel MEA (ITO electrodes) and measured the neuronal response after using TMPP (a drug inducing convulsion). FET is fabricated by semiconductor technology. This type of sensor signifies another transducer used to record extracellular signals, as shown in Figure 1.3 [6]. As a highly organized living microstructure, the cell itself expresses much information depending on the changing environment outside the entity. Up to now, the standard MOS process has been applied to fabricate cell-based FET sensors with tiny changes compared with an insulated-gate field-effect transistor (IGFET). The metal gate connection of the FET structure is replaced by a reference electrode in the solution. Sensitive films such as silicon dioxide and silicon nitride are deposited on the gate area which is covered by different types of electrogenic cells. Environmental factors influence the cells, which can be simulated through the fluid perfusion system. After receiving different stimuli from the chamber electrolyte, the cell response inside seems like a complete black box to us with regard to parallel activation of different signaling pathways. The simple way to analyze cell response is to decode the information from the cell metabolism expressed extracellularly. Cell-based FET sensors focus on the ionic concentration variations near the gate area and the action potential on the cell membrane. If there is any change, the

6

Introduction

Figure 1.3 Schematic diagram of FET for cell-based biosensors. (From: [6]. Reproduced from Solid State Electronics, © 2008, with permission from Elsevier B.V.)

surface potential of the dielectric layer will be lifted, which induces the change in the density of mobile defect electrons. The cell-silicon interaction causes a modulation of the current along the inversion layer driven by a voltage between source and drain. To record ac modulation signal, it was defined by the source-voltage relative to the reference potential [7]. The first attempt to apply an ISFET in neurophysiological measurements was made in 1970. Subsequently, a single invertebrate neuron and a vertebrate neuron, as well as cardiac myocyte monolayers and brain slice, were cultured on the gate of cell-based FET sensors for biological measurement. The mechanism explanation was modeled and described in several papers. Yates et al. described the electrical double layers at colloidal oxide-water interfaces with the site-binding model [8]. Bergveld et al. [9] discussed the determination factors of interfacial potential by double-layer capacitor equations. Fromherz et al. [6] developed a planar two-dimensional area-contact model and a point contact model to evaluate the ac-signal transfer on the basis of linear response theory. Recently, extracellular acidification and respiration detection are hot spots by ion-sensitive field-effect transistor (ISFET). Cell-based FET arrays in high density are also highlighted in the recent development trend. Combined with an additional insulated spot of silicon, a capacitive extracellular stimulation of an individual cell is demonstrated to be possible. This idea develops a two-way communication chip between cell and silicon chip that could simultaneously implement stimulation and record activities on the cell-silicon interface. The cell monitoring system (CMS) even combines different microsensors, including arrays of different ion-sensitive FETs and cell potential FETs, with different gate areas and materials. With the rapid development of the semiconductor industry, high-density cell-based FET sensor arrays with better resolution could be achieved. Before long, this type of sensor can be widely used in drug screening and

1.3 Types of Cell-Based Biosensors

7

neural network transduction research, with advantages such as fast response, low cost, and noninvasive long-term recordings. LAPS is a semiconductor device proposed by Hafeman et al. in 1988 [10]. Hafeman et al. detected the extracellular pH with LAPS, instead of conventional electrodes, to indicate the metabolism of cells. On the basis of LAPS, the commercially available cytosensor microphysiometer was then released in 1990 by Molecular Device Corporation. This can monitor small fluxions of acidification in extracellular microenvironment of 104–106 cells. Nowadays, most research work on LAPS is on the basis of this microphysiometer. The microphysiometer is a typical application of silicon technique in biology. Living cells are cultured and confined in micro environment on the sensor, as shown in Figure 1.4. Variations of certain molecules are then monitored by detecting biological responses of living cells. microphysiometer is different from most other analyzing equipment. Instead of determining the characteristics of analytes, it detects the effect on cells induced by the analyte. In most circumstances, acidic products of cell metabolism are related to the consumption of ATP. During important cell metabolic process (i.e., the metabolism of glucose, aminophenol, and fatty acid), produced proton released into the microenvironment causes the extracellular pH change. The microphysiometer can measure such a small change and can indicate the undertaken cell metabolism. The microphysiometer has been widely used in measurement in biology, pharmacology, toxicology, and so on. Another realization of LAPS as a cell-based biosensor is the cell-semiconductor hybrid LAPS device. Excitable cells such as cardiac myocytes or neurons are cultured and firmly attached to the sensor surface. Then, by detecting the resulting photocurrent, extracellular potential signals of cells can be measured. The cell-semiconductor hybrid LAPS device can possibly be used for single cell analysis [11]. ECIS can be used in electrical impedance spectroscopy and chemical analysis techniques to investigate bioelectrical properties of cell membranes and mechanisms

Figure 1.4 Schematic diagram of LAPS for cell-based biosensors.

8

Introduction

of the excitable cell responses to chemical or electrical stimuli, as shown in Figure 1.5. Compared with conventional methods such as patch-clamping and fluorescent microscopy, the electrochemical means has the advantages mentioned previously. In particular, during the maturation of microelectromechanical system and nanoscale technologies, it creates great opportunities for simplified automated and high-through approaches for basic research in cells. Furthermore, these techniques can be incorporated to simultaneously measure parameters as much as possible by integrating different methods into one chip. Thus, time is supposed to be reduced while obtaining more information about cells [12]. Dittami and Ayliffe designed and fabricated a platform for EIS of small regions of the cell membrane and the measurement of the chemical concentration adjacent to the cell membrane [13]. He reported that the neurotransmitter release was modulated in phase with the positive peak of the sine stimulus, which highlighted the potential of the device to spatially resolve the cell membrane’s electrical properties, as well as the intracellular components. The patch clamp chip technology is widely used in the fields of electrophysiology and neuroscience, as shown in Figure 1.6. It is now a golden standard in the research of electrophysiology. However, patch clamp technology has certain inevitable limitations, such as a low throughput. It can hardly be applied to drug screening and cellular communication in neural networks. The emergence of a planar patch clamp technology makes the highly parallel and automatic electrophysiology recording of ionic channels possible. Scientists have made great efforts to improve conventional patch clamp technology by developing a new configuration of the

Figure 1.5 The basic schematic diagram of ECIS for cell-based biosensors.

Figure 1.6 Schematic diagram of patch clamp chip for cell-based biosensors.

1.3 Types of Cell-Based Biosensors

9

conventional microelectrode or a new generation of microelectrode. Improvement of the electrode configuration was first achieved by Sophion Bioscience. Afterward, Neuropatch and Flyion produced a novel automatic patch clamp instrument. However, these systems were still based on a single microelectrode and could not be used in high-throughput applications. In the late 1990s, scientists developed a patch clamp chip and raised the concept of guiding cells onto a microaperture, which replaced the glass microelectrode with a planar structure. This novel chip can record many cells simultaneously and can be combined with multiple measurement methods easily. Planar patch clamp technology will be a potential and effective approach in the study of ion channels and drug discovery. The key technique is how to fabricate a smooth chip with an aperture and with a diameter of 1 μm or even smaller. So far, some materials, such as silicon, quartz crystal, glass, and polymers, have been utilized to fabricate patch clamp chips. At first, silicon was chosen undoubtedly because of the convenient application of the standard semiconductor technology, while the choice of glass is for its application in conventional patch clamp. Polymer, such as PDMS, is well known as Sylgard, which is widely applied in microfluidics. They all had different advantages and disadvantages, and were chosen for different interests. QCM and SPR are now also widely used commercially available analytical techniques suitable for the detection of biomolecular interactions, as shown in Figure 1.7. Recent experimental results have demonstrated that the cells adhering to the sensor chip and responding to stimulators could introduce the changes of response signals in both QCM and SPR. Based on the piezoelectric effect, QCM is a very sensitive technique to detect mass changes in the field of biosensors. The primary advantages of QCM over traditional methods include high sensitivity (in the range of nanograms), noninvasiveness, long measurement periods, and being label free. The technique possesses a wide detection range from a monolayer of small molecules to much larger masses bound to the surface, even including complex arrays of wholes cells. The signal transduction mechanism of the QCM relies on the piezoelectric effect in quartz crystal. When an alternating electric field is applied across the quartz crystal through electrodes covering the quartz surface, a mechanical oscillation of characteristic frequency is produced in the crystal. Thus, the mass changes can cause the pressure changes on the crystal surface and subsequently lead to the resonant frequency of the crystal shifting. Lots of successfully used cell-based biosensor applications of QCMs, such as drug analysis, cell adhesion, cell exocytosis, and epithelial cell-microparticle interaction, have been introduced.

Figure 1.7 The basic schematic diagram of QCM for cell-based biosensors.

10

Introduction

SPR is an optical technique that uses the evanescent wave phenomenon to measure changes in the refractive index very close to a sensor surface. In recent years, the SPR-based biosensors have been widely used to study biomolecular interactions. SPR is an optical detection process that occurs when a polarized light hits a prism covered by a gold layer. Free electrons at the surface of the gold layer absorb incident light photons and convert them into surface plasmon waves. Perturbations at the gold surface of SPR, such as an interaction between probe molecules immobilized on the chip and captured target molecules, induce a modification of resonance conditions, which in turn are seen as a change in reflectivity and can be measured. This is the basis for molecule SPR measurements. Recent experimental results demonstrated that when cells are cultured on the surface of SPR, the attachment and spreading could be monitored in real time with intracellular signal transduction events reflected. Finally, the immune system is one of the most complex biological systems. It protects against disease by identifying and killing pathogens and tumor cells. Because immune cells can recognize and respond to antigens with very high sensitivity and specificity, a number of immune cells have been investigated to explore the feasibility of being used as sensitive elements in cell-based biosensors. Both mast cells and B cells are used as examples of the immune cells–based biosensors for promising applications in pathogens detecting. At the same time, those cell array– based biosensors can be used in fundamental studies of multicellular interactions in the immune system and other areas of cell biology. This chapter has first summarized the various kinds of cell-based biosensors and then introduced their working principles, design, and fabrication methods. Afterward, it introduced their typical applications in biomedicine, environment monitoring, and so on. Finally, it described the future development trends and possible commercial applications.

1.4

Summary The most important reason for developing cell-based biosensors is that by using living cells components it is possible to respond directly to incoming information from an external physical or chemical stimulus. This functional information, with additional qualitative or quantitative analytical information, can be very important with respect to clinical diagnostics, pharmacology and drug screening, cell biology, toxicology, and environmental monitoring. By means of such biosensors, it is possible to study the effects of pharmaceutical compounds, toxic substances, pollutants, and so on in a physiological system and especially in cellular metabolism. The model of the cell-silicon, cell-metal electrode interface and the detection models of MEA, FET, LAPS, and ECIS are very important for improving the property of cell-based biosensors. For example, the characteristics of transmembrane ionic current are given based on the conductance and permeability of cellular membrane. With the development of micro electronic mechanical system (MEMS) and cell biology, the research on cell-based biosensors has reached the cellular and molecular level. Cells provide and express a series of elements such as naturally evolved receptors, ion channels, and enzymes that can be the targets of biological

1.4

Summary

11

active analytes. When stimulated, the living cell responds and takes action—induce electronic activity, excrete something, or absorb something. Cell-based biosensors that treat cells as biological sensing elements have the capacity to respond to analytes in a physiologically relevant manner.

References [1] [2] [3] [4] [5]

[6] [7] [8]

[9]

[10] [11] [12] [13]

Neher, E., “Molecular Biology Meets Microelectronics,” Nat. Biotechnol., No. 19, 2001, p. 114. Xu, G. X., et al., “Cell-Based Biosensors: Towards the Development of Cellular Monitoring,” Chin. Sci. Bull., Vol. 47, No. 22, 2002, pp. 1849–1856. Michael, P. M., et al., “The Neurochip: A New Multielectrode Device for Stimulation and Recording from Cultured Neurons,” J. Neurosci. Methods, No. 87, 1999, pp. 45–56. Harsanyi, G., Sensors in Biomedical Applications: Fundamentals, Technology and Applications, Lancaster, PA: Technomic, 2000. Grattarola, M., and S. Martinoia, “Modeling the Neuron-Microtransducer Junction: From Extracellular to Patch Recording,” IEEE Trans. Biomed. Eng., Vol. 40, No. 14, 1993, pp. 35–41. Fromherz, P., “Joining Microelectronics and Microionics: Nerve Cells and Brain Tissue on Semiconductor Chips,” Solid State Electronics, No. 52, 2008, pp. 1364–1373. Fromherz, P., “Semiconductor Chips with Ion Channels, Nerve Cells and Brain,” Physica, No. E16, 2003, pp. 24–34. Yates, D. E., S. Levine, and T. W. Healy, “Site-Binding Model of the Electrical Double Layer at the Oxide/Wafer Interface,” J. Chem. Soc. Faraday Trans., Vol. I, 1974, pp. 1907–1818. Bergveld, P., et al., “Theory and Application of the Material Work Function for Chemical Sensors Based on the Field Effect Principle,” Meas. Sci. Technol., Vol. 9, 1998, pp. 1801–1808. Hafeman, D. G., J. W. Parce, and H. M. McConnell, “Light-Addressable Potentiometric Sensor for Biochemical Systems,” Science, No. 240, 1988, pp. 1182–1185. Xu, G. X., et al., “Cell-Based Biosensors Based on Light-Addressable Potentiometric Sensors for Single Cell Monitoring,” Biosen. Bioelectron., No. 20, 2005, pp. 1757–1763. Giaever, I., and C. R. Keese, “Micromotion of Mammalian Cells Measured Electrically,” Proc. Natl. Acad. Sci. USA, Vol. 88, No. 17, 1991, pp. 7896–7900. Dittami, G. M., et al., “A Multilayer MEMS Platform for Single-Cell Electric Impedance Spectroscopy and Electrochemical Analysis,” J. MEMS, Vol. 17, No. 4, 2008, pp. 850–862.

CHAPTER 2

Cell Culture on Chips Wei Zhang and Qingjun Liu

2.1

Introduction Cell culture is one of the essentials that could impact the whole cell-based biosensor work procedure, and it is quite different from common cultures. In vivo, cell adherence and spreading are influenced by the interaction mediated between cell-cell and cell-extracellular matrix (ECM). However, it is still uncertain about the adherence mechanism in vitro. It is consistent with the ECM complex molecule, which plays similar role when cells adhere onto substrate coated with laminin or fibronectin. The most common methods place cells directly on petri dish, slide, and other glass or polymeric plastic culture dishes. Attributing to a good surface hydrophilicity, most cells could locate and spread well on the glass or plastic dish. Besides, many conventional operation manuals and protocols are available for different types of typical cells, such as cardiac myocytes, neurons, glial cells, epithelial cells, embryonic cells, and so on. On cell-based biosensors, a complete contact between the bilayer and insulator silicon dioxide results in an affinitive medium. For example, the electric field changes elicited by action potentials could pass through SiO2 thin layer onto the polar surface of the chip. This is mainly attributed to the outstanding protein molecules in the lipid bilayer of the cell membrane, which could deposit on the substrate to mediate the cell adherence. These molecules could fill the gap between cell membrane and substrate with electrolyte and make it copolar. In the action potential duration, corresponding currents are elicited by ion streams on the cell membrane in the conductive gap, which enhances the voltage coupling between cells and chip. It is a big challenge to culture cells on chips comprised of silicon or other conductive materials and, furthermore, to gain a good contact between substrate and cell membrane. However, the main problem is that the material itself is not suitable to cells in roughness, hydrophilicity, surface functional groups, or viscosity. Therefore, a lot of work is needed to improve the chip surface characteristics.

13

14

Cell Culture on Chips

2.2 Cell Immobilization Factors Typical substrate materials are glass and silicon. Silicon dioxide is a kind of stable, harmless, nontoxic inertia material. In modern biosensors, many types of substrates like silicon nitride and photoresist such as SU-8 and other organic materials are used. Silicon carbide is another kind of good substrate material, as it is stable and could avoid ion or chemicals invasion. Silicon nitride is also widely used in substrate, because of its good properties in spreading and depositing in spite of a high dielectric constant. All the materials used in surface treatment process need to be biocompatibility tested first. Biocompatibility is an important concept used extensively within biomaterials science, but there still exists a great deal of uncertainty regarding means and mechanisms. As biomaterials are being used in increasingly diverse and complex situations, more and more applications involve in tissue engineering, invasive sensors, drug delivery, gene transfection systems, medical nanotechnologies, and biotechnology. Materials are selected, or occasionally developed, on the basis that would be nontoxic, nonimmunogenic, nonthrombogenic, noncarcinogenic, nonirritating, and so on. Cell immobilization on the surface of chips is essential for biosensor designs and applications. Furthermore, a fine coupling with the substrate is expected to be obtained. Fundamentally, some factors that affect cell immobilization on the surface of chips should be investigated first. In terms of materials, several factors such as roughness, elasticity, functional groups, and landscape on the surface are drawing the most attention. Because the materials chosen in biosensor designs are commonly nonelastic, the majority of factors that affect immobilization of cell culture on chips are grouped into three main aspects. One is a physical factor, including surface roughness and landscape, and the others are chemical factors, including surface functional groups and electric charge, and biological factors. 2.2.1

Physical Factors

Cell adherence and spreading on the substrate is a dynamic process. The cell has to change shape to make a better contact with the substrate. It is quite different to culture cells on the microstructure surface with channels or on edges and corners compared with that on smooth surface. Generally, a cell is apt to be directed, and it migrates along the protuberant part or fiber orientation, which is called contact inducement in cell culture. Cell orientation is influenced evidently by depth and width of the channel. The reason is that cell adherence and spreading is a kind of microcosmic mechanical response, like fibroblast cells. Most adherent cells press the substrate to adhere and spread on it. As a result, cells can hardly survive on absolutely smooth substrates due to not having enough stress. Roughness of the chip surface is also very crucial for cell adherence. Usually, surfaces of all kinds of materials are not absolutely polished, but fluctuant in random. In a given length of L, using the function f(x) in profile description and the arithmetic square difference of setover between profile and center is called Ra, which could be considered as a parameter of surface roughness (2.1).

2.2 Cell Immobilization Factors

15

Ra =

(∑ f (x)dx) L

(2.1)

A cell membrane itself is not absolutely smooth, but with transmembrane proteins, surface antigens, receptors, and so on. Neither too smooth nor too rough is suitable for cell adherence. It should match the surface roughness with the membrane, and this should be chiefly considered in biosensor designs. 2.2.2

Chemical Factors

On the surface of silicon insulator layer, some chemical groups such as Si-O-Si, Si-O-O, and Si-O-H exist. Among those bond states, Si-O-H group is most contributive to the material hydrophilicity, which is approved by the measurement of the silicon surface energy spectrum. Enhancement of hydrophilicity makes it easier for cells to adhere onto the surface. Embedding the hydroxyl ions into the surface of silicon dioxide could bring in the Si-O-H group, which plays an essential role in changing the surface hydrophilicity. Additionally, amino silane is a good choice for surface modification because of its hydrophilicity. It depends on the property, toxicity, or other respects of the different kinds of silanes in practical operation. Diethylenetriamine (DETA) is typically used in surface modification [1]. When applied onto the surface, it could make it easier to form a self-assembled monolayer and promote the cell adherence to a certain extent, with the amino group extruding out of the surface. Another important factor is electric charge. Mammalian cell membrane surface is charged. As a result, some work is needed for immobilization on glass or polystyrene surface, which has a similar charge density. Glass is comprised of silicon and oxygen atoms. One oxygen atom could combine with a pair of silicon atoms to make a bonding to capture a hydrogen atom, or to get charged. There is approximately one oxygen atom per square nanometer of glass surface. In soft glass, one in three atoms is charged on average, and surface electric charge density is near 0.3 per square nanometer. Electric charge density in cell lipid bilayer is less than 1 per square nanometer. Glass surface is easier for cell adherence. When the charge density matches with the density on cell membrane, it facilitates cell adherence. Aminosilane is commonly used in modifying the properties of glass surface. Usually glass is chosen for cell culture because its charge property matches with cell membrane after being treated. It was discovered that it could enhance the electric charge density of the chip surface via immobilizing the amino acid group with abundant positive charging, such as laminin. Thus, it could significantly improve the cell adherence. 2.2.3

Biological Factors

Recent researches on cell growth factors and extracellular matrix convey that some segments of the peptide own the special function, which could determine the property of the whole factors or matrix, such as RGD, IKVAV, and YIGSR [2, 3]. And these segments could bind with the intergrin on a cell membrane to promote a series

16

Cell Culture on Chips

of biochemical reactions on the membrane and inside the membrane. It may help the cell adhere and spread onto the substrate. The RGD segment is the binding site of collagen protein, which facilitates the adherence of many types of cells. Meanwhile, YIGSR segments are the active peptide sites of axon spreading promotion on laminin protein molecular. IKVAV sequence on PA22-2 can help neurites extend on the surface. Immobilization of these segments on the chip could improve the surface property, as neurons are prone to land on them and spread. Some peptide sections are immobilized on chips to help cell adherence. Under room temperature, dip the chip into a phosphatic buffer solution with 100 μg/mL poly ornithine for 24 hours. After rinsing, dip the chip into phosphatic buffer solution with 8 μg/ml laminin protein for 24 hours. Thus, a membrane 4 nm thick was formed on the chip, which could help the adherence of cells and avoid the hydration of silicon dioxide on chip surface. Besides the factors of these materials, environmental controls during incubation are also very important for cell survival on chips, including temperature, medium components, pH, osmotic pressure, and gas pressure. Cell culture in vitro is sensitive to environment condition changes. The most suitable temperature for mammalian cells is 35°C–37°C. Generally, cells could survive in lower temperatures—even in 4°C. Low temperatures only stunt the growth of cells. Nevertheless, high temperatures over 40°C are fatal to cells. It is because most kinds of enzymes are inactivated under high temperature, which affect cell metabolism and growth speed predominantly. They are also related to gene expressions, action potential transduction, and so on. As a result, it is important to guarantee precise temperature control during the cell incubation and measurement process. Temperature control units have been designed in most chip platforms. However, a slight change, like 0.5°C, has little effect on chicken embryonic myocyte action potential measurements. The components in this medium include salts, glucose, amino acids, and vitamins. Now serum is also requested in many cell line cultures, in which some peptides could promote cell growth, differentiation, and adherence. Cells are also very sensitive to pH and osmotic pressure changes in solution. pH value is adjusted to 7.4±0.2 by bicarbonate or other typical buffers. And the osmotic pressure of sample should be 260–320 mOsm/L, with a fluctuation of 10 mOsm/L. Gas control during incubation is also very important for cell metabolism. Carbon oxide is one of the components in pH balance system. Typical CO2 pressure in air is about 0.1 kPa; however, it should be kept in 5–10 kPa in cell culture process. The whole operation of chip packing and cell culture should be ensured in a sterilized environment. Additionally, all the subjects involved in the cell’s culture, including substrate, petri dish, and other materials, should have good biocompatibility.

2.3 Basic Surface Modification Rules Patterning cells effectively on chips has recently drawn considerable attention due to its important role in fundamental cell biology, tissue engineering, cell-based biosensors, and other bio-MEMS devices. Many methods in biofunctionalized polymer

2.3 Basic Surface Modification Rules

17

surfaces analysis are well considered in surface characterization, including spectral methods (x-ray photoelectron spectroscopy, Fourier transform infrared spectroscopy, atomic force microscopy (AFM), and others) as well as nonspectral methods (water contact angle, dye assays, biological assays, and zeta potential). Focusing on the factors that affect the cell immobilization, a series of methods are performed on the surface. Traditionally, some physical methods such as the surface hydrophilicity improvement, roughness changing, building special structure and chemical substance or biochemical molecular printing are all available for cell immobilization on biosensors. 2.3.1

Hydrophilicity Improving

Hydrophilicity is one of the fundamental factors that affect the soakage property of material. A uniform hydrated layer exists outside the cell membrane, which determines the suitable parameter of the cell adherence surface. Water contact angle measurement reveals that methyl (CH3), bromine (Br), and vinyl (CH=CH2) groups result in hydrophobic surfaces, while amine (NH2) and carboxyl (COOH) function groups lead to moderately wettable surfaces. Meanwhile, polyethylene glycol (PEG) and hydroxyl (OH) groups create more wettable surfaces. It helps to improve hydrophilicity of silicon surface by the means of hydroxyl ion embedding or silane pretreatment. Hydroxyl ion implantation into the silicon significantly affects structural properties of the surface. It is known that a surface with better hydrophilicity facilitates cell adherence and proliferation. By modulating the energy and the dosage of implantation, Fan et al. pursued an appropriate experiment condition and made it work in tissue repair engineering to enhance the cell adherence ratio on hydroxyl ion modified silicon surface [4]. On silicon surface treated with hydroxyl ion, cell adherence increases significantly. In the aggregation region, the axon outgrowth and formation of synapses are well developed. Ion implantation increases neither the thickness of chip surface nor the conductivity of plating layer. However, it indeed improves the signal-to-noise ratio in spite of some sensitivity loss. That is because the essential aspect of ion channel and action potential measurement is to detect the concentration of hydrogen ions, which is decreased to an extent due to the implantation of Si-O-H groups. Amino silane is a kind of chemical coupling reagent. KH550 is one of the widely used reagents with a molecular structure as H2N(CH2)3Si(OC2H5)3, in which both amino and triethoxy groups exist. The amino end group combines with the organic material firmly, while the triethoxy end group reacts with a little water to become part of the silane alcoholic group, which should combine with silicon surface after disassembling. Thus, amino silane can improve the adherence property between glass fiber and resin, in term of glass fiber complex material intensity promotion. As a result, it is commonly applied in protein chips, DNA chips, or other cell chip fabrications. Additionally, because of amino group hydrophilicity, amino silane is a good choice for surface modification. In practical operation, different kinds of silanes are applied depending on their properties. Taking KH550 as an example; commonly,

18

Cell Culture on Chips

it is diluted into 0.5%–2% with water, alcohol, or a mixture, especially deionized water, ethanol, or isopropyl alcohol without fluoride ion. Usually it is diluted with 95% ethanol and adjusted pH to 3.0–5.5 with hydrochloric acid or acetum solution. Hydroxyl group on the surface could react with silane molecular to construct the structure on a glass surface, which could change the property of glass and make it easier for cell adherence. More applications on a silicon surface are presented in the following passages. 2.3.2

Roughness Changing

Silicon is a kind of proper material for cell chip fabrication. However, its current polishing surface is difficult for cells to adhere to. Fan et al. studied neuron culture on different roughness silicon surfaces and concluded that neurons could adhere to and spread on the silicon surface with appropriate roughness [5]. Roughness is basic physical parameter of material surface. Cells tended to be normal morphology and function on silicon surfaces with an average roughness of 20–50 nm. Under the same conditions, it is not suitable for neuron cultures with roughness lower than 20 nm or higher than 50 nm. In their experiments, silicon was dipped in hydrofluoric acid to enhance the roughness to 25 nm from the original 3 nm; thus, it dramatically improved the surface property for neurons adherence. Compared with chemical components or reagent depositions, changing roughness needs no coating but promotes cell adherence effectively. It not only makes great progress in biocompatibility, but also avoids noise due to some additional medium or chemical groups changing. Hence, it ensures the normal signal conduction between cell and chip. After pretreatment in reactive ion etching, some columnar structures in nanoscale, called silicon grasses, appeared on the silicon surface. With normal lithography technology as a mask, part of the silicon region was reformed with wet chemical etching. In this way, a structure with different roughness (silicon grass and wet chemical etching area) was achieved. Meanwhile, astrocytes were cultured on a nano-patterned surface of silicon. The results showed that a certain type of deformed astrocytes preferred to adhere on wet chemical etching areas, while primary astrocytes of newborn rats would like to land on silicon grass instead [6]. 2.3.3

Chemical Coating

Silicon or silicon dioxide itself is not suitable for cell adherence, so that some chemical substances with special functional groups are necessary to improve the surface for attachment. This is called chemical coating. It is widely used in inducing cell adherence and spreading along the pattern coated on the substrate. The most direct way to change the substrate adherence property is to coat a well-biocompatible material on the whole surface, such as immobilizing a certain adherence molecular or extracellular matrix. Dissociated cells adherence process could be divided into four steps, as shown in Figure 2.1:

2.3 Basic Surface Modification Rules

19

Figure 2.1 Fresh dissociated cells spreading and adherence process.

1. Glycoprotein secoisolariciresinol diglucoside (SDG) from medium blood serum first absorbs on the surface of the culture dish due to electric charge. 2. Cells dissociated from integrate tissue are deposited on the surface after 1–2 hours. 3. Condition factor (CF) combines with glycoprotein and cell surface glycoprotein (CSG), which mediates cell to touch the surface. 4. Following cell immobilization and spreading flat, about 24 hours later, a single layer appears. As a result, either the relation acquisition between cell surface and substrate or the structure and function stabilities relies on the existence of absorption factors in glycoprotein. Most neurons are anchorage-dependent cells, which behave poorly on glass or silicon substrate. Hence, a good sticker like poly-lysine or rat tail collagen is needed to be coated first on the substrate to help neuron adhering and spreading. Matsuzawa et al. coated laminin as lines on the surface of glass, and then adsorbed butyldimethysilane (BDMS) and aminopropyldimethysilane (APDMS), respectively, on it to promote hippocampal neurons to adhere and spread onto the surface [7]. Thus neurons well adhered along the pattern lines. Additional results were visualized where neurons cultured on APDMS adsorption surfaces showed a special appearance with several short branches and a long axon; neurons on a BDMS adsorption surface changed little in axon length, with a basic neuritis and branches reduction. It is necessary to choose the appropriate substance to coat on the surface for the final target cell pattern acquisition. Clark et al. did further research where their group combined simple chemical coating and topology [8]. In their study, they cultured chicken embryo brain neurons on micro-patterned laminin surface. They modulated the width between laminin lines and found the lines’ interval reduction could enhance the neuron adherence, especially when reducing to 2 μm. The neuron cluster grew more uniform and glomerate along the laminin-coated track. With the technology developing dramatically, AFM makes it possible to control the thickness of protein coating. Antonik et al. coated the laminin on the silicon surface and utilized the cantilever tip of AFM to scratch on the surface to reduce the thickness of protein coating [9]. However, a few disadvantages limit this simple method of application in cellbased biosensors. First, coating on chip surfaces influences the interface between cell and substrate. Additional coating might cause the current leakage to culture medium or other cells via a material plating layer or even other electrodes. As a

20

Cell Culture on Chips

result, a lot of superimposed noise is brought into a single electrode, which enormously reduced the signal-to-noise ratio. In other ways, the risk of bacteria contamination raises and coating on substrate means additional sterilization.

2.4

Typical Methods In recent years, cell-based biosensor is becoming a hot topic in science. It means to immobilize the living tissue or cells directly on sensors or transducers, which could specially response to chemical substances, potential change, or other immune interaction in intra- or extracellular microenvironments. One of the most important parts of biosensor is living cell or tissue, which receives or sends out biological signals, such as ion concentration change, electrical current, or voltage fluctuation. The other parts are mainly physical components, including devices and periphery equipment. As a result, extraction, dissociation, purification of cells, and suitable device design determine the system together. Solving a well interface and obtaining a precise combination and a swift response reveals the orientations of the future development. A fast MEMS development has obviously reformed biological, chemical, and medical areas. Technologies such as 3D DNA chip, lab on chip, and μTAS chip are all based on biological theory and MEMS fabrication technology. The main problem in cell and biosensor chip combination is how to immobilize the cell on a target position and obtain a good contact between the cell membrane and chip, because most of the time target cells may be surrounded by many other cells or tissues such as support cells and extracellular matrix. Meanwhile, it is vital to ensure the stability of the gap between cell and electrode because of its sensitivity to action potential during the recording process (in Figure 2.2 [10]). Different types of chips are applied in a biosensor area, and methods used for cell immobilization are also various. Some efforts are needed for cell immobilization. Considering the signal detection and amplification, neither complicated 3D structures nor changes are allowed in chip surface and shape. Electric property makes it difficult to etch or implant ions into the surface. If biodegradable materials are applied in chip surface, the signal detected would always be changing. Some other methods must be developed. Several recent novel techniques, with or without protein mediation, were developed, including the metallic and perforated electrode fabrication [11], microcontact printing [12], microfluidic channels [13, 14], and ink-jet printing [15]. In addition, extracellular matrix such as poly-lysine, poly-ornithine, laminin, fibronectin, and collagen result in a better adherence and spreading of cells and tissues on surface. However, most of these patterning techniques focused on guiding cells on substrates of a single type material—generally a mechanical device in contact with substrate or controlling the immobilization process. Palyvoda et al. coated NH2-terminated SAM on Au surface to guide neurons immobilization on glass wafers and found that dissociated neurons from rat embryos could form good neuronal networks on the 11-amino-1-undecanethiol–coated surface after 6 days [16]. The high adherence and survival of neural cells are evidence of biocompatibility and nontoxicity. Other groups [17, 18] also discovered that SAM might play a role as the support

2.4

Typical Methods

21

Figure 2.2 Micrograph of cells cultured directly on surface of a chip. (From: [10]. Reproduced from Sensors and Actuators B: Chemical. © 2004, with permission from Elsevier B.V.)

structure for the cell culturing due to its long-term stability. However, the subject of extracellular potential detection has not been studied very much. Currently, microfluidic technology has been widely utilized in precise immobilization. Commonly, three types of strategies are applied in immobilizing cells on sensors. First, electrode array design combines with topology method, which guides synapse orientation by artificial microstructures [19]. Thus, it helps to dissociate the cells for a long-term measurement,or record the distribution and current change of neuronal network typically via field effect transistor (FET) array [20]. A practical method is provided for long-period detection between cells and microelectronic circuits. Various microstructures are fabricated on silicon for trapping cells and test separately. Some researchers pointed out that a microhole with a diameter of 150 µm was appropriate for the cell adherence [21]. However, it was supposed that the network oriented as the ideal model and formed synapses artificially, which might not tell the truth in vivo. In addition, it is difficult to control the process exactly to fabricate the complicated structure on a chip. Another way is to utilize some chemicals or reagents to guide the neurites spreading along the track. Some chemicals and proteins such as poly lysine, amino silane, poly ornithine, some peptide sections, and complex compounds, could promote the adherence and combination of neural cells and neurites on chips. However, technology is still lacking in single cell measurement. Many cells prefer to cluster in a coating region, which make it difficult to locate the target during the experiment. The microcontact printing method integrates the lithography technology and chemical coating using a self-assembled monolayer, which guides cells to attach to the target region effectively.

22

Cell Culture on Chips

Lately, amazing materials development plays a helpful role in cell culture on chips. Polydimethyl siloxane (PDMS) is satisfactorily biocompatible, stable, nontoxic, and suitable for cells as a novel organic material. Scaffolds made in PDMS could be used to hold cells and support them for a more complex 3D structure, which is more suitable for cell culture. 2.4.1 Special Physical Structure

Cells, especially neurons, could hardly survive on raw silicon or silicon dioxide, due to its poor biocompatibility, physical properties, and chemical properties. For the sake of a good interface between silicon and cells, some modifications are needed on the silicon surface. Fromherz and Zeck suggested that a single neuron body could be fixed by a picket fence of polyimide, while the neurite could spread through the poles and form a network in plane, and it was supported by a stable test model (Figure 2.3). The stimulation by electric pulse from capacitance stimuli unit on a chip did no harm to the neuron. At the same time action potentials and other signals propagated via a neuronal network from one former nerve cell to the next were recorded [22]. Maher et al. suggested that we could reduce contact resistance by etching a neural well on chips [23]. It could prevent a cell from growing outside the surface of an electrode, which was outstanding progress in cell chip fabrication. On a silicon chip, with common lithography technology, microwells slightly larger than the neuron were etched. There was a 16-well array on a chip, and each electrode leader connected with an exterior circuit at the bottom of the well. Thus, electrode and cell were one-to-one correlative, which made progress in efficiency and consistency in measurement. Hippocampal neurons dissociated from mice were posited in these wells via micromanipulation technology. The main problem was that neurites had to move outside the neural well to synapse with other neurons, which made

Figure 2.3 Neurons culture on silicon chip. (a) Stimulator wings (St) and transistor (S, source; D, drain; G, gate) were marked on neuron silicon chip. Scale bar: 20 μm. (b) A single neuron was trapped inside the poles, neurites were spread outside and connected with others. Scale bar: 20 μm. (c) Micro polyimide poles on biosensor. Scale bar: 100 μm. (From: [22]. Reproduced from Proceedings of the National Academy of Sciences. © 2001, with permission from the National Academy of Sciences, U.S.A.)

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23

it difficult to form a more complex network. Additionally, the responses were difficult to analyze due to signals from different neurons in each well. Many researchers dedicate themselves to the study of cell culture on a nanopatterned silicon surface. Turner and his coworkers utilized reactive ion etching technology to deal with the silicon, after which some special grass with nanoscale was achieved [24]. The morphology of the cells cultured on the special structure could be detected by confocal scanning microscope (CSM) and scanning electronic microscope (SEM). The results revealed that primary cortex astrocytes preferred to land on the special grasses than other astrocytes. It is a good idea to reconstruct neuronal network in 3D cell culture, which has been proposed to replace the traditional 2D cell culture systems. Microfabrication is an ideal way to produce inexpensive 3D cell culture microenvironment [25]. Wu et al. at the University of Georgia had an idea to utilize SU-8 to form a microstructure on silicon, in which they rebuilt the neuronal network and marked it with fluorescence dye. SU-8 has good stability and biocompatibility, and it is easy to fabricate into any kind of microstructure. Human neuroblastoma cells (SH-SY5Y) were cultured in SU-8 microfabricated microwells, as shown in Figure 2.4 [26]. Furthermore, SH-SY5Y cells within microwells were characterized with voltagegated calcium channel (VGCC) function and resting membrane potential by using confocal microscopy. Neural progenitor cells within microwells were characterized with cell morphology and neuronal network formation. SH-SY5Y cells cultured in SU-8 microwell microstructures formed 3D tissue and exhibited higher membrane potential than cells on 2D substrates. In response to 50-mM high K+ depolarization, cells in microwells were less responsive in comparison to cells on 2D substrates in intracellular Ca2+ uptake [27].

Figure 2.4 SH-SY5Y cells cultured in microstructures. (a) Phase contrast images show SH-SY5Y cells interfaced with the microwell network patterned on day 5 and (b) into differentiation on day 13. (c) Fluorescent image for the same field as in (a), showing neuronal extensions along the microchannels. Cells were labeled with FITC. (d) SEM image shows the cross-sectional profile of patterned network in microwell after cell culture. Scale bar: 100 μm. (From: [26]. Reproduced from Colloids and Surfaces B: Biointerfaces. © 2006, with permission from Elsevier B.V.)

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Cell Culture on Chips

A special structure called adherence islands was made, which had some certain size, shape, and pattern. After gold plated onto the silicon, self-assembled monolayer (SAM) was adsorbed on to the micro physical structure. The first layer of SAM is alkanethiol. A stamp made of PDMS with rubber property could adsorb this kind of compound after plasma oxidation and make a new pattern on chip. Peeling off the PDMS mold, another poly glycol with a termination of alkanethiol combined with gold region, which resist the protein adsorption. As a result, the process used a simple way to make gold plating layer adsorbing protein with a certain size (2–8 μm) on alkanethiol region, which resisted protein originally. When exposed to pure ECM, such as laminin and fibronectin, these adsorption regions could immobilize cells to islands with protein covered. 2.4.2

Microcontact Printing

Microcontact printing is probably best known as the soft lithography technique. It could effectively guide cell to survive on chips and become more and more important in neuronal network research. In the elastomeric membrane method, proteins and cells could be patterned on substrates through the holes of a micropatterned PDMS membrane, which is peeled off later to allow cells to spread from the initial pattern. This method can be applied on a variety of surfaces, such as glass and plastic, and provides a unique approach for studying cell spreading and migration when the surfaces are precoated with mediating proteins. With the rapid development of material science, a lot of novel biological materials are gradually applied in the chemistry engineering, biology, electronics, and medicine industries. PDMS is a novel organic material with excellent antioxidation, thermal stability, biocompatibility, and low surface tension. It is convenient to be fabricated to any required pattern. The fabrication of the stamp in microcontact technology is very important. The first step is to pattern on PDMS by lithography. Molds for the production of PDMS stamps were usually produced as described by Love and his coworkers [28]. A photomask was created in a CAD drawing tool and printed on transparent film at a high resolution. Then, the photomask was placed at the field to transfer the reduced image to a substrate. To prepare silicon wafers for the deposition of photoresist, Si wafers were sonicatied in acetone and methanol and dried at 180°C. Positive photoresist was spin-coated onto the wafer at 4,000 rpm for 40 seconds to a thickness of about 1.3 μm. The coated wafers were baked at 105°C for 4 minutes. Photoresist-coated silicon must be protected from light during preparation. The next step was to choose an appropriate cell intimate substance, which was usually silicon or glass, and load on the surface to mediate the cell adherence. Some proteins with special peptide terminated are usually used for immobilizing different types of cells. The fabrication process of a stamp is shown in Figure 2.5(a). Compared with other technologies, microcontact technology could behave well on patterning cells to the target region, especially on two or more different kinds materials mix regions. This technology makes it much easier and more efficient to pattern cells on planar or nonplanar surfaces. It could also be employed to form a

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Typical Methods

25

Figure 2.5 Microcontact fabrication process and reconstruct network in vitro. (a) PDMS stamp fabrication scheme; (b) microcontact stamp mold, scale bar 100 μm; and (c) reconstructing a neuronal network in vitro. (From: [12]. Reproduced from the Journal of Neuroscience Methods. © 2006, with permission from Elsevier B.V.) (d) Fluorescent image of patterned hippocampal neurons after 4 days’ incubation on gold coated with PA22-2. (From: [29]. Reproduced from Biomaterials. © 2004, with permission from Elsevier Ltd.)

cell network in vitro on a required region, even at a low cell density, as shown in Figure 2.5(b, c). Neurons in cell culture make synaptic connections with each other to form interactive networks. The random nature of in vivo network organization in culture complicates the investigation of synaptic interactions. A novel strategy to create cultured neuronal networks in vitro with defined composition and connectivity is needed. The Offenhäusser group reported a self-ordered in vitro culture on a grid of laminin A, which featured an array of 14 × 14-μm nodes connected by 6-μm tracks [30, 31]. Heller et al. patterned networks of hippocampal neurons on peptide-coated gold substrates prepared by microscope projection photolithography and microcontact printing, as shown in Figure 2.5(d). A 19-amino acid peptide segment of laminin A (PA22-2), which included IKVAV cell adherence domain, was used to direct patterns of cell adherence in primary culture [29]. Dispersed hippocampal cells isolated from a neonatal mouse were grown on peptide-patterned gold substrates for 7 days, and then membrane potential was recorded. Neurons showed a preferential adherence onto peptide-coated arrays. As a result, peptidemodified gold surface served as a convenient and effective substrate for growing ordered neuronal networks that were compatible with existing multielectrode array recording technology.

26

Cell Culture on Chips

This method works dramatically in patterning neural cells and networks artificially, especially for cells at a low density. However, it may adsorb many cells onto protein-covered regions, which could break uniform distribution of cells and hardly track single cells. The stamp microfabrication process is also difficult in micro scale. 2.4.3 Fast Ink-Jet Printing

Former methods have mentioned that some small islands of adhesive protein or cells arranged over an inhibitory background are employed in patterned cell cultures, especially in neuron cultures. Lately, more techniques have included contact masks using photolithography, photochemistry, and microstamping. However, they are limited by their use of an unalterable master pattern, such as one mask or solid stamp. Ink-jet printing, a method using low-cost desktop printers that patterns material by depositing microscopic droplets under programmable hardware control has been developed recently. In contrast to desktop ink-jet printers, which print dyes or pigments onto paper, Sanjana and Fuller printed biologically active materials onto coverslips or chips [15]. Ink-jet printing has outstanding advantages in that the patterns are programmable by computer and it is flexible. It becomes much easier to deposit multiple layers of different materials to build 2D or more complicated structure. Less cost is also necessary. In their work, polyethylene glycol (PEG) was chosen as the inhibitory background material, which has been shown to provide a long-term inhibition of cell adherence in several studies of patterned neural cell culture [32, 33]. PEG is a kind of material, which is nonreactive with the glass print head, as well as nontoxic to tissues and cells. In their experiment, a collagen and poly-D-laminin (PDL) mixture was chosen as an adhesive part, which was printed by ink-jet on top of the PEG background according to the pattern set in the program. An ink-jet printing system consists of tightly controlled microscopic droplets that are 10–100 μm in diameter when put onto the substrate. While continuous jet printers elicited a continuous pressurized stream that broke ink up into small droplets, the printer ejected single droplets in response to a pressure impulse in the ink chamber [34]. The pressure impulse is generated by a piezo crystal that deforms in response to a voltage pulse generated under computer control, usually such as mouse and keyboard clicking. The droplet loaded and adhered onto the substrate. Liquid evaporated and left a round protein deposition that would administer to cell adherence. The surface chemistry presented using a uniform covalently bounded layer of PEG as the inhibitory background and a mixture of collagen and PDL printed on top as the cell adhesive foreground. In principle this chemistry also has flexibility. Neurons and glia cells were plated onto the substrate (Figure 2.6). Patterns for neurons included controlled micro-islands, lines of dots as narrow as 65 μm, gaps as small as 8±2 μm, and arbitrary shapes. The mean density of synapses in pattern and control cultures was observed. Synapses are abundant phosphoproteins found in virtually all presynaptic terminals [35]. In both pattern and control

2.4

Typical Methods

27

Figure 2.6 Fast ink-jet pattern and cell culture. (a) Diagram of a piezo drop-on-demand ink-jet printing operation process. (b) Microscopic ink droplets were ejected individually through an orifice by means of a pressure impulse delivered by a piezo crystal. Each droplet, 10–100 μm in diameter, took a small distance (~1 mm) to the glass substrate. The print head was moved robotically in twodimension above the substrate as droplets ejected, leaving a pattern of round dots onto the substrate. (From: [15]. Reproduced from the Journal of Neuroscience Methods. © 2004, with permission from Elsevier B.V.)

cultures, synapses were not significantly different, and similar synaptic density results had been found in high density, unpatterned hippocampal cultures [36]. The advantages of the method are that it is programmable and relatively inexpensive. In addition, ink-jet printing has some other potential advantages in neuroscience, including the ability to fabricate gradients, layered patterns, and nonplanar structures much easier than many mold pattern-based approaches. Meanwhile, much smaller dot size is still expected, which is optimal lower than the 65-μm resolution. New technologies in drop generation based on print heads are different from the way it is used in this area, such as acoustic droplet generators, which may push to an even lower resolution and printing reliability. 2.4.4

Perforated Microelectrode

In the past few years, microelectrode arrays (MEAs) have been widely developed in parallel, noninvasive, real-time, long-term, and extracellular monitoring of cell electrophysiological activity [37–39]. And it is a powerful tool for high-throughput drug screening, intercellular signal propagation for networks, and so on. However, electrodes on MEA are limited to less than 100, so it is becoming vital for measurements to enhance the distribution of seeding cells available. Furthermore, extracellular signals of cells are rather small, on the order of 10–400 μV for neurons and 500–1,000 μV for cadiomyocytes. The probability of harvesting cells on an electrode can be easily increased by utilizing high-density cultures. But it does not work in all cases. Low-density cultures allow for a study on signal propagation along the pathways. Without a particular cells placement technique, it is hard to place cells on

28

Cell Culture on Chips

a single electrode. Thus, a perforated electrode technology has been used to enhance the cell coverage on electrodes. The device was a 5 × 5 mm2 chip fabricated by standard industrial CMOS technology with subsequent dedicated micromachining steps for electrodes deposition and immobilization features realization [11]. The area for cells culture was in the center of chip, including a 4 × 4 array platinum electrodes, and the platinum layer was fabricated during the post-CMOS micromachining (Figure 2.7). For cell sucking, the chip was mounted on a modified dual-in-line (DIL) package that was able to apply pressure from the chip backside. The DIL package had a 1-mm hole in the body, which was fabricated by laser cutting. And a silicon tube connected to the pressure setup was inserted into the hole and sealed by using particular glue. Cell placement was achieved via applying slight underpressure from the backside setup. Thus, it could pull the cells toward the electrode center. Surface chemistry was also inevitable before underpressure was applied. Neonatal rat cadiomyocytes were used in measurements of the electrical activities. The cadiomyocytes were purified by using gradient centrifugation and seeded on chips. Plating medium-featured higher serum content enabled better adherence to the surface of chip [40]. During placement, the underpressure was 20 kPa lower than ambient pressure until all electrodes or holes had been occupied with cells. Cell clusters were assumed to be an incomplete cell separation during the trypsinization. Most of the orifices (90%) were occupied with single cells, 4% of the orifices were occupied with two or more cells, and 6% were not occupied. Unoccupied orifices were most probably a consequence of clogging. This technology is advantageous in a wide variety of applications ranging from low- to high-density cultures and from cardiomyocytes to neurons. The cells were sucked for just a few seconds during the placement, and no other pressure was applied afterward. In consequence, it was not obviously harmful in cells, and it dramatically enhanced the coverage rate on a single electrode. Different applications are allowed for signal recording of individual cells or clusters and even signal transmission in networks.

Figure 2.7 Microholes on the surface of electrode and myocytes culture. (a) Perforated platinum electrodes. (b) Immobilized neonatal rat cardiomyocytes on the orifices. (From: [11]. Reproduced from Journal of Micromechanics and Microengneering. © 2007, with permission of IOP Publishing Limited.)

2.4

Typical Methods

2.4.5

29

Self-Assembled Monolayer

Self-assembled monolayer (SAM) technology has emerged as a promising approach to define the length scale of material science as small as those conveniently accessible by lithography [41, 42]. SAM of thiols on gold has been intensively researched in recent years because of its promising future in microelectronics and biotechnology. It can be a model system for more complex membranes [43, 44]. There are many approaches in SAM, including headgroup-substrate interaction, endgroupsubstrate interaction, chain-chain interaction, and endgroup-endgroup interaction [17, 45]. All of these approaches are attempted to realize the contact attraction and repulsion that guide cells to locate onto the target region. The surface was modified with a novel SAM, which could efficiently guide the cell adherence on gold region [46]. To ensure the surface is totally clean, the chip was first immersed in acetone for 10 minutes in an ultrasonic bath, and then 5 minutes in an ethanol ultrasonic bath. The gold regions were consequently immersed in a 20-mM mixture of alkane thiols of 11-Mercaptopropionic acid (11-MUA) and 3-Mercaptopropionic (3-MPA, 1:10 V/V) for at least 16 hours to create SAM. And the silicon oxide background was passivated with methoxy-polyethyleneglycolsilane (methoxy-PEG-silane) solution prepared in nitrogen-filled reaction flasks by adding 3 mM PEG-silane in anhydrous toluene containing 1% triethylamine. The reaction proceeded under nitrogen at 60°C for 18 hours. Nonbounded groups were removed by ultrasonic washing in toluene and ethanol for 5 minutes each, followed by rinsing in deionized water and drying under nitrogen. Then it was exposed to a mixture of 30-mM NHS (N-hydroxy succinimide) and 150-mM EDAC (1-ethyl3-(3-dimethylamino-propyl) carbodiimide esters for 30 minutes, which could activate the terminated carboxyl in alkane thiols. Then the chip was sterilized in 75% ethanol for 15 minutes, and soon treated with adherence peptides (KRGD) in a phosphate buffer solution (PBS) with a concentration of 0.1 mg/ml. After each step, the chip was rinsed with its original solvent and deionized water for 5 minutes, respectively, to remove other unbound molecules. A density of 2 × 105 cells/mL NIH3T3 cells were plated onto the peptidepatterned substrate, and cells were allowed to adhere to the substrates for 24 hours under the standard culture condition. Also, three different sizes of gold electrodes were designed for a single cell impedance measurement. Cells attached to covalently bound electrodes increased the measurable electrical signal strength by 48.4%, 24.2%, and 19% for three sizes of electrodes, respectively. Compared with cells attached to physically adsorbed electrodes, the results demonstrated that both electrode size and surface chemistry played key roles in cell adhering, spreading, and the impedance characteristics of cell-based sensors. A report on a simple technique to precisely position cortical neurons in a serumfree medium on 2D electrode arrays and pad size effects on neuron cell culture and immobilization was also well investigated [16]. Gold patterns were produced on glass substrates using microfabrication processes. Also, 1-amino-1-undecanethiol SAM was coated only on the gold surface. Cortical neurons were cultured on the arrays for measurement. And the undecanethiol thin film was essential for generating cell adhesive areas for the rat cortical neurons. A 50 μm × 50 μm SAM pad size was found to be suitable for single cortical neuron immobilization, while larger

30

Cell Culture on Chips

pads provided excellent neuron coverage (Figure 2.8). The technology might enable neurons precisely localized during stimulation and surveillance for both biological research and medical applications. Monolayers with different terminated groups were found to have different effects on cell adherence [43]. The interaction of human fibroblasts with CH3, PEG, and OH terminated SAMs was similarly weak when cell spreading, and fibronectin matrix formation and growth were observed on COOH and NH2. Compared with other methods to immobilize cells onto the target region, SAM is a simple and effective technology, which does not need complicated microfabrication, any expert experiences, or precise alignment equipments. Meanwhile, it is a novel method available for cell manipulation in micrometer scale, although the signal decreases to some extent due to the multimonolayer. 2.4.6

Microfluidic Technology

Micro total analysis systems (μTAS), also called lab-on-chip, integrate analytical processes for sequential operations such as sampling, sample pretreatment, analytical separation, chemical reaction, analyte detection, and data analysis in a single microfluidic device. Microfluidic-based research has made significant progress over the past few years and has recently become a hot topic. Microfluidic chip-based systems for biological cell studies have attracted significant attention because of their advantages, including low reagent and power consumption, short reaction time, portability for in situ use, low cost, versatility in design, and potentials for parallel operation and for integration with other miniaturized devices. Microfluidic technique has played an increasingly important role in cell biology discoveries, neurobiology, pharmacology, and tissue engineering. A comprehensive review of microfluidics for cellomics was presented, which covered the microfluidic devices for cell sampling, trapping, sorting, cell treatment,

Figure 2.8 SAM deposit process on substrate and neurons on gold regions. (a) SAM deposit process. (b) Fluorescence images of (1) single rat embryo cortical neuron cultured on 50-μm × 50-μm SAM coated gold pads, and (2) cell-to-cell connection on the 50-μm × 50-μm SAM pads with 50μm gap. (From: [16]. Reproduced from Biosensors and Bioelectronics. © 2006, with permission from Elsevier B.V.)

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31

and cell analysis [47]. Sequentially Erickson focused on integrated microfluidic devices for cell handling and cytometry, dielectrophoretic cellular manipulation and sorting, and general cellular analysis [48]. Manz et al. presented a series of comprehensive reviews concentrated on μTAS that covered development history and theory of miniaturization, fabrication of microfluidic system, and microfluidic standard operations, including sample preparation, injection, manipulation, reaction, separation, and detection [49–51]. Instead of a solid stamp, the microfluidic patterning uses a stamp with a network of microchannels to deliver mediating proteins or cells to the substrate [14, 52, 53]. The technique reduces the potential damage to the mediating proteins by the solid stamp and can pattern cells on substrates of polymers or metals. Compared with solid stamps, this technology allows more complex pattern design, which could realize multitype protein or cell deliveries onto the inconsistent surface. A 3D microfluidic system was fabricated and used to deliver proteins and mammalian cells on a planar substrate in Figure 2.9. The 3D topology of the microfluidic network in the stamp made it a versatile one with which to pattern multiple

Figure 2.9 Fluorescence and phase-contrast pictures of two cell types deposited on a tissue culture dish in a concentric square pattern by using the 3D stamp. (a) Two kinds of cell suspensions of ECVs and BCEs were introduced into the three sets of channels of stamps and were allowed to land and attach to the surface of culture dish. These cells were cultured with the stamp in place for 24 hours to grow and spread into a confluent layer. The pictures were taken immediately after the PDMS stamp was removed. (b) Fluorescence image of cells. (c, d) Phase-contrast pictures of cells. (From: [13]. Reproduced from Proceedings of the National Academy of Sciences. © 2000, with permission from the National Academy of Sciences, U.S.A.)

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Cell Culture on Chips

types of proteins and cells in a complex, discontinuous structures surface. Channels formed by the stamp when it was in contact with the substrate surface. It limited the migration and growth of cells in the channels with the channel structure in contact with the surface. And the cells could land and form a confluent layer [13]. The membrane had two levels of structures. One level provided a plane of channels, which opened for contact with the substrate that was to be patterned. The other level provided the vertical channels that connected these channels in the membrane to those in the slab. The master in the bottom layer required two steps of photolithography, while the master for the top layer was fabricated with a single step. To fabricate the PDMS membrane, a drop of PDMS prepolymer between the master and a Teflon sheet was sandwiched. To ensure the vertical channels were not blocked by a thin underlayer of PDMS, pressures in the range of 10 kPa to 50 kPa were required. Once the PDMS had cured, it was peeled off and attached to the wafer by van der Waals interactions. Another fabrication of microfluidic membrane was designed with an array of 50 μm × 50 μm vertical wells buried horizontally, interconnecting microfluidic channels [54]. The wells were spaced by 150 μm, and horizontal channels were formed with widths of 40, 20, and 10 μm. Then the PDMS membrane was aligned onto a commercially available microelectrode array (Panasonic MED64). Patterns were etched so that lines of varying widths could connect eight electrodes in a line, as shown in Figure 2.10. Patterning of neurons was achieved. Alignment of the microfluidic structures to MED64 probes was also achieved via an XYZ stage. Microfluidic technology could directly realize cells selection, transport, and position on biosensors. This successful hybrid system helps to reduce the cell damage during the transport process, which could ensure a better cell activity than the solid stamp mediated process mentioned earlier. Nevertheless, a precise microfluidic platform fabrication is quite complicated and not flexible and alternative enough.

Figure 2.10 Patterning on the electrode chip. (a) Perfluoropolymer patterned in the dark field on the chip. (b) A bright field micrograph of a fixed cell culture patterned on top of the electrodes. (From: [54]. Reproduced from Sensors and Actuators B: Chemical. © 2002, with permission from Elsevier Science B.V.)

2.5

2.5

Summary

33

Summary In this chapter, basic factors including physical, chemical, and biological factors that affect cell immobilization are introduced. Cells prefer to locate onto the substrate with appropriate roughness, hydrophilicity, and electric charge density. A series of basic surface modification ways on biosensors are also presented. Some effective typical methods are already well developed, like direct microscale structures, indirect solid or soft stamp mediated patterns, some metallic electrode fabrication, and SAM. Some novel pathways are being developed based on electrophoretic and electromagnetic technologies, which guide cells using electric or magnetic field force. Different methods are utilized to help to obtain an affinitive coupling between cell membrane and substrate. However, there are still some problems, and a lot of efforts are needed for more effective, alternative, and feasible technology development.

References [1] [2]

[3]

[4] [5] [6] [7]

[8] [9]

[10]

[11] [12]

Stenger, D. A., et al., “Surface Determinants of Neuronal Survival and Growth on Self Assembled Monolayers in Culture,” Brain. Res., Vol. 630, 1993, pp. 136–147. Clemece, J. F., et al., “Photoimmobilization of a Bioactive Laminin Fragment and Patter-Guided Selective Neuronal Cell Attachment,” Bioconjugate. Chem., No. 6, 1995, pp. 411–417. Huber, M., et al., “Modification of Glassy Carbon Surfaces with Synthetic Laminin-Derived Peptides for Nerve Cell Attachment and Neurite Growth,” J. Biomed. Mater. Res., Vol. 41, 1998, pp. 278–288. Fan, Y. W., et al., “Improvement of Neural Cell Adhesion to Silicon Surface by Hydroxyl Ion Implantation,” Surf. Coat. Tech., Vol. 131, 2000, pp. 355–359. Fan, Y. W., et al., “Culture of Neural Cells on Silicon Wafers with Nano-Scale Surface Topograph,” J. Neurosci. Meth., Vol. 120, No. 1, 2002, pp. 17–23. Turner, S., et al., “Cell Attachment on Silicon Nanostructures,” J. Vac. Sci. Technol. B., Vol. 15, No. 6, 1997, pp. 2848–2854. Matsuzawa, M., P. Liesi, and W. Knoll, “Chemically Modifying Glass Surfaces to Study Substratum-Guided Neurite Outgrowth in Culture,” J. Neurosci. Meth., Vol. 69, No. 2, 1996, pp. 189–196. Clark, P., S. Britland, and P. Connolly, “Growth Cone Guidance and Neuron Morphology on Micropatterned Laminin Surfaces,” J. Cell. Sci., Vol. 105, No. 1, 1993, pp. 203–212. Antonik, M. D., N. P. D’Costa, and J. H. Hoh, “A Biosensor Based on Micromechanical Interrogation of Living Cells,” IEEE Eng. Med. Biol. Mag., Vol. 16, No. 2, 1997, pp. 66–72. Migrew, M. J., P. A. Hammond, and D. R. S. Cumming, “The Development of Scalable Sensor Arrays Using Standard CMOS Technology,” Sensor. Actuat. B, Chem., Vol. 103, No. 1–2, 2004, pp. 37–42. Greve, F., et al., “A Perforated CMOS Microchip for Immobilization and Activity Monitoring of Electrogenic Cells,” J. Micromech. Microeng., Vol. 17, 2007, pp. 462–471. Jun, S. B., M. R. Hynd, and D. M. Natalie, “Low-Density Neuronal Networks Cultured Using Patterned Poly-L-Lysine on Microelectrode Arrays,” J. Neurosci. Meth., Vol. 160, No. 2, 2007, pp. 317–326.

34

Cell Culture on Chips [13]

[14]

[15] [16] [17] [18]

[19]

[20] [21]

[22]

[23] [24] [25] [26] [27]

[28]

[29] [30] [31]

[32]

Chiu, D. T., et al., “Patterned Deposition of Cells and Proteins onto Surfaces by Using Three-Dimensional Microfluidic Systems,” Proc. Natl. Acad. Sci. USA, Vol. 97, No. 6, 2000, pp. 2408–2413. Folch, A., et al., “Molding of Deep Polydimethylsiloxane Microstructures for Microfluidics and Biological Applications,” J. Biomech. Eng-T. ASME., Vol. 121, No. 1, 1999, pp. 28–34. Sanjana, N. E., and S. B. Fuller, “A Fast Flexible Ink-Jet Printing Method for Patterning Dissociated Neurons in Culture,” J. Neurosci. Meth., Vol. 136, No. 2, 2004, pp. 151–163. Palyvoda, C. O., and G. W. Chen, “Culturing Neuron Cells on Electrode with Self-Assembly Monolayer,” Biosens. Bioelectron., Vol. 22, 2007, pp. 2346–2350. Nicolau, D. V., et al., “Patterning Neuronal and Glia Cells on Light-Assisted Functionalised Photoresists,” Biosens. Bioelectron., Vol. 14, 1999, pp. 317–325. Veiseh, M., B. T. Wicks, and D. G. Castner, “Guided Cell Patterning on Gold-Silicon Dioxide Substrates by Surface Molecular Engineering,” Biomaterials, Vol. 25, 2004, pp. 3315–3324. Chang J. C., G. J. Brewer, and B. C. Wheeler, “Microelectrode Array Recordings of Patterned Hippocampal Neurons for Four Weeks,” J. Biomed. Microdev., Vol. 2, No. 4, 2000, pp. 245–300. Jenker, M., B. Müller, and P. Fromherz, “Interfacing a Silicon Chip to Pairs of Snail Neuron Connected by Electrical Synapses,” Biol. Cybern., Vol. 84, 2001, pp. 239–249. Peterson, S. L., et al., “Poly (Dimethylsiloxane) Thin Films as Biocompatible Coatings for Microfluidic Devices: Cell Culture and Flow Studies with Glial Cells,” J. Biomed. Mater. Res-A., Vol. 72, No. 1, 2005, pp. 10–11. Zeck, G., and P. Fromherz, “Noninvasive Neuroelectronic Interfacing with Synaptically Connected Snail Neurons Immobilized on a Semiconductor Chip,” Proc. Natl. Acad. Sci. USA, Vol. 98, 2001, pp. 10457–10462. Maher, M. P., et al., “The Neurochip: A New Electrode Device for Stimulating and Recording from Cultured Neurons,” J. Neurosci. Meth., Vol. 87, 1999, pp. 45–56. St. John, P. M., et al., “Preferential Glial Cell Attachment to Microcontact Printed Surfaces,” J. Neurosci. Meth., Vol. 75, No. 2, 1997, pp. 171–177. Weibel, D. B., et al., “Pumping Fluids in Microfluidic Systems Using the Elastic Deformation of Poly (Dimethylsiloxane),” Lab. Chip, Vol. 7, No. 12, 2007, pp. 1832–1836. Wu, Z. Z., Y. Zhao, and W. S. Kisaalita, “Interfacing SH-SY5Y Human Neuroblastoma Cells with SU-8 Microstructures,” Colloid. Surface B., Vol. 52, No. 1, 2006, pp. 14–21. Wang, L., Z. Z. Wu, and W. S. Kisaalita, “High-Aspect-Ratio SU-8 Microstructures for Quasi-Three-Dimensional Neural Network Ex Vivo with Application in Cell-Based Biosensing,” 10th World Congress on Biosensors, Shanghai, China, 2008. Love, J. C., et al., “Microscope Projection Photolithography for Rapid Prototyping of Masters with Micron-Scale Features for Use in Soft Lithography,” Langmuir, Vol. 17, 2001, pp. 6005–6012. Heller, D. A., et al., “Patterned Networks of Mouse Hippocampal Neurons on PeptideCoated Gold Surface,” Biomaterials, Vol. 26, 2005, pp. 883–889. Scholl, M., et al., “Ordered Networks of Rat Hippocampal Neurons Attached to Silicon Oxide Surfaces,” J. Neurosci. Meth., Vol. 104, 2000, pp. 65–75. Yeung, C., et al., “Modulation of the Growth and Guidance of Rat Brain Stem Neurons Using Patterned Extracellular Matrix Proteins,” Neurosci. Lett., Vol. 301, 2001, pp. 147–150. Branch, D. W., et al., “Long-Term Maintenance of Patterns of Hippocampal Pyramidal Cells on Substrates of Polyethylene Glycol and Microstamped Polylysine,” IEEE Trans. Biomed. Eng., Vol. 47, 2000, pp. 290–300.

2.5

Summary [33] [34] [35] [36]

[37] [38]

[39] [40] [41]

[42] [43]

[44]

[45]

[46] [47] [48] [49] [50] [51] [52] [53]

[54]

35

Wheeler, B. C., et al., “Microcontact Printing for Precise Control of Nerve Cell Growth in Culture,” J. Biomech. Eng., Vol. 121, 1999, pp. 73–78. Heinzl, J., and C. H. Herz, “Ink-Jet Printing,” Adv. Electron. El. Phys., Vol. 65, 1985, pp. 91–171. Greengard, P., et al., “Synaptic Vesicle Phosphoproteins and Regulation of Synaptic Function,” Science, Vol. 259, 1993, pp. 780–785. Renger, J. J., C. Egles, and G. Liu, “A Developmental Switch in Neurotransmitter Flux Enhances Synaptic Efficacy by Affecting AMPA Receptor Activation,” Neuron., Vol. 29, 2001, pp. 469–484. Thomas, C. A., Jr., et al., “A Miniature Microelectrode Array to Monitor the Bioelectric Activity of Cultured Cells,” Exp. Cell. Res., Vol. 74, No. 1, 1972, pp. 61–66. Gross, G. W., “Simultaneous Single Unit Recording In-Vitro with a Photoetched Laser Deinsulated Gold Multimicroelectrode Surface,” IEEE Trans. Biomed. Eng., Vol. 26, No. 5, 1979, pp. 273–279. Pine, J., “Recording Action-Potentials from Cultured Neurons with Extracellular MicroCircuit Electrodes,” J. Neurosci. Meth., Vol. 2, No. 1, 1980, pp. 19–31. Auerbach, D., et al., “Different Domains of the M-Band Protein Myomesin Are Involved in Myosin Binding and M-Band Targeting,” Mol. Biol. Cell., Vol. 10, 1999, pp. 1297–1308. Nam, Y., et al., “Gold-Coated Microelectrode Array with Thiol Linked Self-Assembled Monolayers for Engineering Neuronal Cultures,” IEEE Trans. Biomed. Eng., Vol. 51, No. 1, 2004, pp. 158–165. Kelm, J. M., et al., “Self-Assembly of Sensory Neurons into Ganglia-Like Microtissues,” J. Biotechnol., Vol. 121, 2006, pp. 86–101. Faucheux, N., et al., “Self-Assembled Monolayers with Different Terminating Groups as Model Substrates for Cell Adhesion Studies,” Biomaterials, Vol. 25, 2004, pp. 2721–2730. Schäferling, M., et al., “Time-Resolved Luminescence Imaging of Hydrogen Peroxide Using Sensor Membranes in a Microwell Format,” Appl. Spectrosc., Vol. 57, No. 11, 2003, pp. 1386–1392. Saneinejad, S., and M. S. Shoichet, “Patterned Glass Surfaces Direct Cell Adhesion and Process Outgrowth of Primary Neurons of the Central Nervous System,” J. Biomed. Mater. Res., Vol. 42, No. 1, 1998, pp. 13–19. Asphahani, F., et al., “Influence of Cell Adhesion and Spreading on Impedance Characteristics of Cell-Based Sensors,” Biosens. Bioelectron., Vol. 23, 2008, pp. 1307–1313. Andersson, H., and A. van den Berg, “Microfluidic Devices for Cellomics: A Review,” Sensor Actuators B, Chem., Vol. 92, 2003, pp. 315–325. Erickson, D., et al., “Electrokinetically Controlled DNA Hybridization Microfluidic Chip Enabling Rapid Target Analysis,” Anal. Chem., Vol. 76, No. 24, 2004, pp. 7269–7277. Reyes, D. R., et al., “Micro Total Analysis Systems. 1. Introduction, Theory, and Technology,” Anal. Chem., Vol. 74, No. 12, 2002, pp. 2623–2636. Auroux, P. A., et al., “Micro Total Analysis Systems. 2. Analytical Standard Operations and Applications,” Anal. Chem., Vol. 74, No. 12, 2002, pp. 2637–2652. Vilkner, T., D. Janasek, and A. Manz, “Micro Total Analysis Systems. Recent Developments,” Anal. Chem., Vol. 76, No. 12, 2004, pp. 3373–3385. Folch, A., and M. Toner, “Cellular Micropatterns on Biocompatible Materials,” Biotechnol. Progr., Vol. 14, No. 3, 1998, pp. 388–392. Takayama, S., et al., “Patterning Cells and Their Environments Using Multiple Laminar Fluid Flows in Capillary Networks,” Proc. Natl. Acad. Sci. USA, Vol. 96, No. 10, 1999, pp. 5545–5548. Laurent, G., et al., “Techniques for Patterning and Guidance of Primary Culture Neurons on Micro-Electrode Arrays,” Sensor. Actuat. B, Chem., Vol. 83, 2002, pp. 15–21.

CHAPTER 3

Mechanisms of Cell-Based Biosensors Hua Cai and Ping Wang

3.1

Introduction In a general sense, cell-based biosensors are useful because they harness highly evolved cellular pathways. These properties make such sensors highly attractive for detection of chemical and biological analyte, for detection of environmental toxins, and for drug screening. The development of cell-based biosensors represents the convergence of several technologies. This type of sensor is divided into two stages: primary transducer and secondary transducer. Living cells serve as the primary transducers, converting the detected molecular signals into signals measured via means such as an extracellular electrode or optical detector [1]. The basic principle of cell-based biosensors is shown as Figure 3.1. The living biological cells serve as the primary transducers to receive the stimulation signals, and the cells will produce different given responses, such as cell metabolism, action potential, and impedance change. The secondary transduction can detect these responses from cells, convert the detected cellular signals into electrical signals, and send to the electric system. All those components make up the whole cell-based biosensors. According to the principle of cell-based biosensors, we need to understand the following two basic mechanisms: • As primary transducers, how do cells receive the stimulation signals? Cell biology cannot be fully understood without knowledge and understanding of biochemistry, metabolism, molecular biology, and genetics. Here, we will focus on the basis of cellular functions as they relate to cellular metabolism, electrical activity in cells, cellular membrane impedance characteristics, and cell adhesion and motion. The specifics of these functions will be explored in detail where it is necessary for an understanding of the signals and responses. However, the sensitivities of cell-based biosensors to biological toxins and chemical agents depend on the type of cells used and the nature of the agents.

37

38

Mechanisms of Cell-Based Biosensors

Figure 3.1 The basic principle of the cell-based biosensor.

• How are cellular responses coupled to the secondary transducers when cells respond to the outside stimulation? As cells sense the stimulation signals, they will produce a great deal of relevant information, such as changes of extracellular acidification, action potential, and cellular membrane impedance. Corresponding to the parameters, there are a variety of different measurement techniques. Microphysiometer can be used to analyze the cell membrane bound receptors, and study nonreceptor mediated events on cell metabolism (e.g., the effect of viral infection and toxicological effects). The action potential system can be used to see changes in the action potential. Impedance techniques can be used to monitor such things as cellular adhesion, motility, and proliferation of both electrically active and nonelectrically active cell types. We will discuss the measurement principle and techniques of these parameters by these questions: • How are the parameters produced, and what do they represent? • What are the mechanisms and models of the biosensors to monitor these parameters? • What are the transducers used to monitor these parameters?

3.2

Metabolic Measurements 3.2.1

Cell Metabolism

A general feature of living, heterotrophic cells is the uptake of metabolites (carbon sources), the production of energy (ATP), and the excretion of acid waste products (e.g., lactic and carbonic acid). Carbon sources include sugars, amino acids, and fatty acids. In regular culture conditions, glucose and glutamine are present in

3.2

Metabolic Measurements

39

high concentrations and are taken up by cells and broken down into energy and waste products. Under aerobic conditions, glucose is converted via pyruvate and acetyl CoA into CO2 yielding energy (respiration). The corresponding pathways are glycolysis, citric acid (TCA) cycle, and oxidative phosphorylation. Under anaerobic conditions, glucose is converted via pyruvate into lactate and energy. The corresponding pathway is glycolysis, connected to the reduction of pyruvate to lactate by lactate dehydrogenase. By comparing the amount of protons produced per ATP molecule (Table 3.1), it is evident that glycolysis yields the greatest number of protons. Many cultured eucaryotic cells might predominantly use glycolysis as the main energy-yielding pathway. The extracellular acidification of cells sitting in the flow chamber can be measured with the cytosensor microphysiometer, and thus a functional response of cells to receptor stimulation can be monitored under noninvasive conditions and in real time (Figure 3.2). Upon stimulation of a membrane-bound receptor, which can be G protein-coupled, tyrosine kinase-coupled, or an ion channel, a signal transduction cascade is initiated. Many steps in this cascade are either directly or indirectly energy dependent—for example, phosphorylations by protein-kinases and production of second messengers (cAMP, IP3). In addition, the opening of channels provokes a subsequent consumption of energy. To maintain ionic homeostasis, pumps must actively transport ions across the plasma membrane at the expense of ATP. Under steady state conditions, one cell produces ≈108 protons per second. After receptor stimulation, this will be raised between 10% and 100%, depending on the cell type, the receptor, and the coupling pathway. In order to yield this increase, additional 107−108 ATP molecules per cell must be produced (and consumed) per second. Calculations suggest that the production of cAMP as a second messenger, as well as protein phosphorylation by kinases, only accounts for a small proportion of the increased acidification. Ion pumps, which pump at the expense of ATP to maintain homeostasis, are mainly responsible for this effect. Some receptors directly activate the Na+/H+ exchanger, an antiport that directs Na+ into the cell and H+ out of the cell. The result is an acidification of the extracellular medium, which

Table 3.1

Summary of Principal Energy-Yielding Pathways ATP Yield

H+ per ATP

2

1.000

Carbon Source

Pathway

Reactiona

Glucose

Glycolysis

Glucose→2 lactate−+2H+

Respiration

Glucose+6O2→6HCO3 +6H

Glutamine

Respiration

Glutamine+9/2 O2+3H2O→5 HCO3 +2NH4 +3H

Pyruvate

Respiration

Pyruvate−+5/2 O2+H2O→3 HCO3−+2H+ C2nH2n−1O2−+(6n−2)O2→2nCO3−+(2n+1)H+

17n-6 0.129(n=9)

Glucose

β-Osidaton, Fatty acid a

respiration

−

+

b

−

+

+

36

0.167

27

0.111

15

0.133

Assumes that all ATP produced is hydrolyzed and all CO2 produces hydrates and dissociates into H+ and HCO3-. At pH 7.4, 95%

of the CO2 does so (pK=6.1). b

Assumes that each cytosolic NADH produced in the glycolytic part of the pathway yields 2 ATP. If a more efficient shuttle system

is used to pass the NADH to the mitochondrion, the yield rises to 3 ATP and the overall yield of ATP for the reaction becomes 38 [2].

40

Mechanisms of Cell-Based Biosensors

can directly be detected with the cytosensor system. This is associated with an increase of intracellular Na+. Other receptors (e.g., nAChR) open cation channels so that Na+ and K+ can follow their concentration gradients. The Na+/K+-ATPase has to pump the cations at the expense of ATP to reverse this effect. In fact, mammalian cells use more than 20% of the total energy to feed the Na+/K+-ATPases of the plasma membrane. Other receptors, which couple via a G protein to PLC, lead to the release of intracellular Ca2+, which then has to be pumped back into intracellular stores with the help of Ca2+-ATPases. Lipid-soluble and some small noncharged molecules, such as alcohol, can pass through the plasma membrane passively, whereas ions (including protons) have to use specific channels or transporters. CO2 and lactic acid, the waste products from aerobic and anaerobic glucose degradation, either are hydrolyzed inside the cell to HCO3-/H+ and lactate/H+, respectively, or can pass the plasma membrane in an unhydrolyzed state and are hydrolyzed outside the cell. Inside the cell, CO2 is hydrolyzed with the help of the enzyme carboanhydrase into H+/HCO3-. The latter can actively be transported outside the cell via an antiport (sodium in, bicarbonate out). Lactate is excreted with facilitated transport by monocarboxylate carriers and anion exchange proteins. With the hydrolysis, either intra- or extracellular protons are generated. Intracellular potons have to be transported outside. Although specific proton channels do exist, the most important transporter for protons is the Na+/H+ exchanger, where one H+ directed outside the cell leads to the influx of one Na+ (antiport). The Na+ influx is counteracted by the Na+/K+ pump at the expense of ATP (active transport) [2]. 3.2.2 Extracellular pH Monitoring

Extracellular pH monitoring is based on the detection of H+ concentration near the cell, and the principal is shown in Figure 3.2. So the chip as extracellular pH monitoring device is composed of the H+ sensitive membrane. Due to distinct membranes, the mechanisms are different, and we will introduce the SiO2 as an example. The H+-sensitivity of the transducer can be explained using the site-binding theory. 3.2.2.1

Site-Binding Theory

This model was first introduced in 1974 by Yates et al. to describe the properties of an oxide aqueous electrolyte interface [3] and was generalized in 1986 by Fung et al. to characterize ISFETs with oxide gate insulators [4]. It is founded on the thermodynamical fundamentals of the equilibrium reactions of the surface groups and the Gouy-Chapman-Stern theory of the ion distribution at an electrolyte-solid interface. In the site-binding model, the oxide surface (i.e., pH-sensitive surface) contains the site in three forms: A-O−, A-OH, and A-OH2+, as shown in Figure 3.3. The acidic and basic characters of the neutral site A-OH are characterized by the equilibrium constants Ka and Kb, respectively, and can be written as the following equations:

3.2

Metabolic Measurements

41

Figure 3.2 Upon receptor stimulation, signal transduction pathways are induced. The corresponding ATP consumption is compensated by the increased uptake and metabolism of glucose, which results in an increase in the excretion of acid waste products. The extracellular acidification is measured by the silicon sensor.

Figure 3.3 Schematic representation of the site-binding model.

Ka A − OH ←⎯⎯ → A − O + H s+

(3.1)

Kb A − OH + H s+ ←⎯⎯ → A − OH 2+

(3.2)

_

where Ka =

⎡⎣ A − O− ⎤⎦ ⎡⎣ H + ⎤⎦ s [A − OH ]

(3.3)

42

Mechanisms of Cell-Based Biosensors

Kb =

⎡⎣ A − H 2+ ⎤⎦ s [A − OH ]⎡⎣H + ⎤⎦

(3.4) S

According to (3.1)–(3.4), A-OH2+, A-OH, and A-O- represent positive, neutral, and negative surface sites, respectively, and [A-OH2+], [A-OH], and [A-O-] are the numbers of these sites per surface area. Note that the HS+ activity results in [H+]S, the surface concentration. The activity coefficient is effectively included in the dissociation constants. The relationship between [H+]S and the bulk concentration of H+ ions ([H+]b) is given by the Boltzmann equation: ⎛ − q ψ0 ⎞ ⎡⎣ H + ⎤⎦ = ⎡⎣ H + ⎤⎦ exp ⎜ S b ⎝ KT ⎟⎠

(3.5)

where q is the elementary charge; K is the Boltzmann’s constant; T is the temperature; ψ0 is the pH-dependent surface potential. In fact this surface potential is generated by the net surface charge, σ0.

(

σ 0 = q ⎡⎣ A − OH 2+ ⎤⎦ − ⎡⎣ A − O− ⎤⎦

)

(3.6)

Knowing that the total number of sites per unit area is N S = [A − OH ]+ ⎡⎣ A − OH 2+ ⎤⎦ + ⎡⎣ A − O− ⎤⎦

(3.7)

The relationship between [H+]b, ψ0, and σ0 can now be derived from (3.3)–(3.7) in terms of Ka, Kb, and Ns, which are the characterized parameters of a specific oxide. ⎛K ⎞ ln ⎡⎣ H + ⎤⎦ − ln ⎜ a ⎟ b ⎝ Kb ⎠

12

=

q ψ0 σ ⎛ 1 ⎞ + sinh −1 0 ⎜ KT qN S ⎝ 4Ka Kb ⎠⎟

12

(3.8)

For this calculation, it is assumed that 2(KaKb)1/2