Multidimensional Liquid Chromatography Theory and Applications in Industrial Chemistry and the Li

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MULTIDIMENSIONAL LIQUID CHROMATOGRAPHY Theory and Applications in Industrial Chemistry and the Life Sciences Edited by

STEVEN A. COHEN Life Sciences R&D, Waters Corporation Milford, MA 01757, USA

MARK R. SCHURE Theoretical Separation Science Laboratory Rohm and Haas Company Springhouse, PA 19477-0904, USA

A JOHN WILEY & SONS, INC., PUBLICATION

MULTIDIMENSIONAL LIQUID CHROMATOGRAPHY

MULTIDIMENSIONAL LIQUID CHROMATOGRAPHY Theory and Applications in Industrial Chemistry and the Life Sciences Edited by

STEVEN A. COHEN Life Sciences R&D, Waters Corporation Milford, MA 01757, USA

MARK R. SCHURE Theoretical Separation Science Laboratory Rohm and Haas Company Springhouse, PA 19477-0904, USA

A JOHN WILEY & SONS, INC., PUBLICATION

Copyright Ó 2008 by John Wiley & Sons, Inc. All rights reserved. Published by John Wiley & Sons, Inc., Hoboken, New Jersey Published simultaneously in Canada No part of this publication may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, electronic, mechanical, photocopying, recording, scanning, or otherwise, except as permitted under Section 107 or 108 of the 1976 United States Copyright Act, without either the prior written permission of the Publisher, or authorization through payment of the appropriate per-copy fee to the Copyright Clearance Center, Inc., 222 Rosewood Drive, Danvers, MA 01923, 978-750-8400, fax 978-750-4470, or on the web at www.copyright.com. Requests to the Publisher for permission should be addressed to the Permissions Department, John Wiley & Sons, Inc., 111 River Street, Hoboken, NJ 07030, 201-748-6011, fax 201-748-6008, or online at http://www.wiley.com/go/permission. Limit of Liability/Disclaimer of Warranty: While the publisher and author have used their best efforts in preparing this book, they make no representations or warranties with respect to the accuracy or completeness of the contents of this book and specifically disclaim any implied warranties of merchantability or fitness for a particular purpose. No warranty may be created or extended by sales representatives or written sales materials. The advice and strategies contained herein may not be suitable for your situation. You should consult with a professional where appropriate. Neither the publisher nor author shall be liable for any loss of profit or any other commercial damages, including but not limited to special, incidental, consequential, or other damages. For general information on our other products and services or for technical support, please contact our Customer Care Department within the United States at 800-762-2974, outside the United States at 317-572-3993 or fax 317-572-4002. Wiley also publishes its books in a variety of electronic formats. Some content that appears in print may not be available in electronic formats. For more information about Wiley products, visit our web site at www. wiley.com. Library of Congress Cataloging-in-Publication Data: Multidimensional liquid chromatography: theory and applications in industrial chemistry and the life sciences / edited By Steven A. Cohen, Mark R. Schure. p. cm. Includes index. ISBN 978-0-471-73847-3 (cloth) 1. Liquid chromatography. 2. Chemical engineering. 3. Chemistry, Technical. 4. Biochemistry. I. Cohen, Steven A., 1953- II. Schure, Mark R.; 1952QD79.C454M85 2007 543’.84–dc22 2007041576

Printed in the United States of America 10 9 8 7 6 5 4 3 2 1

CONTENTS

Foreword Preface

xiii xv

Contributors

xvii

1 Introduction

1

1.1 Previous Literature Which Covers MDLC 1.2 How this Book is Organized References

PART I

THEORY

2 Elements of the Theory of Multidimensional Liquid Chromatography 2.1 2.2 2.3 2.4 2.5 2.6 2.7 2.8 2.9

Introduction Peak Capacity Resolution Orthogonality Two-Dimensional Theory of Peak Overlap Dimensionality, Peak Ordering, and Clustering Theory of Zone Sampling Dilution and Limit of Detection Chemometric Analysis

4 5 6

9

11 11 13 17 19 21 23 24 26 27 v

vi

CONTENTS

2.10 Future Directions References 3 Peak Capacity in Two-Dimensional Liquid Chromatography 3.1 Introduction 3.2 Theory 3.3 Procedures 3.4 Results and Discussion 3.5 Conclusions Appendix 3A Generation of Random Correlated Coordinates Appendix 3B Derivation of Limiting Correlation Coefficient r References 4 Decoding Complex 2D Separations 4.1 4.2

Introduction Fundamentals: The Statistical Description of Complex Multicomponent Separations 4.3 Decoding 1D and 2D Multicomponent Separations by Using the SMO Poisson Statistics 4.4 Decoding Multicomponent Separations by the Autocovariance Function 4.5 Application to 2D Separations 4.5.1 Results from SMO Method 4.5.2 Results from 2D Autocovariance Function Method 4.6 Concluding Remarks Acknowledgments References

PART II COLUMNS, INSTRUMENTATION AND METHODS DEVELOPMENT 5 Instrumentation for Comprehensive Multidimensional Liquid Chromatography 5.1 5.2 5.3 5.4

Introduction Heart-Cutting Versus Comprehensive Mode Chromatographic Hardware 5.3.1 Valves CE Interfaces 5.4.1 Gated Interface for HPLC–CE 5.4.2 Microfluidic Valves for On-Chip Multidimensional Analysis

28 30 35 35 37 41 42 49 50 54 56 59 59 62 68 74 78 81 84 88 88 88

91

93 93 95 97 97 104 104 105

CONTENTS

5.5

Columns and Combinations 5.5.1 Column Systems, Dilution, and Splitting 5.6 Detection 5.7 Computer Hardware and Software 5.7.1 Software Development 5.7.2 Valve Sequencing 5.7.3 Data Format and Storage 5.8 Zone Visualization 5.8.1 Contour Visualization 5.8.2 2D Peak Presentation 5.8.3 Zone Visualization in Specific Chemical (pI) Regions 5.8.4 External Plotting Programs 5.8.5 Difference Plots 5.8.6 Multi-channel Data 5.9 Data Analysis and Signal Processing 5.10 Future Prospects References 6 Method Development in Comprehensive Multidimensional Liquid Chromatography 6.1 6.2 6.3 6.4

Introduction Previous Work Column Variables Method Development 6.4.1 The Cardinal Rules of 2DLC Method Development 6.5 Planning the Experiment 6.6 General Comments on Optimizing the 2DLC Experiment: Speed–Resolution Trade-off Acknowledgment References 7 Monolithic Columns and Their 2D-HPLC Applications 7.1 7.2

7.3

Introduction Monolithic Polymer Columns 7.2.1 Structural Properties of Polymer Monoliths 7.2.2 Chromatographic Properties of Polymer Monolithic Columns 7.2.3 Two-Dimensional HPLC Using Polymer Monoliths Monolithic Silica Columns 7.3.1 Preparation 7.3.2 Structural Properties of Monolithic Silica Columns 7.3.3 Chromatographic Properties of Monolithic Silica Columns

vii

106 108 109 109 110 111 113 115 115 117 117 117 118 118 119 120 121

127 127 128 130 130 132 143 143 144 144 147 147 148 148 150 152 153 154 154 156

viii

CONTENTS

7.4

Peak Capacity Increase by Using Monolithic Silica Columns in Gradient Elution 7.5 2D HPLC Using Monolithic Silica Columns 7.5.1 RP-RP 2D HPLC Using Two Different Columns 7.5.2 RP–RP 2D HPLC Using Two Similar Columns 7.5.3 Ion Exchange–Reversed-Phase 2D HPLC Using a Monolithic Column for the 2nd-D 7.5.4 IEX-RP 2D HPLC Using a Monolithic RP Capillary Column for the 2nd-D 7.6 Summary and Future Improvement of 2D HPLC References 8 Ultrahigh Pressure Multidimensional Liquid Chromatography 8.1

Background: MDLC in the Jorgenson Lab 8.1.1 Cation Exchange–Size Exclusion 8.1.2 Anion Exchange–Reversed Phase 8.1.3 Cation Exchange–Reversed Phase 8.1.4 Size Exclusion–Reversed Phase 8.2 Online Versus Off-Line MDLC 8.3 MDLC Using Ultrahigh Pressure Liquid Chromatography: Benefits and Challenges 8.3.1 An Introduction to UHPLC 8.3.2 UHPLC for LC LC: High Speed Versus High Peak Capacity 8.3.3 LC UHPLC for Separations of Intact Proteins 8.4 Experimental Details 8.4.1 Instrumentation 8.4.2 Data Analysis 8.4.3 Chromatographic Conditions 8.4.4 Samples 8.5 Results and Discussion 8.6 Future Directions for UHP-MDLC References

PART III

LIFE SCIENCE APPLICATIONS

9 Peptidomics 9.1 State of the Art—Why Peptidomics? 9.2 Strategies and Solutions 9.3 Summary and Conclusions References

158 159 161 164 166 168 171 171 177 177 178 180 181 183 188 189 190 191 191 193 193 194 195 196 196 202 203

205 207 207 208 218 218

CONTENTS

10 A Two-Dimensional Liquid Mass Mapping Technique for Biomarker Discovery 10.1 10.2

Introduction Methods for Separating and Identifying Proteins 10.2.1 pI-Based Methods of Separation 10.2.2 Chromatofocusing-A Column Based pH Separation 10.2.3 Nonporous Separation of Proteins 10.2.4 Electrospray-Time of Flight-Mass Spectrometry 10.2.5 MALDI Peptide Mass Fingerprinting 10.2.6 Data Analysis and Recombination 10.3 Applications 10.3.1 Proteomic Mapping and Clustering of Multiple Samples—Application to Ovarian Cancer Cell Lines 10.3.2 2D Liquid Mass Mapping of Tumor Cell Line Secreted Samples, Application to Metastasis-Associated Protein Profiles 10.3.3 Identification Annotation and Data Correlation in MCF10 Human Breast Cancer Cell Lines 10.4 Summary and Conclusions Acknowledgments References 11 Coupled Multidimensional Chromatography and Tandem Mass Spectrometry Systems for Complex Peptide Mixture Analysis 11.1 SCX-RP/MS/MS 11.2 SCX/RP/MS/MS 11.3 MudPIT 11.4 Alternative First Dimension Approaches 11.5 Conclusion References 12 Development of Orthogonal 2DLC Methods for Separation of Peptides 12.1 12.2 12.3

Introduction Previous Work Developing Orthogonal 2DLC Methods 12.3.1 LC Selectivity for Peptides: Experimental Design 12.3.2 Investigation of 2DLC Orthogonality for Separation of Peptides 12.3.3 Geometric Approach to Orthogonality in 2DLC 12.3.4 Practical 2DLC Considerations in Proteome Research 12.3.5 Evaluation of Selected 2DLC MS/MS Systems

ix

221 221 223 223 225 226 228 229 230 230

230

233 235 237 238 238

243 245 248 251 254 255 255

261 261 263 264 264 266 271 275 276

x

CONTENTS

12.3.6 Peak Capacity in 2DLC-MS/MS 12.3.7 Considerations of Concentration Dynamic Range 12.4 Conclusions Acknowledgment References 13 Multidimensional Separation of Proteins with Online Electrospray Time-of-Flight Mass Spectrometric Detection 13.1 13.2 13.3 13.4

Introduction Chromatographic Parameters Analyte Detection and Subsequent Analysis Building a Multidimensional Protein Separation 13.4.1 Selection of an Ion-Exchange–Reversed-Phase Separation System for Protein-Level Separations 13.4.2 Chromatographic Sorbent Considerations 13.4.3 Chromatographic Behavior of Proteins 13.5 Comprehensive Multidimensional Chromatographic Systems 13.6 Coupling 2DLC with Online ESI–MS Detection 13.6.1 Interactions between the Two Dimensions of Chromatography (Step Vs. Linear) 13.6.2 Recognizing Increased Selectivity in 2DLC Separations 13.7 Expanding Multidimensional Separations into a “Middle-Out” Approach to Proteomic Analysis 13.8 Future Directions in Protein MDLC 13.8.1 Protein Chromatography 13.8.2 MS Analysis of Proteins 13.8.3 Data Interpretation 13.9 Conclusion References 14 Analysis of Enantiomeric Compounds Using Multidimensional Liquid Chromatography 14.1 14.2

Online Achiral-Chiral LC-LC Applications 14.2.1 Analysis of Enantiomers in Plasma and Urine 14.3 Amino Acids 14.3.1 Physiological Fluids or Tissues 14.3.2 In Food, Beverages, and Other Products 14.4 Other Applications 14.4.1 Analysis of Enantiomers from Plant and Environmental Sources 14.5 Miscellaneous Applications 14.6 Conclusion References

280 282 284 284 284

291 291 293 293 294 295 295 296 296 299 304 306 308 311 312 313 314 314 315

319 320 323 323 328 328 333 334 334 336 338 339

CONTENTS

xi

PART IV MULTIDIMENSIONAL SEPARATION USING CAPILLARY ELECTROPHORESIS

345

15 Two-Dimensional Capillary Electrophoresis for the Comprehensive Analysis of Complex Protein Mixtures

347

15.1 15.2

Introduction Previous Work 15.2.1 Miniaturized IEF/SDS-PAGE 15.2.2 One-Dimensional Capillary Electrophoresis for Protein Analysis 15.3 Two-Dimensional Capillary Separations for Analysis of Peptides and Proteins 15.3.1 Capillary Liquid Chromatography Coupled with Capillary Electrophoresis for Analysis of Unlabeled Peptides and Proteins 15.3.2 Two-Dimensional Capillary Electrophoresis for Analysis of Proteins 15.3.3 High-Speed Two-Dimensional Capillary Electrophoresis 15.3.4 The Analysis of a Single Fixed Cell 15.4 Conclusions 15.5 Abbreviations References 16 Two-Dimensional HPLC–CE Methods for Protein/Peptide Separation 16.1 Introduction 16.2 Off-line Versus Online 16.3 HPLC Fractionation 16.4 2D HPLC–CE 16.5 CE–MS Detection 16.6 Applications 16.7 Concluding Remarks Acknowledgment References

PART V

INDUSTRIAL APPLICATIONS

17 Multidimensional Liquid Chromatography in Industrial Applications 17.1 17.2

Introduction Principles of Multidimensional Liquid Chromatography as Applied to Polymer Analysis

347 348 348 349 352

352 352 356 358 360 360 360

365 365 366 366 367 368 370 380 381 381

385

387 387 390

xii

CONTENTS

17.3 17.4

Experimental Analysis of Alkylene Oxide-Based Polymers 17.4.1 Amphiphilic Polyalkylene Oxides 17.5 Excipients 17.6 Polyether Polyols 17.7 Analysis of Condensation Polymers 17.8 Polyamides 17.9 Aromatic Polyesters 17.10 Aliphatic Polyesters References

393 395 395 399 403 406 407 414 417 420

18 The Analysis of Surfactants by Multidimensional Liquid Chromatography

425

18.1 18.2

Introduction Analytical Characterization Methods 18.2.1 CE and CGE 18.2.2 SEC 18.2.3 NPLC 18.2.4 RPLC 18.3 Detection Methods 18.4 2DLC 18.4.1 RPLC Coupled to SEC 18.4.2 NPLC Coupled to RPLC 18.5 Conclusions References

425 428 429 430 431 433 434 434 435 435 442 443

Index

447

FOREWORD

The principal rationale for multidimensional separations is that they offer a more effective as well as efficient way to generate high peak capacity and thus permit more complete resolution of complex mixtures. I suspect, however, that there is another motivation that attracts people to multidimensional separations: the resulting twodimensional chromatograms make fascinating pictures. Two-dimensional separation patterns are somehow more satisfying than a series of peaks in a one-dimensional chromatogram. The human mind is highly adept at dealing with complex information presented in the form of images and, despite the complexity, is able to quickly spot differences among such patterns. My own inspiration for pursuing multidimensional separations came from J. Calvin Giddings. I was invited to present a seminar at the University of Utah in May 1987, where I spoke on our current research project concerning liquid chromatography in open-tubular columns. That night over dinner Cal told me that he liked the work I presented and also our work on capillary electrophoresis, and suggested that I consider multidimensional chromatography as a more practical approach for the resolution of complex mixtures. On the trip back to Chapel Hill, I thought of nothing other than Cal’s recommendation and how I might set about to implement it. I was interested in analyzing samples in their entirety by two-dimensional separations, so I did not want to settle for the well-established “heart-cutting” approach, where only a single portion of the effluent from the first separation dimension is subjected to a second dimension of separation. Instead, I wanted to subject the entire sample to the full two dimensions of resolution. Also, I did not want to do two-dimensional separations in space, as in two-dimensional thin-layer chromatography, but to use coupled columns instead. This latter consideration was driven at least in part by the ready coupling to mass

xiii

xiv

FOREWORD

spectrometry that columns (but not slabs) provide. Upon arriving home, I made plans with my graduate student, Michelle Bushey, to initiate a project on protein separation by two-dimensional liquid chromatography. Michelle also went on to develop comprehensive two-dimensional liquid chromatography-capillary electrophoresis in my lab. We searched for a suitable term to differentiate our approach from the “heartcutting’’ style of two-dimensional separations and settled for “comprehensive’’ in order to emphasize that all of the sample components were subjected to the full two dimensions of separation. Multidimensional separations have proven to be quite successful, as evidenced by the wealth of examples of hardware and applications described in this book. It is hoped that increased awareness and use of multidimensional separations will open up possibilities for meaningful analyses of truly complex samples and permit the routine analysis of thousands of components from a single sample in a single run. James W. Jorgenson Chapel Hill, North Carolina, USA September 9, 2007

PREFACE

At least two driving forces have contributed to the recent increased use and development of multidimensional liquid chromatography (MDLC). These include the high resolution and peak capacity needed for proteomics studies and the independent size and chemical structure selectivity for resolving industrial polymers. In this regard, separation science focuses on a system approach to separation as individual columns can contribute only part of the separation task and must be incorporated into a larger separation system for a more in-depth analytical scheme. Separation techniques are increasingly used to resolve molecular structure at a finer and finer scale and in chemical environments that are fundamentally complex. This applies not only to small and medium-sized ( 10 and repetitive (comprehensive)

LC/LC (six-port valve) LC/CE Thermal FFF/GPC LC/LC (eight-port valve) Gel electrophoresis/LC CE/GE TLC/TLC IEF/PAGE

References Majors (1980) Augenstein and Stickler (1990) Pasch et al. (1992) Balke and Patel (1980) Chapter 11 Holland (1995) Lemmo and Jorgenson (1993) Venema et al. (1997) Erni and Frei (1978) Bushey and Jorgenson (1990) Rose and Opiteck (1994) Liu and Sweedler (1996) Rezanka (1996) Celis and Bravo (1984)

in the first dimension separation, whereas comprehensive 2DLC samples the entire first dimension into the second dimension. The difference in sampling also results in different data presentations. Heart-cut 2DLC is presented as stacked plots of each injection into the second dimension, and comprehensive 2DLC can be represented as a contour or projection. Table 5.1 lists several heart-cut and comprehensive techniques. Heart-cut 2DLC is very common and has great application for the increased resolution of one or several components from the first dimension (Augenstein and Stickler, 1990; Majors, 1980; Pasch et al., 1992; and Dixon et al., 2006). Heart-cut 2DLC for the analysis of polymers is often referred to as “cross-fractionation’’ (Balke and Patel, 1980). Protein digest analysis with MS/MS identification has been called “multidimensional protein identification technology’’ or “MUDPIT.’’This is described in detail in Chapter 11. In comprehensive 2DLC, partial sampling is used when the entire volume cannot be injected into the second dimension (Holland and Jorgenson, 1995; Venema et al., 1997) or when capillary electrophoresis (CE) is utilized in the second dimension (Lemmo and Jorgenson, 1993). 2DLC utilizing an eight-port or ten-port valve is commonly used for sampling the entire volume of the first dimension (Erni and Frei, 1978; Bushey and Jorgenson, 1990a). Gel electrophoresis (GE) was coupled to HPLC in a comprehensive mode (Rose and Opiteck, 1994), as well as CE and GE (Liu and Sweedler, 1996). Two-dimensional thin layer chromatography (2DTLC) (Rezanka, 1996) and polyacrylamide gel electrophoresis (PAGE)/isoelectric focusing (IEF) (Celis and Bravo, 1984) are also considered comprehensive since the entire sample is subjected to both dimensions noting that all two-dimensional planar techniques are comprehensive by definition.

CHROMATOGRAPHIC HARDWARE

97

Far more information can be obtained by using comprehensive sampling as opposed to the heart-cutting method as higher resolution is typically obtained across the whole range of first-dimension elution (Erni and Frei, 1978; Bushey and Jorgenson, 1990b; Kilz et al., 1995; Murphy et al., 1998a). In some respect, heart cutting is useful when it is known where the zone is overlapped a priori but this case is rarely known for samples where composition is variable. The instrumentation used to implement this comprehensive sampling is the main topic of this chapter. The most common comprehensive mode uses a sampling valve so that second dimension elution can begin as soon as a sampling loop has stored the necessary amount of first column solute. In this case, there is no need for storing the effluent from the first column; it is continuously allocated to a sampling loop with subsequent injection into the second-dimension column. Schoenmakers et al. (2003) define a two-dimensional separation as comprehensive if 1. every part of the sample is subjected to two different separations; 2. equal percentages (either 100% or lower) of all sample components pass through both columns and eventually reach the detector; 3. the separation (resolution) obtained in the first dimension is essentially maintained. These authors clarify these criteria but the essential operation is that the comprehensive separation takes a one-dimensional data representation and through the use of a second separation mechanism converts this to a two-dimensional presentation of the data, as seen in most of the chapters of this book. Topics which will be presented in this chapter include the hardware, software, automation, valve and column configurations, and integration used in comprehensive 2DLC. Aspects of the 2DLC experiment in conjunction with multichannel detectors such as UV diode array optical detectors and mass spectrometers are discussed along with the handling of the data, which is expected to expand in scope in the future as chemometric methods are more widely used for data analysis. 5.3 CHROMATOGRAPHIC HARDWARE 5.3.1

Valves

Many different types of valves are used to control the collection and injection of stored column effluent. We will review most of the valve systems that have been used in 2DLC and will highlight the advantages of these systems where possible. Note that there is an endless combination of these configurations. We note one excellent review (Shalliker and Gray, 2006) that contains details of valve configurations with a good level of detail. When elution chromatography is used in both dimensions, the valve configurations are similar for the different column combinations. However, when CE is utilized as the second dimension, other types of interfaces not based on valves have been implemented with unique advantages. These and the microfluidic implementation of sampling systems for chip-based two-dimensional separations will be discussed below.

98

INSTRUMENTATION FOR COMPREHENSIVE

5.3.1.1 2DLC with Partial Sampling or Heart-Cut Mode A six-port valve is generally used for the partial sampling of the first dimension separation. Fig. 5.2 shows the valve configuration for heart cutting parts of zones for further analysis in a second column. This setup can be used for sampling the first dimension one or several times depending on the run time in the second dimension and the problem being solved. The most common application of this valve is for analyzing one fraction that is unresolved in the first dimension, shown in Fig. 5.1. Operation with heart-cut 2DLC is not as complex as comprehensive 2DLC since the sampling is less frequent and most often done manually. Fig. 5.3 displays a schematic for the HPLC configuration using two six-port valves along with the steps employed for the quantitative analysis of antibodies in serum

FIGURE 5.3 2DLC configuration and sequence utilizing a protein A affinity column in the first dimension and an SEC column in the second dimension. In step 1, the sample is injected onto the affinity column and the first-dimension separation takes place while the SEC column is being equilibrated. In step 2, valve 1 moves to position 2 and a fraction of the affinity separation is collected into the loop. In step 3, valve 1 moves back to position 1 and the collected sample is injected onto the SEC column for MS analysis. In step 4, after the protein elutes from the SEC column valve 2 is switchedtoposition2andtheSECcolumneffluentissenttowastetoavoidsaltsfromenteringtheMS.


99

FIGURE 5.4 Chromatograms of 2DLC (affinity/SEC/MS). Bottom trace is affinity separation with UV detection and 2 min fraction specified. Middle trace is MS total ion chromatogram showing protein elution and salts diverted to waste. Top trace in MS extracted ion chromatogram of protein of interest.

(Murphy et al., 2005). The system utilizes affinity chromatography in the first dimension for the retention of antibodies, and the six-port valve is used to capture the antibody fraction in the loop during elution, then transferred to a size exclusion chromatography (SEC) column. The SEC column separates the proteins from the lower mass salts so that mass spectrometry (MS) can be used as a detector without introducing deleterious effects due to the presence of salt. The resulting chromatograms are shown in Fig. 5.4. This system allows for the automated analysis of serum and is used for both quantitative and qualitative analyses. There are many more applications of 2DLC with a six-port valve and a list of biomedical applications was previously reviewed (Somsen and deJong, 2002). 5.3.1.2 2DLC with Complete Sampling or Comprehensive Mode Table 5.2 lists several comprehensive 2DLC methods with the corresponding columns in each dimension, valves, and detectors utilized. Many 2DLC systems use 8-port valves, 10-port valves, or fraction collectors to retain the entire sample or increase the concentration for the second dimension detection. An eight-port valve with matching sample loops is typically used for the coupling and repetitive sampling of the first-dimension separation system when the comprehensive mode of operation is utilized, as shown in Fig. 5.5. This valve configuration can be used for the heart-cut mode where only a portion of the sample from column 1 enters column 2, or it can be used in the comprehensive mode where the total sample from column 1 enters column 2 depending on the sampling rate. This was the valve configuration used by Erni and Frei (1978) in the first comprehensive 2DLC

100


TABLE 5.2 Sampling First dimension

Two-Dimensional Liquid Separation Examples with Comprehensive Second dimension

Detection

Separation

Valve

References Bushey and Jorgenson (1990a) Isobe et al. (1991) Holland and Jorgenson (1995) Kilz et al. (1995) Opiteck et al. (1997)

CEC

SEC

UV

Protein mix

8-port

AEC

RPLC

UV

Brain extracts

6-port

AEC

RPLC

LIF

Protein digest

6-port

NPLC

SEC

UV, RI

6-port

CEC

RPLC

UV, MS

SEC

RPLC

UV

Copolymer blends Protein mix and E.coli lysate E.coli lysate

RPLC

SEC

ELS

Polymer mix

NPLC

RPLC

ELS

AEC/CEC

RPLC

UV

Surfactant oligomers Protein mix

HILIC

RPLC

UV, MS

CF

RPLC

MS

CEC

RPLC

UV

RPLC

RPLC

UV

Protein mix, cell lysates Breast cancer cell lysates Protein digest Maize metabolites

8-port

2 4-port

Opiteck et al. (1998) 8-port Murphy et al. (1998a) 8-port Murphy et al. (1998b) 8-port Unger et al. (2000) 8-port Murphy (2001) Fraction Chong et al. collector (2001) 10-port Stoll and Carr (2005) 2 6-port Stoll et al. (2006)

AEC: anion-exchange chromatography; CEC: cation-exchange chromatography; CF: chromatofocusing; HILIC: hydrophilic interaction chromatography; NPLC: normal-phase liquid chromatography; SEC: sizeexclusion chromatography; RPLC: reversed-phase liquid chromatography; ELS: evaporative light scattering; LIF: laser-induced fluorescence; MS: mass spectrometry; RI: refractive index; UV: ultraviolet.

instrument and in the first automated comprehensive 2DLC instrument (Bushey and Jorgenson, 1990a). Instead of using sample loops in the eight-port valve, several groups utilize matching columns to alternate sampling in the second dimension (Opiteck et al., 1997; Wagner et al., 2002). This can be difficult, however, because the elution volume of the columns must be exactly matched or adjacent rows in the 2D matrix will be offset and scaled due to variation between columns. This can be partially compensated for in software but it places severe demands on matching columns. Figure 5.6 shows the configuration of an eight-port valve with dual columns in the


101

FIGURE 5.5 2DLC with eight-port valve and two sample loops. In position A, loop 1 (L1) is being loaded with sample from column 1, and loop 2 (L2) is being injected onto column 2. In position B, L2 is being loaded with sample from column 1, and L1 is being injected onto column 2.

second dimension. Sample loops are not necessary for 2DLC under a variety of conditions as highly aqueous solvent conditions can be used to load hydrophobic solutes into reversed-phase columns with subsequent elution under gradient conditions (Unger et al., 2000). The most common case of this is where components elute from an ion-exchange column into a reversed-phase column in the second dimension and these systems are commonly utilized in protein and peptide separations (Unger et al., 2000; Liu et al., 2002) and in other applications, for example, the separation of

Pump 1

Pump 1

Position A

Position B Column 1

Column 1

C2-B C2-A

Pump 2

Detectors Waste

Pump 2

Detectors Waste

FIGURE 5.6 2DLC with eight-port valve and two columns in second dimension. In position A, column 2-A is being loaded with sample from column 1, and column 2-B is being analyzed utilizing pump 2. In position B, column 2-B is being loaded with sample from column 1, and column 2-A is being analyzed.

102


complex surfactant mixtures (Haefliger, 2003). In these cases, a 10-port valve is most commonly used, as discussed below. All of the valves used in 2DLC are two-position valves as they alternate between two positions (A and B in Figs. 5.2, 5.5 and 5.6). Two-position 10- and 12-port valves may also be used similarly to six- and eight-port valves and the differences in application with these are now discussed. It is commonly assumed in 2DLC instruments that employ sample loops that there is complete mixing within the sample loop prior to flushing the loop volume to the second column. However, it was discovered (Van der Horst and Schoenmakers, 2003) that if the fluids in the two sample loops are pumped into the second column in different order (e.g., in the forward direction in loop 1 followed by the reverse direction in loop 2) the resulting 2D chromatograms show irregularities. This is typically the case with an eight-port valve and will depend on loop volume and the inherent broadness of the component zones. This can be corrected by allowing the fluid in the loop to be pumped in the same direction for both sample loops by using a 10-port valve. Figure 5.7 shows the 10-port valve configuration that allows the flow to enter and exit the sample loops in the same direction as they were filled. In fact, 10-port valves are becoming more popular for 2DLC. One commercial 2DLC system, the Perceptive Biosystems Integral 100Q workstation, utilized multiple 10-port pneumatically-driven twoposition valves for column switching and other functions. This unit is no longer manufactured. Higher performance systems where gradient elution is conducted at high speed (Stoll and Carr, 2005; Stoll et al., 2006) utilize 10-port valves. It is also common with 10-port valves to dispense with the use of the sample loops and put two Pump 1 Position A

Pump 1 Position B

Column 1

Pump 2

Column 1

Pump 2

L1

L2

Column 2

Detectors

L1

L2

Waste Column 2

Waste

Detectors

FIGURE 5.7 2DLC with 10-port valve and two sample loops. In position A, loop 1 (L1) is being loaded with sample from column 1, and loop 2 (L2) is being injected onto column 2. In position B, L2 is being loaded with sample from column 1, and L1 is being injected onto column 2.


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second-dimension columns in place of the sample loops. This is a much more difficult experiment to balance but does have advantages while the columns are new and remain balanced. This configuration typically requires very narrow bore firstdimension columns to be used to minimize the volume of effluent that will be flushed into the next dimension. A 12-port valve was used for the periodic sampling of the first column onto multiple second-dimension columns for the 2DLC analysis of aromatic amines and other species (Venkatramani and Zelechonok, 2003). The utility of the 12-port valve is that two columns can be utilized in the second dimension and flow is kept constant through both columns. This configuration requires three sample loops for implementation. The output of the second-dimension columns are connected so that both columns continuously feed the detector. Multiposition valves direct the flow to multiple ports that may be useful for multiple columns in the second dimension. This approach eliminates or greatly reduces the constraints of slowing down the first dimension to get an adequate sampling rate. A potential configuration of using multiple columns in the second dimension is shown in Fig. 5.8. This system would need eight pumps and eight detectors to match the eight columns and would allow for every eighth sample to enter the same column, thus increasing the sampling rate over using one column in the second dimension. As with the dual-column arrangement shown in Fig. 5.6, the adjacent rows may not match perfectly, resulting in increased variability and compensation may be necessary via software. However, this approach may be useful if configured with parallel-column chromatography systems that are now being offered commercially.

First Dimension

Second Dimension

Detectors

FIGURE 5.8 2DLC with multipositional valve. Effluent for the first dimension column can be directed to one of eight columns in the second dimension for increased sampling rate.

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5.4 CE INTERFACES 5.4.1

Gated Interface for HPLC–CE

Because of the small volumes encountered in CE, implementing CE as a second dimension is difficult if a valve is used. More efficient, lower volume unions have been utilized in a number of cases. The main types of these interfaces include optical gating and flow gating, which are discussed below. Electrical gating is described in detail in Chapter 15. Fraction collection is also used, as discussed in Chapter 16, although this takes longer and is a less efficient method than the other comprehensive 2D schemes. Chip-based separation systems typically use some form of electrical gating and these systems will be discussed below. The first instrument to couple LC and CE in a comprehensive multidimensional separation system was described by Bushey and Jorgenson (1990b). This system used a six-port valve and utilized a unique timing diagram for applying the voltage to the CE system. It used a valve that was synchronized so as to utilize electroinjection of the zone into the CE system. Further applications of the electromigration method with valves include protein separations by SEC/CE (Lemmo and Jorgenson, 1993a). Optically gated interfaces (OGI) for diverting solute into a CE column from a firstdimension chromatography column were pioneered by Monnig et al. (1991a and 1991b). The principle of this type of interface is as follows. The components in the mixture to be analyzed are tagged with a fluorescent molecule and continuously introduced into the capillary inlet. Near the capillary inlet, a relatively high powered laser is utilized to photodegrade the fluorescent tag. When the tag is degraded, the solute becomes undetectable to a fluorescence detector, which is placed near the capillary outlet. The sample zone is generated by a transient blocking of the laser beam that opens a small width “fluorescent enabled’’ region in the solute mixture, which is separated within the CE column. In this way, the second-dimension column can be just an extension of the first-dimension column; no special tee or valve is needed for this dimension with the possible exception of having a splitting union so that column volumes may be matched to a smaller diameter capillary used for CE. This mode of operation is known to yield very narrow injection widths. However, this form of sample introduction to the CE capillary is limited to fluorescence detection and does not work for detection by mass spectrometry. Applications of OGI for the reversed-phase liquid chromatography (RPLC)/CE analysis of peptides have been described (Moore and Jorgenson, 1995a) as has the SEC/RPLC/CE separation of peptides (Moore and Jorgenson, 1995b)—a threedimensional separation system. The zone broadening mechanics of the OGI system have been studied (Moore and Jorgenson, 1993). Because of the sampling requirements for comprehensive MDLC, as discussed in Chapter 2, CE with an OGI in the third dimension is ideal because the total run time of the third-dimension separator must be very fast. Otherwise, the preceding dimension separator must be slowed down to allow proper sampling. OGI techniques have been used extensively in single capillary systems where a narrow injection width is needed along with a simplified interface. These techniques are summarized by Hapuarachchi et al. (2006). There are many advantages to this

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Stainless steel tubing

µA 1/16” channel

RPLC capillary

CZE capillary

1/16” o.d. teflon sleeve

Lexan

PEEK tubing

FIGURE 5.9 The flow gating interface from Hooker and Jorgenson (1997). The cross-flow of buffer prevents LC effluent from electromigrating onto the CE capillary until an injection is desired. This figure is used by permission of the American Chemical Society.

configuration including measuring certain chemicals within complex mixtures in real time in a small volume measurement system. Microdialysis interfaces that employ OGI have been described (Tao et al., 1998; Thompson et al., 1999). This type of interface is also very useful for 1D CE systems that are implemented on-chip through microfluidic systems, as described below. The flow gating interface (FGI) is another type of interface that couples columns to CE shown in Fig. 5.9. This interface is especially useful for very small volumes of column effluent, which would be impractical to store in a sample loop, as in the normal valve configuration. In the FGI, a cross flow of buffer is used in a unique interface to divert solute into the capillary for CE analysis. This system was originally described by Lemmo and Jorgenson (1993b). A revised design was published by Hooker and Jorgenson (1997). The FGI has been interfaced to other separation systems, for example, microdialysis, with subsequent CE detection; see Lada and coworkers (Lada and Kennedy, 1996, 1997; Lada et al., 1997). Multidimensional LC–LC and LC–CE separations were recently reviewed for biological molecules (Evans and Jorgenson, 2004). 5.4.2

Microfluidic Valves for On-Chip Multidimensional Analysis

Chip-based systems that employ microfluidics have become a popular research area and a number of systems are now available commercially that utilize electrophoresis-

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based separations. Implementing MDLC systems on a chip can be difficult. However, 1D chip-based systems that employ electrophoresis for biochemical analysis have been used, which employ optical gating (Pittman et al., 2003; Roddy et al., 2003), flow-through sampling (Chen et al., 2002), and microdialysis sampling (Huynh et al., 2004). For implementing 2D systems on-chip, a number of approaches can be followed. One approach to implementing multidimensional separation systems on-chip is to use micellar electrokinetic chromatography (MEKC) as the first dimension, followed by CE in the second dimension (Ramsey et al., 2003). This allows some degree of orthogonality as the MEKC separation mechanism is sensitive to the degree of hydrophobicity of the solute. The second-dimension separation mechanism is sensitive to the charge and size of the solute. Gating of the solute into the electrophoresis channel is done by electrical switching. Detection is on-chip by laser-induced fluorescence. An image of this chip system is shown in Fig. 5.10. Note that MEKC is a popular alternative to a packed-column RPLC system due to the difficulty in packing columns on the chip. However, it is not unreasonable to think that monolithic columns will soon be integrated into the chip. Regardless, the MEKC system has a reversed-phase like behavior with respect to retention mechanism but is somewhat limited to samples that are soluble in MEKC buffer solutions that tend to be aqueous. For most samples of biological origin, this is not typically a problem. The use of electrically-gated solute injection into the electrophoresis system simplifies the chip design as electrical connections are easy to implement as compared to the microfluidics part of the chip. Voltage waveform manipulation via hardware and software are relatively easy to control and implement. Other combinations of first and second dimensions on chip-based separation systems are becoming more common. A particularly interesting combination is IEF/PAGE. This is an analog of the planar system used by biochemists for decades. However, IEF does not typically allow for fluorescent tags that are essential for laserinduced fluorescent detection; one of the most common on-chip detection systems. These and other first- and second-dimension combinations are given in a recent paper that discusses the implementation of MEKC in the first dimension and GE in the second (Shadpour and Sopor, 2006). This is a very powerful system. Again, sampling of the first dimension is by electrical switching, which is the most convenient method for on-chip systems.

5.5 COLUMNS AND COMBINATIONS There are many combinations of separations techniques and methods of coupling these techniques currently employed in MDLC systems. Giddings (1984) has discussed a number of the possible combinations of techniques that can be coupled to form twodimensional systems in matrix form. This matrix includes column chromatography, field-flow fractionation (FFF), various types of electrophoresis experiments, and more. However, many of these matrix elements would be difficult if not impossible to reduce to practice.

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FIGURE 5.10 The image of a microchip used in 2D MEKC-CE described in Ramsey et al. (2003). Note that injections are made at valve 1 (V1) for the first-dimension MEKC separation. Solute is sampled into the CE system at V2. Other designations are provided in the original reference. This figure is used by permission of the American Chemical Society.

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We have categorized the various two-dimensional separation systems by the number of fractions and amount of effluent sampled into the second dimension (Table 5.1). This discussion will be restricted to two-dimensional systems, although most of the principles will apply to more than two coupled separation systems. Furthermore, we limit our discussion here to dimensions that utilize column chromatography. Our discussion of 2DGE is mentioned in passing although we recognize that a great deal of the methodology and analytical outcomes apply jointly to the planar techniques that have been available for years prior to coupling columns with automated valves. The choice of columns used for 2DLC is based upon experience with the sample and resolution required. The HPLC column descriptors of selectivity, resolution, peak capacity, sample capacity, degree of sample recovery, and speed of separation have been discussed previously (Unger et al., 2000). Columns with higher peak capacity and sample capacity (IEC, HIC, NPLC, and RPLC) are preferred in the first dimension, and higher speed columns (SEC and RPLC) are better in the second dimension. This is discussed in detail in Chapters 2 and 6. For optimum resolution in two dimensions, the columns selected need to be orthogonal. Orthogonality is discussed in Chapters 2, 3, and 12. If two particular chemical functionalities are to be separated, then the ease of interpretation of the resulting data is more important than the optimal resolution. For some separations, a mixed mode of separation is observed, and having two dimensions facilitates the study of the differences in separation mechanisms. Overall, there are many combinations of 2DLC separation systems, and as long as the two mobile phase solvents are miscible, they can be coupled for improved resolution. Temperature is another variable to be considered to speed up the separation in the second dimension and will be discussed below. The variety of columns that are utilized in 2DLC to separate complex mixtures is exemplified in Table 5.2. Column selectivity in 2DLC was recently reviewed by Jandera (2006). 5.5.1

Column Systems, Dilution, and Splitting

The scale (capillary or standard bore) of chromatography systems used in 2DLC can vary widely and is dependent on the amount of sample available, detection system, and HPLC availability in the laboratory. Since the chromatography is taking place in two dimensions, zone broadening and dilution are taking place on both columns. Zone broadening leads to sample dilution. The dilution effect has been studied (Schure, 1999) in the context of MDLC and certain combinations of columns are more beneficial in minimizing dilution. This dilution effect is also discussed in Chapter 2. In industrial laboratories, where samples are not typically volume limited, analytical-scale HPLC systems are routinely used. To combine two analytical HPLC systems would involve standard valves with loop volumes from 10 to 1000 mL, and control of each system. If a six-port valve is utilized, then sampling a portion of the effluent from the first to second dimension would allow lower volumes to be injected into the second dimension, but with a loss in sensitivity. A system employing an eight-port valve in the comprehensive mode with analytical flow rates has been used to separate polymers and surfactants (Murphy et al., 1998a, 1998b). A fast second-dimension column flow rate is generally used to accommodate the higher sampling rate. When a narrow range of

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analyte properties are examined, then isocratic techniques can be used in the second dimension. In Chapter 18, isocratic reversed-phase HPLC is used to separate the alkyl portion of a complex surfactant mixture at 1-min intervals during a normal phase gradient experiment. In samples where there is a broad range of analyte properties, such as biological samples, gradient HPLC is needed in both dimensions to improve resolution of the entire sample. Chong et al. (2001) have used gradient chromatofocusing in combination with gradient reversed-phase HPLC for the mapping of protein samples from breast cancer cell lysates. Holland and Jorgenson (1995) coupled capillary anion-exchange and reversed-phased HPLC columns for the analysis of tryptic digests of the contents of a single cell. Recent work by Carr and coworkers (Stoll et al., 2006) has shown the utility in using fast gradient separations in 2DLC that have been facilitated by working at elevated temperature. The use of elevated temperatures in 2DLC is important, especially in the second dimension because of the importance in speeding this dimension up. Thus, the column diameters chosen for the two dimensions are determined by the amount of sample available and will dictate the flow rate ranges available to use. In split-flow systems, where only a portion of the first-dimension effluent is injected into the second dimension, the choice of column size is unlimited and the two methods can be developed independently. In comprehensive systems where the entire sample from the first dimension is injected into the second dimension, the flow rates are generally lower in the first dimension to accommodate the lower injection volumes into the second dimension. For example, for a 1-mm ID column in the first dimension with a flow rate of 50 mL/min and a sampling rate of 1 min, 50 mL could be injected onto the second dimension. A 50-mL injection onto a 4.6-mm ID column flowing at 1 mL/min should be accommodated fairly well based upon its composition. In Chapter 6, the first dimension column diameters are estimated based upon the injection volume and sampling rate into the second dimension. 5.6 DETECTION Detection in 2DLC is the same as encountered in one-dimensional HPLC. Avariety of detectors are presented in Table 5.2. The choice of detector is dependent on the molecule being detected, the problem being solved, and the separation mode used for the second dimension. If MS detection is utilized, then volatile buffers are typically used in the second-dimension separation. Ultraviolet detection is used for peptides, proteins, and any molecules that contain an appropriate chromophore. Evaporative light scattering detection has become popular for the analysis of polymers and surfactants that do not contain UV chromophores. Refractive index (RI) detection is generally used with size exclusion chromatography for the analysis of polymers. 5.7 COMPUTER HARDWARE AND SOFTWARE The equipment used in 2DLC is the same as in HPLC with the addition of valves and integrated control of both HPLC systems. Most chromatography data systems allow

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control over one HPLC system, but few can control two HPLC systems and the repetitive sampling for a 2DLC system. Thus, for 2DLC the control of each HPLC system is done by selecting one system as the main system and the second one as a slave to the first. This allows initiation control of the second HPLC system by contact closure such as during valve actuation. If mass spectrometry detection is used, this method of control is utilized since MS systems can currently control one HPLC. There are some integrated 2DLC systems on the market that can control two HPLC systems and valves. Computer hardware and software used in 2DLC generally take care of three critical operations. These include real-time control of valves and sequencing functions such as autosampler control, formatting the time series data into a 2D data matrix, and analyzing the data. These will be described in some detail. The control of valves and sequencing of autosamplers and injectors is typically a slow speed operation for 2DLC. For example, valves must be controlled on the chromatographic timescale so that switching times on the order of tens of seconds to minutes is not uncommon. However, the sequencing time control must be highly accurate to achieve reproducible chromatograms. Data formatting is accomplished by taking one or more detector signals, for example ultraviolet and refractive index detector signals, and formatting the data to be in matrix format. The computer program must manipulate 2D chromatograms including scaling, chromatogram subtraction, signal processing, and moments analysis. For multiwavelength detectors and mass spectrometer detectors, the software should also allow individual spectral analysis functions such as selection of wavelengths or mass to charge ratios for display. More details of these aspects will be covered below. 5.7.1

Software Development

There are a few commercially available systems that are loosely or tightly integrated for 2DLC. There are commercial systems for multidimensional gas chromatography as the interface between columns can be accomplished without valves using thermal modulation (Marriott, 2002) and the thermal modulator is tightly coupled to the software. Most scientists who work on developing 2DLC systems write their own software and valve sequencing systems. The software for these systems can be developed in higher level languages with graphical icon-oriented systems; one example of this is National Instrument’s LabVIEW system (National Instruments, 2007), which is popular for real-time instrument control on both Microsoft Windows-based operating systems and on Apple’s Mac OS X-based computers. Other popular languages include Microsoft’s Visual Basic and Visual Cþþ programming languages that feature good integration and graphical user interfaces (GUI’s) on Windows-based systems. For the examples used in this chapter, we utilize software that is available commercially (Kroungold, 2007) and is an “add-on’’ for existing chromatography systems. This is one of a few commercially available packages that allow scientists to add columns, pumps, valves, and sample loops to make their own 2DLC systems.

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FIGURE 5.11 Timing diagram for comprehensive 2DLC with either a two-position valve (bottom) or four-position valve (top). Repetitive sampling of the first dimension at each time (T1, T2, T3, T4, . . .,) results in an injection onto the second-dimension column.

5.7.2

Valve Sequencing

The sequence control of valves, autosamplers, fraction collectors, and other devices is typically performed under program control although dedicated hardware controllers can be used here. Valves can be controlled by standard signals such as RS-232 serial lines, a USB (universal serial bus) port, digital signals such as those from TTL voltages (transistor–transistor logic where signal levels are less than 5 V) and relay closures. A good reference to these terms and details are available in a well-known electronics book (Horowitz and Hill, 1989). The program must utilize a clock for an accurate time base and the clock is usually the internal clock of the computer that can be accurate to better than a millisecond. The timing diagrams for 2DLC are shown in Fig. 5.11 for two-position and multiposition valves. A two-position valve has the option of positions A or B, whereas the four-position valve has positions A, B, C, and D available. For twoposition valves, the valve is initially in position A at the beginning of the experiment and the duration interval of the first segment is denoted as T1. During T1, effluent from the first dimension column fills the first sample loop. At the end of the T1 interval, the position of the valve is changed to position B and the solute contained in sample loop 1 is separated on the second-dimension column. Sample loop 2 is now filled with the effluent from the first column as the elution of the second column takes place into the detector. This procedure is repeated until the entire first-column effluent is sampled (i.e., all of the injected sample components have eluted from the first-dimension column) or the experiment is stopped. This timing plan is simple but alternative schemes can be easily accommodated. Two common modifications include provision for a predelay period occurring prior to commencement of the experiment and letting a second valve shunt a portion of the

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second-dimension effluent that may contain salt to waste. Both of these will now be explained. Elution in the second dimension need not be executed until solute is present in the first sample loop. This predelay period allows the first-dimension column void solvent to be dumped to waste collection prior to the arrival of the first retained component. After some initial time, the regular sampling interval T1 ¼ T2 ¼ T3 . . . is started. Mass spectrometers that use electrospray ionization (ESI) do not function well if the eluent contains low volatility salts. This is a major concern when an ion-exchange column is used as a first-dimension column and the salt concentration is used to modulate the retention in this column. In this case, another valve can be connected between the second-dimension column and the detector so that any salt from the second-dimension elution process that is either unretained or weakly retained can be diverted prior to feeding zones to the mass spectrometer. These parameters are typically entered into a program that accepts the parameters as shown in Fig. 5.12. As shown here, the user enters the total run time, seconddimension time (equal to T1 ¼ T2 ¼ T3 . . .), any initial delay time, and operation information regarding triggering and valve states. Once the unit is triggered through a user’s start signal (keyboard initiated, contact sense initiated from an autosampler, data acquisition module, etc.) the software presents a real-time view of the current valve position as shown in Fig. 5.13. The sequence of repetitive valve switching continues until the data acquisition cycles are completed or the user interrupts the sequence. At that time data analysis is initiated by the user. In automatic operation mode, which is one of the user-selected modes in the software, the autosampler triggers another data acquisition and valve switching cycle so that a tray of samples can be readily analyzed via 2DLC.

FIGURE 5.12 User interface for the simplest of the 2DLC modes with no fraction collection or salt diversion. Figure courtesy of Kroungold Analytical (2007).

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FIGURE 5.13 Real-time display of valve sequencing. Figure courtesy of Kroungold Analytical (2007).

5.7.3

Data Format and Storage

If the data system is tightly integrated with the 2DLC system, the native data format can be used to convert the successive one-dimensional data vectors into a matrix form for visualization and analysis. However, there are currently few commercially available 2D systems that have tight integration of data acquisition and analysis. Consequently, the data are stored as raw data that can be easily imported into external software in a number of ways. An especially easy way to do this is to export the raw data into a spreadsheet program like Microsoft Excel using the comma-separated variable (CSV) data format and then use a graphics program that can read CSV files. This has its limits though because data files can get very large in certain cases, which we will now discuss. Chromatography equipment manufacturers and users have embraced a series of exchange formats and data standards that expedite this process. The standard for this was formerly known as the AIA (Analytical Instrument Association) format. Approximately 20 years ago, the AIA developed a set of standards for chromatography and mass spectrometry data. The standard is now called the ANDI/netCDF Chromatography Data Interchange format and is available as standards documents from ASTM International (formerly the American Society for Testing and Materials) as the E 1948-98 standard guide for analytical data interchange protocol for chromatographic data and the E 1947-98 standard specification of analytical data interchange protocol for chromatographic data. A similar set of standards exists for mass spectrometry data. These include the E 2077-00 standard specification for analytical data interchange protocol for mass spectrometric data and the E 2078-00 standard

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guide for analytical data interchange protocol for mass spectrometric data. These systems use the NetCDF packaging standard (NetCDF, 2007) software that can package (encapsulate) or unpack data. This system has been utilized extensively for both chromatography and mass spectrometry data exchange and is a de facto standard for getting data between third party software and an instrument vendor’s proprietary data formats. One packaging format that lacks the efficiency of storage that NetCDF offers but is gaining universal acceptance as a data structure encapsulation standard is the eXtensible Markup Language or XML (Benz and Durant, 2003). Currently, there are activities for both chromatography and mass spectrometry in this area in the project called AnIML (Analytical Information Markup Language), which is now driven by the ASTM subcommittee E13.15. In the case of mass spectrometry data, there is an XML data standard, mzXML, which is used for proteomics data. Furthermore, the mzData format has been touted among equipment manufacturers for proteomics-related data. Information about these formats is easily found on the World Wide Web. However, there does not appear to be a standard yet for multidimensional chromatographic data. The size of the datasets for 2DLC depends on the detector. For a single wavelength detector such as an evaporative light scattering detector (ELSD), we assume the following: there are 80 samples of the first-dimension effluent with 3 min runs in the second dimension. With the signal sampled 10 times a second, the resulting number of data values is 144,000. If the data are sampled by a 20-bit analog-to-digital converter this could be stored in 8 bytes of character data and this would give files that were on the order of 1.2 megabytes in length. If a UV–vis multiwavelength detector, for example, a photodiode array detector, is utilized with a range of 200–450 nm and with a resolution of 0.1 nm, this dataset would comprise 2500 amplitude values at each time slice. If sampled twice a second, the entire 2DLC dataset would contain 2500 28,800 ¼ 72,000,000 or 72 million numbers. If this is kept as a binary file with 4 bytes per data storage value, the file is of the order of 288 megabytes in length. Others have reported file sizes of approximately 100 megabytes for multiwavelength detectors when 100 wavelengths are stored with sampling rates of 80 samples per second and with run times on the order of an hour. In this case, some file compression may also be utilized to reduce file size. Higher sampling rates and higher spectral resolution will increase the file size into the gigabyte range. If the file is to be stored in character format instead of binary, the file size would double or triple. The problem here is not as critical for data storage length, as desktop computers often have mass storage capabilities larger than 300 gigabytes. However, the time needed to read these large datasets becomes a critical parameter when routine handling and analysis of these datasets is to take place. Furthermore, these data cannot be completely memory resident when large datasets greater than perhaps 500 megabytes are present. This is often the case for 2DLC when using mass spectrometry and the data must be partitioned into memory, which leads to far more complex analysis software. In many cases, data can be obtained offline and analyzed in a comprehensive manner from a fraction collector. For example, LC and gas chromatography (GC) can be utilized to form a 2D experiment — LC/GC. This is quite useful as the retention

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FIGURE 5.14 Software to read individual files from an offline GC instrument into the 2D software. Figure courtesy of Kroungold Analytical (2007).

mechanism of LC is different than the GC step. This is a powerful combination when components are sufficiently volatile to be analyzed by GC. The GC detector is typically a flame ionization detector or a mass spectrometer. The GC experiment is run on every LC collected fraction. After data acquisition, the data are converted to the AIA format and then a program is used to collimate the individual data files back into a two-dimensional format. Such a program for doing this is shown in Fig. 5.14. Note that the GC step here need not be fast; in the case of off-line techniques, the seconddimension speed requirement can often be relaxed and run times up to 15 min can be accommodated for an overnight analysis of 60 collected fractions.

5.8 ZONE VISUALIZATION 5.8.1

Contour Visualization

There are two common means of visualizing comprehensive 2DLC data. One is to draw the data as contours with component zones showing as spots, much like 2DGE, and the other is to show zones as a two-dimensional amplitude plot where peaks are shown. A small variation on contour plotting is to map the data value to a color or gray scale and present the color or gray scale as a pixel. The contour plot and pixel mapping are more convenient as these modes of plotting do not tend to hide or exclude features due to high amplitude peaks in the vicinity of low amplitude peaks, although visualizing any low amplitude contour or pixel map is difficult when high amplitude and low amplitude regions are adjacent. The contour drawing capabilities of most graphics software finds the regions of constant peak amplitude through interpolation. Typically, 10–20 contour amplitudes are picked from maximum to minimum. This interpolation can be sophisticated and may include a noise minimizing basis function. However, the use of this filtering may sometimes distort the data presentation. Because 2DLC data are typically performed

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on matrices with sizes larger than 60 rows (first dimension) and 100 columns (second dimension), another approach is to simply map the peak amplitude into a color map and deposit the color as a pixel or in a small rectangular region in the plot. The user typically has control on the color mapping function that gives the most esthetically pleasing plot. This technique is commonly used and provides a fast computational approach to data presentation. However, interpolation is often used and the interpolation function, while appearing to reduce noise, can also artificially broaden the 2D chromatogram and must be used with care. Plotting the logarithm of the amplitude with contours, mapped pixels, or peak plotting can help distinguish large and small adjacent peaks. Taking the logarithm of the amplitude is essentially a compression operation. However, small values of noise in the data tend to become amplified in this approach and more sophisticated techniques may then be necessary for noise suppression, such as gating the noise floor. Gray-scale contour plots are useful and one is shown in Fig. 5.15. These data are quite typical of the zone profiles from a 2DLC experiment and appear similar to the data from 2DGE when digitized in a densitometer after the proper staining treatment. Color contours and pixel maps are useful as they help display more of the zone shape. Other examples of gray and color contouring and pixel mapping of data are plentiful throughout this text.

FIGURE 5.15 A typical gray-scale contour plot of the 2DLC separation of tryptic digest peptides from BSA. From Stoll and Carr (2005). Reprinted with permission of the American Chemical Society.

ZONE VISUALIZATION

5.8.2

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2D Peak Presentation

A good example of a 2D peak plot where the amplitude is directly visualized showing peaks is given in Fig. 15.7 of Chapter 15. In this plot, peaks are shown at an angle projection of the two independent variable axes. One problem, besides obscuring data with this approach, is that it is difficult to determine the retention time on each axis; the scientist must be creative in the interpolation of the peak maxima back to the axes. However, aside from esthetic preferences, the data are essentially the same as the contour or direct pixel representation. An important point to note is that since the data are contained in digital form, it is easy to obtain peak maxima information through programmed analysis. Hence, the visualization step is largely a cosmetic step that should be viewed as an information-conveying process, rather than as a substitution for experimental parameter analysis. 5.8.3

Zone Visualization in Specific Chemical (pI) Regions

A number of variations to contour, pixel mapping and peak plotting exist and these tend to depend on the type of column used in the first or second dimension. For example, as shown in Fig. 1.2 in Chapter 1, chromatofocusing can be used to exploit the pI dependence of proteomics samples as one of the dimensions. Hence, one dimension shows a pI range and the other shows the temporal zone production in gradient elution RPLC. By plotting the peak amplitude as a function of color, it is easy to see the individual zones and these can be compared by examination of neighboring pI regions as well as the evolution as a function of gradient modifier or of time although the two do not need to be linearly related. This approach is extremely powerful and can be further elaborated by substituting specific mass-to-charge (m/z) ratio amplitudes from a mass spectrometer. Visualizing analytical chemical concentration data from multivariate instrumentation has for many years been a fruitful area for more research and 2DLC fuels this need. Chemometric analysis may provide a better handle on where (i.e., what regions of multidimensional space) to visualize the data. 5.8.4

External Plotting Programs

Plotting data with two independent variables can be very subjective as to the most pleasing form of information display. Therefore, it is common, once the data are assembled into matrix form, to use an external plotting and graphics program to provide customization to the data plot. In addition, these types of packages can be used to distinguish and label individual peak features. Many PC graphics packages, including the plotting software inside Microsoft Excel, can be used for this purpose. As mentioned previously, for spreadsheet-based plotting systems, data can be imported as a CSV data file where data are separated by commas on a row-by-row basis. Other packages can be used that will add color coding to the data amplitude on the contours and/or peaks and some packages will accept more sophisticated data formats in binary. One example of these software packages was Transform that was sold as a general data analysis package for data packaged in matrix form or as a linear dataset

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that could be converted to matrix form. It was used by Bushey and Jorgenson (1990a) in their original publication on 2DLC. These features are now standard in high performance visualization software. Other plotting software is contained within more elaborate mathematics and analysis integrated systems including MATLAB (2007) and Mathematica (2007). The software used to create Fig. 5.15 was done by programming within the MATLAB package. 5.8.5

Difference Plots

One useful visualization technique is to show the difference between two 2DLC chromatograms. The peak shape reproducibility must be high as difference measurements are inherently noisy operations, as opposed to integration measurements that tend to smooth data irregularities. An interesting paper entitled “Comparative visualization for comprehensive two-dimensional gas chromatography’’ (Hollingsworth et al., 2006) shows difference plots from two-dimensional gas chromatographic data. Difference plots have been used routinely in 2DLC polymer analysis and now are used in proteomics applications. 5.8.6

Multi-channel Data

Interesting data presentations can be made with UV–visible multi-wavelength datasets and mass spectral datasets. In these cases, there are many interesting possibilities. For example, one can show the integrated signal (e.g., the total ion current in the case of mass spectrometry) as a function of the 2D retention. Alternatively, one can show the signal at a particular wavelength or m/z ratio in the 2D space. Or one can show the selective signals as a function of the highest masses or the most intense wavelength or m/z ratio. These presentations are typically dependent on the application, for example, top-down or bottom-up proteomics (see Chapter 13) or polymer analysis (Chapter 17). The presentation mode of the dataset can be utilized to explore the data and see interesting features. In the case of mass spectrometry, we show the data in Fig. 5.16 of an analysis of a protein sample mixture. The user clicks on a peak of interest in the 2D chromatogram and the mass spectrum appears in the graph below the colored pixel map plot. This is the amplitude, as a function of the m/z ratio, of the mass spectrometer at that location in the 2D chromatogram. If the user clicks on a part of the mass spectrum of interest or enters a mass range to view, the 2DLC contours are then shown as in Fig. 5.17. In this way one can check if other peaks with a specific m/z ratio are present. This software also has an automated browser mode that displays the 2D chromatogram repeatedly at different m/z ratios within a specified range. Much like 1D LC/MS, the data presentation has many possibilities because the data are part of a multivariate dataset. This means that there are multiple independent variables; besides the two retention time variables in 2DLC, one also has the m/z ratio so that 2DLC/MS has three independent variables. This can be challenging to visualize; the problem increases dramatically when MS/MS systems are utilized, for example, as in proteomics studies.

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FIGURE 5.16 Mass spectrometry data and the corresponding mass spectrum of a selected spot. Figure courtesy of Kroungold Analytical (2007). (See color plate.)

5.9 DATA ANALYSIS AND SIGNAL PROCESSING 2DLC in many ways parallels 2DGE when it comes to signal processing and data analysis. Many of the same operations, from zone subtraction to the determination of adjacent resolution, are quite similar and software is available for these operations in 2DGE. One of the data operations researched for 2DGE is the similarity in electropherograms. This can also be determined in 2DLC using the same type of methodology. One important difference, however, is that in 2DGE or any type of planar detection system, the signal quality and dynamic range of optical densitometry detectors are much smaller than with the types of detectors found in column chromatography and in 2DLC. Advanced processing methods using Fourier-based techniques such as autocovariance methods, described in Chapter 4, have not found their way into commercially available systems. There is no reason that these cannot be developed further for measuring the similarity between 2DLC chromatograms and estimating the number of peaks present in the 2DLC chromatograms. Chemometric analysis of 2DLC data is further discussed in Chapter 2.

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FIGURE 5.17 The 2DLC contours for m/z ¼ 1301 with a window width of 2 m/z units. Figure courtesy of Kroungold Analytical (2007).

Moment analysis is one of the simplest types of analysis and is useful for measuring the performance of the chromatography. Moments can be used to measure the same things that are measured in 1D chromatographic systems; these include the first, second, and third moments, which are more accurate than the related peak maximum, peak width, and peak asymmetry. In 2D, however, these values each have a component in each dimension and this can be easily determined in software-based measurement systems.

5.10 FUTURE PROSPECTS 2DLC has great potential for the analysis of complex samples from industrial or biological origin. It provides a powerful tool for the analysis of membrane proteins (Lohaus et al., 2007) that cannot be detected in 2D gels. When combined with high resolution mass spectrometry, 2DLC has the resolution to analyze and identify approximately 1300 proteins in human plasma (Jin et al., 2005). There is no doubt that the instrumentation will evolve and will provide an excellent chromatographic platform for in-depth analysis that single-column methods cannot provide. Although

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2DLC may be the method of choice for specific applications, these applications will need to be turnkey from the viewpoint of instrumentation and this has not yet been achieved in a wide context. When this desired goal is achieved, 2DLC will evolve to be a standard instrumental approach to the qualitative and quantitative analyses of complex materials.

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6 METHOD DEVELOPMENT IN COMPREHENSIVE MULTIDIMENSIONAL LIQUID CHROMATOGRAPHY Robert E. Murphy Kroungold Analytical, Inc., Encinitas, CA 92024, USA

Mark R. Schure Theoretical Separation Science Laboratory, Rohm and Haas Company, Springhouse, PA 19477-0904, USA

6.1 INTRODUCTION Q1

In one-dimensional chromatographic method development, an analyst is often faced with a complex task where the choice of column packing material, particle size, flow rate, detector(s), and sample preparation is intimately interlinked. In two-dimensional chromatographic method development, there are two columns for which individual method development needs to take place as well as the integration into one analysis scheme. In this chapter, we will review the present state of knowledge regarding method development in multidimensional liquid chromatography (MDLC). In addition, we will propose some rules that can guide the analyst with a recommended order of steps. Our discussion will be restricted to those methods that utilize two columns. Methods that utilize capillary electrophoresis (CE) as one of the dimensions will not be discussed here, although many of the principles remain the same.

Multidimensional Liquid Chromatography: Theory and Applications in Industrial Chemistry and the Life Sciences, Edited by Steven A. Cohen and Mark R. Schure. Copyright 2008 John Wiley & Sons, Inc.

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We are guided by both literature and experience in this regard. Recognizing that there are many sources of information on single-column method development and optimization (Berridge, 1985; Schoenmakers, 1986; Glajch and Snyder, 1990; Snyder et al., 1997), we will not dwell on the method development and optimization step of any one-dimensional method, except for guiding the user of twodimensional liquid chromatography (2DLC) on issues specific to 2DLC. However, we will be pointing out many observations from previous method development experiences for biomolecules, biopolymers, and synthetic polymers we have learned by performing 2DLC. For 2DLC to gain wide acceptance, the two column methods must be separately optimized using the well-known 1D method development approaches. However, the main thrust here is to understand and control the variables that connect the two 1D method development processes into a successful 2D separation.

6.2 PREVIOUS WORK There have been very few method development processes proposed for 2DLC. One study (Schoenmakers et al., 2006) is titled “A protocol for designing comprehensive two-dimensional liquid chromatography separation systems.’’ This study advocates that one initially chooses the first-dimension maximum acceptable analysis time, the first-dimension maximum workable pressure drop, and the smallest first-dimension column diameter. The first two variables are then used to construct a “Poppe plot’’ (Poppe, 1997)—pronounced “Pop-puh’’ (Eksteen, 2007). The Poppe plot is a log–log plot of H/u0 ¼ t0/N versus the number of plates with different particle sizes and with lines drawn at constant void time, t0. H is the plate height, N is the number of plates, and u0 is the fluid velocity (assumed equal to the void velocity). The quantity H/u0 is called the “plate time,’’ which is the time for a theoretical plate to develop and is indicative of the speed of the separation, with units of seconds. In the Poppe plot, a number of parameters including the maximum allowable pressure drop, particle diameter, viscosity, flow resistance, and diffusion coefficient are held constant. A typical Poppe plot is shown in Fig. 6.1. For smaller particle diameters, the time to generate a plate is smaller than that for larger particles. Furthermore, the region above the line (longer time) is experimentally accessible with a pressure drop lower than the stated maximum, but the region below these lines is not accessible as the maximum pressure will be exceeded. There are vertical asymptotes to these curves; there is a limit where the column cannot deliver any higher number of plates. In addition to the curves for each particle diameter, there are also constant t0 lines on these plots, which are shown as dashed lines in Fig. 6.1. These dashed lines are determined by multiplying the number of plates N by H/u0, which gives units in seconds. The points where these dashed lines intersect the solid lines give the void time with a certain plate number and a certain rate of plate generation. In this regard, the Poppe plot gives a good indication of the overall performance of a column. However, in practice this type of plot cannot be expected to yield exact results because columns do not perform close to the theoretical

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–1

7.0 µm 5.0 µm –2

Log H/u

0

3.0 µm 2.0 µm 1.4 µm

–3

100 s 10 s

4

5

6

7

Log plate number

FIGURE 6.1 A Poppe plot for the required plate number in conventional HPLC. The parameters are taken from Poppe’s original paper (Poppe, 1997). The parameters are maximum pressure DP ¼ 4 107 Pa, viscosity h ¼ 0.001 Pa/s, flow resistance factor j ¼ 1000, diffusion coefficient D ¼ 1 109 m2/s, and reduced plate height parameters using Knox’s plate height model are A ¼ 1, B ¼ 1.5, C ¼ 0.05.

limit owing to instrumental limitations (Guiochon, 2006). However, we believe that these types of plots can give very useful ideas about column performance and they can be very helpful, at least, in a rough design of the first- and the second-dimension column. Given the construction of the Poppe plot, the number of plates, the column length, the peak capacity, and the particle diameter are determined in the Schoenmakers et al. (2006) scheme all for the first-dimension column. These are then used to determine the second-dimension parameters that include the particle diameter, the number of plates, column length, and peak capacity. Other variables are utilized and optimized from this scheme. Another study (Bedani et al., 2006) starts from the multidimensional sampling theory (Murphy et al., 1998a), which is discussed in Chapter 2. This sampling theory states that one needs to sample the first dimension separation system at least three to four times per peak width for maximum resolution. Bedani et al. then equate the second-dimension total analysis time to the first-dimension narrowest peak standard deviation. This defines the second-dimension operational parameters. All other parameters can be derived from this balance and Bedani’s study goes through this and discusses how the rest of these variables are obtained. There are two very important principles that appear in Bedani et al.’s study. First, the sampling theory dictates the constraints on speed between the dimensions. Second,

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unlike Schoenmaker’s approach, Bedani et al.’s approach does not establish limits to the columns or separation processes a priori, but rather assumes that the seconddimension parameters have been established and that the total system peak capacity is selected prior to determining everything else. The authors then establish from equations what the first dimension column length, efficiency, velocity, and other parameters should be. These authors also advocate the use of a stopped-flow method instead of running the first dimension column at low velocities to match the second column performance. Others have examined the necessary parameters that should be optimized to make the two-dimensional separation operate within the context of the columns that are chosen for the unique separation applications that are being developed. This is true for most of the applications shown in this book. However, one of the common themes here is that it is often necessary to slow down the first-dimension separation system in a 2DLC system. If one does not slow down the first dimension, another approach is to speed up the second dimension so that the whole analysis is not gated by the time of the second dimension. Recently, this has been the motivation behind the very fast seconddimension systems, such as Carr and coworker’s fast gradient reversed-phase liquid chromatography (RPLC) second dimension systems, which operate at elevated temperatures (Stoll et al., 2006, 2007). Having a fast second dimension makes CE an attractive technique, especially with fast gating methods, which are discussed in Chapter 5. However, these are specialized for specific applications and may require method development techniques specific to CE.

6.3 COLUMN VARIABLES Many of the possible column combinations that are useful in 2DLC are listed in Chapter 5. Besides the actual types of column stationary phases, for example, anionexchange chromatography (AEC), size exclusion chromatography (SEC), and RPLC, many other column variables must be determined for the successful operation of a 2DLC instrument. The attributes that comprise the basic 2DLC experiment are listed in Table 6.1. We will discuss these attributes individually and how they interact between dimensions. For example, a fast, high efficiency column in the first dimension places a huge burden on the second-dimension system to sample extremely fast so that typically four samples can be obtained across a peak. These interactions will become more apparent as we follow the proposed rules in the following section.

6.4 METHOD DEVELOPMENT Here we suggest the steps needed for developing a 2D method. These recommendations can result in either an application that far exceeds a one-column method or an application that fails and is replaced by a one-column method. In the case of a separation that can be adequately resolved with a one-dimensional method, the added

METHOD DEVELOPMENT

TABLE 6.1

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Typical Parameters Necessary for Consideration in 2DLC

Parameters for first dimension column Stationary phase Particle size or length scale Column inner diameter Column length Particle type (monolith, pelicular, nonporous, etc.) Temperature Isocratic or gradient, gradient parameters Solvent system Flow rate Maximum run time Parameters for second dimension column Stationary phase Particle size or length scale Column inner diameter Column length Particle type (monolith, pelicular, nonporous, etc.) Temperature Isocratic or gradient, gradient parameters Solvent system Flow rate Maximum run time Parameters for sampling interface Column configuration: sample loops or columns Loop or column volume

Typical value

Units

Reversed phase 3.5 1.0 25 Porous

mm mm cm

40 Gradient

C

Water:acetonitrile 50 120

mL/min min

Typical value

Units

Size exclusion 5.0 4.6 5 Porous

mm mm cm

40 Isocratic

C

Water:methanol 1.0 1

mL/min min

Typical value

Units

Sample loops 50

mL

complexity of 2DLC is not worth the effort. We assume that the methods described here apply to reasonably complex separations that require a multidimensional approach. Many of these choices have come with experience and with further applications development from the 2DLC literature. The order of presentation below is intended to explain theprocess of method developmentasa rule or learning.We haveattempted to do this in a hierarchical manner so that each aspect must be satisfied, or else the method will perform poorly even if lower ranking considerations are closely adhered to and highly

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optimized. We call these rules “The cardinal rules of 2DLC method development’’ because they have been utilized repeatedly and although developed heuristically, have appeared to be necessary for achieving good method performance. 6.4.1

The Cardinal Rules of 2DLC Method Development

1. Column selection: Column selection must lead to a minimum correlation between retention mechanisms (orthogonality must be maximized). 2. Sampling: The second column method must be as fast as possible to allow for optimal sampling of the first dimension. The method development of the second dimension should be done first. The sampling rate for the second-dimension column should maintain three to four samples across the narrowest peak in the first dimension for optimum 2DLC resolution. Less than three samples across the narrowest peak in the first dimension allow for faster analyses with lower 2DLC resolution. 3. Solvent systems and gradient elution: Solvent systems used in the first dimension must be “compatible’’ with the second-dimension solvent system. Gradient elution is highly desirable when RPLC is used in either the first or the second dimension as it can help limit the elution range and can be used to sharpen zones through its focusing effect. Salt gradients can be run for ion exchange in either dimension. However, these introduce additional complications when mass spectrometry is used as a detector. 4. Second-dimension elution time range: The second-dimension elution time range must be determined. The flow rate needs to be optimized for maximum resolution and speed. This will establish the performance of the second dimension. The elution time range can be tuned with either gradient elution and/or by flow rate to determine the sampling rate. 5. Sample loop volumes: Thevolumes of the sample loops that store eluent from the first dimension and inject eluent into the second-dimension column system must be determined. The loop volume divided by the second-dimension elution time range determines the first-dimension flow rate in comprehensive 2DLC. If the dilution factor is small in the second column, a flow splitter can maintain a small loop volume even with a substantial flow rate from the first-dimension column. 6. First-dimension optimization: The flow rate, elution time range, and the efficiency of the first-dimension column must be carefully controlled and matched to the second-dimension column, the sample loop volume, and the sampling rate. 6.4.1.1 Column Selection The selection of the two types of columns to be used is perhaps the most important consideration in 2DLC method development. This is driven by the need to have orthogonal dimensions for the solutes under investigation, otherwise the solutes will elute along the diagonal of the separation space, as discussed in Chapter 2. We have observed a number of 2DLC applications in the literature,

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especially those for small molecules, where peaks are crowded in the diagonal space that indicates a “less than optimal’’ retention mechanism for solutes between the two columns. This must be avoided for a successful application of high resolution 2DLC. Additionally, the choice of columns and solvents must spread the peaks across the retention dimensions as uniformly as possible. In this way, “cross-column’’ selectivity maximizes the resolution within the two-dimensional space. Some examples of the pairs of columns that have been previously utilized in 2DLC are given in Chapter 5 and in Stoll et al.’s (2007) study. The choice of these column types is critical and one should consider trying different columns in, at least, one dimension to see if the orthogonality and the spreading across the retention space work better with one column as opposed to another. If the 2DLC separation application has been described in the literature previously, those column types can be accepted if the separation was satisfactory. However, a little chemical knowledge can be extremely useful in the choice of columns. For example, if a class of molecules has two outstanding attributes for separation (e.g., there is charge and hydrophobicity differences between molecules), then the two candidates might include ion exchange and RPLC. This combination is well established for peptide and protein separations. Additionally, normal-phase liquid chromatography (NPLC) and RPLC may be appropriate when there are two distinct groups that are unique within the molecule. In any case, RPLC, the “workhorse’’ of liquid chromatography, is an ideal dimension for either the first or the second dimension, but is better suited to the first dimension when run times are long because of highly retained compounds. When run times are shorter in RPLC, either because of fast gradients that can be utilized or a chosen solvent system that limits the elution range, RPLC can be utilized as a fast second-dimension column system (Murphy et al., 1998b). For synthetic and naturally occurring polymers, a few well-established techniques have proven useful. The first column pair to try is RPLC, followed by SEC. As SEC has a limited elution range, it can be used as a very fast second-dimension technique with run times on the order of 1–2 min. There are many examples of fast second-dimension SEC columns in the literature (Murphy et al., 1998a; van der Horst and Schoenmakers, 2003). If molecules are small and polar and if the number of different solutes is large, RPLC and NPLC can be combined into a very powerful 2DLC separation system (Murphy et al., 1998b); see Chapter 18. Other applications that utilize different types of reversed-phase columns in both dimensions have been advocated by Carr (Stoll et al., 2006) for metabolomics work in small-molecule separations. These stationary phases include a pentafluorophenylpropyl stationary phase in the first dimension and a carbon-coated zirconia material stationary phase in the second dimension. A common mistake in 2D method development is to mismatch the solvent system; the two solvent systems must be miscible as discussed below. 6.4.1.2 Sampling One of the limitations of comprehensive 2DLC, as presently practiced, is that one must sample the eluent of the first column with the second column for a sufficient number of times so that peaks in the first dimension are not

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“deresolved.’’ This problem is discussed in Chapter 2, under Section 2.7. The essence of this problem is that undersampling the first dimension would allow a zone that has been separated in the first dimension to be mixed with other resolved zones within the confines of the sample loop. This can often result in a need to either speed up the second dimension or slow down the first dimension. As discussed in Chapter 2, we make the point here that there is a suggested sampling rate for the second column of three or four samples per minimum peak width of the first column. In other words, over the duration of a first-dimension peak, the second dimension should sample a peak three or four times. If the sampling rate is reduced to less than 1.5 times of the first-dimension peak width, the quantitative precision of total peak area and retention time is rapidly reduced as compared to higher sampling rates (Seeley, 2002). We now present some 2DLC chromatograms that suggest this point and the consequences of undersampling, from the viewpoint of chromatogram visualization. Fig. 6.2 shows the result of varying the sampling rate and Fig. 6.3 shows the result of varying the sampling phase. As shown clearly in Fig. 6.2, the sampling rate has a great influence on the resolution of neighboring zones. The trade off of resolution and sampling rate is evident from Fig. 6.2; a fast sampling rate will allow the first dimension to be resolved; however, the second dimension will exhibit a small decrease in resolution. But overall, the faster second-dimension analysis is desirable compared to the less sampled first-dimension analysis. The sampling phase is defined as the start of sampling relative to an eluting peak. The sampling phase is important because one may think that three samples per peak are adequate. However, if a small part of the peak, either at the beginning or at the end of the peak, is included in the sample, the phase effect would lead to a distinct undersampling of the first-dimension peak. It has been demonstrated that for more than four samples per peak, the phase of sampling has a minor effect (Murphy et al., 1998a). A lower sampling rate is more affected by variations in the phase since not all peaks elute at regular intervals. Thus, 2DLC chromatograms at higher sampling rates give better reproducibility because of the sampling phase not being a factor but above four samples per peak width this slows down the first dimension and becomes a deleterious effect. Because frequent sampling is necessary, the second dimension must be fast. Hence, the second-dimension technique must be developed first because it sets the stage for the type of performance that can be driven by the first dimension. This process has worked out well for a number of systems that we have studied. But the philosophy is simple: Why develop a high efficiency first dimension separation if this efficiency is distorted by the sampling process? Hence, the first dimension is matched in performance to the second-dimension system and related through the sampling criterion. 6.4.1.3 Solvent Systems and Gradient Elution Picking a compatible solvent system is extremely important. Strange effects happen when the solvents are not totally miscible in both dimensions and over all of the gradient ranges if one or more dimensions use gradient elution techniques. If gradient elution is feasible, it should be used, as is the case for a standard chromatographic analysis, because gradient elution

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FIGURE 6.2 The sampling time effect on two-dimensional resolution from Murphy (1998a). Amplitude changes because of peak overlap cause a corresponding change in gray scale between chromatograms. The sampling time is labeled on the 2D chromatograms. Reprinted with permission of the American Chemical Society.

separations generally produce higher resolution separations per unit time than isocratic separations. However, gradient elution in the second dimension requires fast gradients and column reequilibration time must be included in the second-dimension time. A number of 2DLC applications have attempted to use liquid chromatography at critical conditions (LCCC) and are discussed in Chapter 17. This mode of operation is useful for copolymer analysis when one of the functional groups has no retention in a very narrow range of the solvent mixture. However, determining the critical solvent composition is problematic as it is very sensitive to small changes in composition. This technique has advantages, but the method development is extremely difficult and is very much dependent on the exact nature of the sample as to whether it will work at all.

136


FIGURE 6.3 The sampling phase effect on two-dimensional resolution for 3.8 samples per first-dimension peak (top 4) and 1.9 samples per first-dimension peak (bottom 4). A sampling time of 1 min was used for the 3.8 samples study and a sampling time of 2.0 min was used for the 1.9 samples study. The sampling phase is expressed as a delay time and noted on each chromatogram. Taken from Murphy (1998a) and reprinted with permission of the American Chemical Society.

Protein applications are extremely sensitive to solvent pH, salt concentration, and small molecular weight additives such as trifluoroacetic acid (TFA), which affect solute equilibria. These effects are known and depending on the specific application, proteins are often run under denaturing conditions, which offer vastly different retention conditions than nondenaturing conditions.

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6.4.1.4 Second-Dimension Elution Time Range We define the elution time range as the time between the start of the first peak and the end of the last peak for complete elution in the second dimension. In isocratic LC for the second dimension, the elution time range can be the same as the minimum sampling time. Alternatively, the sampling time can be run out to the end of the last peak of the second dimension. These two cases will be discussed in detail later. In gradient elution for the second dimension, the elution time range plus the equilibration time add up to the minimum sampling time. Thus, the sampling time must be greater than or equal to the elution time range plus the equilibration time for gradient elution operation; otherwise, peak overlay effects or peak wraparound will occur. For isocratic LC, the solute does not need to fully elute from the second-dimension column prior to the next sampling period. This is illustrated in Fig. 6.4, where it is shown that more than one sample can be resident in the column at one time. Using the chromatograms shown in Fig. 6.5, which show the effect of various injection volumes that will be discussed later, it is not necessary to wait for the full 2 min of sampling time. This significantly helps to speed up the sampling process and is most useful for SEC, where short elution time ranges are typically found and short times between the injection and nonretained peaks are typical of column operation. Figure. 6.4 shows that after 1 min, the first sampled components are about to elute. However, the next solute sample is introduced into the column at 1 min; the zone

FIGURE 6.4 Zone evolution on the second column showing zones for two different times at each sampling number. Note that there is more than one injection of the sample loop on the second-dimension column after the first injection.

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(a) 400,000

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FIGURE 6.5 Size exclusion chromatograms of a mixture of PEG 8000, PEG 1000, and PEG 200 at different injection volumes. Sample concentrations of 800 ppm (a), 400 ppm (b), 200 ppm (c), and 133 ppm (d). Run conditions: Polymer Standards Service styrene–divinyl ˚ pores; tetrahydrofuran benzene linear mixed bed, 50 mm 8 mm, 3 mm particles and 100 A flowing at 1 mL/min; evaporative light scattering detection. Taken from Murphy (1998a) and reprinted with permission of the American Chemical Society.

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appears visually at the half-minute intervals shown in Fig. 6.4. This sample is largely unresolved in Fig. 6.4. Hence, there can be more than one sample on the seconddimension column at the same time with the restriction that sampling cannot be faster than the elution time range. The initial data, which are composed of baseline, are usually removed from the 2D chromatogram prior to construction of the matrix. In complex samples, when the range of elution times may not be known beforehand, there is the possibility of wraparound where components from the previous run are still eluting on the next second-dimension elution (Micyus et al., 2005). This situation is of concern and should be eliminated in the method development process for all but the most exploratory of work. This may require collecting fractions and injecting these fractions into the second-dimension column to determine the most retained compound retention time as part of the method development process. Reducing the analysis time in the second dimension will allow faster sampling times and higher 2DLC resolution, at least in the first dimension. The run time in the second dimension can be reduced by employing steeper solvent gradients and/or by increasing the flow rate. Fig. 6.6 gives an example of the separation of several proteins at different flow rates and injection volumes using RPLC on a monolithic column. At 1 mL/min the elution range is 5 min, whereas at 2 mL/min the elution range is 2.5 min. The higher flow rates reduce the elution range, but decrease resolution. The use of monolithic columns in the second dimension may have advantages since they allow higher flow rates (i.e., higher velocities) with reduced back pressures without major losses in resolution. However, the use of short monolithic columns may not possess the necessary efficiency needed for fast operation in the second dimension. Elevated temperature can also be a very effective way to increase column performance and reduce the second-dimension elution time range as retention is generally reduced at higher temperatures. This has been utilized by Carr and coworkers (Stoll et al., 2006, 2007) to perform very fast second-dimension elution time ranges, and it should be considered for faster chromatographic analysis in general. 6.4.1.5 Sample Loop Volumes The maximum injection volume of the seconddimension column is determined using the strongest solvent in the first dimension to minimize effects on band broadening and improve 2DLC resolution. For isocratic separations in the first dimension, the maximum injection volume onto the seconddimension column should be determined attheisocraticsolventcomposition.In Fig.6.5, variousinjectionvolumesareshownforSECanalysesofpolyethyleneglycolselutingina 1 min elution time range. Good resolution is obtained with 25–50 mL injection volume; 100 mL is still tolerable, but a 150 mL injection results in a significant loss in resolution. If gradient elution high performance liquid chromatography (HPLC) is used in the first dimension, several solvent compositions should be injected into the seconddimension system along with a test solute to see if there is an effect of solvent composition. Typically, the highest solvent strength of the first dimension will show the largest effect if the effect is present. If an effect is seen, then the largest possible injectionvolume is used before deleterious effects become noticeable. As long as these two solvent systems are miscible, there are generally few problems, and the effect of

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100 2.60 %

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100 2.61 %

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1.69

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FIGURE 6.6 Merck Chromolith monolithic RPLC column at 1 mL/min (top) and 2 mL/min with various injection volumes. Protein standards: A ¼ aprotinin, B ¼ cytochrome C, C ¼ carbonic anhydrase.

injection volume can be determined by monitoring peak resolution with various injection volumes at different compositions similar to Fig. 6.5. The first-dimension maximum flow rate can now be determined from the injection volume divided by the sampling time. In Fig. 6.5, if 100 mL injections are utilized, the

METHOD DEVELOPMENT

TABLE 6.2

Method Development Examples Second dimension

Diameter, mm 4.6 4.6 1.0 1.0 0.3 0.3

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First dimension

Injection volume, mL

Minimum sampling time, min

Maximum flow rate, mL/min

Diameter, mm

100 100 10 10 1.0 1.0

1.0 5.0 1.0 5.0 1.0 5.0

100 20 10 2.0 1.0 0.2

2.0 0.5 0.5 0.3 0.1 0.05

first-dimension flow rate should be set equal to 100 mL/min. Table 6.2 examines three different column diameters in the second dimension and the effect that sampling time has on the first-dimension maximum flow rate. At a constant flow velocity, the largest flow rates from the first dimension are accommodated with larger column diameters in the second dimension. In addition, smaller sampling times allow larger first-dimension flow rates. As the sampling time increases (i.e., a decrease in sampling rate), the first-dimension column flow rate must be decreased to accommodate the longer time required to fill the loop. Thus, faster sampling times allow larger first-dimension column diameters (and higher loading capacity), which may be critical for trace analysis or high dynamic range samples such as the plasma proteome. 6.4.1.6 First Dimension Optimization After the second-dimension separation has been developed, the first-dimension flow rate is determined. This includes selecting a first-dimension column diameter to work at the flow rate selected. We illustrate the selection process with an application that addresses a column method for proteins that functions as a replacement for planar 2D gel electrophoresis (2DGE) within a narrow molecular weight and pI range. In the planar experiment, isoelectric focusing is performed in the first dimension and sodium dodecylsulfate polyacrylamide gel electrophoresis (SDS/PAGE) in the second dimension. We use the second-dimension separation from Fig. 6.6 with a 25 mL injectionvolume and 2.5 min sampling time; the separation is an RPLC method that uses a monolithic column. Thus, 10 mL/min is the maximum flow rate in the first-dimension. Fig. 6.7 shows the development of the first-dimension column that utilizes a hydrophilic interaction (or HILIC) column for the separation of proteins at decreasing flow rates. The same proteins were separated in Fig. 6.6 (RPLC) and 6.7 (HILIC) and have a reversed elution order, which is known from the basics of HILIC (Alpert, 1990). It is believed that HILIC and RPLC separations are a good pair for 2DLC analysis of proteins as they appear to have dissimilar retention mechanisms, much like those of NPLC and RPLC; it has been suggested that HILIC is similar in retention to NPLC (Alpert, 1990). Because the HILIC column used in Fig. 6.7 gave good resolution at 0.1 mL/min and no smaller diameter column was available, the flow was split 10-fold to match the second-dimension requirement.

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100

0.1 mL/min

%

C B

85.73 89.56

A

101.80

40.11

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38.91

13.21

%

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33.63

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3.34

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1.0 mL/min 10.00

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80.00

90.00

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FIGURE 6.7 A HILIC column (Tosoh Amide-80, 25 cm 4.6 mm, 5 mm particles) at different flow rates with a solvent of ACN/water/0.1%TFA. The gradient is 80% to 15% ACN. Protein standards: A ¼ aprotinin, B ¼ cytochrome C, C ¼ carbonic anhydrase.

The resulting 2DLC chromatogram combining HILIC and RPLC is shown in Fig. 6.8 and compared to the electropherogram produced by 2DGE. The 2DLC result shown in Fig. 6.8 gives similar resolution to the 2DGE result in a 2 hour analysis time. The analysis could have been optimized further by decreasing the flow rate and

FIGURE 6.8 Right: 2DLC of Sigma M3411 proteins standards. The standards are A ¼ amyloglucosidase (pI 3.8, MW 89,000), B ¼ ovalbumin (pI 5.1, MW 45,000), C ¼ carbonic anhydrase (pI 6.6, MW 29,000), and D ¼ myoglobin (pI 7.6, MW 17,000). Left: 2DGE from Sigma Product Information sheet M3411: markers for two-dimensional electrophoresis.

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slowing down the first dimension separation. The chromatographic peaks in Fig. 6.7 are about 5 min wide, which allows sampling twice of the peak width. Overall, increasing 2DLC resolution to four samples per chromatographic peak would double the analysis time. Depending on the equipment available and the analytical problem, a reduction in resolution is a trade-off for increased speed, a situation that is similar to one-dimensional chromatography.

6.5 PLANNING THE EXPERIMENT It is worthwhile to plan the 2D experiment and understand the type of 2D separation one is planning to conduct before going through the cardinal rules. For example, is the goal of the separation to try to resolve all of the peaks possible? This scheme has often been the goal for complex samples of biological origin where the increased resolution is the purpose of performing 2DLC. If the desired goal is to utilize the two dimensions to sort out specific interactions with each phase, as is the case for multifunctional surfactants and other polymers (see Chapter 18), then the goals of the separation are very much different. In this case, speed can be more important than the resolution of most zones. If one is willing to wait, much higher resolution of fused zones is possible with 2DLC, as is the case with one-dimensional LC. Again, the types of trade-offs between speed, resolution, and selectivity must be sorted out before one goes through the cardinal rule choices and designs.

6.6 GENERAL COMMENTS ON OPTIMIZING THE 2DLC EXPERIMENT: SPEED–RESOLUTION TRADE-OFF As with any separation technique, the desired goal is to maximize peak resolution at the fastest speed. Higher resolution in 2DLC is easier to achieve than when using onedimensional chromatography because selectivity differences between the two different columns can give a resolution enhancement. This is easily seen through the simplified resolution equation, discussed in Chapter 2, qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi Rs ¼ Rs21 þRs22 þ ð6:1Þ where the individual subscripted Rsi are the resolution in the first and second dimensions and these lead to the total resolution. This is the same formula as the distance d between two points x1,y1 and x2,y2 on a plane, also referred to as the ‘2 norm or Euclidean norm: d ¼ ½ðx1 x2 Þ2 þðy1 y2 Þ2 1=2

ð6:2Þ

Equation 6.1 demonstrates that if resolution is low in one of the dimensions and high in the other dimension, the result is at least as large as the higher resolution. However, limited sampling of the first dimension can make the standard deviation of the zone in

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the first dimension appear from 1.5 to 3 times larger than if it were continuously sampled, as given in Fig. 2.7 of Chapter 2. Since the resolution is inversely proportional to the standard deviation of one of the zones to be resolved (assuming the neighbors have equal width), this implies that Rs1 is one third (2 samples per zone width) to two thirds (3 samples per zone width) that of a continuously sampled zone. For two zones with resolution of 1.0 in each dimension, the total resolution is 1.414 showing the increase in resolution because of the use of two dimensions; we assume here that this is approximate and that the two zones in question lie at 45 angles. For the case where the two zones are sampled with two or three samples per zone width, the value of Rs1 is reduced by 0.33 and 0.66, respectively, and the results after plugging into Equation 6.1 give a total resolution of, respectively, 1.05 and 1.20, a reduction of approximately 25% and 15% from the “ideal’’ continuously sampled zones. This is not a drastic amount of resolution loss, but it highlights one of the major difficulties in utilizing 2DLC or any multidimensional technique based on zone sampling. Faster sampling times result in higher resolution and decrease the total analysis time. Thus, the speed and efficiency of the second dimension is critical in maintaining the higher resolution advantage of 2DLC. We feel that the method development of fast second dimension column methods is the major challenge to the effective utilization of 2DLC.

ACKNOWLEDGMENT We thank Dwight Stoll, of the University of Minnesota, for helpful discussions.

REFERENCES Alpert, A.J. (1990). Hydrophilic-interaction chromatography for the separation of peptides, nucleic acids and other polar compounds. J. Chromatogr. A 499, 177–196. Bedani, F., Kok, W.Th., Janssen, H.-G. (2006). A theoretical basis for parameter selection and instrument design in comprehensive size-exclusion chromatography liquid chromatography. J. Chromatogr. A 1133, 126–134. Berridge, J.C. (1985). Techniques for the Automated Optimization of HPLC Separations. John Wiley & Sons, Inc., New York. Eksteen, R. (2007). Personal communication. Glajch, J.L., Snyder, L.R., editors (1990). Computer-Assisted Method Development for HighPerformance Liquid Chromatography. Elsevier, Amsterdam. Guiochon, G. (2006). The limits of the separation power of unidimensional column liquid chromatography. J. Chromatogr. A 1126, 6–49. Micyus, N.J., Seeley, S.K., Seeley, J.V. (2005). Method for reducing the ambiguity of comprehensive two-dimensional chromatography retention times. J. Chromatogr. A 1086(1–2), 171–174. Murphy, R.E., Schure, M.R., Foley, J.P. (1998a). Effect of sampling rate on resolution in comprehensive two-dimensional liquid chromatography. Anal. Chem. 70(8), 1585–1594.

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Murphy, R.E., Schure, M.R., Foley, J.P. (1998b). One and two-dimensional chromatographic analysis of alcohol ethoxylates. Anal. Chem. 70, 4353–4360. Poppe, H. (1997). Some reflections on speed and efficiency of modern chromatographic methods. J. Chromatogr. A 778, 3–21. Schoenmakers, P.J. (1986). Optimization of Chromatographic Selectivity. Elsevier, Amsterdam. Schoenmakers, P.J. Vivo-Truyols, G., Decrop, W.M.C. (2006). A protocol for designing comprehensive two-dimensional liquid chromatography separation systems. J. Chromatogr. A 1120, 282–290. Seeley, J.V. (2002). Theoretical study of incomplete sampling of the first dimension in comprehensive two-dimensional chromatography. J. Chromatogr. A 962(1–2), 21–27. Snyder, L.R., Kirkland, J.J., Glajch, J.L. (1997). Practical HPLC Method Development. 2nd edition. Wiley Interscience, New York. Stoll, D.R., Cohen, J.D., Carr, P.W. (2006). Fast, comprehensive online two-dimensional high performance liquid chromatography through the use of high temperature ultra-fast gradient elution reversed-phase liquid chromatography. J. Chromatogr. A 1122, 123–137. Stoll, D.R., Li, X., Wang, X., Carr, P.W., Porter, S.E.G., Rutan, S.C. (2007). Fast, comprehensive two-dimensional liquid chromatography. J. Chromatogr. A 1168 (1–2), 3–43. Van der Horst, A., Schoenmakers, P.J. (2003). Comprehensive two-dimensional liquid chromatography of polymers. J. Chromatogr. A 1000, 693–709.

7 MONOLITHIC COLUMNS AND THEIR 2D-HPLC APPLICATIONS Tohru Ikegami, Hiroshi Aoki, Hiroshi Kimura, Ken Hosoya, and Nobuo Tanaka Department of Polymer Science and Engineering, Kyoto Institute of Technology, Matsugaski, Sakyo-Ku, Kyoto 606-8585, Japan

7.1 INTRODUCTION As chromatography is going to be used for the separation of very complex mixtures, new methods and materials aiming at much higher efficiency have been examined in liquid-phase separations. Higher chromatographic performance than obtainable by using particle-packed columns with common high performance liquid chromatography (HPLC) instrumentation has been achieved by employing capillary electrochromatography (CEC) (Rozing et al., 1996), ultrahigh pressure HPLC (UPLC) (MacNair et al., 1997), and monolithic columns (Minakuchi et al., 1996). The reason why monolithic columns attract attention in spite of limited availability at present is that they can potentially provide higher performance than conventional particle-packed columns under similar operating conditions. In this chapter, current performance of monolithic columns, made of organic polymers or silica, will be briefly reviewed before describing examples of two dimensional – high performance liquid chromatography (2D-HPLC) methods using these monolithic columns.


147

148

MONOLITHIC COLUMNS AND THEIR 2D-HPLC APPLICATIONS

7.2 MONOLITHIC POLYMER COLUMNS This section provides an overview of properties of polymer monolith columns related to 2D-HPLC. Monolithic organic polymer columns, having longer history than silica monoliths, have been reviewed in detail recently by Svec and by Eeltink including their preparation methods and performance (Eeltink et al., 2004; Svec, 2004a). Polymer monolith columns commercially available include poly(styrene-co-divinylbenzene) (PSDVB) columns and poly(alkyl methacrylate) columns. Polymeric packing materials have been used in HPLC and have shown to be particularly suitable for applications for the separation of biological substances. They are free from silanol effects associated with silica-C18 phases and showed higher performance and recovery for proteins than silica-based materials in reversed-phase mode. They are also more chemically stable in aqueous, buffered mobile phases for ion-exchange (IE) or size-exclusion (SEC) mode applications, leading to frequent use in proteome studies. The efficiency and the mechanical stability of columns packed with organic polymer particles were somewhat lower than those of silica-based materials. Similar tendencies observed with monolithic materials seem to be the reason for their 2D applications in off-line mode so far reported. Polymer monolithic columns, however, will find a greater market share in monolithic columns when compared with polymer particulate columns in whole particle-packed columns. Polymer monolithic columns are also attractive for the application in CEC (Zou et al., 2002; Svec, 2004b), especially for chip-based separation systems, because of their flexibility in surface chemistry and higher chemical stability than silica materials. 7.2.1

Structural Properties of Polymer Monoliths

The major design concept of polymer monoliths for separation media is the realization of the hierarchical porous structure of mesopores (2–50 nm in diameter) and macropores (larger than 50 nm in diameter). The mesopores provide retentive sites and macropores flow-through channels for effective mobile-phase transport and solute transfer between the mobile phase and the stationary phase. Preparation methods of such monolithic polymers with bimodal pore sizes were disclosed in a US patent (Frechet and Svec, 1994). The two modes of pore-size distribution were characterized with the smaller sized pores ranging less than 200 nm and the larger sized pores greater than 600 nm. In the case of silica monoliths, the concept of hierarchy of pore structures is more clearly realized in the preparation by sol–gel processes followed by mesopore formation (Minakuchi et al., 1996). The monomers commonly used for the preparation of polymer monoliths are either hydrophobic, for example, styrene/divinylbenzene and alkyl methacrylates, or hydrophilic, for example, acrylamides. The polymerization is usually accomplished by radical chain mechanisms with thermal or photochemical initiation, as detailed in the reviews (Eeltink et al., 2004; Svec, 2004a and b). Internal structures of polymer monoliths are described to be corpuscular rather than spongy; this means throughpores were found to be interstices of agglomerated globular skeletons as shown in Fig. 7.1 (Ivanov et al., 2003). Porosity is presumably predetermined by the preparation

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149

FIGURE 7.1 Scanning electron micrographs of a polystyrene–divinylbenzene monolithic column prepared in a 20-mm fused silica capillary tube (reproduced from the reference, Ivanov et al. (2003), with permission from American Chemical Society).

feed as the volume of porogen in the total mixture and the extent of shrinkage of skeletons during polymerization. The porosity predicted by the feed composition, or slightly smaller, has been found in many cases. It is of much interest to compare polymer monoliths with monolithic silica columns for practical purposes of column selection. Methacrylate-based polymer monoliths have been evaluated extensively in comparison with silica monoliths (Moravcova et al., 2004). The methacrylate-based capillary columns were prepared from butyl methacrylate, ethylene dimethacrylate, in a porogenic mixture of water, 1-propanol, and 1,4-butanediol, and compared with commercial silica particulate and monolithic columns (Chromolith Performance). Table 7.1 shows the pore properties of several polymer monolithic columns prepared from styrene/DVB, methacrylates, and acrylamides along with the feed porosity and column efficiency, summarized from several recent publications. Some important points seem to be clearly shown in Table 7.1, especially by the comparison of the properties between methacrylate-based polymer monoliths and silica monoliths. A rather limited range of mesopores in terms of size and volume were observed in the skeletons of polymer monoliths. The porosity of the polymer monolith seems to be lower than that of silica monolith. The total porosity of these monoliths is in the range of 0.61–0.73, whereas interstitial (through-pore) porosity and mesopore porosity are 0.28–0.70 and 0.03–0.24, respectively. In the case of poly(butyl methacrylate-co-ethylene dimethacrylate), the observed porosity is around 0.61– 0.71, resulting in permeability 0.15–8.43 1014 m2, whereas the observed porosity of silica monoliths prepared in a capillary is 0.86–0.96 and the permeability is 7–120 1014 m2. Higher permeability will be advantageous for 2D applications, as mentioned later. High performance monolithic columns were prepared from styrene and divinylbenzene (PSDVB, 200 mm i.d.) (Oberacher et al., 2004). The monoliths possess 5– 300 nm pores with porosity of ca. 50% and 20% for external and internal pores, respectively, with specific surface areas of 30–40 m2/g. The column showed permeability K ¼ 3.5 1015 m2 in water and slightly less in acetonitrile. The pore size

150


distribution was not necessarily bimodal. Small-sized pores and skeletons seem to be the origin of high performance and high back pressure. A short column can be used for a high speed gradient separation of biological molecules. 7.2.2

Chromatographic Properties of Polymer Monolithic Columns

The monolithic PSDVB polymer columns mentioned above showed relatively low permeability but exceptionally high efficiency with plate heights of about 10 mm at optimum mobile phase linear velocity. The columns were particularly useful for gradient separation of peptides. Not many other examples, however, were found for characterization of polymer monoliths with respect to fundamental chromatographic properties, including column efficiency in an isocratic mode, because the primary application areas of polymer monoliths are gradient separations of biological macromolecules. According to the comparative evaluation cited above (Moravcova et al., 2004), the methacrylate-based monolithic columns showed retention behavior similar to that of the silica columns by HPLC in 70% ACN, although methylene selectivity (aCH2) was lower. The results were attributed to the lower surface hydrophobicity of the polymeric columns having polar ester groups. The van Deemter curves showed that the efficiency of the columns for homologous alkylbenzenes was lower than that of silica columns. In a review on the comparison between microparticulate and monolithic capillary columns (Eeltink et al., 2004), the efficiency of a wide range of polymer monoliths, including acrylamides, styrene/divinylbenzene, methacrylate, and acrylate, were discussed in detail. It was shown that better efficiency has been achieved with CEC mode, where flow is electrodriven, than with a pressure-driven mode. Acrylamide monoliths showed plate heights somewhere around 9–40 mm in CEC mode, though some better results were observed, whereas plate heights ranging 9–20 mm were reported for PSDVB, or methacrylate-based monoliths in CEC mode. Recently, methacrylamide-based C16 columns, prepared from methacrylamide (monomer), N,N0 -methylenebisacrylamide (crosslinking agent), 1-octadecene (functional monomer) in 1-propanol as porogen, have been proposed for CEC application (Zhang et al., 2005). The separation efficiency of 25,000/m (plate height 40 mm) was reported for neutral PAH compounds like fluorene. As shown in Table 7.1 listing the comparison among the polymer monoliths prepared from three major monomers, styrene/DVB, methacrylates, and acrylamides, the column efficiency of polymer monoliths (in terms of plate height) at optimum linear velocity of mobile phase seems to be a little lower (in HPLC mode, H ¼ 22–25 mm for methacrylate-based), compared to that of silica monolith (in HPLC, H ¼ 8–16 mm) and packed particles (in HPLC, H ¼ 7–19 mm with 5 mm particles), although the polymer monoliths performed relatively well in CEC mode, compared to the monolithic and particulate silica. The agglomerated globular structure of polymer monoliths may cause the lower permeability and efficiency of polymer monoliths, presumably because of the slow mass transfer in stationary phase and the irregularity in the structure, skeleton size, and channel size. The formation of agglomerated globules may be attributed to the choice of a

151

1.90

1.92

2.0–8.0

NA

NA

Skeleton 1.0–2.0 Particulate, 5 mm Particulate 3 mm

NA

NA

4.35

NA

9

13.1–25.2

NA

NA

NA

NA

0.84–1.3

NA

3–4

0.69–3.87

Globule 0.15–1.2 Granule 5

NA

340

140–340

2.0

2.5

0.3

NA

5.4–41.3

32–43

7–183

Surface Area, m2/g

Reference: aEeltink et al. (2004). bVirklund et al. (2001). cGusev et al. (1999). (2001). gJung and Hahn (2004). hJanc et al. (2002). iTanaka et al. (2002).

Silica particlea

Silica particlea

Styrene/ divinylbenzeneh Glycidyl MA/EDMAh 2,3-Dihydroxypropyl MA/EDMAh Silica monolitha, i

100

0.5

Globule

Styrene/ divinylbenzened Methacrylates (BMA/EDMA)a,e Acrylamidea,f,g

Meso, nm 3–4

Globule 2–5

Styrene/ divinylbenzenea–c

Macro, mm

0.6–1

Skeleton size, mm

Pore size

Properties of Polymer Monoliths for HPLC

Column materials (reference)

TABLE 7.1

3–13

5–20

5

NA

NA

NA

9–40

13–25

-

9–20

CEC

NA

NA

0.96

NA

NA

NA

NA

0.65

0.86–0.96

1.08 mL/g

1.05 mL/g

0.68 mL/g

NA

0.61–0.71

0.71

0.4–0.65

Observed Porosity

NA

4

7–120

NA

NA

NA

NA

Alkylbenzenes, PAHs Drugs, proteins (Commercial product) (Commercial product) (Commercial product) Alkylbenzenes, PAH Alkylbenzenes, PAHs Alkylbenzenes, PAHs

PAHs, pharmaceuticals, peptides Peptides

Analytes

Hoegger and Freitag

f

0.15–8.4

0.35

5–18

Permeabili ty (K), [m2] 1014

Moravcova et al. (2004).

e

0.71–0.85

0.55–0.65

0.6

0.6–0.65

Feed Porosity

Oberacher et al. (2004).

d

4–19

7–19

8–16

NA

NA

NA

6–330

22–25

8

15–85

HPLC

Plate Height, nm

152


poor solvent for the monomers as porogen for the formation of through-pores, though solvent mixtures of poor and good solvents, binary or ternary, are occasionally used for pore size control. Karger and coworkers used tetrahydrofuran (THF) and 1-decanol as porogen for the preparation of PSDVB capillaries of bimodal pore distribution (Ivanov et al., 2003). The higher porosity greater than 0.7 is not easily achieved in the case of brittle polymer monoliths. 7.2.3

Two-Dimensional HPLC Using Polymer Monoliths

Polymer monolithic columns with small diameter have been successfully employed for proteome analysis. Karger and coworkers reported MALDI-TOF of separated fractions spotted on a plate from a polymeric reversed-phase column that showed high peak capacity (Chen et al., 2005). Huber and coworkers reported separation and detection of about 200 peaks within 5 min by using a PSDVB column (Premstaller et al., 2001). As to the application to 2D analysis, there seem to be still few examples of polymer monolith application. It is presently our understanding that polymer monoliths are used either in off-line approach, or in slow 2D separations that are slow elution at the first dimension with fractionation and storage, and subsequent slow elution of each stored fraction at the second dimension. The usefulness of polymer monolithic columns in 2D HPLC is nicely shown in the recent publication on 2D separation of protein digests (Toll et al., 2005; Dragan et al., 2005). The use of PSDVB columns for proteome analyses was demonstrated to achieve comprehensive 2D-HPLC separation by using PSDVB columns in an off-line mode. In one case PSDVB columns were operated in acidic and basic mobile phases of different pH values (Fig. 7.2, Toll et al., 2005) (also see Chapter 12; by Gilar et al. for examples of hybrid silica columns using different pH mobile phases for 2D peptide separations). PSDVB-based monolithic columns were also shown to be effective for characterization of posttranslational modification of proteins (Tholey et al., 2005). As mentioned earlier, high-speed separation is necessary to carry out fast, comprehensive 2D HPLC. The polymer monoliths have not been employed in such 2D HPLC, probably because permeability of polymer monoliths is not high enough to allow fast elution of the second dimension (2nd-D) in simple 2D operation, and the gradient cycle at the 2nd-D cannot be so fast to allow online 2D operation without reducing peak capacity at first dimension (1st-D). The application of polymer monoliths in 2D separations, however, is very attractive in that polymer-based packing materials can provide a high performance, chemically stable stationary phase, and better recovery of biological molecules, namely proteins and peptides, even in comparison with C18 phases on silica particles with wide mesopores (Tanaka et al., 1990). Microchip fabrication for 2D HPLC has been disclosed in a recent patent, based on polymer monoliths (Corso et al., 2003). This separation system consists of stacked separation blocks, namely, the first block for ion exchange (strong cation exchange) and the second block for reversed-phase separation. This layered separation chip device also contains an electrospray interface microfabricated on chip (a polymer monolith/

MONOLITHIC SILICA COLUMNS

153

FIGURE 7.2 Two-dimensional separation of tryptic peptides from a mixture of 10 proteins by reversed-phase separation at high pH followed by reversed-phase separation at low pH. 1stD: 50 mm 530 mm i.d., PSDVB monolith, linear gradient 0–30% acetonitrile in 72 mmol/L triethylamine-65 mmol/L acetic acid, pH 10.0, flow rate 18 mL/min, temperature 50 C, detection negative-mode ESI-MS. 2nd-D: 50 mm 100 mm i.d. PSDVB monolith, linear gradient 0%–35% acetonitrile in 6.5 mmol/L trifluoroacetic acid, pH 2.1 in 20 min, flow rate 500 nL/min, temperature 50 C, detection: positive mode ESI-MS. One microliter injection of 10 fractions from 1st-D, (reproduced from the reference, Toll et al. 2005, with permission from Elsevier).

multiple nozzle electrospray device) and is hyphenated to a mass spectrometer. The development of polymer monoliths of high permeability will enable their wide use in 2D HPLC.

7.3 MONOLITHIC SILICA COLUMNS Chromatographic use of monolithic silica columns has been attracting considerable attention because they can potentially provide higher overall performance than particle-packed columns based on the variable external porosity and through-pore size/skeleton size ratios. These subjects have been recently reviewed with particular interests in fundamental properties, applications, or chemical modifications (Tanaka et al., 2001; Siouffi, 2003; Cabrera, 2004; Eeltink et al., 2004; Rieux et al., 2005). Commercially available monolithic silica columns at this time include conventional size columns (4.6 mm i.d., 1–10 cm), capillary columns (50–200 mm i.d., 15–30 cm), and preparative scale columns (25 mm i.d., 10 cm).

154


7.3.1

Preparation

Monolithic silica columns possess three-dimensional network structures consisting of silica skeletons of 0.5–2 mm diameter and through-pores of 1–8 mm (Ishizuka et al., 2002; Motokawa et al., 2002). They are prepared by sol–gel reactions starting from tetraalkoxysilanes, namely, tetramethoxysilane (TMOS) or tetraethoxysilane (TEOS). The characteristic cocontinuous structures of monolithic silica columns are produced by spinodal decomposition of initially homogeneous solutions of tetraalkoxysilanes. They undergo hydrolysis and polymerization in the presence of acetic acid to form silica gel. By controlling the rates of hydrolysis and polymerization causing gelation and phase separation between the silica-rich phase and the water-rich phase, the sizes of skeletons and through-pores can be varied. Domain size (combined size of through-pore and skeleton) can be controlled by the concentration of water-soluble polymer, namely, poly(ethylene glycol) (PEG). Mesopores are formed in silica skeletons by treatment with ammonia introduced after the formation of the network structure of silica skeletons. Ammonia can be generated by the hydrolysis of urea charged in an initial reaction mixture. The preparation procedure was developed, and discussed in detail, by Nakanishi and coworkers (Nakanishi, 1997). Monolithic silica columns can be prepared either in a test tube or in a fused silica capillary tube. In the case of preparation in a test tube, silica network structures undergo shrinkage during reaction and subsequent aging process, typically to 70% of the mold size (Minakuchi et al., 1996, 1997, 1998 a,b), whereas in a capillary the silica skeletons must be covalently attached to the wall so that no void can be formed. The monolithic silica structure can be formed in a capillary tube of up to 200 mm i.d. starting from TMOS, whereas a successful preparation of up to 530 mm ID capillary columns was reported starting from a mixture of TMOS and methyltrimethoxysilane (MTMS) (Motokawa, 2006). For the preparation in a test tube, the resulting silica monoliths are covered by PEEK resin to fabricate a column to be used under pressure of up to 200 bar. High-temperature treatment is commonly carried out at above 600 C for preparation in a test tube, and at 330 C for those in a capillary to prevent damage to the polyimide coating of the tube. Chemical modification of silica is carried out by oncolumn reaction with octadecyldiethylaminosilane in a capillary. In the case of monolithic silica prepared in a test tube, batch modification prior to the cladding process is possible.

7.3.2

Structural Properties of Monolithic Silica Columns

Monolithic silica columns currently available consist of silica skeletons of 0.5–2 mm diameter and through-pores of 1–8 mm. Figure 7.3 shows scanning electron micrographs (SEM) of monolithic silica prepared in a mold (a) and those prepared in a capillary (b–d) (Motokawa et al., 2002). Through-pores of a monolithic silica column are relatively large compared to those of a column packed with particles with (throughpore size)/(skeleton size) ratio in the range 1–4, much larger than that in a particlepacked column, 0.25–0.4 (Unger, 1979). Mesopores of 10–30 nm can be formed.


155

FIGURE 7.3 Scanning electron micrographs of monolithic silica prepared from sol–gel methods. (a) monolithic silica prepared from TMOS in a test tube, and monolithic silica columns prepared from a mixture of TMOS and MTMS, (b) in a 50-mm fused silica capillary, (c) in a 100-mm fused silica capillary, and (d) in a 200-mm fused silica capillary tube (reproduced from the reference, Motokawa et al. (2002), with permission from Elsevier).

External porosity is above 60% for conventional size columns prepared in a test tube (Al-Bokari et al., 2002), and above 80% for those prepared in a capillary. The high external porosity and large (through-pore size)/(skeleton size) ratios can lead to much higher permeability of monolithic silica columns than that of a column packed with particles of similar column efficiency (Leinweber et al., 2002; Leinweber and Tallarek, 2003). For example, a Chromolith column provided by Merck shows permeability, K ¼ 7 1014 m2, twice as high as a column packed with 5 mm particles. Capillary columns with large through-pores show up to 30 times higher permeability, K ¼ 1.2 1012 m2 (Eq. 1, u: linear velocity of mobile phase, h: solvent viscosity, L: column length, and DP: column pressure drop) (Ishizuka et al., 2002). High permeability is an important feature of monolithic silica columns, particularly for 2D-HPLC applications. Higher flow rates generally afford a greater peak capacity for a short separation time at 2nd-D. A faster 2nd-D analysis is also advantageous as it permits more frequent sampling of the first dimension. An additional advantage of a monolithic silica column is the increased mechanical stability provided by the integrated network structure. Although columns with smaller domain size show high column efficiency and high pressure drop, those with larger domain size show low pressure drop and suitable for fast operation. K ¼ uhL=DP

ð7:1Þ

Disadvantages of monolithic silica columns include the labor-intensive preparation of individual columns with possible reproducibility problems, limited availability, and relatively short retention caused by the smaller amount of silica existing in a column

156


than in a particle-packed column. The k values found with monolithic silica-C18 columns were smaller than those with particle-packed columns by a factor of 2–5 depending on the total porosity, 80–85% for conventional size and 90–95% for capillary type. This can be a limitation for a 2nd-D column. Large volume injections frequently needed in 2D HPLC need highly retentive columns for maintaining the column efficiency. 7.3.3

Chromatographic Properties of Monolithic Silica Columns

50

180 150

Plate height, H / µm

Pressure drop, ∆P / kg cm–2

Correlation was found between domain size and attainable column efficiency. Column efficiency increases with the decrease in domain size, just like the efficiency of a particle-packed column is determined by particle size. Chromolith columns having ca. 2 mm through-pores and ca. 1 mm skeletons show H ¼ 10 (N ¼ 10,000 for 10 cm column) at around optimum linear velocity of 1 mm/s, whereas a 15-cm column packed with 5 mm particles commonly shows 10,000– 15,000 theoretical plates (H ¼10–15) (Ikegami et al., 2004). The pressure drop of a Chromolith column is typically half of the column packed with 5 mm particles. The performance of a Chromolith column was described to be similar to 7–15 mm particles in terms of pressure drop and to 3.5–4 mm particles in terms of column efficiency (Leinweber and Tallarek, 2003; Miyabe et al., 2003). Figure 7.4 shows the pressure drop and column efficiency of monolithic silica columns. A short column produces 500 (1 cm column) to 2500 plates (5 cm) at high linear velocity of 10 mm/s. Small columns, especially capillary type, are sensitive to extra-column band

120 90 60 30 0

0

1 2 3 4 5 Linear velocity, µ /mm s–1

40 30 20 10 0 0

1 2 3 4 Linear velocity, µ /mm s–1

5

FIGURE 7.4 (a) Plots of column back pressure against linear velocity of mobile phase (29, 32, 37). Mobile phase: 80% methanol. The pressures were normalized to the column length of 15 cm. Columns: 5 mm silica-C18 particles, Mightysil RP18 (.), Inertsil ODS-3(~). Monolithic silica column prepared in a mold, MS-PTFE(B)S-C18 (), MS-PEEK(&). Monolithic silica column in capillary, MS-FS(50)-A (*), MS-FS(50)-B (~), MS-FS(50)-C (!). MS-FS(50); 50 mm i.d. (A) 33.5 cm (effective length 25 cm), (B) 53.5 cm(effective length 45 cm), (C) 138.5 cm (effective length 130 cm). The van Deemter plots obtained for C18 monolithic silica columns and silica-C18 packed columns with hexylbenzene as a solute. (b) For columns of 4.6–7 mm i.d. Mobile phase: 80 % methanol. Symbols as in Figure 7.4 for the columns. Solute: hexylbenzene (reproduced from the reference, Tanaka et al. (2002) with permission from Elsevier).


157

broadening. Injection and detection as well as line connection must be carried out not to reduce the performance of small size columns by minimizing extra-column effects (Ikegami et al., 2004). E ¼ DPt0 =hN 2 ¼ ðDP=NÞðt0 =NÞð1=hÞ ¼ H 2 =K

ð7:2Þ

Separation impedance (E value, Eq. 7.2) is calculated by multiplying a reciprocal number of theoretical plates per unit pressure drop with a reciprocal number of theoretical plates per unit time. Separation impedance is a measure of total column performance in terms of permeability and column efficiency. The separation impedance for a monolithic silica column was as low as 200–300, about 10 times smaller than those of particle-packed columns. Although the performance of monolithic silica columns can be higher than that of particle-packed columns at similar pressure drop in a slow-elution range, the column efficiency for high-speed operation comes closer to that of a column packed with particles. High porosity or large through-pores can be responsible for the reduction of column efficiency at high speed due to the increased contribution of slow mass transfer in the mobile phase. According to a simulation study, monolithic columns can show advantage over particle-packed columns for the separations that require more than 50,000 theoretical plates at a pressure limit of 400 bar (Desmet et al., 2005). Monolithic columns with small domain size, especially those with high porosity prepared in capillary, could not produce performance expected from the reduction of their unit size (domain size). This is partly because of the increased inhomogeneity of the network structure of a monolithic silica column. It has been suggested that an increase in homogeneity of monolithic silica can increase the performance by several times (Gzil et al., 2004). The silica skeletons prepared in a test tube are smoother and more homogeneous than those prepared in a capillary (Fig. 7.3). The inhomogeneity and large-sized through-pores seem to explain the lower performance of monolithic silica than a particle-packed column, particularly for capillary columns operated at high speed. High column efficiency at high linear velocity is particularly important for a second-dimension column in 2D HPLC. The use of monolithic silica columns in 2D HPLC is, however, advantageous in terms of mechanical stability of the column. It is highly desirable to use short monolithic silica columns that can achieve high efficiency at high linear velocity. In a sense each monolithic column is unique, or produced as a product of a separate batch, because the columns are prepared one by one by a process including monolith formation, column fabrication, and chemical modification. Reproducibility of Chromolith columns has been examined, and found to be similar to particle-packed-silicabased columns of different batches (Kele and Guiochon, 2002). Surface coverage of a Chromolith reversed-phase (RP) column appears to be nearly maximum, but greater silanol effects were found for basic compounds and ionized amines in buffered and nonbuffered mobile phases than advanced particle-packed columns prepared from high purity silica (McCalley, 2002). Small differences were observed between monolithic silica columns derived from TMOS and those from silane mixtures for planarity in solute structure as well as polar interactions (Kobayashi et al., 2004).

158


Monolithic silica columns with various surface derivatization, including ion exchange (Xie et al., 2005), and chiral functionalities (Chen et al., 2002; Lubda et al., 2003; Chankvetadze et al., 2004), as well as protein-immobilized monoliths (Kato et al., 2005) have been reported. The latter will be an important part of an integrated multidimensional separation/identification system.

7.4 PEAK CAPACITY INCREASE BY USING MONOLITHIC SILICA COLUMNS IN GRADIENT ELUTION A typical HPLC separation using a 15-cm column of 15,000 theoretical plates produces peak capacity (Giddings, 1991) of about 80–100 under isocratic conditions and up to 150 under gradient conditions in 1 h (Eq. 7.3, n: peak capacity, N: number of theoretical plates of a column, and tR and t1: retention time of the last and the first peak of the chromatogram, respectively). An increase in the number of separated peaks per unit time can be achieved by increased separation speed made possible by monolithic silica columns (Deng et al., 2002; Volmer et al., 2002). This has also been shown for peptides and proteins (Minakuchi et al., 1998; Leinweber et al., 2003). pffiffiffiffi ð7:3Þ n ¼ 1þð N =4ÞlnðtR =t1 Þ Shallow gradient elution using a long monolithic silica capillary column is an easy and a viable approach to increase the number of separated peaks, or peak capacity, per unit time. In a liquid chromatography-mass spectrometry (LC-MS) system, the use of a monolithic silica capillary column resulted in better resolution in LC and detection and identification of greater number of peaks in MS. Figure 7.5 shows comparison of chromatograms for a gradient elution of methanol extracts of arabidopsis thaliana using 30–90 cm monolithic silica columns in capillary (Tolstikov et al., 2003). The increase in the column length, a greater number of peaks has been resolved without much increase in gradient time. The major factor for the improved detection/resolution seems to be the reduction of ion suppression. It is known that less easily ionizable solutes in unresolved peaks tend to be suppressed from ionization. The increased efficiency of a long capillary column, ca. 60,000 theoretical plates by 90-cm column, compared to 30,000, resulted in a greater resolution and less ion suppression to produce higher sensitivity and a greater number of peaks detected (Tolstikov et al., 2003). The results indicate that some peaks showed consistent intensity in MS trace, whereas other peaks showed increase in peak intensity up to certain level, with the increase in resolution provided by the longer column indicating the elimination of ion suppression. Small sample size, small amount of mobile phase required, and compatibility with micro- and nano-ESI interface are the features of capillary HPLC that are most conveniently carried out by using a long monolithic silica column. This was shown very nicely in a proteomic study using a 20 mm i.d., 70 cm monolithic silica capillary column (Luo et al., 2005) providing a peak capacity of 420 in a single run in 4 h. The advantage of using long capillary columns was also shown when they were applied to

2D HPLC USING MONOLITHIC SILICA COLUMNS

159

FIGURE 7.5 Replicate injections of an Arabidopsis leaf methanol extract on capillary monolithic C18 columns in positive ionization fullscan MS, given as base peak chromatograms. (a) 0.2 300 mm, (b) 0.2 600 mm, (c) 0.2 900 mm column. Mobile phase A: 6.5 mM ammonium acetate (pH 5.5, adjusted by acetic acid), B: acetonitrile. (a) 5% B–20%B (15 min)–70%B (22 min) to 100%B (57 min), 2.6 mm/s, (b) 5%B–20%B (15 min)–70%B (23 min) to 100%B (75 min), 2.6 mm/s, (c) 5% B–20%B (16 min)–70%B (23 min) to 100%B (110 min), 1.8 mm/s (reproduced from the reference, Tolstikov et al. (2003), with permission from the American Chemical Society).

2D separations. A monolithic silica-C18 capillary column was used as a 1st-D column coupled off-line to capillary electrophoresis (CE) in a metabolome study (Jia et al., 2004). Although it is not a simple 2D chromatographic system, the example showed that high peak capacity obtained by gradient elution with a long monolithic silica capillary under shallow gradient can result in high peak capacity in 2D separation when coupled with fast 2nd-D separation.

7.5 2D HPLC USING MONOLITHIC SILICA COLUMNS Recent proteomic or metabolomic analyses often require the separation/identification of several hundreds to several thousands of species in a mixture using LC–MS. It is practically impossible to achieve complete separation for a sample with complexity of

160


this magnitude by single HPLC run. Such a separation needs a system able to produce a very high peak capacity. In UPLC, peak capacity of 300–500 has been achieved under gradient conditions (MacNair et al., 1999; Mellors and Jorgenson, 2004; Patel et al., 2004). As mentioned earlier, a monolithic silica capillary column can also provide separation of several hundred species (Tolstikov et al., 2003; Luo et al., 2005). Twodimensional HPLC can potentially provide high peak capacity, because the peak capacity of 2D HPLC is theoretically a product of the peak capacities of the two systems (Eq. 7.4) (Giddings, 1991). n2DHPLC ¼ n1stD n2ndD

ð7:4Þ

The two HPLC systems should have differences in retention mechanism preferably orthogonal to each other (see Chapters 3 and 12 by Davis and Gilar et al., respectively). Various combinations have been employed in the past, including IE- RP, RP— SEC, SEC—SEC, and RP—RP (Bushey and Jorgenson, 1990; Opiteck et al., 1997a; K€ohne and Welsch, 1999; Wagner et al., 2002). In proteome analysis, the ion-exchange mode using salt gradient elution for the first dimension is commonly followed by the reversed-phase mode gradient elution to separate a very complex mixture of digested peptides. In a so-called shot gun approach, a single column packed successively with ion-exchange and reversed-phase packing materials is commonly employed (Wolters et al., 2001). Tens of thousands of peaks per day are processed to detect various proteins. The use of a long monolithic silica capillary for the 2nd-D resulting in increased detection/identification of proteins was also reported (Wienkoop et al., 2004). In the examples mentioned above, both 1st-D and 2nd-D HPLC systems are operated relatively slowly, because every fraction from the 1st-D is separated by a relatively slow gradient elution in the 2nd-D to complete the comprehensive separation. Sample storage loops or trapping columns may be used after the 1st-D column to hold every fraction. In fast and comprehensive online 2D HPLC, the separation of the 2nd-D must be very fast, to be completed within each sampling interval of the 1st-D. Various approaches were taken in the past to alleviate the problem of slow elution, or a limited volume or frequency of injection, for the 2nd-D (Bushey and Jorgenson, 1990; Opiteck et al., 1997a; K€ ohne and Welsch, 1999; Wagner et al., 2002). These approaches included the following: (i) smaller diameter column was employed in the 1st-D than in the 2nd-D, (ii) the 1st-D column was eluted slowly or intermittently, or (iii) two or more sets of columns and chromatographs were used for the 2nd-D. Simple 2D HPLC can be carried out by connecting the outlet of the 1st-D to the column or an injector loop of the 2nd-D chromatograph. The effluent of the 1st-D is fractionated at certain intervals, and the fraction is injected into the 2nd-D. While the next 1st-D fraction is loaded onto the 2nd-D, the 2nd-D separation of the previous fraction must be completed. Such an operation scheme needs very high speed separation for the 2nd-D. Monolithic columns seem to be suited as a 2nd-D column, because they possess high permeability and relatively high efficiency at high linear velocity of mobile phase, as well as high mechanical stability against fast flow.


161

Utilizing the difference in selectivity between a monolithic silica-C18 column (2nd-D) and another particle-packed column of C18 phase (1st-D), 2D HPLC separation was shown mainly for basic compounds and other species (Venkatramani and Zelechonok, 2003). The authors also reported other examples of reversed-phase 2D HPLC, using amino- and cyano-derivatized particle-packed columns for 2nd-D separation. The combination of normal-phase separation for the 1st-D and reversedphase separation on monolithic C18 column for the 2nd-D was reported (Dugo et al., 2004). The use of a microbore column and weak mobile phase for the 1st-D and a monolithic column for the 2nd-D was essential for successful operation. Improvement in the 2D separation of complex mixtures of Chinese medicines was also reported (Hu et al., 2005). Following are practical examples of comprehensive 2D HPLC using monolithic silica columns that have been reported. 7.5.1

RP-RP 2D HPLC Using Two Different Columns

Simple and comprehensive 2D HPLC was reported in a reversed-phase mode using monolithic silica columns for the 2nd-D separation (Tanaka et al., 2004). Every fraction from the 1st-D column, 15 cm long (4.6 mm i.d.), packed with fluoroalkylsilyl-bonded (FR) silica particles (5 mm), was subjected to the separation in the 2nd-D using one or two octadecylsilylated (C18) monolithic silica columns (4.6 mm i.d., 3 cm). Monolithic silica columns in the 2nd-D were eluted at a flow rate of up to 10 mL/min with separation time of 30 s that provides fractionation every 15–30 s for the 1st-D, which is operated near the optimum flow rate of 0.4–0.8 mL/min. The 2D-HPLC systems were assembled, as shown in Fig. 7.6, so that the sample loops of the 2nd-D injectors were back flushed to minimize band broadening. In the simplest scheme of 2D HPLC, effluent of the first dimension (1st-D) was directly loaded into an injector loop (500 mL) of the 2nd-D HPLC for 28 s, and 2 s were allowed for injection. This operation was accompanied by the loss of 1st-D effluent for 2 s out of 30 s in each cycle. The flow rate of 10 mL/min allowed the elution of solutes having retention factors (k values) up to 8 for the 2nd-D within the 30-s separation window, with t0 of 3.5 s. Figure 7.7 a and b shows the chromatograms for the 1st-D and the 2nd-D, respectively, obtained for a mixture of hydrocarbons and benzene derivatives. The 1st-D chromatogram showed many overlapping peaks. PAHs were eluted as mixtures from the FR column, and some are separated in the 2nd-D. The loss of about 7% of the 1st-D effluent caused by a 2-s injection in a 30-s operation cycle, which could cause up to 20% loss of a peak in the most unfavorable case, or the narrowest peak at the beginning, can be avoided by using two six-port valves each having a sample loop (Fig. 7.6b); an alternative system uses a 10-port valve with two holding loops. The loops hold the effluent of the 1st-D alternately for 30 s during a complete separation cycle on the 2nd-D column to effect comprehensive 2D HPLC. From the 2nd-D chromatograms in Fig. 7.7b, a contour plot was obtained, as shown in Fig. 7.8. The 2D plots indicate that several types of hydrocarbons and benzene

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(a)

(b) 1st-D column

1st-D column

1st-D detector 2nd-D column

2nd-D column

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Loop B (500 µl)

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.

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Loop A Load, Loop B Inject Loop A Inject, Loop B Load

1st-D Pump

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Loop A

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FIGURE 7.6 (a) Tubing connection at 2nd-D injector of simple 2D-HPLC. (b) Tubing connection of two six-port valves used as 2nd-D injector in simple and comprehensive 2DHPLC. (c) Scheme of comprehensive 2D-HPLC using two 2nd-D columns (reproduced from the reference, Tanaka et al. 2004, with permission from American Chemical Society).

derivatives were clearly distinguished from each other. A group of compounds showed similar behavior determined by their relative affinity to the two stationary phases; thus 2D reversed-phase HPLC can afford structural information for the solutes, especially when the separation mechanism on each stationary phase is known and widely different. The three stationary phases, FR, C18, and (pentabromobenzyloxy)propylsilyl-bonded (PBB), represent stationary phases providing the smaller and the greater dispersion interactions for solute retention (Turowski et al., 2003) to show the widely different selectivity from each other, making 2D separations possible. They are effective for the separation and characterization of organic compounds as in 2D separations in normal-phase–reversed-phase combination (Dugo et al., 2004). When two monolithic silica columns were used for two sets of 2nd-D chromatographs (Fig. 7.6c) separating each fraction of the 1st-D effluent alternately,


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FIGURE 7.7 Two-dimensional separation of a mixture of hydrocarbons and benzene derivatives in simple 2D HPLC. (a) Chromatogram obtained in the 1st-D on FR column in 60% methanol/water. (b) Chromatograms obtained in the 2nd-D on C18 column in 80% methanol/water. The insets 3(a) and 3(b) are expanded views of Fig. 7.7(a) and (b) respectively. Sampling every 30 s at the 1st-D. Flow rate: 0.4 mL/min for 1st-D, and 10 mL/min for 2nd-D (reproduced from the reference, Tanaka et al. 2004, with permission from American Chemical Society).

fractionation every 15 s of the 1st-D and separation time of 30 s at the 2nd-D were possible. In this case, two columns of the same stationary phase (C18) or different phases, C18 and PBB, could be employed for the 2nd-D, although the latter needed two complementary runs. The systems produced peak capacity of about 1000 in ca. 60 min with one column used for the 2nd-D, and in about 30 min when two columns were used for the 2nd-D (Fig. 7.8). Injection of a large volume sample can cause significant band broadening in the 2nd-D. The use of a smaller 1st-D column with a larger 2nd-D column is a possible approach to avoid this problem, but it is associated with considerable dilution of solutes. When a mobile phase of lower elution strength is used for the 1st-D separation than for the 2nd-D, the band broadening can be minimized. This is the reason why a FR column with lower retention was used in 60/40 methanol/water in the 1st-D, and a C18 phase was used in the 2nd-D with 80/20 methanol/water mobile phase. The loss of column efficiency in the 2nd-D was avoided even with the injection of 200 mL-fractions into a 3-cm column containing ca. 400 mL of mobile phase as the analytes were concentrated at the head of the more strongly retentive 2nd-D column.

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FIGURE 7.8 A contour plot obtained for 2D HPLC using two 2nd-D columns in a system shown in Figure 7.6c. Sampling every 15 s at the 1st-D. Flow rate: 0.8 mL/min for 1st-D, and 10 mL/min for 2nd-D (reproduced from the reference, Tanaka et al. (2004) with permission from American Chemical Society).

7.5.2

RP–RP 2D HPLC Using Two Similar Columns

A comprehensive 2D HPLC can be carried out with two very similar columns in reversed-phase liquid chromatography (Ikegami et al., 2005). A mixture of water and tetrahydrofuran was used as a mobile phase in the 1st-D separation, and a mixture of water and methanol (CH3OH) in the 2nd-D separation with a common C18 stationary phase. In a RPLC separation that is usually dominated by hydrophobic interactions, the interactions between the solute and the stationary phases include instantaneous dipole–induced dipole interactions along with contribution of polar interactions depending on the structure of stationary phases. As each organic modifier undergoes different solute–solvent interactions based on the difference in dipole moment, polarizability, and hydrogen bond basicity as well as acidity, RPLC using mixtures of water–THF, water–CH3CN, and water–CH3OH exhibits significantly different selectivities (Tanaka et al., 1978; Dzido et al., 2002). Two-dimensional HPLC was carried out by using two C18 silica monolithic columns, a 10-cm column for the 1st-D, and a 5-cm column for the 2nd-D (Ikegami et al., 2005). The flow rate of each dimension was 0.65 and 9.5 mL/min, respectively, covering a k value range of up to 8 for the 2nd-D with the sampling time for the 1st-D of 45 s. A contour plot for 2D HPLC is shown in Fig. 7.9b, whereas


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FIGURE 7.9 Comparison of spots in a 2D-chromatogram and plots of retention factors in individual measurement. (a) Plots of k (45% CH3OH) values against k (22% THF) values on a Chromolith C18 column. Each mark in the plots stands for a group of benzene derivatives of similar functionality. (b) Contour plot for 2D HPLC. Mobile phase: 1st-D: 22% THF (0.1% HCOOH). Flow rate 0.65 mL/min, 2nd-D: Mobile phase: 45% CH3OH (0.1% HCOOH), Flow rate 9.5 mL/min. Sampling every 45 s (reproduced from the reference, Ikegami et al. (2005) with permission from Elsevier).

Fig. 7.9a shows plots of k values of sample compounds in 45% MeOH against k values in 22% THF. These two sets of plots are very similar. Figure 7.9 indicates that the 2D HPLC based on the difference in selectivity provided by an organic modifier that could be used for 2D HPLC to provide nominally large PC, but the selectivity difference was not quite orthogonal. The practically usable area for the 2D separation will be about half the orthogonal plotting area, because there is some similarity between the selectivities obtained with the two mobile phases. Gradient elution was employed in both dimensions to increase effective PC. In the 1st-D, THF concentration was increased from 15% to 30% linearly with a gradient time (tG) of 60 min, whereas in the 2nd-D, CH3OH concentration was changed from 30% to 45% linearly from 10 to 30 min. In the 2nd-D, each fraction from the 1st-D was separated under nearly isocratic conditions, because the separation time in the 2nd-D was only 45 s, which corresponds to a 0.6% change in CH3OH concentration for each separation. The results shown in Fig. 7.10 indicate that compared with Fig. 7.9b, the total analysis time was reduced by the elution in gradient mode in the 1st-D (15%–30% THF), early-eluting polar solutes showed larger k values than the isocratic mode that increased resolution, and that late-eluting solutes showed smaller retention times that compressed the scattered spots, as shown in Fig. 7.9b. The gradient elution mode was found to provide a more orthogonal 2D chromatogram, where the peaks were more evenly spaced and the blank region was reduced in the 1st-D and in the 2nd-D. As a consequence, the peak capacity in the 2nd-D separation could be used more efficiently,

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FIGURE 7.10 2D-chromatogram in gradient mode. Columns: Chromolith performance, 4.6 mm, 10 cm for 1st-D, and Chromolith speed 4.6 mm, 5 cm for 2nd-D. Mobile phase: 1st-D: 15% ! 30% THF (0.1% HCOOH) linear gradient, 0–60 min, flow rate 0.50 mL/min, Mobile phase: 2nd-D 30% ! 45% CH3OH (0.1% HCOOH), linear gradient, 10–30 min, Flow rate 9.5 mL/min. Sampling every 45 s (reproduced from the reference, Ikegami et al. (2005) with permission from Elsevier).

resulting in a significant improvement of separation efficiency compared with the isocratic mode. THF and methanol employed as organic modifiers of mobile phase provided a considerable difference in selectivity based on the polar interactions between solutes and the organic solvent molecules in the stationary phase. Acidic compounds, phenols and nitroaromatics, were preferentially retained in the THF-based mobile phase, whereas esters and ketones were preferentially retained in the methanol (a hydrogenbond donor) containing mobile phase. The system presented here seems to be very practical because any laboratory possessing two sets of HPLC equipment and two C18 columns can attempt similar 2D HPLC by simply changing the mobile phase for the two dimensions. 7.5.3 Ion Exchange–Reversed-Phase 2D HPLC Using a Monolithic Column for the 2nd-D Fast and simple 2D HPLC was also shown to be effective for the separation of a tryptic digest of bovine serum albumin (BSA) (Kimura et al., 2004). Every


167

fraction from the first column, 5 cm long (2.1 mm i.d.) packed with polymer-based cation exchange beads eluted at 50 mL/min, was subjected to separation in the 2nd-D using an octadecylsilylated (C18) monolithic silica column (4.6 mm i.d., 2.5 cm, Chromolith Flash). The salt gradient in the 1st-D was provided by changing the mixing ratio of the two eluents, A, an aqueous 5 mM ammonium formate solution buffered at pH 3.1, and B, an aqueous 5 mM ammonium formate/ 500 mM ammonium chloride solution buffered at pH 3.1, with a typical gradient run from 100% A to 85% B within 50 min. The flow-rate in the 2nd-D was 5.0 mL/min with a typical gradient starting with 100% A (water with 0.1% formic acid) for 0.5 min for sample enrichment, and then increased to 50% B (acetonitrile containing 0.1% formic acid) within 1.17 min followed by a 0.33 min column regeneration step with the initial eluent. The linear velocity in the column was 6.6 mm/s. The loop for the 2nd-D was loaded with the effluent of the 1st-D at 50 mL/min for 1 min 58 s, and then the injection valve was turned to inject the 100 mL fraction for 2 s onto the 2nd-D HPLC. The flow rate was 5 mL/min, and the valve was turned back for the next loading, resulting in fractionation of the 1st-D every 2 min. In this case less than 2% of the effluent from the 1st-D was wasted during sample injection. The 2nd-D effluent eluted at 5 mL/min from the 2nd-D column, passed through a UV detector, and then was split by using a T-joint at approximately a 1/140 split ratio, resulting in a flow rate of ca. 36 mL/min going into the spray capillary for ESI-TOFMS detection. Figure 7.11a shows the separation of a tryptic digest of BSA by the ion-exchange mode under gradient conditions in the 1st-D, showing apparently overlapping peaks within 40 min. The peak width found for unretained Tyr-Gly-Gly (retention time, 3.70 min) was 0.79 min in this system. Therefore, the maximum possible loss of a solute band from the 1st-D during injection time of 2 s at the 2nd-D was estimated to be ca. 14%, assuming Gaussian peaks of similar width for retained substances under gradient conditions. Figure 7.11b shows total ion chromatograms for gradient runs of the 2nd-D between 18 and 24 min. Figure 7.12 shows a 2D chromatogram for the tryptic digest of BSA obtained from total ion monitoring by ESI-TOF-MS. From the 1st-D, 18 fractions were injected at 2 min intervals onto the 2nd-D reversedphase system generating 18 chromatograms that were used to produce a 2D chromatogram. The fractionation interval of 2 min was longer than the minimum peak width at the 1st-D, indicating considerable loss of peak capacity obtained for the 1st-D. Two minute sampling for the 1st-D, 118 s loading, and 2 s injection for the 2ndD injection, allowed 1 min for gradient separation in the 2nd-D. This resulted in maximum peak capacity of about 700 within 40 min. Although this may be an overly optimistic estimate, the results obtained in this work can be compared favorably in terms of numbers of detectable peaks per unit time with the results obtained in other previous studies, including gradient elution using a long capillary column. Peak capacity of well over a few thousand can be expected by adding the separation capability of MS detection (Opiteck et al., 1997b; Wolters et al., 2001).

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FIGURE 7.11 Chromatograms of tryptic digest of BSA. (a) 1st-D separation with gradient elution on a cation exchange column. Column: MCI CQK-31S, 50 mm long, 2.1 mm i.d. Flow rate: 50 mL/min. Mobile phase: A, aqueous 5 mM ammonium formate solution buffered at pH 3.1, and B, an aqueous 5 mM ammonium formate/500 mM ammonium chloride solution buffered at pH 3.1. Gradient from 100% A to 85% B in 50 min. (b) 2nd-D chromatograms of simple 2D-HPLC separation of a tryptic digest of BSA. Monolithic silica-C18 column (4.6 mm i.d., 2.5 cm) as 2nd-D column. Mobile phase A: 0.1% formic acid, B: acetonitrile containing 0.1% formic acid. Gradient started with 0% B at 0.5 min, increased to 50% B at 1.67 min followed by 0.33 min column regeneration with the initial eluent, A. Flow rate: 5.0 mL/min. ESI-TOF-MS detection, total ion chromatogram for the mass range 400–2000. Monolithic silica-C18 column (4.6 mm i.d., 2.5 cm) as 2nd-D column (reproduced from the reference, Kimura et al. (2004) with permission from Wiley).

7.5.4 IEX-RP 2D HPLC Using a Monolithic RP Capillary Column for the 2nd-D In the examples described in Sections 7.5.1–7.5.3, an extremely high linear velocity was employed in the 2nd-D, with the resulting high flow rate leading to large solvent consumption. Thus, miniaturization of the system is highly desirable, especially for the 2nd-D. In another 2D-HPLC separation of BSA digest, a capillary-type monolithic silica-C18 column (100 mm i.d., 10 cm, 1.6 mm through-pore size and 0.8 mm skeleton size) was employed as a 2nd-D column with split flow/injection following the first column. The 1st-D column was 5 cm long (2.1 mm i.d.), packed with polymer-based cation exchange beads, and was eluted at 50 mL/min (Kimura et al., 2004). The split ratio before the 2nd-D column was controlled to be 3/2000 with the flow rate in the capillary column at 3.0 mL/min and solvent delivery by the 2nd-D pump at 2 mL/min. In this case, the exit of the capillary column was directly connected to the ESI spray capillary with a union. The linear velocity in the 2nd-D column was 7.7 mm/s. The gradient was started with 100% A (water with 0.1% formic acid) until 0.5 min, increased to 50% B (acetonitrile containing 0.1% formic acid) at 3.3 min, then further


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FIGURE 7.12 Two-dimensional separation of tryptic digest of BSA in simple 2D HPLC. Conditions as in Fig. 7.11 (reproduced from the reference, Kimura et al. (2004), with permission from Wiley).

increased to 100% B within 0.2 min to wash the column, then returned to the initial condition, and held for 0.5 min for reequilibration. As the eluent was split after the injector, a very high flow rate was employed at the pump compared to the flow in the column, resulting in very little delay in the gradient for the capillary 2nd-D column. The use of a capillary column for the 2nd-D led to better MS detectability compared to a larger-sized column. The loop of the 2nd-D HPLC was loaded with the effluent from the 1st-D HPLC at 50 mL/min for 3 min 53 s, then the injection valve was turned to inject the 200 mL fraction for 7 s onto the 2nd-D HPLC at 3 mL/min, and turned back for loading for the next 3 min 53 s, resulting in fractionation of the 1st-D every 4 min. Thus, ca. 300 nL or 0.15% of each fraction from 1st-D (200 mL) was introduced into the 2nd-D column having approximately 800 nL column volume. Fig. 7.13 shows a 2D chromatogram obtained by using a 10-cm capillary column for the 2nd-D. Portions (0.5–2.5 min) where peaks were observed during the gradient elution are shown for the 2nd-D. The longer gradient time (tG) needed than the case shown in Fig. 7.12 resulted in the longer sampling interval for the large 1st-D band width, ca. 4 min for most of the bands observed in the 2D

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FIGURE 7.13 Two-dimensional separation of tryptic digest of BSA in simple 2D-HPLC. Capillary monolithic silica-C18 column (0.1 mm i.d., 10 cm) was used as 2nd-D column. Mobile phase for 2nd-D: gradient started with 0% B at 0.5 min, increased to 50% B at 3.3 min, to 100% B at 3.5 min, then returned to the initial condition and held for the last 0.5 min. Flow rate: 3.0 mL/min in capillary, and 2 mL/min at the pump. Other conditions are similar to those for Figure 7.11 (reproduced from the reference, Kimura et al. (2004) with permission from Wiley).

chromatogram. Because a longer column and longer separation time were employed for the 1st-D, a greater number of solutes were separated in the 2nd-D based on the higher column efficiency and longer gradient time in the 2nd-D. This also provided greater MS detection sensitivity due to the nearly optimum flow rate (3 mL/min) on the capillary column, the greater amount of sample introduced to the 2nd-D column because of the longer sampling interval, and the smaller extent of dilution due to the use of small diameter column. The peak capacity, however, was much smaller than in Fig. 7.12 because of the longer sampling time in the 1st-D. It has been recommended to take at least 3–4 fractions from one peak in the 1st-D to preserve the separation (Murphy et al., 1998), whereas less frequent sampling of 2s–4s for the 1st-D peaks, where s is the standard deviation of a Gaussian peak in the 1st-D, was reported to give optimum peak capacity (Horie et al., 2007). Although a low flow rate was provided by splitting the flow after the injector, this rules out the injection of very small sample size. It is desirable to develop a simple 2D-HPLC system consisting of two capillary columns with a short column for the 2nd-D.

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7.6 SUMMARY AND FUTURE IMPROVEMENT OF 2D HPLC At present the monolithic columns used in 2D HPLC, described in Sections 7.5.1– 7.5.4, commonly provide plate height, H, 10–20 mm at a linear velocity of 1–10 mm/s. Improvement in monolithic column performance to 2500 theoretical plates with 2.5 s t0 will increase peak capacity by 40% to ca. 30 under similar conditions. The performance of monolithic silica columns available at present is much lower at high linear velocity than the most advanced particle-packed columns, especially UPLC columns. If we were able to use a 25-mm-long UPLC column for the 2nd-D generating 5000 theoretical plates at 10 mm/s mobile phase velocity, we can get PC of about 43 in 30 s to further increase PC by 40% (see Chapter 8 by Evans and Jorgenson for further discussion of UPLC). It has been predicted that a peak capacity of 15,000 can be obtained in 8 h by using a high efficiency 2nd-D column and gradient elution for both dimensions (Gilar et al., 2004). Reversed-phase-mode 2D HPLC is particularly facile, as shown in Section 7.5.2. Even if one cannot use a comprehensive 2D HPLC system, and a simple 2D HPLC system is accompanied by loss of several percent of the 1st-D effluent, short sampling intervals will prevent the loss of a peak to effect 2D separation of all solutes present. Development of various stationary phases showing selectivity difference (Jandera et al., 2005) and improvement in the performance of monolithic silica columns having mechanical stability, as well as the improvement in miniaturized column-switching device will promote 2D HPLC as a useful high-peak-capacity separation tool. Band broadening due to undersampling for 1st-D effluent will cause a decrease in peak capacity of 2D-HPLC (Davis et al., 2008; Horie et al., 2007; Murphy et al., 1998, Seely, 2002). When considering the effect of undersampling, peak capacity of ca. 3000 or ca. 5000 within one hour will be possible by using a 1-cm monolithic silica column or a 1-cm column packed with 2-mm particles at 2nd-D, respectively, in combination with 1st-D gradient elution producing peaks of 10-s band width by employing an optimum sampling period of 2.2-4 s of 1st-D peaks (Horie et al., 2007). REFERENCES Al-Bokari, M., Cherrak, D., Guiochon, G. (2002). Determination of the porosities of monolithic columns by inverse size-exclusion chromatography. J. Chromatogr. A 975, 275–284. Bushey, M.M., Jorgenson, J.W. (1990). Automated instrumentation for comprehensive twodimensional high-performance liquid chromatography of proteins. Anal. Chem. 62, 161–167. Cabrera, K. (2004). Applications of silica-based monolithic HPLC columns. J. Sep. Sci. 27, 843–852. Chankvetadze, B., Yamamoto, C., Tanaka, N., Nakanishi, K., Okamoto, Y. (2004). Highperformance liquid chromatographic enantioseparations on capillary columns containing monolithic silica modified with cellulose tris(3,5-dimethylphenylcarbamate). J. Sep. Sci. 27, 905–911. Chen, H.S., Rejtar, T., Andreev, V., Moskovets, E., Karger, B.L. (2005). High-speed, highresolution monolithic capillary LC-MALDI MS using an off-line continuous deposition interface for proteomic analysis. Anal. Chem. 77, 2323–2331.

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8 ULTRAHIGH PRESSURE MULTIDIMENSIONAL LIQUID CHROMATOGRAPHY Charles R. Evans and James W. Jorgenson Department of Chemistry, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA

8.1 BACKGROUND: MDLC IN THE JORGENSON LAB By the year 1990, many aspects of the field of multidimensional separations were well developed. On the theoretical front, Giddings (1987) had already described the multiplicative rule of peak capacity for ideal multidimensional separations and had set forth two fundamental criteria to define “ideal’’: that all dimensions must be fully orthogonal and that no resolution gained by one dimension may be lost on any subsequent dimension. In terms of experimental science, O’Farrell (1975) had long since published on 2D gel electrophoresis, a technique that continues to set the standard for high resolution separations of proteins. MDLC, however, remained comparatively delayed in its development. Although numerous investigations into MDLC had already been performed, most of them involved some form of heart cutting, wherein only a certain portion of the effluent from the first dimension column was analyzed on a second column. The heart-cutting approach has been reviewed in detail elsewhere (Cortes, 1990). The only major pre-1990 example resembling comprehensive MDLC, or LC LC, was reported by Erni and Frei (1978). Their technique used a size exclusion column coupled to a reversed-phase column with an eight-port switching valve. This instrument was used to analyze a complex plant extract. In this report,


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only seven fractions were transferred from the first column to the second. Thus, the contribution of the first separation to the overall resolution of the technique was quite limited. No further reports of online LC LC were published for some time, perhaps due to the apparent difficulty of making two different separation methods compatible to operate in direct connection with each other and the complexity of the instrumentation necessary to do so. 8.1.1

Cation Exchange–Size Exclusion

It was in this context that the first true comprehensive online LC LC separation was reported (Bushey and Jorgenson, 1990). Mixtures of intact proteins were analyzed using cation-exchange chromatography (CEX) as the first dimension and size exclusion chromatography (SEC) as the second. This research demonstrated that the practical difficulties of coupling two dissimilar LC modes for a comprehensive 2D separation are relatively easy to overcome when instrumentation is properly configured. The instrumentation used by Bushey and Jorgenson is depicted in Fig. 8.1. The first separation is run on a cation-exchange column using a gradient of increasing salt concentration. The outlet of the cation exchange column is coupled to an eight-port valve, which directs the effluent from the column to one of the two storage loops. After a sufficient period of time passes to precisely fill the sample loop with effluent from the first column, the valve is switched, causing the contents of the loop to be injected onto the size exclusion column using mobile phase flow from a second LC pump operating in isocratic mode. Meanwhile, the effluent of the first column is directed to the second storage loop. Once the second storage loop fills, the valve is actuated again and the contents of the second loop are injected onto the size exclusion column. The process is repeated until the cation-exchange separation is complete and all fractions have been analyzed by the size exclusion column. Because it is desirable to sample the first column many times over the duration of the run (Murphy et al., 1998), the size exclusion separations must be

FIGURE 8.1 Schematic diagram of a 2D CEX SEC instrument. Reprinted from Evans, C. R. and Jorgenson, J. W. (2004) Anal. Bioanal. Chem. 378, 1952–1961, with kind permission of Springer Science and Business Media.

BACKGROUND: MDLC IN THE JORGENSON LAB

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very rapid compared to the time necessary to complete the run. This is accomplished by using a relatively long anion-exchange gradient to spread the first separation over about 6 h and by using a fast mobile phase flow rate through the size exclusion column. The result is that the cation-exchange column can be sampled every 6 min, which is the time necessary to complete each size exclusion run. An UV absorbance chromatogram for a 2D separation of a mixture of nine proteins is shown in Fig. 8.2. The proteins are all resolved into single peaks, their positions determined by their charge and size. It is notable that no peaks appear during the first half of all of the size exclusion runs. This is because the exclusion volume— that is, the volume of mobile phase required to elute a molecule too large to enter the pores of the size exclusion particles—is roughly equal to the inclusion volume—the additional volume of mobile phase needed to elute a molecule small enough to completely permeate the pores. To eliminate this “wasted space’’ in the chromatogram, the eight-port valve can be cycled twice as frequently to overlap the useful, peak-containing portion of one size exclusion run onto the nonuseful

FIGURE 8.2 2D chromatogram of a mixture of eight proteins separated using CEX SEC. Detection was performed using UVabsorbance at 215 nm. Peak identities: (A) glucose oxidase, (B) ovalbumin, (C) b-lactoglobulin, (D) trypsinogen, (E) a-lactoglobulin, (F) conalbumin, (G) ribonuclease A, (H) hemoglobin, and (M) exclusion volume “pressure’’ ridge, and (N) inclusion volume “salt’’ ridge. Reprinted with permission from Bushey and Jorgenson (1990), copyright 1990, American Chemical Society.

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portion of another. This allows the first dimension separation to be run twice as fast, thereby reducing the total analysis time by half. The total peak capacity for this method was estimated to be 130, based on the product of the peak capacities visually estimated for each dimension. 8.1.2

Anion Exchange–Reversed Phase

The next major development in MDLC from the Jorgenson lab was a method designed for the analysis of peptides (Holland and Jorgenson, 1995). This method used gradient anion-exchange (AEX) chromatography as the first dimension and gradient reversed-phase liquid chromatography (RPLC) as the second. A schematic diagram of the instrumentation is depicted in Fig. 8.3. Two capillary columns with an inner diameter of 100 mm are used instead of LC column of conventional diameter. The first column is a 90 cm long capillary packed with 5-mm diameter anion-exchange particles. The anion-exchange mobile phase consists of 50% water/ 50% acetonitrile, a buffering component 5 mM 3-(N-morphilino) propanesulfonic acid (MOPS) at pH 7.9, and a salt component guanidine thiocyanate whose concentration increases from 2.5 to 170 mM over the course of a 40-h gradient. The long gradient and a slow flow rate (33 nL/min) cause the peptides to be eluted over a period of up to 25 h. Because the first dimension mobile phase contains 50% acetonitrile, to reduce any reversed-phase contribution to retention of the peptides

FIGURE 8.3 Schematic diagram of an AEX RPLC instrument. Reprinted with permission from Holland and Jorgenson (1995), copyright 1995, American Chemical Society.


181

on the anion-exchange column, the effluent from this column must be diluted approximately 10-fold with a makeup flow of water to allow the peptides to be effectively retained on the second dimension. The diluted first column effluent is loaded into a single storage loop through an eight-port valve. When the loop is full, the valve is actuated and a second pump loads the sample onto a 3 cm long capillary column packed with 5 mm reversed-phase particles. The loading of the sample onto the RPLC column takes approximately 30 s, during which time the effluent from the first column is directed to waste. After this period, the valve is switched back and the peptides are eluted from the column using a reversed-phase gradient. The gradient begins at 24% acetonitrile/76% water plus 0.1% triflouroacetic acid and increases to 50% acetonitrile/50% water plus 0.1% triflouroacetic acid over 1.6 min, followed by a 0.5-min wash at 100% acetonitrile and a 0.9-min period of reequilibration to the starting conditions. Detection is accomplished by monitoring fluorescence at the outlet of the reversed-phase column; therefore, all peptides must be derivatized with a fluorescent tag prior to the injection onto the first column. For this work, the fluorescent molecule tetramethylrhodamine 5-isothiocyanate (TRITC) was used as the derivatization reagent. A further improvement to the system was later reported in which the 5 mm reversed-phase media was replaced with equivalent perfusion-based reversedphase stationary phase media (Holland and Jorgenson, 2000). These particles suffer less band broadening when run at the high flow rates necessary to carry out a complete reversed-phase separation every 3 min. The resolving power of this system is relatively high. Fig. 8.4 shows a 2D chromatogram obtained for the separation of tryptic digest of porcine thyroglobulin. Approximately 150 spots can be seen, and the peak capacity is estimated to be about 2030. The detection limit of this system was determined to be approximately 0.8 attomoles for TRITC-tagged glycine. 8.1.3

Cation Exchange–Reversed Phase

The first report of the coupling of a mass spectrometer to a comprehensive online LC LC separation was published in 1997 as a collaboration between Jorgenson research group and Robert Anderegg of Glaxo Wellcome, Inc. (Opiteck et al., 1997b). The configuration of the instrumentation is essentially the same as that of the CEX SEC method previously described, using two columns and an eight-port valve with two storage loops. Instead of a size exclusion column for the second dimension, a reversed-phase column was used. Both the cation-exchange and reversed-phase columns are packed in-house, having inner diameters of 750 and 500 mm, respectively. The first- and second-dimension separations are both carried out using gradient elution; the cation-exchange gradient lasts 2 h and each reversed-phase gradient lasts 3 min. Direct coupling to a mass spectrometer is achieved by splitting the flow from the outlet of the UV detector such that 10% of the column’s effluent flows to the electrospray interface of the mass spectrometer. This instrument was used to analyze mixtures of intact proteins, including protein standards and a cell lysate of the bacterium Escherichia coli. A UV

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FIGURE 8.4 2D chromatogram of a AEX RPLC separation of reduced porcine thyroglobulin. Reprinted from Holland and Jorgenson (2000), by permission of John Wiley & Sons, Ltd.

absorbance 2D chromatogram for a separation of the E. coli lysate is shown in Fig. 8.5. The chromatographic peak capacity of this system was estimated to be 512, based on a cation-exchange peak capacity of 16 and a reversed-phase peak capacity of 32. The mass spectrometer can be effectively considered to be a third dimension for this 2D system as it can distinguish two or more coeluting components not resolved by chromatography. If the peak capacity of a mass spectrometer is taken to be 5, the total peak capacity of the system is over 2500. In addition to increasing the total peak capacity of the system, the mass spectrometer allows the determination of the molecular weight of all analytes in the 2D separation.


183

FIGURE 8.5 2D chromatogram of a CEX RPLC separation of an E. coli lysate. Reprinted with permission from Opiteck et al. (1997), copyright 1997, American Chemical Society.

8.1.4

Size Exclusion–Reversed Phase

Another approach to MDLC, also developed in collaboration with Robert Anderegg and Glaxo Wellcome, Inc., used SEC coupled to RPLC for the analysis of peptides (Opiteck et al., 1997a). Although size exclusion chromatography is sometimes considered a low resolution separation technique, its resolving power can be improved substantially by simply increasing the column length. To this ˚ pores end, six 30 cm long, 7.8 mm diameter size exclusion columns with 125 A (G2000SWXL, Toso-Haas, Montgomeryville, PA) were connected in series to give 1.8 m of effective column length and were used as the first dimension in a 2D separation. The schematic diagram of the instrument, shown in Fig. 8.6, differs slightly from the dual-storage-loop approach used in the previous methods. Two four-port valves are used to interface the first dimension directly with two 3.3 cm

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FIGURE 8.6 Schematic diagram of a 2D SEC RPLC instrument. Reprinted from Evans, C. R. and Jorgenson, J. W. (2004) Anal. Bioanal. Chem. 378, 1952–1961, with kind permission of Springer Science and Business Media.

long, 4.6 mm diameter reversed-phase columns placed in parallel (RP18, Micra Scientific, Northbrook, IL). Instead of storing the effluent from the size exclusion column assembly in a storage loop prior to the injection onto the second dimension, it is loaded directly onto one of the two reversed-phase columns. The 100% aqueous mobile phase used for the size exclusion separation is a weak eluent for RPLC, so all peptides eluting from the size exclusion column are retained in a narrow band at the head of the reversed-phase column. After a set length of time, the valve is switched, resulting in the connection of the column to a LC pump at its inlet and a dual UV/MS detector setup at its outlet. A 4-min reversed-phase gradient is then run to separate the peptides by hydrophobicity and elute them from the reversed-phase column. Meanwhile, the effluent from the size exclusion column assembly is directed to the second reversed-phase column. This parallel column arrangement is advantageous over the use of storage loops because it eliminates the time required to load the contents of the loop onto the second column. Peak position reproducibility proved to be excellent. A 2D UVabsorbance chromatogram of a tryptic digest of ovalbumin


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FIGURE 8.7 2D chromatogram of a SEC RPLC separation of a tryptic digest of ovalbumin. Reproduced with permission from Opiteck et al. (1997), copyright 1997, American Chemical Society.

separated on this system is shown in Fig. 8.7. The estimated chromatographic peak capacity of the separation is nearly 500. Several variations of this SEC RPLC system were also implemented. In one such enhancement, two 4.6 mm diameter reversed-phase columns were replaced with 1.0 mm diameter columns (Opiteck et al., 1998a). As the amount of sample injected and the flow rate of the first dimension were the same as in the previous study, the analyte is trapped at a higher concentration at the front of the second column. Much slower second dimension flow rates can be used since the columns are narrower, which results in increased peak concentration and allows much better signal to be attained from the mass spectrometer. This enhanced signal made it possible to perform online partial peptide sequencing by increasing the orifice voltage of the mass spectrometer to fragment the peptides. The SEC RPLC approach was also used to separate intact proteins rather than peptides, in a collaborative effort with Arthur Moseley at Glaxo Wellcome, Inc. (Opiteck et al.1998b). To optimize the size exclusion separation for proteins, six ˚ pores were exchanged for a series of eight columns with 250 A ˚ columns with 125 A ˚ ˚ pores or a combination of six columns with 250 A pores and six columns with 450 A

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FIGURE 8.8 2D chromatogram of a SEC RPLC separation of a native E. coli lysate. Reprinted from Opiteck et al. (1998), by permission of Academic Press.

pores for a maximum column length of 3.6 m. The system was configured with a UV detector, and mass spectrometry was performed off-line by collecting fractions and analyzing those of interest using MALDI-TOF-MS and/or electrospray-TOF-MS. A 2D chromatogram of a native E. coli lysate separated on this instrument is shown in Fig. 8.8. The chromatographic peak capacity was determined to be 1500. The system was also used to isolate particular proteins for further analysis. Figure 8.9 shows a UV chromatogram of an E. coli lysate grown to overexpress b-lactamase. The fraction containing an intense peak suspected to be the overexpressed protein was examined using MALDI-TOF/MS and ESI/MS, and was confirmed to be within 0.4% of the expected mass of b-lactamase, 28,907 Da. The fraction was also analyzed by Edman sequencing, which confirmed that the N-terminus of the protein had the expected amino acid sequence. The relative ease of isolating proteins in free solution and the corresponding option to analyze them using other techniques clearly highlight the utility of MDLC for proteomics compared to techniques such as 2D gel electrophoresis.


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FIGURE 8.9 2D chromatogram of a SEC RPLC separation of an E. coli lysate grown to overexpress b-lactamase. Reprinted from Opiteck et al. (1998), by permission of Academic Press.

Table 8.1 summarizes the MDLC separations reported to date by the Jorgenson lab. Both size exclusion and ion-exchange chromatographies have been used with essentially equal frequency and success as the first separation dimension. The most commonly used separation mode for the second dimension was RPLC, due to its high peak capacity, its potential for short run times, and its compatibility with online coupling to mass spectrometry via ESI. Using RPLC for the second dimension also allows on-column concentration of fractions from the first dimension as the aqueous buffers typically used for ion exchange or size exclusion separations are weak eluents for reversed-phase columns. Peak capacities of these MDLC separations are varied; however, not even the best has a peak capacity approaching that of 2D gel electrophoresis. Clearly, there is ample motivation for continuing to develop more powerful MDLC separation techniques.

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TABLE 8.1

Summary of MDLC Separations in the Jorgenson Lab

Year

Sample

Peak Capacity

Separation Modes

Detection

Reference

1990 1995 1997 1997 1997 1998 2000

Proteins Peptides Proteins Peptides Peptides Proteins Peptides

126 1050 512 520 495 1500 2028

CEX SEC AEX RPLC CEX RPLC SEC RPLC SEC RPLC SEC RPLC AEX RPLC

UV FL UV þ MS UV þ MS MS UV þ MS FL

Bushey and Jorgenson (1990) Holland and Jorgenson (1995) Opiteck et al. (1997b) Opiteck et al. (1997a) Opiteck et al. (1998a) Opiteck et al. (1998b) Holland and Jorgenson (2000)

8.2 ONLINE VERSUS OFF-LINE MDLC The majority of recent research involving comprehensive multidimensional LC has employed online coupling of dimensions, in which fractions from the first column are transferred directly to the second column using automated switching valves. Indeed, all the MDLC research from the Jorgenson lab that has been summarized to this point in the chapter lab has used online coupling. An alternative is off-line MDLC, where the effluent of the first column is physically collected as fractions that are later individually injected onto the second column. Although off-line coupling may seem to be a less sophisticated approach, a more thorough discussion of the relative advantages and disadvantages of online and off-line MDLC is merited. The primary advantages of the online approach to MDLC are automation and speed. An entire 2D separation can be carried out with no user intervention beyond the initial injection of the sample. This is very advantageous for the purpose of routine, high throughput analyses. The entire run can be finished in essentially the time that it takes for the first dimension separation to complete; therefore, the technique can be made quite rapid, especially compared to 2D gel electrophoresis and related techniques. However, these advantages impose certain limitations. Most significantly, the second dimension separation must be configured such that it is very rapid compared to the first. This is necessary so that many second-dimension runs can be carried out during the time it takes to complete a single run on the first dimension in order to adequately sample the column. This usually means that the resolution contributed by the second dimension must be compromised to improve the analysis speed. Secondly, the online approach requires relatively complex instrumentation. Two LC pumps must be operated simultaneously, and the operation of the switching valves used to connect the two dimensions must be timed precisely so that all of the effluent from the first column is transferred to and analyzed by the second column. The relative complexity and the cost of the instrumentation may discourage the average user from adopting online MDLC as an alternative to proven techniques such as 2D gel electrophoresis. Off-line MDLC is a comparatively simple approach. When minimally configured, it requires only a single LC pump, two separation columns of different types, and

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suitable containers for collecting fractions. A greater degree of automation can be achieved if a fraction collector is used to collect the fractions from the first column and an autosampler is used to inject the fractions onto the second column. There is no limit to the number of fractions that can be collected, so off-line coupling can be just as thorough in sampling the first dimension as online coupling. Fraction collection also permits sample manipulation between separation dimensions – for example, fractions could be concentrated via lyophilization or reconstituted in a solvent more appropriate for the analysis on the second dimension. Another major advantage of the off-line approach is that there is no need for the second dimension separation to be run faster than the first because fractions can be stored and run when time permits. Therefore, both separations can be optimized to provide the highest possible resolution, which could allow the second dimension to contribute a much higher peak capacity to the overall 2D method. Off-line MDLC also allows flexibility in the amount of each fraction transferred to the second dimension. This amount could be increased to improve sensitivity, decreased to prevent second-dimension column overload, or a fraction could be skipped altogether if the detector monitoring the first-dimension column effluent indicates that no analytes are present. Finally, an off-line 2D separation allows the flexibility of analyzing any fraction multiple times on the second dimension without repeating the entire 2D separation, should the sample prove of particular interest or if an instrument failure occurs. Of course, off-line sampling also has numerous disadvantages. Foremost is speed—off-line sampling is certain to take longer than an online approach because the fractions are usually run after the first dimension separation is complete, rather than as it is in progress. An additional factor is that the operator of the instrument may need to spend more time on actively working to carry out the 2D separation, performing tasks such as setting up for fraction collection, collecting the fractions, and manipulating the fractions such that they are ready to be analyzed on the second dimension. Finally, as the liquid fractions come in contact with more tubes and surfaces when fraction collection is performed than when online coupling is used, there is a greater possibility of sample losses with off-line coupling. In summary, both off-line and online approaches have substantial advantages and disadvantages. In general, it is probably best to use online coupling as a first choice due to its advantages in speed and automation. If, however, some portion of the technique is not amenable to online coupling, or the extra flexibility of off-line coupling is particularly advantageous for the task at hand, then off-line MDLC is a highly suitable option.

8.3 MDLC USING ULTRAHIGH PRESSURE LIQUID CHROMATOGRAPHY: BENEFITS AND CHALLENGES In two-dimensional separations, as in any other form of chromatography, it is desirable to generate very high peak capacity in as short a time as possible. In reality, some compromise between speed and resolution must be made, the specifics of which depend on the nature of the sample to be analyzed. A relatively new technique known

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as ultrahigh pressure liquid chromatography offers improved chromatographic performance without long run times (MacNair et al., 1997, 1999; Jerkovich et al., 2003; Mellors and Jorgenson, 2004; Patel et al., 2004). Therefore, UHPLC has the potential to substantially enhance either the speed or the peak capacity of LC LC, or even both simultaneously to some extent. The basis for this potential improvement is briefly described below, in terms of standard one-dimensional chromatographic theory. 8.3.1

An Introduction to UHPLC

The efficiency of a chromatographic separation can be described by the height equivalent of a theoretical plate (H), where lower values of H correspond to more efficient separations. The Van Deemter equation describes the relationship between H and mobile phase flow velocity (u) as the sum of three major terms, A, B, and C, each of which represents a different contribution to band broadening in a chromatographic column. H ¼ Aþ

B þ Cu u

where A corresponds to Eddy diffusion, B to longitudinal diffusion, and C to resistance to mass transfer. For more efficient separations, it is advantageous to minimize the value of all these terms. The magnitude of two of the terms is known to be related to the diameter of the particles (dp) with which the column is packed: A is proportional to dp and C is proportional to d2p . Therefore, it is desirable to use the smallest possible particles in order to achieve the highest efficiency. An added benefit of smaller particles is that the optimum linear velocity – that is, the value of u that gives the most efficient separation – increases as particle diameter decreases. Therefore, it is possible to perform both faster and more efficient separations by decreasing particle diameter. The drawback to small particles is that they have higher flow resistance than conventional-sized particles when packed in a column, and therefore generate greater backpressures. Conventional LC pumps are limited to a maximum operating pressure of around 400 bar (6000 psi), which can quickly be exceeded with particles of 1–2 mm diameter. One option is to decrease the column length to a few centimeters or less; this results in faster separations (Majors, 2003). However, no gain in separation efficiency is achieved due to the loss of column length. An alternative is to use specialized pumps capable of producing substantially higher pressures to overcome the increased flow resistance. The resulting technology is termed ultrahigh pressure liquid chromatography. One initial concern with UHPLC was that frictional heating caused by forcing mobile phase through a packed bed of small particles at high flow rates might have negative effects on separation efficiency. For conventional 4.6 mm diameter columns this would indeed be problematic due to the poor heat dissipation of such columns. This problem is easily overcome by using packed fused-silica capillaries with internal diameters ranging 10–150 mm because their increased surface area-to-volume ratio allows any heat generated to be quickly dissipated in air (MacNair et al., 1997). Runs have been performed successfully at pressures up to 100,000 psi using packed capillary columns (Patel et al., 2004). UHPLC has demonstrated plate counts over

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350,000 for isocratic reversed-phase separations and peak capacities over 500 using gradient elution (Jerkovich et al., 2003). 8.3.2

UHPLC for LC LC: High Speed Versus High Peak Capacity

If the main goal for a 2D separation is a short total analysis time, UHPLC is potentially useful. The limiting factor for the speed of a 2D separation is typically the time it takes to complete each second-dimension run. If this time is decreased, the first column can be sampled at shorter intervals and therefore can be run faster, thus reducing the total analysis time. To this end, the high pressure capabilities of UHPLC pumps could be devoted to generating very fast runs in relatively short columns. With the small particles used in UHPLC, fast runs do not result in excessive compromise in terms of chromatographic efficiency, so relatively high performance can be maintained. Therefore, fast UHPLC is potentially well suited to use as the second dimension of an LC LC separation. At present, there remains a practical hurdle preventing UHPLC from being used to enhance the speed of an online LC LC separation. Suitable low dead volume automated switching valves capable of operating at ultrahigh pressures are not yet widely available, although preliminary versions of such valves have been reported (Wu et al., 2001). For the time being, this limitation precludes the online coupling of UHPLC to another separation method. There is no reason to expect that UHPLC-compatible valves cannot be manufactured on a wider scale; however, so as interest in UHPLC continues to grow and new instrumentation is developed, online LC LC using UHPLC may become a realistic and attractive option for high speed 2D analyses. Another very important benefit of UHPLC in the context of MDLC is its potential to generate very high resolution separations. As previously discussed, the peak capacity of a multidimensional separation is equal to the product of the peak capacities of each dimension as long as all separation dimensions are orthogonal and no resolution gained on one dimension is lost on any subsequent dimension (Giddings, 1987; also see Chapters 3 and 12 by Davis and Gilar et al., respectively). Since reversed-phase UHPLC can give peak capacities well into the hundreds as a 1D technique (Jerkovich et al., 2003), it is easy to conceive that extremely powerful comprehensive 2D separations could be performed if UHPLC were appropriately combined with a second, orthogonal separation method. As already discussed, off-line coupling is an excellent option for MDLC separations when the main goal is to maximize resolution. For this reason, as well as due to the lack of valves compatible with ultrahigh pressures, the research reported in this chapter uses off-line coupling to interface the first dimension to the second. 8.3.3

LC UHPLC for Separations of Intact Proteins

One of the primary challenges facing the field of separation science is the analysis of the entire complement of proteins produced by an organism—a field of research known as proteomics. 2D gel electrophoresis remains the gold standard in protein separations, with the ability to resolve as many as 5000 proteins in a single gel

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(O’Farrell, 1975). However, 2D gel electrophoresis has pronounced limitations in several respects. It is a labor-intensive analysis usually requiring multiple days to complete. It is not readily coupled to mass spectrometry. Additionally, it is biased against certain classes of proteins. Very large or hydrophobic proteins may not enter the gel at all, and resolution of very acidic or basic proteins is often poor (Wehr, 2002). There is, therefore, widespread interest in devising a liquid-phase protein separation technique to supplement or replace 2D gel electrophoresis. Since UHPLC offers the potential to generate very high peak capacities, especially if used as part of a multidimensional separation, it is a logical candidate for application to the challenge of intact protein separations. In this chapter, research is reported in which UHPLC was used as one dimension in an LC LC separation of the soluble proteins produced by the bacterium E. coli. A schematic diagram of the general procedure used is shown in Fig. 8.10. Conventional-pressure anion-exchange chromatography, which separates proteins based on their charge, is used for the first dimension. Interfacing between the two dimensions is accomplished by fraction collection after dimension 1, followed by lyophilization of the volatile mobile phase and reconstitution of the fractions in order to concentrate the proteins. All fractions are then analyzed on the second dimension, ultrahigh pressure reversed-phase liquid chromatography (UHPRPLC), which separates proteins based on hydrophobicity. The outlet of the reversed-phase capillary column is directly interfaced with a mass spectrometer to carry out online electrospray time-of-flight MS, which provides intact molecular weight information for all detectable proteins.

FIGURE 8.10 Basic configuration of instrumentation used for off-line AEX UHP-RPLC.

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8.4 EXPERIMENTAL DETAILS 8.4.1

Instrumentation

The instrumentation used in this study consists of two separate LC systems, a lyophilization apparatus, and a mass spectrometer. The first-dimension separation is performed on commercially available instrumentation. AWaters Corporation 600E quaternary gradient LC pump (Milford, MA) is used to generate a salt gradient. The pump is connected to a Valco (Houston, TX) six-port valve with a 100-mL sample loop used to inject a sample onto the column. A 7.5 cm long, 7.8 mm diameter Waters Biosuite Q, 10 mm strong anion-exchange column, which contains polymeric particles bonded with a quaternary amine functionality, is used to carry out the first-dimension separation. Detection is performed using an Applied Biosystems 785AUVabsorbance detector (Foster City, CA) set at 280 nm. Fractions with a volume of 1.5 mL each are collected in microcentrifuge tubes using a Waters Fraction Collector II. Fractions are flash frozen using liquid nitrogen and are then placed in a SpeedVac Concentrator (Thermo Electron, Bellefonte, PA), which is pumped down to pressures between 102 and 103 Torr using an Edwards high vacuum pump (Wilmington, MA). Once the fractions have been lyophilized to dryness, they are reconstituted in 100 ml of a solution of 10% acetonitrile and 90% water (v/v). The fractions are then ready for analysis on the second dimension. The second LC system, used to perform gradient reversed-phase separations at ultrahigh pressures, is of a more unusual design. A simplified schematic diagram of this instrument, described in greater detail elsewhere (Link, 2004), is shown in Fig. 8.11. The instrument consists of two separate pumps. A Waters CapLC pump is responsible for injecting the sample and generating the reversed-phase gradient under low pressure conditions. A custom-designed hydraulic-amplifier pump is used

FIGURE 8.11

Simplified diagram of the gradient ultrahigh pressure instrument.

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to produce the ultrahigh pressures needed to carry out the separation. The two pumps are interfaced with the separation column at a four-port stainless steel injector block. The UHP pump connects to the injector block through the gradient storage loop, which is a several meter long length of 250 mm diameter stainless steel tubing that stores the gradient generated by the CapLC pump and subsequently allows it to be pumped onto the column by the ultrahigh pressure pump. Valves 1 and 2 are air-actuated on/off valves, model ASFVO, from Valco (Houston, TX). The separation column is a 50 mm ID capillary, 45 cm in length, packed in-house with 1.5-mm ethyl-bridged hybrid C18 reversed-phase particles provided by Waters Corporation (Milford, MA). An open-tubular splitter capillary, 10 mm in internal diameter and 1.3 m in length, is also connected to the injection block. To perform a run, a sample vial is loaded into the autosampler of the CapLC instrument. With valves 1 and 2 in the open position, the CapLC first generates an acetonitrile/water gradient, which travels into the injection block. Since the column and the splitter are both capillaries with very high flow resistance, essentially all the flow from the CapLC enters the gradient storage loop. Since the gradient will later be pumped onto the column by the ultrahigh pressure pump, located on the opposite end of the loop, the gradient must be loaded in reverse – that is, beginning with the highest desired acetonitrile content and ending with the lowest. The loading of the gradient is usually performed at a flow rate of 40 mL/min, whereas the ultrahigh pressure pump operates at 2 mL/min when a run is in progress. Therefore, a gradient that will run for 60 min only takes 3 min to load onto the gradient storage loop. Once loading of the gradient is complete, the CapLC loads the sample, typically 1 mL in volume, onto the storage loop in the same manner. After sample has been loaded, valve 1 is closed to isolate the CapLC pump from ultrahigh pressure. Valve 2 is also closed, and then the ultrahigh pressure pump is activated to pressurize the system to the desired run pressure. Once this pressure is reached, the pump operates at a constant flow rate of 2 mL/min. The splitter capillary diverts most of the flow from the gradient storage loop and keeps the backpressure at approximately 23,000 psi. The flow rate through the separation capillary at this pressure is approximately 100 nL/min, which means the split ratio is approximately 20:1. The sample in the gradient storage loop is the first liquid forced onto the column. Next, the column experiences the gradient, which although loaded in reverse, is now “played back’’ in the normal manner of low to high acetonitrile concentration. The outlet of the separation column is coupled using a Teflon sleeve to a nanoelectrospray tip purchased from New Objective (Woburn, MA). Online positive ion mode electrospray time-of-flight MS is performed using a Micromass Q-TOF Micro instrument (Waters Corp., Milford, MA). Mass spectra were acquired at a frequency of 2 Hz for the duration of the run. All mass spectra were acquired using the software package MassLynx 4.0 (Waters Corp., Milford, MA). 8.4.2

Data Analysis

2D chromatograms were prepared by loading total ion current (TIC) data from each reversed-phase chromatogram from MassLynx onto the data analysis software

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program Igor Pro 4 (Wavemetrics, Lake Oswego, Oregon), and the data from all fractions in an anion-exchange run were combined into a two-dimensional dataset called a “wave.’’ Since peaks in the chromatogram vary in intensity over several orders of magnitude, the logarithm of the TIC intensity was taken for all points in the wave to more clearly show both the high and low intensity peaks. The wave was plotted as an image to generate a 2D chromatogram. The upper limit of the intensity scale was set by the most intense peak in the chromatogram, whereas the lower limit was set in a manner such that the majority of background noise that did not correspond to detectable proteins was kept to the lowest portions of the scale. To determine the molecular mass of the proteins in the sample from multiply charged ions in the electrospray mass spectra, all chromatograms were thoroughly surveyed for peaks that gave a detectable charge envelope. When such a peak was found, all MS scans under the peak were summed, and the resulting mass spectrum was background subtracted, smoothed, and centered according to MassLynx default parameters to convert the spectrum from continuum data to a line spectrum. Then an adjacent pair of ions from the same charge envelope in the mass spectrum was identified visually. Using the MassLynx “Find Manual’’ dialog, the ion pair’s m/z values were used to discover all remaining ions in the same series and calculate the actual molecular weight of the intact protein. If more than one charge envelope was present in the same mass spectrum, all other detectable protein masses were also measured. This procedure was repeated in the same manner for all peaks in all of the chromatograms, and protein mass data were recorded in tabular form. Identification of the proteins based on their molecular weight was not attempted. Instead, the data were used to determine the number of probable unique protein masses detected in each fraction and in each 2D run. If the same protein mass appeared in more than one fraction of a 2D run, it was counted as being found only in the fraction where its base peak intensity was the greatest. 8.4.3

Chromatographic Conditions

A volatile salt in the mobile phase of the anion-exchange separation was preferred so that it could be removed from the fractions by lyophilization. Ammonium acetate was used because it sublimes under vacuum and can serve as both the elution salt and a buffer at pH 8.5. All anion-exchange runs were performed using a 0.5 mL/min flow rate. Flow was held isocratic at 100% mobile phase A for 10 min following sample injection, after which a linear gradient from 0% to 50% mobile phase B was run over 30, 60, or 120 min. The mobile phase was then ramped to 75% B over 5 min and held at this composition for 15 min to maximize protein elution. The mobile phase was then returned to 100% A. Buffer A was 25 mM ammonium acetate, pH 8.5. Buffer B was 1 M ammonium acetate, pH 8.5. Fractions were collected every 3 min beginning immediately after the injection of the sample. For the 30-min gradient, 15 fractions were collected; for the 60-min gradient, 25 fractions were collected; and for the 120-min gradient, 40 fractions were collected. The reversed-phase gradient separation was run at a flow rate near 100 nL/min and pressure around 23,000 psi. The gradient used to elute the proteins from the

196


reversed-phase column is as follows: the mobile phase composition was held isocratic at 90% mobile phase C/10% mobile phase D for the first 15 min of the run to allow the sample in the gradient storage loop to be fully transferred to the column. Then a linear gradient was run from 10% to 65% mobile phase D over 60 min. The mobile phase composition was then returned to 90% C/10% D. Mobile phase C was deionized water with 0.2% formic acid. Mobile phase D was acetonitrile with 0.2% formic acid. 8.4.4

Samples

An extract of the soluble proteins of the bacterium E. coli was provided by the Giddings lab in the Department of Microbiology and Immunology at the University of North Carolina at Chapel Hill. The details of the procedure for the preparation of this extract have been reported elsewhere (Link, 2004). 8.5 RESULTS AND DISCUSSION Figure 8.12 shows the UV absorbance chromatograms of three anion-exchange separations of an E. coli lysate carried out using gradients ranging 0.025–0.5 M ammonium acetate over 30, 60, and 120 min. Fraction collection was performed during each of these runs; fractions were changed at the intervals shown as vertical lines on the chromatograms. The sample is too complicated to be fully resolved by a single anion-exchange separation, as indicated by the presence of multiple overlapping peaks in all chromatograms. Some additional resolution is apparently gained by extending the gradient from 30 to 60 or 120 min. It is difficult to ascertain how much actual improvement in the separation of the proteins is achieved from the UV chromatograms because no peaks are fully resolved even with the longer gradients. Although it is difficult to assign a peak capacity to these separations without fully resolved peaks to examine, 20 would be a reasonable, conservative estimate for the 120-min gradient separation. Fractions from the anion-exchange separations were analyzed on the second dimension UHP-RPLC. For each anion exchange separation, a series of reversedphase chromatograms are produced. A representative chromatogram of a reversedphase separation of one anion-exchange fraction is shown in Fig. 8.13. The chromatogram is a plot of the total ion current measured by mass spectrometer as a function of retention time. Peaks typically do not appear until after 25–30 min in most of the reversed-phase chromatograms, mainly because of the delay associated with transferring the sample from the gradient storage loop onto the reversed-phase column. Further inspection of the chromatogram reveals that there is a great deal of variability in peak shape. Some peaks are sharp and symmetrical, having base widths as small as 10 s. Some proteins coelute, causing overlapping peaks and distorted peak shape, although more peaks are fully resolved in this chromatogram than in the anionexchange separation. Some nonoverlapping peaks are also broad and asymmetrical, having notable tailing and widths as large as 30 s. Although this inconsistency in peak shape is clearly not desirable, it is not atypical of reversed-phase protein separations. The high total resolving power of UHPLC helps to compensate somewhat for the fact

RESULTS AND DISCUSSION

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FIGURE 8.12 UV absorbance chromatograms of three anion-exchange separations of an E. coli lysate. Vertical lines represent the times at which fractions were changed.

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FIGURE 8.13 Total ion current chromatogram for a UHP-RPLC–MS separation of one anion exchange fraction (fraction number 6 from the 30-min anion-exchange gradient).

that some peaks are wider than expected. Assuming an average peak width of 20 s and an elution window of 30 min, a typical reversed-phase peak capacity for this set of runs would be 120. All the reversed-phase chromatograms from the fractions of an anion-exchange separation can be combined and presented as a 2D chromatogram. In Fig. 8.14a–c, the 2D chromatograms for separations of the E. coli lysate using three different anion exchange gradients—30, 60, and 120 min in length—are shown. Reversed-phase retention time is plotted on the X-axis, anion-exchange retention time is plotted on the Y-axis, and the intensity scale (“Z-axis’’) represents the logarithm of the total ion current measured by the mass spectrometer. The data are essentially equivalent to an online comprehensive LC LC separation, even though off-line coupling was used. As such, it is possible to estimate a peak capacity for this 2D separation. For the longest anion-exchange separation, a total of 40 fractions were collected. Assuming that peaks may be split over two fractions, the peak capacity of the first dimension is taken to be half of this number, or 20, which is not unreasonable based on the visual estimate from the UV chromatogram. If the second-dimension peak capacity is 120 as previously estimated, the resulting total peak capacity of this 2D separation method is approximately 2400. In comparing the three chromatograms, it is apparent that the general shape of the elution profile is the same for all the three runs. Numerous peaks appear within the first 12 min of anion-exchange retention time in all three 2D chromatograms. These correspond to the proteins that were not-retained or very weakly retained on the anion-exchange column, which implies that they are the most basic proteins in the sample. There is then a gap of several minutes where few peaks appear; the gap is longer for the shallower anion-exchange gradient and shorter for the steep one. The time after this gap at which peaks reappear corresponds to the point on the gradient when the salt concentration is high enough to begin eluting proteins that were retained on the anion exchange column. Peaks are spread over a fairly wide portion of the


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FIGURE 8.14 2D chromatograms from AEX RPLC separations of an E. coli lysate using different anion-exchange gradient lengths. The scale at the right-hand side of the figure represents the signal intensity, as measured by the total ion current (TIC) from the mass spectrometer. Parts (a), (b), and (c) represent anion exchange gradient lengths of 30, 60 and 120 min, respectively, all plotted using the same intensity scale range. Part (d) is the same chromatogram as Part (c), except that the intensity scale range has been altered to enhance peak visibility (see the text for explanation). Chromatograms have been cropped to show only the separation space in which proteins were found to elute. (See color plate.)

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available separation space, although certain areas of the 2D plot clearly contain greater concentrations of protein than others. Additionally, there seems to be a weak correlation between retention in the two dimensions—proteins that elute early from the anion-exchange column (the most basic proteins) also tend to elute earlier in the reversed-phase runs. This correlation is expected, however, since many of the most basic proteins of E. coli are ribosomal proteins, which also tend to be fairly hydrophilic and thus are expected to be weakly retained on the reversed-phase column. In general, however, the two separation methods appear to be relatively orthogonal in that peaks are spread over a relatively wide range of the available 2D separation space. As the anion-exchange gradient is lengthened, more detail becomes visible in the 2D chromatograms. Regions that appear laden with many overlapping high intensity bands in the chromatogram from the steepest anion-exchange gradient begin to separate into resolved peaks with the longer gradients. Another trend is that the intensity of all peaks seems to diminish as the anion-exchange gradient is lengthened. This is most apparent in Fig. 8.14c, which is the 2D chromatogram generated from the longest anion-exchange gradient. To visually compensate for this decrease in sensitivity, Fig. 8.14d shows the same 2D chromatogram as Fig. 8.14c with the “Z-axis’’ scale adjusted in the manner such that many of the peaks hidden by the diminished signal intensity are revealed. The notable decrease in sensitivity with the shallower gradient is consistent with previous observations from gradient reversed-phase and ion-exchange separations of proteins. (Mal’tsev et al., 1990) Additionally, it has been shown that the peak capacity of a gradient separation will reach a maximum at certain gradient length and that making the gradient shallower beyond this point will result in no further improvement of peak capacity (Stout et al., 1986; Gilar et al., 2004). Both these factors imply that the point of diminishing returns must be considered when attempting to improve resolution by lengthening the gradient. Many of the trends that can be visually observed by examining the 2D chromatograms are also supported by the mass spectrometry data. Figure 8.15 presents graphs of the number of probable unique protein masses found in each fraction for the same three 2D runs. As is noted in the chromatograms, in all the runs there is a spike in the number of proteins detected in the second fraction that corresponds to proteins not retained by the anion-exchange column. This is followed by a gap of several fractions where few proteins are detected. After the gap formation, the majority of the proteins elute over in the next 10–25 fractions, depending on the gradient length. Several differences among the three runs are also apparent in the MS data. For one, lengthening the anion-exchange gradient does spread the proteins over a greater number of fractions, as anticipated. All proteins elute within 15 fractions for the 30-min gradient, whereas they are spread over 31 fractions for the 120-min gradient. Also notable is the fact that the number of proteins detected in each fraction is substantially less for the 120-min anion-exchange gradient run than for the 30- or 60-min gradients. As the gradient is lengthened, many of the proteins that elute in only one fraction in the shorter gradients are spread over two or more fractions in the longest gradient, and thus are more dilute. This reduces their MS signal intensity and causes


201

FIGURE 8.15 Histograms representing the number of unique proteins found for three 2D separations of an E. coli lysate using anion-exchange gradients of different length (A ¼ 30-min gradient, B ¼ 60 min, C ¼ 120 min). The vertical bars represent the number of proteins found in each fraction. The upward-sloping line represents the cumulative number of unique proteins found up to and including the indicated fraction.

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TABLE 8.2 Summary of the Data from Three LC UHPLC Separations of an E. coli Lysate Anion-Exchange Gradient Length, min 30 60 120

# of probable unique proteins found

# of fractions with probable unique proteins

209 247 176

15 21 31

some proteins to fall below the detection limit, which prevents them from being counted. Just as is the case for the trend observed from visual inspection of the 2D chromatograms, the decrease in the number of proteins detected is consistent with expected drop-off in sensitivity as gradient length is increased (Mal’tsev et al., 1990). The data from the three 2D runs are summarized in Table 8.2. The total number of unique protein masses found in all fractions increased from 209 to 247 for the 30- and 60-min anion-exchange gradients, respectively. This suggests that the increased peak capacity contributed by lengthening the anion-exchange gradient allows more proteins to be resolved and detected. This trend did not continue when the gradient was lengthened further, however, as the number of proteins detected decreased to 176 for the 120-min anion-exchange gradient. As already discussed above, this drop-off probably results from the fact that the proteins become too dilute when spread over several fractions. The resulting diminished signal intensity offsets any gain in resolution achieved by lengthening the anion-exchange gradient beyond a certain point. Therefore, for the instrumentation used in this study, the 60-min anion-exchange gradient proved best. The chromatographic peak capacity of this separation method, estimated as 2400, represents one of the highest peak capacities for a comprehensive LC LC separation of intact proteins reported to date. Much potential still exists to further enhance the capabilities of this system through optimization of both the anion-exchange and the UHP-RPLC separations. Although the peak capacity of this system still falls substantially short of the capabilities of 2D gel electrophoresis, it is free from many of the cumbersome problems that plague gel-based separations. As time passes and further refinements are made, it is expected that ultrahigh pressure multidimensional liquid chromatography will become a practical technique for the separation of complex samples.

8.6 FUTURE DIRECTIONS FOR UHP-MDLC One of the major trends to be anticipated is greater availability of instrumentation that can be used to perform multidimensional separations at ultrahigh pressures. Some instrument makers have recently introduced LC pumps capable of operating at pressures up to 15,000 psi. As time progresses, ultrahigh pressure separations are likely to become more routine and the pressure limit of commercial instruments is likely to increase

REFERENCES

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further. Given these trends, it is also reasonable to anticipate that valve technology will soon be available that will allow online coupling of dimensions compatible with ultrahigh pressures. Once online coupling becomes possible, UHPLC may find much more widespread applicability to MDLC separations due to its potential to enhance the speed of reversed-phase separations without excessive loss of peak capacity. Another potential application for multidimensional separations using UHPLC is the “bottom-up’’ approach to proteomics. This is an alternative to intact protein analysis, which involves enzymatically digesting the proteins to be analyzed to produce a mixture containing an extremely large number of peptides. The peptides are then separated using chromatography and analyzed via MS/MS. One advantage of this approach is that, unlike proteins, peptides are almost always well behaved on reversed-phase columns. The main disadvantage is that digesting a mixture of proteins further increases the complexity of an already complex sample, thereby making the separation of its components more challenging. UHPLC is well suited to this challenge—peak capacities of over 500 have been demonstrated for gradient UHPLC separations of peptides, while conventional reversed-phase HPLC typically gives peak capacities of 200 or less with similar samples (Jerkovich et al., 2003). If coupled to an appropriate orthogonal separation method, it is not difficult to envision that a MDLC technique using UHPLC could find use as part of a method for bottom-up proteomics. Another concept worthy of consideration is a MDLC separation in which both dimensions would be operated at ultrahigh pressures. The challenge on this front is that essentially all work using UHPLC to date has been performed using the reversed-phase separation mode. It remains to be shown whether UHPLC can give the same improvement in separation efficiency and peak capacity for other separation modes. If UHPLC does prove useful for separation modes, such as ion exchange or size exclusion, the coupling of one UHPLC separation to a second may still offer greater peak capacities and faster separation times than are presently possible with MDLC.

REFERENCES Bushey, M.M., Jorgenson, J.W. (1990). Automated instrumentation for comprehensive twodimensional liquid chromatography of proteins. Anal. Chem. 62, 161–167. Cortes, H.J. (1990). Multidimensional Chromatography: Techniques and Applications. Marcel Dekker, Inc., New York. Erni, F., Frei, R.W. (1978). Two-dimensional column liquid chromatographic technique for resolution of complex mixtures. J. Chromatogr. 149, 561–569. Giddings, J.C. (1987). Concepts and comparisons in multidimensional separation. J. High. Resolut. Chromatogr. Chromatogr. Commun. 10, 319–323. Gilar, M., Daly, A.E., Kele, M., Neue, U.D., Gebler, J.C. (2004). Implications of column peak capacity on the separation of complex peptide mixtures in single- and two-dimensional high-performance liquid chromatography. J. Chromatogr. A 1061, 183–192. Holland, L.A., Jorgenson, J.W. (1995). Separation of nanoliter samples of biological amines by a comprehensive two-dimensional microcolumn liquid chromatography system. Anal. Chem. 67, 3275–3283.

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Holland, L.A., Jorgenson, J.W. (2000). Characterization of a comprehensive two-dimensional anion exchange-perfusive reversed phase liquid chromatography system for improved separations of peptides. J. Microcolumn. Sep. 12, 371–377. Jerkovich, A.D., Mellors, J.S., Jorgenson, J.W. (2003). The use of micrometer-sized particles in ultrahigh pressure liquid chromatography. LCGC N. Am. 21, 600, 604, 606, 608, 610. Link, J.C. (2004). Development and application of gradient ultrahigh pressure liquid chromatography for separations of complex biological mixtures: Dissertation, University of North Carolina, Chapel Hill. Available from UMI ProQuest Digital Dissertations, Ann Arbor, MI, AAT 3156171. MacNair, J.E., Lewis, K.C., Jorgenson, J.W. (1997). Ultrahigh-pressure reversed-phase liquid chromatography in packed capillary columns. Anal. Chem. 69, 983–989. MacNair, J.E., Lewis, K.C., Jorgenson, J.W. (1999). Ultrahigh-pressure reversed-phase capillary liquid chromatography: isocratic and gradient elution using columns packed with 1.0-m m particles. Anal. Chem. 71, 700–708. Majors, R.E. (2003). HPLC column packing design. LC-GC Eur. 16(6a), 8–13. Mal’tsev, V.G., Nasledov, D.G., Trushin, T.B., Tennikova, L.V., Vinogradova, I.N., Volokitina, I.N., Zgonnik, V.N. (1990). High-performance liquid chromatography of proteins on short capillary columns. J. High Resolut. Chromatogr. 13, 185–192. Mellors, J.S., Jorgenson, J.W. (2004). Use of 1.5 mm porous ethyl-bridged hybrid particles as a stationary-phase support for reversed-phase ultrahigh-pressure liquid chromatography. Anal. Chem. 76, 5441–5450. Murphy, R.E., Schure, M.R., Foley, J.P. (1998). Effect of sampling rate on resolution in comprehensive two-dimensional liquid chromatography. Anal. Chem. 70, 1585–1594. O’Farrell, P.H. (1975). High resolution two-dimensional electrophoresis of proteins. J. Biol. Chem. 250, 4007–4021. Opiteck, G.J., Jorgenson, J.W., Anderegg, R.J. (1997). Two-dimensional SEC/RPLC coupled to mass spectrometry for the analysis of peptides. Anal. Chem. 69, 2283–2291. Opiteck, G.J., Lewis, K.C., Jorgenson, J.W., Anderegg, R.J. (1997). Comprehensive on-line LC/ LC/MS of proteins. Anal. Chem. 69, 1518–1524. Opiteck, G.J., Jorgenson, J.W., Moseley, M.A. III, Anderegg, R.J. (1998). Two-dimensional microcolumn HPLC coupled to a single quadrupole mass spectrometer for the elucidation of sequence tags and peptide mapping. J. Microcolumn. Sep. 10, 365–375. Opiteck, G.J., Ramirez, S.M., Jorgenson, J.W., Moseley, M.A. III (1998b). Comprehensive twodimensional high-performance liquid chromatography for the isolation of overexpressed proteins and proteome mapping. Anal. Biochem. 258, 349–361. Patel, K.D., Jerkovich, A.D., Link, J.C., Jorgenson, J.W. (2004). In-depth characterization of slurry packed capillary columns with 1.0-mm nonporous particles using reversed-phase isocratic ultrahigh-pressure liquid chromatography. Anal. Chem. 76, 5777–5786. Stout, R.W., Sivakoff, S.I., Ricker, R.D. (1986). Separation of proteins by gradient elution from ion-exchange columns. J. Chromatogr. 353, 439–463. Wehr, T. (2002). Multidimensional liquid chromatography in proteomic studies. LCGC N. Am. 20, 954, 956–958, 960–962. Wu, N., Lippert, J.A., Lee, M.L. (2001). Practical aspects of ultrahigh pressure capillary liquid chromatography. J. Chromatogr. A 911, 1–12.

PART III LIFE SCIENCE APPLICATIONS

9 PEPTIDOMICS Egidijus Machtejevas and Klaus K. Unger Institute of Inorganic Chemistry and Analytical Chemistry, Johannes Gutenberg-University, Duesbergweg 10-14, 55099 Mainz, Germany

9.1 STATE OF THE ART—WHY PEPTIDOMICS? Peptides often have very specific functions as mediators and indicators of biological processes. They play important roles as messengers, for example, as hormones, growth factors, and cytokines, and thus have a high impact on health and disease. Peptidomics comprises not only peptides, originally synthesized by an organism to perform a certain task, but also degradation products of proteins (degradome). Therefore, proteolytic cleavage of proteins leads to peptides as indicators of protease activity, degradation, and degeneration. The degradome is a very important part of protein metabolism, and thus also reflects the organism state. However, peptidomics is far more challenging compared with genomics and proteomics. The dynamic range of protein expression and posttranslational modification makes the identification of the entire proteome a far bigger and more complex challenge than the sequencing of the genome. The sensitivity of proteomics and peptidomics suffers from the lack of an amplification method, analogous to the polymerase chain reaction method, to reveal and quantify the presence of low abundance proteinaceous constituents. Proteome analysis usually includes the following strategies: native protein preseparation, then digestion followed by separation and identification (Figure 9.1a), or alternatively straight digestion, separation and identification by mass spectrometry (Figure 9.1b). Therefore, starting with one protein, after digestion we will end up with approximately 30 to 70 short peptide fragments. Identification of only very few of them will provide sufficient information as to which protein was present in the sample. Peptidomics does not possess such a feature: from Multidimensional Liquid Chromatography: Theory and Applications in Industrial Chemistry and the Life Sciences, Edited by Steven A. Cohen and Mark R. Schure. Copyright 2008 John Wiley & Sons, Inc.

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FIGURE 9.1 Liquid chromatography workflow strategy options in proteomics. (a ) “bottomup’’ approach; (b) “top-down’’ approach; (c) selective sample cleanup directly combined with chromatographic separation; (d) peptide capture with affinity restricted access material.

the beginning of the analysis to the end we have only one peptide at a certain concentration and we have to identify it. However, when peptides come from the degradome of proteins, then, naturally, peptidomics is in a similar situation as proteomics. The display level is difficult because of the wide range of peptide concentrations that spans over ten orders of magnitude. These challenges motivate researchers to develop reliable analytical platforms. Shortcomings in throughput are due to the absence of technologies that can deliver fast and parallel quantitative analysis of complex protein distributions in an automated fashion.

9.2 STRATEGIES AND SOLUTIONS Physiological and pathological changes are reflected in the production and the metabolism of proteins and peptides. Such changes are detectable in extracellular fluids, including blood plasma, cerebrospinal fluid, synovial fluid, and urine (Clynen et al., 2003). Protein samples of biological origin are by nature highly complex and require sophisticated analytical tools to provide reliable analysis of the components. Proteomics especially challenges the need for robust, automated, and sensitive high throughput technologies. Most single-dimension separations lack sufficient resolution capability to resolve complex biological matrixes. Separations employing multiple dimensions offer a better promise for such applications (Giddings, 1984; Wagner et al., 2002). Liquid chromatographic separation methods using different physical properties of peptides for molecular discrimination have been combined with varying degree of success. The ultimate goal is a rapid separation strategy that can be coupled with mass spectrometry, to provide a comprehensive monitoring of the changing concentration. Multidimensional liquid chromatography (LC) separation typically relies on utilizing

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two or more independent physical properties of the peptides to fractionate the mixture into individual components. Physical properties commonly exploited include size, shape, charge, hydrophobicity, and biomimetic or affinity interactions. These processes are the underlying phenomena for peptide/protein separations using different chromatographic modes, such as size exclusion, reversed phase, cation/anion exchange, and hydrophobic interaction columns. Liquid chromatographic techniques are fast, quantitative, easy to automate, and can be coupled more readily to mass spectrometry than two-dimensional gel electrophoresis (Premstaller et al., 2001). The drawback of LC is the limited peak capacity of a single column. Thus, multidimensional LC is the logical choice for increasing peak capacity, fractionating the eluent and transferring the fractions between different columns through automated valve switching (Cortes, 1990). Mass spectrometry has limitations with respect to sensitivity, therefore, a certain number of analyte molecules should be injected in order to be identified. Thus, detection is favored by applying higher amounts of the sample. Knowing the target analyte concentration in the sample and the mass spectrometer detection limits provides the answer to the question: how much we should inject? According to Geigy scientific tables (Lentner, 1984) human plasma contains only 0.03% peptides (dry mass). It might be estimated that in plasma there could be tens of thousands of different peptides with vast concentration differences. Therefore, huge injection volumes might be required. For example, Tatemoto (1982) extracted 0.6 mg of Peptide YY from four tons of porcine intestine. Dart et al. (1985) obtained 47 mg of transforming growth factor-b from 8.8 kg of human placenta. Another important prerequisite for the suitability of a separation system for proteomic analysis is the ability to handle very small amounts of biological material (Premstaller et al., (2001). These methods allow one to detect low concentrations of peptides from complex mixtures with a high degree of automation. Biological, individual, and variations between individuals (such as gender, age, and nutrition) affect peptidomes and require careful consideration in order to find valid biomarkers. A few, equally important factors for successful proteomic biomarker research are high sample quality, high sensitivity, and reproducibility that depend on proper selection of the high quality samples. Proteins are found in different cell compartments (cytoplasm, a range of intracellular organelles) or as secreted extracellular proteins (in various body fluids). Furthermore, proteins range from highly soluble hydrophilic proteins, to membrane associated and transmembrane proteins containing multiple hydrophobic transmembrane domains. Moreover, proteins often exist as multisubunit complexes or can form large macromolecular complexes with other proteins. No optimized conditions exist to suit the wide range of physical and chemical properties of proteins. When nucleic acids are purified from different samples it is assumed that all different DNAs or RNAs are extracted to the same or equivalent degree and such extraction is reproducible between samples. It would be naive to believe that all cellular proteins can be solubilized and efficiently extracted and that such extraction can be reproducibly repeated for many different samples. This means that the protein composition of two different tissues (e.g., liver and brain) cannot be quantitatively compared even if suitable affinity assays

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were available (Figure 9.1d), since inherent variability in the protein extraction step will make it impossible. A proteomic analysis of a sample usually consists of four steps. These are extraction of the proteins from the sample, their separation, detection and finally identification/ analysis of the individual separated peptides. It is of major importance to pay particular attention to the sample extraction, as any error or losses during this stage will strongly influence the results (see discussion above). Analyzing body fluids, sample collection protocols, and variations in sample treatment procedures play a major role in determining sample quality. Tammen et al. (2005) concluded that specimen collection is a crucial step for successful peptide biomarker discovery in human blood samples. The proteome of a blood sample throws light on the metabolic state of an individual at the moment of blood withdrawal. Furthermore, it represents a collection of information about physiological as well as patho-physiological processes occurring at the same time. Plasma samples are one of the major substances that could provide an adequate answer about the state of an organism in total. Initial sample treatment is the major step that ensures how representative the data are and to what degree component losses are acceptable. A large number of peptides, many of them in rather high abundance, are only present in serum and were not detected in plasma (Tammen et al., 2005). This is not surprising, because proteases are participating in clotting events, cleaving many proteins and releasing large quantities of peptides. In general, sample preparation protocols that limit the number of preparation steps, circumvent the loss or dilution of the sample, and concentrate the sample are preferred. Therefore, the most desirable sample pretreatment methods are those that are totally automated. Automation eliminates human type errors and also drastically increases throughput. Another important issue while working with patient biofluids is safety of the researcher who is at higher risk of exposure to an infectious illness. Fully automated sample treatment significantly reduces that risk. However, improvements in sample preparation, resolution, and data analysis are necessary before multidimensional liquid chromatography can be applied for the study of the peptidome. Several promising attempts have been made to analyze the peptidome (e.g., Richter et al., 1999). This group constructed a database of human circulating peptides. To establish a mass database, all 480 fractions of a peptide bank generated from human hemofiltrate were analyzed by MALDI-TOF mass spectrometry. Using this method, over 20,000 molecular masses representing native, circulating peptides were detected. Estimation of repeatedly detected masses suggests that approximately 5,000 different peptides were recorded. More than 95% of the detected masses were smaller than 15,000, indicating that the human hemofiltrate predominantly contains peptides (Richter et al., 1999). Silica-based restricted access materials (RAM) have been developed for cleanup in bioanalysis, first for low molecular weight compounds in biofluids (Rbeida et al., 2005) and subsequently for biopolymers such as peptides (Wagner et al., 2002). A classification of different types of RAM has been given by Boos and Rudolphi (1997). Novel RAMs with strong cation-exchange functionality have been synthesized and implemented in the sample cleanup of biofluids. Racaityt_e et al. (2000) have shown that this type of RAM is highly suitable for the online extraction and analysis of


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neuropeptides in plasma. Machtejevas et al. (2006) analyzed the pore structural parameters and size exclusion properties of LiChrospher strong cation- exchange and reverse-phase RAM. For peptide analysis out of the biofluids, the strong cationexchange functionality seems to be particularly suitable mainly because of the high mass capacity of the strong cation-exchange restricted access material (SCX-RAM) and the fact that one can work under nondenaturing conditions to perform effective chromatographic separations. Additionally, proper column operating conditions lead to a total effective working time for the RAM column of approximately 500 injections, depending on the type of sample, is comprehensively described. The restricted access principle is based on the concept of diffusion-based exclusion of matrix components and allows peptides, which are able to access the internal surface of the particle, to interact with a functionalized surface (Figure 9.2). The diffusion barrier can be accomplished in two ways: (i) the porous adsorbent particles have a topochemically different surface functionalization between the outer particle surface and the internal surface. The diffusion barrier is then determined by an entropy controlled size exclusion mechanism of the particle depending on the pore size of adsorbent (Pinkerton, 1991) and (ii) the diffusion barrier is accomplished by a dense hydrophilic polymer layer with a given network size over the essentially functionalized surface. In other words, the diffusion barrier is moved as a layer to the interfacial

FIGURE 9.2

Cartoon of a RAM particle.

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layer inside the adsorbent particles, and the exclusion properties are controlled by the size of the polymeric network protecting the internal surface that is no longer dependant on the average pore diameter of the adsorbent (Mazsaroff and Regnier, 1988). It should be emphasized that the restricted access principle is independent of the type and composition of the adsorbent, that is it can be applied to silica adsorbents as well as to polymeric packings. The size exclusion process is entropically driven, that is, proteins with decreasing shape and size penetrate an increasing volume of the porous particles. Size exclusion chromatography (SEC) of proteins is commonly carried out with buffer solutions containing a high salt concentration, for example, 0.1 M, at pH 5–7, which is needed to suppress electrostatic interactions between the solute and the charged surface of silicabased packings. In sample cleanup using a RAM-SCX column the concentration of salt is much lower, for example, less than 20 mM, and the pH is kept at approximately 3. Under these conditions, electrostatic attraction forces are dominant between the positively charged peptides and proteins whereas the negatively charged species are excluded from the pores of the RAM-SCX column through electrostatic repulsion forces (Figure 9.3a). After loading the RAM-SCX column an isocratic washing step elutes all the excluded compounds between the start and 15 min. After 15 min the trapped analytes are eluted from the RAM-SCX column with a strong eluent under gradient conditions during the period between 15 and 45 min (Figure 9.3b). Thus it is a charge and charge distribution selective process combined with SEC. Use of RAMSCX allows the direct application of biofluids onto the column. Small peptides are selectively trapped in the pores by the cationic functional groups while large molecular weight biopolymers (e.g., proteins) are directed to waste. This strategy performs the sample cleanup and selective peptide enrichment in one simultaneous step (Figure 9.1c). mAU (a)

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FIGURE 9.3 Typical SCX-RAM column separation profile: peaks (a) represent physical exclusion by pore size. Trapped retained biomolecules are separated by a gradient in the second step (b). Conditions: column—LiChrospher 60 XDS (SO3/Diol), 25 4 mm I.D., flow rate— 0.5 mL/min, gradient from 0 to 1 M NaCl in 20 mM KH2PO4 pH 2.5, containing 5% ACN in 30 min. Sample: 100 mL Human Hemofiltrate (3.7 mg/mL), UV detection at 214 nm.


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FIGURE 9.4 Schematic diagram of the on-line comprehensive two-dimensional HPLC system including an integrated sample preparation step.

Restricted access materials with a strong cation-exchange functionality have demonstrated a high potential for sample cleanup after online direct biofluid injections (Unger et al., 2004). For example, Wagner et al. (2002) used online 2D HPLC with sample cleanup (Figure 9.4), employing restricted access materials for mapping the small peptides and proteins in human hemofiltrate. The basic idea of the 2D-HPLC system is to employ an online sample cleanup strategy using two directly connected separation dimensions. In the first dimension, a strong cation-exchange restricted access media column is followed by an analytical cation-exchange column. In the second dimension, four parallel reversed-phase columns enable a fast separation. Enrichment of the fractions directly on top of the column does not require any sample storage, hence there is no vial contamination, wall adsorption or sample loss due to additional sample handling procedures such as fraction collection and reinjection. The procedure avoids sample dilutions and automatically desalts the analytes, thus preventing eluent incompatibilities. After sample loading, the cation-exchange RAM-column was placed in-line with the analytical cation-exchange column and analytes were eluted with a salt gradient. A total of 24 fractions of 4 min duration (2 mL of eluent) were transferred to the

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second dimension for subsequent reversed-phase chromatography on MICRA ODS I columns. These ion-exchange fractions were then separated on the four reversedphase columns running in parallel, resulting in six reversed-phase chromatograms for each RP column. The second dimension RP separations produced 60 or more resolved peaks in a single analysis, with little overlap between fractions, thus confirming the orthogonal nature of the two separation dimensions. This system reproducibly resolved a total of approximately 1000 peaks within a total analysis time of 96 min. Selected peaks from the RP step were sampled to analyze the molecular weights of the collected peptides by MALDI-TOF mass spectrometry and to determine their amino acid sequence by Edman degradation (Machtejevas et al., 2004). Though the potential for comprehensive peptide mapping and identification was demonstrated, the system complexity and the operation procedures were too complicated for routine analysis. An alternative system proved to be both simpler and more user friendly (Unger et al., 2004; Machtejevas et al., 2006). Thus far we have used this configuration to analyze human plasma, sputum, urine, cerebrospinal fluid, and rat plasma. For each particular analysis we set up an analytical system based on a simple but specific strategy (Figure 9.5). The analysis concept is based on an online sample preparation and a two-dimensional LC system: preseparating the majority of the matrix components from the analytes that are retained on a RAM-SCX column followed by a solvent switch and transfer of the trapped peptides. The SCX elution used five salt steps created by mixing 20 mM phosphate buffer (pH 2.5) (eluent A1) and 20 mM phosphate buffer with 1.5 M sodium chloride (eluent B1) in the following proportions: 85/15; 70/30; 65/45; 45/55; 0/100 with at the constant 0.1 mL/min flow rate. Desorption of the

FIGURE 9.5

2D-LC system set-up with integrated on-line sample preparation.


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adsorbed species from the RAM-SCX column could be accomplished by employing an eluent with a higher solvent strength or pH than the eluent used for loading the column. We preferred to use salt steps as pH elution required twice the time for reequilibration. The desorption step was repeated several times to eliminate memory effects. In order to avoid sample-to-sample cross contamination, two blank gradients were typically applied, although with specific analytes or higher loadings it could require up to five blank gradients. The transfer from the RAM-SCX column to the next (analytical cation-exchange column) is heavily dependant on the way this transfer is performed. Three different modes could be chosen to elute the trapped sample from the RAM-SCX column. 1. Isocratic elution with a strong solvent: After the elution of the unretained component from the RAM-SCX column a single step of 1.5 M sodium chloride in 19 mM phosphate buffer (pH 2.5) containing 5% methanol was applied for 10 min using a constant 0.5 mL/min flow rate. This method is particularly attractive when relatively noncomplex samples are analyzed. In this case, RAM-SCX is used only as a sample precleaning column. Eluted components could then be separated, for example, according to hydrophobicity. This elution method is particularly suitable for column cleaning, eliminating carry over effects, and avoiding sample cross contamination. 2. Elutionwith alineargradient:Thisisperformed with a 20 mingradient from0% to 100% of 1.5 M sodium chloride in 19 mM phosphate buffer (pH 2.5) containing 5% methanol at 0.5 mL/min. More complex mixtures require employing RAMSCX as an ion-exchange separation column. Linear gradients require more sophisticated switching techniques. The draw back is that in a multidimensional system, the next dimension should be very fast. A serious disadvantage is that a repeating series of the same component is observed in consecutively eluted fractions when the switching valve switches in the middle of the peak. This reduces MS sensitivity and complicates data analysis. 3. Elution with salt pulses: A multiple step elution is performed by the introduction of, for example, 5%, 10%, 25%, 50%, and 100% of 1.5 M sodium chloride in 19 mM phosphate buffer (pH 2.5) containing 5% methanol. Each step is for 10 min and run at 0.5 mL/min. This elution method compromises analytical system dimensionality, as the peak capacity of the ion-exchange chromatography (IEX) step is equal at most to the number of salt steps. However, in the second dimension only one or two columns are needed and there is no particular limitation in the second dimension separation time as peptides are eluted in portions in a controlled manner. However, the number of salt steps is limited by the total analysis time. In this case the multidimensional system is relatively simple. Each of these operations will lead to different results, because the desorption conditions of the target substances are not identical. As proteins trapped in a RAMSCX column will be subjected to different conditions (salt composition and molarity, pH), these changes might lead to conformational changes.

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Following elution from the IEX dimension, desalting and preconcentration of the fractions containing proteinaceous components were performed on two identical trap columns (Zorbax 300 SB-C18, 5 mm particles, 5 0.3 mm I.D., Agilent, Waldbron, Germany). As a final column a monolithic fused silica RP-18 endcapped capillary column of dimensions 150 0.1 mm I.D. (Chromolith CapRod, Merck KGaA, Darmstadt, Germany) was used. We preferred the monolithic type of column over a particulate capillary column for the following reasons: (a) the monolithic silica columns can be operated over a wide range of flow rates, which is particularly useful in the setup of multidimensional LC MS system to adjust for different column sizes; (b) in a MDLC-MS system the monolithic silica columns meet the requirement of high reproducibility comparable to particulate columns; (c) in terms of column robustness and usage flexibility monolithic silica columns are superior to packed particulate columns, for example, one could cut the top-end column when damaged, there is no change in the permeability as a result of pressure fluctuation and no frits are required at the end of the capillary directly connected to the MS. A 40 min acetonitrile gradient with 0.1% formic acid operating at constant 2 mL/min flow rate was employed to separate the trapped peptides. The end of the reversephase capillary column was directly inserted into a homemade robotic spotting apparatus so that the droplets were accumulated on a MALDI plate at 2 min intervals, filling a 100 spot MALDI plate per sample (5 fractions from the RAM-SCX column (salt steps), 20 fractions from the monolithic capillary RP 18e column). After the plate positions were filled the fraction were dried and 0.5 mL of matrix solution, consisting of a-cyano-4-hydroxycinamonic acid in 50% acetonitrile/4% formic acid/water (v/v/v) was spotted on the top. The MALDI plate was kept in the dark and analyzed within 12 h. Restricted access materials with a strong cation functionality and an average pore diameter of 6 nm were chosen to extract peptides from complex plasma samples. Ionexchange chromatography was employed in the second dimension as a mild separation technique that minimizes the risk of denaturation. Another important advantage of using strong cation adsorption compared to reverse-phase chromatography is the elimination of lipid-like plasma components, which preserves the column loading capacity, as the lipids are not bound, and allows an increase in sensitivity in mass spectrometry. Prefractionation can also be based on different physiochemical properties, such as net charge, mobility, size, hydrophobicity and affinity. By adjusting the average pore diameter of the RAM-SCX column the binding of highly abundant human serum albumin and other common large proteins in plasma can be minimized. Incorporating RAM-technology for sample preparation along with IEX and reversephase separations enables: (i) automation of the sample cleanup; (ii) adjusting the column mass loadability by choosing the appropriate column dimensions; (iii) combining sample cleanup with a selective prefractionation according to charge. With such sample pretreatment we obtained a peptide profile for the molecular weight range from 700 to 4500 Da. For the second dimension, reverse phase columns were chosen as they offer high capacity and efficient separation. As the sample is highly pretreated after the RAM-SCX column and separated into fractions on the analytical


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FIGURE 9.6 The peptide and small protein map from a 100 mL human plasma injection. Columns: sample preparation SCX RAM; analytical column: chromolith performance RP-18, 100 0.1 mm I.D. Minute fractions were analyzed using MALDI-TOF MS. Fraction numbers correspond to the time scale. Dot size is related to signal intensity.

SCX column, the final separation has the capability to resolve a high number of low concentration substances/peptides. In order to use MS as an elegant and effective detection technique there are some requirements to maximize sensitivity and resolution. For example, there is a need to avoid salts. Reverse phase chromatography is an effective means to desalt fractions from the RAM-SCX column. Two trap columns packed with reverse phase material perform this task. These also compensate for the flow-rate differences between the analytical and capillary columns. To make the last chromatographic step highly efficient and also to avoid dilution of the fractions, a reversed-phase capillary column was employed. Droplets/fractions from the reversed-phase capillary column were directly spotted onto the MALDI target plate thus avoiding dilution. Combined with sample volumes of 0.5 mL used in classical MALDI-MS sample spotting, sample concentrations can be so low that adhesion to tubes, tips and other surfaces might result in a substantial sample loss. As already mentioned the system can be used to analyze unfractionated human plasma, cerebrospinal fluid and urine samples in a fully automated way with high sensitivity. In all peptide displays (Figure 9.6), between 1000 and 4000 mass spectrometric signals were observed, which correspond to 500–2000 individual peptides. This number of additional observed peaks usually reflects redundancy (peptides that elute in more than one fraction), peptide species with and without oxidative states, or a small number of MS artifacts, such as fragment ions.

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9.3 SUMMARY AND CONCLUSIONS We predict that peptide analysis both for biomarker discovery and degradome studies will gain substantial interest in future diagnostic research. The presented 2D(3D)-LC technology platform with integrated sample cleanup provides a powerful tool and a sustainable platform for research in this field. The approach is highly flexible with respect to sample volume and loading capacity of the probes. System optimization enables the handling of a variety of biofluids and is highly flexible to choose appropriate LC modes for further separation.

REFERENCES Baggerman, G., Verleyen, P., Clynen, E., Huybrechts, J., De Loof, A., Schoofs, L. (2004). Peptidomics. J Chromatogr B 803, 3–16. Boos, K.S., Rudolphi, A. (1997). The use of restricted-access media in HPLC, Part I– Classification and review. LC-GC Int. 15602–611. Clynen, E., Baggerman, G., Veelaert, D., Cerstiaens, A., Van der Horst, D., Harthoorn, L., Derua, R., Waelkens, E., De Loof, A., Schoofs, L. (2001). Peptidomics of the pars intercerebralis-corpus cardiacum complex of the migratory locust, Locusta migratoria. Eur. J. Biochem. 268, 1929–1939. Clynen, E., De Loof, A., Schoofs, L. (2003). The use of peptidomics in endocrine research. Gen. Comp. Endocr. 132, 1–9. Cortes, H.J. (1990). Multidimensional Chromatography Techniques and Applications. Marcel Dekker, New York. Dart, L.L., Smith, D.M., Meyers, C.A., Sporn, M.B., Frolik, C.A. (1985). Transforming growth factors from a human tumor cell: characterization of transforming growth factor b and identification of high molecular weight transforming growth factor Ó. Biochem. 24, 5925–5931. Giddings, J.C. (1984). Two-dimensional separations: concept and promise. Anal. Chem. 56, 1258–1270. He, Q.Y., Chen, J., Kung, H.F., Yuen, A.P., Chiu, J.F. (2004). Identification of tumorassociated proteins in oral tongue squamous cell carcinoma by, proteomics. Proteomics 4, 271–278. Hunt, D.F. (2002). Personal commentary on proteomics. J. Proteome Res. 1, 15–19. Lentner, C. (1984). Human plasma composition (Physical chemistry, composition of the blood, haematology, sonatometric data). Geigy Scientific Tables, Vol. 3. Basle, Switzerland: Ciba Geigy. Machtejevas, E., Denoyel, R., Meneses, J.M., Kudirkaite, V., Grimes, B.A., Lubda, D., Unger, K.K. (2006). Sulphonic acid strong cation-exchange restricted access columns in sample cleanup for profiling of endogenous peptides in multidimensional liquid chromatography. Structure and function of strong cation-exchange restricted access materials. J. Chromatogr. A 1123, 38–46. Machtejevas, E., John, H., Wagner, K., St€andker, L., Marko-Varga, G., Forssmann, W.-G., Bischoff, R., Unger, K.K. (2004). Automated multi-dimensional liquid chromatography:

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sample preparation and identification of peptides from human blood filtrate. J. Chromatogr. B 803, 121–130. Machtejevas, E., Marko-Varga, G., Lindberg, C., Lubda, D., Hendriks, R., Unger, K.K. (2007). Sulphonic acid strong cation exchanger restricted access columns in sample cleanup for profiling of endogenous peptides in multidimensional liquid chromatography: on-line automated sample cleanup procedures for human urine. In preparation for J. Chromatogr. A. Mazsaroff, I., Regnier, F.E. (1988). Phase ratio determination in an ion-exchange column having pores partially accessible to proteins. J. Chromatogr. 442, 15–28. Pinkerton, T.C. (1991). High-performance liquid chromatography packing materials for the analysis of small molecules in biological matrices by direct injection. J. Chromatogr. 544, 13–23. Premstaller, A., Oberacher, H., Walcher, W., Timperio, A.M., Zolla, L., Chervet, J.-P., Cavusoglu, N., van Dorsselaer, A., Huber, Ch.G. (2001). High-performance liquid chromatography-electrospray ionization mass spectrometry using monolithic capillary columns for proteomic studies. Anal. Chem. 73, 2390–2396. Racaityt_e, K., Lutz, E.S.M., Unger, K.K., Lubda, D., Boos, K.S. (2000). Analysis of neuropeptide Y and its metabolites by high-performance liquid chromatography—electrospray ionization mass spectrometry and integrated sample cleanup with a novel restricted-access sulphonic acid cation exchanger. J. Chromatogr. A 890, 135–144. Rbeida, O., Christiaens, B., Hubert, Ph., Lubda, D., Boos, K.-S., Crommen, J., Chiap, P. (2005). Integrated online sample cleanup using cation exchange restricted access sorbent for the LC determination of atropine in human plasma coupled to UV detection. J. Pharm. Biomed. Anal. 36/5, 947–954. Richter, R., Schulz-Knappe, P., Schrader, M., Standker, L., Jurgens, M., Tammen, H., Forssmann, W.-G. (1999). Composition of the peptide fraction in human blood plasma: database of circulating human peptides. J. Chromatogr. B 726, 25–35. Tammen, H., Schulte, I., Hess, R., Menzel, C., Kellmann, M., Mohring, T., Schulz-Knappe, P. (2005). Peptidomic analysis of human blood specimens: comparison between plasma, specimens and serum by differential peptide display. Proteomics 5, 3414–3422. Tatemoto, K. (1982). Neuropeptide Y: complete amino acid sequence of the brain peptide. Proc. Natl. Acad. Sci. USA 79, 5485–5489. Unger, K.K., Machtejevas, E., Hennessy, T.P., Ditz, R. (2004). Multidimensionale LC/MS in der proteomanalyse – eine kritische Bestandsaufnahme. Laborwelt 5/4, 4–10. Verhaert, P., Uttenweiler-Joseph, S., de Vries, M., Loboda, A., Ens, W., Standing, K.G. (2001). Matrix-assisted laser desorption/ionization quadrupole time-of-flight mass spectrometry: an elegant tool for peptidomics. Proteomics 1, 118–131. Wagner, K., Miliotis, T., Marko-Varga, G., Bischof, R., Unger, K.K. (2002). An automated online multidimensional HPLC system for protein and peptide mapping with integrated sample preparation. Anal. Chem. 74, 809–820.

10 A TWO-DIMENSIONAL LIQUID MASS MAPPING TECHNIQUE FOR BIOMARKER DISCOVERY David M. Lubman Department of Chemistry, Department of Surgery, Comprehensive Cancer Center, The University of Michigan, Ann Arbor, MI 48109, USA

Nathan S. Buchanan, Paweena Kreunin, and Yanfei Wang Department of Chemistry, The University of Michigan, Ann Arbor, MI 48109, USA

Fred R. Miller Karmanos Cancer Institute, Wayne State University, Detroit, MI 48201, USA

Kathleen Cho and Rong Wu Department of Pathology, The University of Michigan, Ann Arbor, MI 48109, USA

Steven Goodison Department of Surgery, University of Florida, Jacksonville, FL 32209, USA

Timothy J. Barder Eprogen, Inc., Darien, IL 60561, USA

10.1 INTRODUCTION Q1

Identification and validation of biomarkers predictive of disease, particularly cancer, is a significant and expanding area in clinical research. Quality cancer biomarkers should facilitate early detection and diagnosis of the disease, with more specific markers used for classification (such as grade/stage) or subtype determination of the Multidimensional Liquid Chromatography: Theory and Applications in Industrial Chemistry and the Life Sciences, Edited by Steven A. Cohen and Mark R. Schure. Copyright 2008 John Wiley & Sons, Inc.

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disease presenting in a patient. Moreover, each cancer subtype will have its own specific histopathology and prognosis and may be more benign or aggressive and consequently require a specific course of treatment. A method which can reliably identify biomarkers to distinguish specific types, subtypes and/or grades/stages of a cancer would ultimately lead to a more individualized targeted therapy, whereby each patient could be treated according to the specific presentation of the disease. Currently, several strategies are being used to search for biomarkers of cancer based upon either gene or protein expression. The advent of DNA microarrays has enabled the study of gene expression profiles for large numbers of tumor samples and has been used to characterize global and specific gene expression patterns of cancer (Golub et al., 1999; Schaner et al., 2003). This technology has enabled the study of gene expression profiles of large numbers of tumor samples, which can be used to classify different cancers based upon characteristic gene expression patterns. For example, Schaner, et. al., used DNA microarrays to identify groups of genes that could distinguish ovarian from breast carcinomas, clear cell subtype from other ovarian carcinomas, and grade I and II from grade III serous papillary carcinomas (Schaner et al., 2003). In other work, Giordano et al. (2001), were able to use gene expression profiles of adenocarcinomas of the lung, colon, and ovary to demonstrate the ability to classify tumors in an organ-specific manner. Numerous other studies have also used gene expression to classify various cancers and their subtypes and their relationship to one another (Ross et al., 2000; Korshunov et al., 2003; Tan et al., 2004). Classification of different types of cancers can also be accomplished by profiling protein expression in cells, biofluids or serum (Jones et al., 2002; Yanagisawa et al., 2003). The use of protein expression profiling may be essential for classification of cancers because many of the mRNAs are never expressed in the cell and it is ultimately the posttranslational protein expression that determines the function and structure of the cell. Moreover, protein expression can be profiled from either tissue, biofluids, or serum. The ability to profile protein expression in each of these media is essential to gaining insights into the systemic aspects of the disease. The analysis of serum is particularly important as it provides a noninvasive means for early detection of circulating biomarkers secreted into the bloodstream from invasive tumors. However, only a limited subset of potential markers may be secreted by the tumor and thus be useful for detection of a specific cancer. Often a biopsy of tissue is required to obtain a more complete picture of the protein expression of the cancer cell so that a more detailed diagnosis can be made. In other cases, analyses of bodily fluids such as those from cysts may be important for detecting early stages of cancer. 2D-polyacrylamide gel electrophoresis (2D-PAGE) is the classical method for profiling large numbers of proteins in cells (Gorg et al., 2000). 2D-PAGE involves an isoelectric separation of proteins based on pI in a first dimension followed by an electrophoretic MW separation in a second-dimension using a polyacrylamide gel. The result is a two-dimensional map of the proteins expressed in the cell. A number of studies using large numbers of quantitative 2D gels for tumor classification have been performed for bladder, breast, lung, prostate, and ovarian cancers (Alaiya et al., 2002; Jones et al., 2002) where, in general, benign and malignant

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tumors were identified by proteins that were differentially expressed and tumor stage classified by marker proteins that were upregulated or downregulated. For example, Alaiya et al. (2002), did extensive work on the classification of ovarian tumors using 2D gel electrophoresis. A total of 40 tumor samples were evaluated and hierarchal cluster analysis used to distinguish borderline ovarian tumors from malignant and benign tumors. Although 2D gel electrophoresis has remained the most powerful method for separating large numbers of proteins, the method has many limitations as a general tool for profiling large numbers of samples. The 2D gel method is generally a slow, manually intensive technique that can require several days to complete. Moreover, the irreproducibility of interlysate comparisons due to varying run conditions between gels makes “spot” comparison sometimes quite difficult and limits their quantitation. Run-to-run reproducibility can be a critical issue in such intralab comparisons and even more significant for interlab comparisons where run conditions can vary significantly. Identification of proteins in gel “spots” is also a key issue. Proteins embedded in the gel require manually intensive procedures to excise and purify the gel spots for further analysis by mass spectrometry. The limited amount of material that can be loaded onto 2D gels before streaking occurs and resolution ruined is also of concern to researchers. Low protein loading, although desirable for improving gel resolution, ultimately limits dynamic range and the amount of protein that can be recovered from each spot. This in turn limits the ability to perform accurate identification and structural analysis especially for low abundance proteins. The drawbacks of current gel-based separation methods have led to the development of new strategies using all liquid-based separations. Some of these methods have involved 2D liquid chromatography (Liu et al., 2002) while others have used batch liquid methods to separate proteins based upon some fundamental property, such as charge, size or hydrophobicity. Each of these methods have distinct advantages and disadvantages, but they all have the advantage of producing purified proteins in the liquid phase, which can readily be interfaced to mass spectrometry. In particular, the configuration described in this chapter (Figure 10.1) involves a 2D liquid separation based upon two phases of chromatography involving pI and hydrophobicity. It will be shown that this method generates a map analogous to 2D gel electrophoresis and can be readily interfaced to mass spectrometry to generate a highly reproducible map for interlysate comparisons for searching for markers of cancer.

10.2 METHODS FOR SEPARATING AND IDENTIFYING PROTEINS 10.2.1

pI-Based Methods of Separation

An important issue in 2D liquid separations is finding a first dimension, which can provide information on the pI of the protein. This is important as pI information has biological significance in proteomics where it is a physical property listed in the databases and can aid in protein identification. The use of pI

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FIGURE 10.1

Schematic overview of protein separation techniques.

also becomes essential for separation of various modified isoforms of proteins that might be poorly resolved, particularly with respect to phosphorylated and glycosylated isoforms. In recent work, there has been an effort to develop gel-free pI-based separations. These include liquid phase isoelectric focusing (IEF) using the Rotofor apparatus (Wall et al., 2001), which is a liquid-based analog of carrier ampholyte gels and the IsoPrime device (Zuo and Speicher, 2000; Zhu and Lubman, 2004) and other related units that employ isoelectric membranes to separate proteins in the liquid phase and are the analogs of IPG-based gels (Ek et al., 1983). Alternatively, continuous flow electrophoresis has been used to separate proteins based upon pI in the liquid phase (Hoffmann et al., 2001). These methods are batch liquid-phase separations and can be performed in either preparative or analytical scale separations. These methods allow higher sample loading capacity, which in turn increases the amount of protein available for subsequent analyses. However, these methods are not readily automated and in the Rotofor and continuous flow methods the fractionation suffers from protein overlap between proteins in adjacent fractions. Capillary isoelectric focusing has also been used to separate proteins based upon pI in one-dimensional and two-dimensional separation schemes. This method provides a means for microscale separation of proteins based upon pI (Tang et al., 1997; Zhou and Johnston, 2005). In current work chromatofocusing (CF), a column based method for separating complex mixtures of proteins according to pI, has been selected for the first separation dimension (Hutchens et al., 1984; Liu and Anderson, 1997). CF uses charge exchange on an ion exchange medium, where a pH gradient is generated by


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titration of a buffer against a start buffer, which sets the initial pH of the system. In the case of weak anion exchangers, proteins are loaded on the column at a high pH and as the titration proceeds, proteins with pI’s greater than the pH will elute down the column, with high pI proteins eluting from the column first sequentially followed by proteins of successively lower pI’s. Chromatofocusing has genuine advantages as a method for separating proteins based upon pH in that it can rapidly fractionate large numbers of proteins and achieve separation of proteins in narrow (0.1) pH fractions. The pH can be measured online and the fractions directly collected as the eluent of the liquid-based separation. Ultimately, it is a method that uses standard HPLC hardware and has been automated and sold under the commercial name Beckman–Coulter ProteomeLab PF2D. A pH gradient pI-based separation has also recently been applied to a microscale format (Andersen et al., 2004). 10.2.2

Chromatofocusing-A Column Based pH Separation

10.2.2.1 Tumor Tissues Lysate Preparation As with any analytical technique sample preparation is of vital importance in 2D liquid separation experiments. Most of the samples studied thus far have been either cell lines or tumor tissues. The tissues are often heterogeneous and require extensive sample preparation to properly lyse the sample and extract the proteins for chromatography. There are a number of procedures that have been used in our laboratory, but one of the most effective involves extensive bead blasting of the sample. The tumor sample preparation described herein has been used with ovarian tumor tissues. In this procedure the tumor tissue samples are cut into small pieces by using razor blades and tissue samples are subsequently sealed into 2 mL screw-cap microcentrifuge vials (BioSpec Products, OK), which also contain hundreds of minute glass beads (BioSpec Products, OK). Vials are subsequently filled full of lysis buffer (7.5 M urea, 2.5 M thiourea, 12.5% glycerol, 50 mM tris, 2.5% n-OG, 6.25 mM Tris-(2-Carboxyethyl) phosphine (TCEP), 1.25 mM protease inhibitor, pH adjusted to start pH-usually 8.5) with no air bubbles. The lysis buffer has been developed to solubilize and denature the proteins in a manner that is compatible with chromatography. In particular, n-octyl-bD-glucopyranoside (OG) is used as a nonionic detergent to solubilize the proteins, where SDS cannot be used. Other detergents such as Triton are incompatible with the mass spec analysis. TCEP or dithiothreitol (DTT) is important for breaking disulfide bonds, where denaturing the protein is important to obtain full interaction with the column in the hydrophobic separation. Tissue samples are then homogenized for 3 min in 10 s increments at 4800 rpm in the minibead beater cell disruptor and followed by centrifuging at 5000 rpm for 10 min at room temperature to pellet the bead mix. The supernatant containing proteins is collected and stored on ice. In order to avoid incomplete tissue disruption and protein extraction, the vials are filled with fresh lysis buffer again and homogenized in 2 min. These two lysis solutions are combined in 10 mL polycarbonate centrifuge tubes and insoluble material was precipitated by centrifugation at 35000 rpm for 1 h (80Ti Beckman Ultracentrifuge). Tissue lysates are stored at 80 C.

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10.2.2.2 Chromatofocusing Procedure CF and NPS RP-HPLC were continuously performed using an integrated protein fractionation system ProteomeLab PF 2D (Beckman Coulter, Inc., Fullerton, CA, USA). A High-performance Chromatofocusing (HPCF)-1D column (250 mm 2.1 mm) was used to perform chromatofocusing. Two buffers, a start buffer (SB) (Beckman Coulter, Inc., Fullerton, CA, USA) and an elution buffer (EB) (Beckman Coulter, Inc., Fullerton, CA, USA), were used to generate the pH gradient on the column. Both buffers were prepared in 6 M urea and 0.2% OG. Before running the CF, the pH of SB was adjusted to 8.5 þ/ 0.1 and EB was adjusted to 4.0 þ/ 0.1 using either a saturated solution (50 mg/mL) of iminodiacetic acid if the buffer was too basic or 1 M NH4OH if the buffer was too acidic. A PD-10 G-25 column (Amersham Pharmacia Biotech) was used to exchange the protein sample from the lysis buffer to the equilibration buffer used in the CF experiment. The HPCF-1D column was first flushed with 100% distilled water (filtered through a 0.45 mm filter) for 10 column volumes at 0.2 mL/min, then equilibrated with 100% SB for 30 column volumes. After equilibration with SB, the HPCF column was ready to start the ProteomeLab PF 2D default method where injection of the sample began the method. After the method had been started, the column was washed with 100% SB to remove material that did not bind to the column at pH 8.5. When the wash was complete, the UV absorbance returned to baseline. Once a stable baseline was achieved, the method was initiated at 100% EB. UV detection was performed at 280 nm and the pH was monitored online by a flow-through pH probe (Beckman Coulter, Inc., Fullerton, CA, USA). As the pH decreased, pH fractions were then collected in 0.2 pH intervals where 23 fractions in total were collected in the range of 8.5–4.0 pH. After the pH of the eluent reached 4.0, the HPCF column was washed with 10 columnvolumes of 1M NaCl and the fractions collected by time. After the salt wash, the HPCF column is washed with 10 column volumes of distilled or deionized water. The CF portion of the method for the ProteomeLab PF 2D required around 185 min including the salt wash. A typical chromatofocusing fractionation (including RPHPLC) of an E. coli sample as a function of pH is shown in Figure 10.2. 10.2.3

Nonporous Separation of Proteins

A unique feature of this multidimensional/liquid phase method is the use of nonporous silica (NPS) as a medium for the second dimension of separation (Nimura et al., 1991; Banks and Gulcicek, 1997; Barder et al., 1997). In any pI fraction obtained using CF of a whole cell lysate of a human cell line or tumor tissue sample, there may be from 50 to 150 proteins. In the mid-pH range 5.0–6.5, there are often large numbers of proteins in each fraction; whereas on the basic and acidic ends, there are generally fewer proteins. In order to separate large numbers of proteins with sufficient resolution, a 1.5 mm HPCF-2D (4.6 mm 33 mm) NPS C18 column (Beckman Coulter, Inc.) has been used. NPS RP-HPLC provides rapid and highly reproducible separations of proteins according to their hydrophobicities. The NPS packing material used in these RP separations eliminates problems associated with porous media where proteins adhere within the pores. In porous materials this often results in “smearing” of protein peaks


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FIGURE 10.2 2D map of a whole cell lysate (top) along with an illustration of the reproducibility of the pI versus hydrophobicity profiling technique (bottom) using the Beckman PF2D automated instrument and software. (See color plate.)

with a corresponding loss in resolution and protein recovery. The use of NPS media improves resolution and reduces separation times by as much as one-third compared to porous media. The fast separation times are due in part to the short columns used (4.6 mm 50 mm) and the elevated temperature that is typically around 60–65 C. This elevated temperature reduces solvent viscosity improving mass transport to/from

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the stationary phase improving resolution and also helps reduce column pressure. The separation efficiency remains high thanks to the use of 1.5 mm particles with highly uniform size distribution in these columns. Of greater significance is the reproducibility of the separation, which is essential for interlysate comparisons. The reproducibility in retention times for NPS separations has been found to be within 1 sec under the conditions used in our work. In addition, the recovery for proteins under 40 kDa may be as high as 90%. These columns have been used for separation of proteins of over 200 kDa MW in our experiments as shown by analysis using a 1D gel. In addition, columns with larger particle sizes have been used to separate proteins of over 400 kDa (55–56). The NPS RP-HPLC method provides a liquid phase method for separating large intact proteins for further analysis. More specifically, it provides a means of separating proteins for interfacing to mass spectrometric analysis. 10.2.4

Electrospray-Time of Flight-Mass Spectrometry

Electrospray-time of flight-mass spectrometry (ESI-TOF-MS) (Fenn et al., 1989) allows rapid determination of intact protein molecular weights from LC effluent or direct injection via syringe pump. Because ESI data are of far higher resolution and mass accuracy than traditional gel techniques not only does it support protein characterization but it can reveal isoforms and modifications in ways that would not be possible otherwise. The electrospray process produces a distribution of charges for each protein, so ESI is free from any inherent limits to the detectable protein molecular weights. The distribution of charges can also be quantitatively deconvoluted allowing determinations of relative protein expression levels as well as the original mass of the parent ion. Consequently, ESI-TOF-MS is an extremely powerful technique for protein mass mapping and further characterization of 2D liquid separations (Wall et al., 2001). Online LC-ESI-TOF-MS experiments are carried out in a very similar fashion to the off-line NPS-HPLC separations described above, with a few notable exceptions. Firstly, 0.3% (v/v) formic acid is added to each mobile phase to counteract the ionization suppression induced by TFA. Because of the formic acid UV detection must be carried out at 280 nm (as opposed to 214 nm). To aid in normalization between runs 1 mg of Bovine insulin (MW ¼ 5734 Da) is added to each chromatofocusing fraction prior to injection onto the column. Finally, the flow is split postcolumn directing 200 mL/min into the ion source and the remaining 300 mL/min through the UV detector and fraction collection. Because online separations provide such a wealth of information about target proteins, interpretation becomes of critical importance in order to make full use of the data. The first step in any analysis of LC-MS data involves integration and deconvolution of sample spectra to determine protein mass and intensity. In manual analysis (Hamler et al., 2004), users identify protein “umbrellas,” create a total ion chromatogram (TIC), integrate the protein peak, and deconvolute the resulting spectrum. Deconvolution of ESI spectra employs a maximum entropy deconvolution algorithm often referred to as MaxEnt (Ferrige et al., 1991). MaxEnt calculates


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a “mock” dataset that most closely resembles the “real” data and the intact mass and intensity mostly likely to result in such a spectrum. The process of selecting individual peaks and running deconvolutions is quite time intensive, both for processing computers and analysts. This has prompted the development of automated analysis tools such as Protein Trawler (Williams et al., 2002). Protein Trawler replaces the manual steps in data analysis by integrating fixed time segments, deconvoluting the spectra and reporting the resulting mass and intensities. The output from complex datasets is quite similar to that obtained manually and allows for far higher data throughput (Buchanan et al., 2005). Output from Trawler is then normalized against other “lanes” in a dataset using the deconvoluted bovine insulin intensity as an internal standard. Comparisons between pI separations or cell lines can take a variety of routes including normalization against the same insulin standard or normalization against a common protein found in the cell lines. Normalized peak lists are copied into plaintext files so data visualization can be carried out with two software tools, ProteoVue (Wall et al., 2001) and DeltaVue (Yan et al., 2003) (as in Figure 10.2). These tools display masses and intensities as banded mass maps or “virtual gels” allowing rapid visual interpretation that can reveal details and differences among samples. 10.2.5

MALDI Peptide Mass Fingerprinting

Proteomics ultimately hinges upon protein identification to reveal the meaning behind the masses, spots, or peaks detected by other means. Because fraction collection is a natural component of HPLC separations, intact proteins can be readily collected either for direct analysis or for proteolytic digestion and identification using peptide mass fingerprinting (PMF) in conjunction with matrix assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF-MS). Following collection, protein fractions are neutralized using ammonium bicarbonate (to counteract the FA/TFA) and digested overnight using modified sequencing grade porcine trypsin. Following digestion, samples are concentrated and desalted using a miniature pipette-based solid-phase extraction cartridge—one common brand is the Millipore ZipTip. Samples are then coprecipitated on an appropriate stainless steel MALDI target along with 1 mL of a standard and matrix solution containing 25% v/v saturated a-CHCA matrix in 60 : 40 acetonitrile: water, 0.1% TFA, 100 pg of angiotensin I (MW ¼ 1296 Da), and 250 pg of ACTH 1–16 and 17–38 (MW ¼ 2093 Da, and 2465 Da, respectively). These peptide standards act as an internal mass calibration over the range where most useful tryptic peptides are observed in MALDI. Samples are then introduced into the source region of the mass spectrometer where a pulsed laser (often N2) gently ablates the peptides from the target and measures the masses. Data analysis consists of summing and calibrating spectra, then recording sample peptide peaks (while excluding standards and noise). When working optimally with internal calibration, MALDI spectra can have a mass accuracy in excess of 20 ppm (0.002%). Mass accuracy is vital in the next step in which the sample peptide lists are searched against online databases (such as NCBI or SwissProt (Boeckmann

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et al., 2003)) using a utility such as Protein Prospector (Clauser et al., 1999) or Mascot (Perkins et al., 1999). Users can specify species, potential modifications, mass accuracy and a number of other parameters that can then be used to compare MALDI peak lists against extensive in-silico digests. The results returned from these searches are then assessed based on molecular weight search score (MOWSE score), percent coverage, potential modifications present, and detected species. Although individual experiments can differ in overall performance, “good” scores will likely have scores over 1000, coverage over 20%, and match the species exactly. 10.2.6

Data Analysis and Recombination

With the completion of these various analyses a final step must be undertaken to recombine the various datasets into a powerful, quantitative proteomic description of a given system. Databases, although an invaluable tool in protein identification, require significant interpretation by analysts in order to draw correlations between identities from MALDI-PMF and intact masses measured by ESI-TOF-MS. Any given database entry for a protein can include numerous isoforms, truncations, and modifications (both observed and theoretical) that contribute to the final molecular weight. With careful examination of database entries using online tools, such as ExPASy (Gasteiger et al., 2003), PMF identities can be annotated to indicate the range of possible molecular weights as well as special modifications, including phosphorylations and glycosylations, that may be of interest for future analysis. ID-mass correlation can also reveal previously unknown modifications provided other data match up. These are commonly revealed as shifts of þ16, þ42, and þ80 Da, for methionine oxidations, acetylations, and phosphorylations respectively. Not only does this suggest possible new avenues for research, but it allows for substantial improvements in correlation between PMF results and intact MW determination. Correlated mass-ID data can take several forms including annotated mass maps to catalog a cell system and tables of differential expression for potential biomarkers.

10.3 APPLICATIONS 10.3.1 Proteomic Mapping and Clustering of Multiple Samples—Application to Ovarian Cancer Cell Lines The two-dimensional liquid mass mapping method has grown to be a useful technique for classification and biomarkers identification. It can serve as a powerful tool for comparing the protein expression profiles of large numbers of samples. The use of protein expression is an informative means for classification to distinguish specific types, subtypes and/or grades/stages of a cancer according to the protein bands observed in their expression maps. The capability for automation of the method allows reproducible comparison of many samples and the use of differential analysis limits the number of proteins that might require further analysis by mass spec techniques.

APPLICATIONS

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FIGURE 10.3 Fraction pI 6.05–6.20 gradient range from 30.0% to 78.0%. The relative intensities of the band are quantitatively proportional to the amount of corresponding protein detected by UV absorption.

In this 2D liquid mass mapping technique, we can use ProteoVue software (Beckman-Coulter) to make comparisons between a large number of samples. Figure 10.3 shows peak patterns of 11 serous and ovarian surface epithelium (OSE) cell lines for fraction pI 6.05–6.20. The peak retention time and intensity can be obtained using the software. The average number of peaks for 11 samples is 74. After comparing protein expression maps, one finds that proteins can be clustered into three groups. One group is likely to be common to most cell lines. For this fraction, around 14 proteins have the same retention time and are likely to be identical for all cell lines. A second set of proteins are linked only to some group of cell lines. This set may provide the basis for detection and classification of serous carcinoma and have the potential to provide identifying biomarkers. A third group of proteins appears to be uniquely expressed on each individual cell line. It is possible to hypothesize that this third group of proteins is responsible for unique aspects of cell behavior. Figure 10.4 shows an example of specific types of serous carcinoma cell lines that clustered together using an automated 2D liquid fractionation system (Beckman PF2D) for the liquid phase separation and mapping of the protein expression for eight serous ovarian cancer and three OSE cell lines. Maps are produced using pI as the separation parameter in the first dimension and hydrophobicity based upon RP-HPLC separation in the second dimension. A dynamic programming method was used to correct for minor shifts in peaks during the HPLC gradient between sample runs. A correlation matrix was formed by calculating the Pearson correlation coefficient between each aligned pair of samples (Falk and Well, 1997). These correlation matrices were then visualized using hierarchical clustering techniques. Figure 10.4 is a hierarchical cluster analysis of “complete linkage,” dendrogram whose distance

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FIGURE 10.4 Cell lines are separately aligned and grouped based on similarities in their protein expression using a hierarchical clustering analysis technique. This technique produces a dendrogram in which pairs of points are joined sooner (i.e., closer to the ends of the dendrogram) if they have greater correlation.

between groups is defined as the distance between the most distant pair of objects, one from each group. Hierarchical clustering analysis was used to classify the different samples according to their corrected protein expression profiles. In the dendrograms, the length and the subdivision of the branches display the relatedness of the cell lines and the expression of the proteins. Several of the ovarian surface epithelial cell lines clustered together, while specific groups of serous carcinoma cell lines clustered with each other. Two sets of samples as IOSE-144-1 and IOSE-144-2 (Auersperg et al., 1994), which is life-extended OSE cells expressing SV40 large TAntigen were used to evaluate the method. It was found that OSE cell lines IOSE-144 and HOSE-A (Gregoire et al., 1998) clustered together, while the two IOSE-144 samples clustered together most closely as expected. In addition, several serous carcinoma cell lines clustered with each other, that is, DOV13, OVCA429, and OVCA433 clustered together. It is interesting that IOSE-80, which was derived from OSE, clusters with the highly invasive serous carcinoma lines HEY and PEO1 (Buick et al., 1985; Langdon et al., 1988). This is not surprising as the IOSE80 cell line has been cultured for many passages and may have obtained some of the characteristics of the carcinoma-derived lines. Although limited information is available on the cell lines, it is shown that the protein expression of certain cell lines is closely related to others and that these cluster together on the dendrogram. Identification of potential marker bands is an essential application of the 2D liquid mass mapping technique. Figure 10.5 shows an example of potential marker identification between two groups of serous carcinoma cell lines, where one group contains OVCA429, OVCA433, and DOV13 and the other group contains IOSE-80, PEO1, and HEY. Standardization and alignment of bands were performed and then comparisons were separately made at each hydrophobicity level between two groups of samples. A differentially expressed band is selected on the basis of having at least a four fold different mean level within the two groups of samples. In

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FIGURE 10.5 Differential analysis. Fraction pI 7.42–7.57, gradient range from 25.0% to 75.0%. The relative intensities of the band are quantitatively proportional to the amount of corresponding protein detected by UV absorption.

addition, at least 25 consecutive hydrophobicity levels were required to meet these conditions in order for the band to be considered as a marker. Figure 10.5 shows peak patterns for these two cluster samples, group OVCA429, OVCA433, and DOV13 and group IOSE-80, PEO1, and HEY. The image is displayed in a format with each different sample on the x axis and hydrophobicity on the y axis. The relative intensities of the band are quantitatively proportional to the amount of corresponding protein detected by UV absorption. By comparing protein expression between these two groups, the four groups of bands marked in figure are only observed in the group IOSE-80, PEO1, and HEY but not in the group of OVCA433, OVCA429, and DOV13. The use of differential analysis allows us to identify proteins that may be common bands for classification and also reduces the potentially large amounts of protein expression data from a large number of samples into a manageable dataset. It thus reduces the number of significant bands that need to be identified by mass spec or other methods. 10.3.2 2D Liquid Mass Mapping of Tumor Cell Line Secreted Samples, Application to Metastasis-Associated Protein Profiles A 2D liquid mass mapping method has been developed in our laboratory for the analytical profiling of proteins in complex biological material. In the present study, we demonstrate the capability of this method for comparative protein mapping of isogenic

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breast tumor cell line clones. Liquid separation/mass mapping was applied to the extracellular component of the proteome of M4A4 and NM2C5 metastasis model cell lines (Urquidi et al., 2002; Goodison et al., 2003) in order to identify proteins, which are differentially expressed with respect to metastatic phenotype. Secreted and cell surface proteins are of substantial interest to disease progression as these secreted fractions are rich of therapeutic targets. Profiling the proteins expressed in these compartments could provide useful information on the molecular mechanism of tumor metastasis. In this study, serum-free conditioned media was collected from the cultured monoclonal cell lines and a mass mapping technique was applied in order to profile a component of each cell line proteome. After the serum-free conditioned media was thawed, a buffer (Kreunin et al., 2004) containing chaotropes, detergents, reducing reagents and protease inhibitors was immediately added. The buffer prevents protein degradation during the sample preparation step. The secreted protein sample was then exchanged from the serum-free conditioned media/buffer to the equilibrium buffer required for the chromatofocusing experiment using a gel filtration. Using the 2D liquid mass mapping approach, over 400 of the proteins were mapped and displayed as a 2D map of pI versus accurate Mr. This was performed over a pI range of 4.0–6.2, and a mass range of 6–80 kDa. An example of a differential display between CF fraction 5.6–5.4 from M4A4 and NM2C5 secreted samples is given in Figure 10.6, indicating the expression of several potential biomarkers as well as a number of common proteins. Confirmation of the identity

FIGURE 10.6 2D differential display of CF fractions from M4A4 and NM2C5 secreted samples. These fractions ranged in pH from 5.6 to 5.4. The differential display map was created using point-by-point subtraction of the areas of the deconvoluted peaks in the TIC. (See color plate.)

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of a number of differentially expressed proteins was achieved through trypsin digestion and analysis by MALDI-TOF MS peptide mapping. Eighty-eight unique proteins were identified and using a relative abundance threshold of more than twofold, 27 of the 88 proteins were confirmed as being differentially expressed with regard to metastatic phenotype. Proteins associated with the metastatic phenotype included osteopontin and extracellular matrix protein 1, whereas the matrix metalloproteinase-1 and annexin 1 proteins were associated with the nonmetastatic phenotype. 10.3.3 Identification Annotation and Data Correlation in MCF10 Human Breast Cancer Cell Lines The MCF10 model of proliferative breast disease (Miller, 2000) has been investigated as a model of human breast cancer. The model includes a number of cell lines encompassing the full range of disease states, from normal epithelial cells to malignant tumors. Several studies have been carried out on this cell line using the PF2D and related techniques. One such study detailed differential expression between MCF10A, MCF10CA1a.cl1 (CA1a), and MCF10CA1d.cl1 (CA1d) (Hamler et al., 2004). MCF10A is a “normal” immortalized epithelial cell line that was compared against two malignant lines (CA1a and CA1d). Work focused on two pI regions (8.0–7.6 and 6.0–5.6), identifying and quantitating all proteins detected therein. As seen in Figure 10.7 these proteins cover a wide range of molecular weights, from 5–75 kDa.

FIGURE 10.7 Annotated mass maps of MCF10A, CA1a, and CA1d. Normalized maps show identities and intensities of proteins from 3 MCF10 cell lines at pH 5.6–6.0 and 7.6–8.0.

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As described above all samples were separated online using LCT ESI-TOF-MS then normalized for relative quantitation using a bovine insulin internal standard. Fractions were then collected for MALDI-TOF-MS PMF, digested with modified porcine trypsin, and analyzed using the TofSpec2E. Following this analysis, three major classes of differentially expressed including proteins were revealed in these TABLE 10.1

Differentially Expressed Proteins in MCF10 Cell Lines Differential Expression

pH 5.6–6.0 Protein Thymosin-beta-10 Barrier to autointegration factor 60S acidic ribosomal P2 Galectin-1 Cyt c oxidase polypeptide Va Hsp 27 Peroxiredoxin 2 Nucleophosmin Inorganic pyrophosphate Actin, beta Keratin type I cytoskeletal 19 Keratin type I cytoskeletal 17 Keratin type II cytoskeletal 7 Keratin type I cytoskeletal 8 Heat shock protein 70 kDa (GRP78) Heat shock cognate 71 kDa protein (Hsp73)

pH 7.6–8.0 Protein ATP synthase coupling factor 6 NADH-ubiquinone oxidoreductase 13 kDa subunit Peptidyl-prolyl cis-trans isomerase A (Rotamase) Adenylate kinase isoenzyme 2 Annexin II Fructose biphosphate aldolase Phosphoglycerate kinase 1 Alpha enolase Elongation factor Tu, m. p. Serine hydroxymethylase, m.p. Pyruvate Kinase, M2 isoenzyme

Differential Expressiona Accession Number

CA1a

CA1d

P13472 O75531 P05387 P09382 P20674 P04792 P32119 P06748 Q15181 P60709 P08727 Q04695 P08729 P05787 P11021 P11142

56X 2.5X X 1.5X 0.2X 2Xb 0.07X 2X 7X 2X 0.3X 0.5X X X 90X 3Xb

30X 10X 10X 22X 2X Xb 0.2X n/d 3X 2.5X 0.5X n/d 3X 4X 35X Xb

Differential Expressiona Accession Number P18859 O75380 P05092 P54819 P07355 P04075 P00558 P06733 P49411 P34897 P14786

Differences are based on deconvoluted MaxEnt peak areas for each protein. a Expression level relative to normal MCF10A cells; nd ¼ not detected. b Proteins detected in CA1a and CA1d lines, but not observed in MCF10A cells.

CA1a

CA1d

6X 19X 3X X 4X 10Xb 3X Xb 0.7X n/d 1,500X

2X X 2X 2X X Xb X Xb 0.4X 0.4X 325X

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cell lines, including cytokeratins (CKs), annexins, and heat shock proteins (HSPs). Relative quantitation for these markers, along with other differentially expressed proteins, is shown in Table 10.1. Traditional proteomics studies, using gel techniques, have also singled out these markers, supporting the validity of 2D liquid separations for biomarker discovery. Cytokeratins are common structural components of the epithelial cells, but have been shown to change in type and distribution with cancer progression particularly, CK17 and 19 (under expressed in malignant lines) and CK7 and 8 (over expressed in CA1d). Upregulation of CK8 and down regulation of CK 19 was correlated with tumor progression and metastasis in breast cancer (Brotherick et al., 1998). Similarly upregulation of CK8 was correlated with poor prognosis (25% after 18 months) in patients with nonsmall cell lung cancer (Fukunaga et al., 2002). It has been suggested that CK changes of this type alter the cytoskeleton in such a way that cellular motility and invasiveness are enhanced. This leads to metastases and the resulting poor patient outcomes. Heat shock proteins are another broadly indicative class of markers in human cancers as they not only protect cells from environmental stressors found in typical tumor sites but also can confer resistance to some chemotherapeutic agents. HSPs, also referred to as molecular chaperones, accomplish this by playing an important role in protein folding, protecting nascent proteins from proteolytic digestion and inhibiting apoptotic pathways (Jaattela, 1999). As seen in Table 10.1 Heat shock proteins HSP70 and GRP78 were over expressed in malignant MCF10 cells, suggesting just such an adaptation to environmental stress. A recent study of hepatocarcinomas (Lim et al., 2005) indicates that HSP70 correlates with histological grade and GRP78 is correlated with grade, increased size, microvascular invasiveness, and tumor stage. Building on this knowledge a simple database of detected proteins has been developed for MCF10 cell lines compositing identified proteins along with their theoretical masses, detected experimental masses, and other critical information including links to their database entries and possible PTMs. This study also provided high expression (but not differentially expressed) “benchmark” proteins, such as a truncated 60 kDa Heat Shock Protein (P10809), which have helped to verify this methodology in subsequent experiments.

10.4 SUMMARY AND CONCLUSIONS The combination of mass spectrometry and liquid separations described here offers proteomic researchers the opportunity to move beyond the 2D gel paradigm by easily separating complex protein mixtures and readily identifying proteins by both pattern recognition and MS techniques. Because the system is flexible enough to allow online and off-line detection by spectrophotometry or mass spectrometry the right toolset can be brought to bear on a problem ensuring the most efficient use of instrumentation and time. Because it allows both rapid screening of cell lines and detailed studies of protein expression, this approach can be not only of diagnostic use but also reveal a wealth of

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knowledge about the underlying biology of cancers and other disease states. With coming advances in instrumentation and bioinformatics this information will continue to gain greater depth, detail, and accessibility.

ACKNOWLEDGMENTS This work was supported in part by the National Cancer Institute under grant R01CA10010 (DML, KRC), R01CA90503 (DML, FRM), R01CA108597 (SG) and the National Institutes of Health under grant R01GM49500 (DML). Support was also generously provided by Eprogen, Inc. and Beckman-Coulter, in particular for HPLC columns and use of the PF2D instrument. REFERENCES Alaiya, A. A., Franzen, B., Hagman, A., Dysvik, B., Roblick, U. J., Becker, S., Moberger, B., Auer, G., Linder, S. (2002). Molecular classification of borderline ovarian tumors using hierarchical cluster analysis of protein expression profiles. Int. J. Cancer 98(6), 895–899. Andersen, T., Pepaj, M., Trones, R., Lundanes, E., Greibrokk, T. (2004). Isoelectric point separation of proteins by capillary pH-gradient ionexchange chromatography. J. Chromatogr. A 1025(2), 217–226. Auersperg, N., Mainesbandiera, S. L., Dyck, H. G., Kruk, P. A. (1994). Characterization of cultured human ovarian surface epithelialcells- phenotypic plasticity and premalignant changes. Lab Invest 71(4), 510–518. Banks, J. F., Gulcicek, E. E. (1997). Rapid peptide mapping by reversed-phase liquid chromatography on nonporous silica with online electrospray time of flight mass spectrometry. Anal. Chem. 69(19), 3973–3978. Barder, T. J., Wohlman, P. J., Thrall, C., DuBois, P. D. (1997). Fast chromatography and nonporous silica. Lc Gc-Mag. Sep. Sci. 15(10), 918–926. Boeckmann, B., Bairoch, A., Apweiler, R., Blatter, M. C., Estreicher, A., Gasteiger, E., Martin, M. J., Michoud, K., O’Donovan, C., Phan, I., Pilbout, S., Schneider, M. (2003). The SWISSPROT protein knowledgebase and its supplement TrEMBL in 2003. Nucleic Acids Res. 31(1), 365–370. Brotherick, I., Robson, C. N., Browell, D. A., Shenfine, J., White, M. D., Cunliffe, W. J., Shenton, B. K., Egan, M., Webb, L. A., Lunt, L. G., Young, J. R., Higgs, M. J. (1998). Cytokeratin expression in breast cancer: phenotypic changes associated with disease progression. Cytometry 32(4), 301–308. Buchanan, N. S., Hamler, R. L., Leopold, P. E., Miller, F. R., Lubman, D. M. (2005). Mass mapping of cancer cell lysates using two-dimensional liquid separations, electrospray-time of flight-mass spectrometry, and automated data processing. Electrophoresis 26(1), 248–256. Buick, R. N., Pullano, R., Trent, J. M. (1985). Comparative properties of 5 human ovarian adenocarcinoma celllines. Cancer Res. 45(8), 3668–3676. Clauser, K. R., Baker, P., Burlingame, A. L. (1999). Role of accurate mass measurement (þ/10 ppm) in protein identification strategies employing MS or MS MS and database searching. Anal. Chem. 71(14), 2871–2882.

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11 COUPLED MULTIDIMENSIONAL CHROMATOGRAPHY AND TANDEM MASS SPECTROMETRY SYSTEMS FOR COMPLEX PEPTIDE MIXTURE ANALYSIS Michael P. Washburn Stowers Institute for Medical Research, 1000 E. 50th St., Kansas City, MO 64110, USA

In mass-spectrometry-based proteomics, the goal is to identify and characterize proteins from a protein complex, an organelle, a whole cell, or a biofluid. In each case, these are highly complex protein mixtures that will be made even more complex when digested into peptides for mass spectrometry analysis. This presents significant challenges to mass spectrometers because many peptides will have very similar mass to charge ratios, and different peptides will vary widely in abundance, making the detection and identification of low abundance proteins from a complex mixture challenging. A driving force in proteomics is the need to introduce into a mass spectrometer a few peptides at a time for identification by database searching algorithms. This requires powerful peptide separation methods. Increasingly, researchers refer to the approach where proteins are digested into peptides followed by peptide separation and tandem mass spectrometry as shotgun proteomics. Shotgun proteomics requires multidimensional separation of peptides to identify the majority of proteins in a complex mixture generated from a protein complex, organelle, whole cell, or biofluid. The multidimensional separations predominantly used include two-dimensional separations of peptides using strong cation


243

244

COUPLED MULTIDIMENSIONAL CHROMATOGRAPHY

exchange (SCX) and reversed-phase (RP) chromatography coupled to tandem mass spectrometry (MS/MS). Because tandem mass spectra contain information based on peptide sequences, database searching algorithms that compare experimental tandem mass spectra to theoretical tandem mass spectra, such as SEQUEST (Eng et al., 1994), MASCOT (Perkins et al., 1999), and SONAR (Field et al., 2002) are used to determine the original protein content of a sample. Shotgun proteomics generates enormously complex datasets and presents complicated data analysis and organization challenges. This is beyond the scope of this review, however, it is the subject of recent reviews (Nesvizhskii and Aebersold, 2005; Sadygov et al., 2004). There exist essentially three categories of SCX/RP/MS/MS approaches. In one approach, SCX is run off-line followed by on-line RP/MS/MS (Fig. 11.1). In the offline SCX approach, fractions do not directly elute onto RP material but rather are collected. In one of the two in-line approaches, SCX is run in line with RP/MS/MS using different columns for SCX and RP (Fig. 11.2). In the multidimensional protein identification technology approach (MudPIT), SCX and RP are run in line in the same column, and this column serves as the ion source for a tandem mass spectrometer (Fig. 11.3). Both the in-line approaches are true SCX/RP/MS/MS approaches; the first approach could be abbreviated as SCX—RP/MS/MS where

SCX Injector

SCX Fractions HPLCautosampler Waste Flow splitter

RP trap

ESI into MS RP

HPLC

RP

200–300 nl/min flow rate

Waste kV

FIGURE 11.1 Off-line strong cation exchange and online RP/MS/MS configuration. Using an HPLC with a strong cation exchange column, a complex peptide mixture may be fractionated using a salt gradient into fractions. In this configuration, volatile salts such as ammonium acetate and ammonium formate, or salts such as KCl and NaCl may be used. Once fractions are collected, they are loaded onto a reversed-phase trap followed by washing of salts via an HPLC pump. A RP gradient is then used to move peptides from the RP trap to a RP analytical column that is the electrospray ionization source for a tandem mass spectrometer.

SCX-RP/MS/MS

245

Multiple samples

Nanoflow HPLC (200–300nL/min) kV

Sample from HPLC-autosampler

ESI into MS RP

RP trap

SCX

RP

200–300 nL/min flow rate

Waste Waste

FIGURE 11.2 Online strong cation exchange RP/MS/MS configuration. Strong cation exchange and reversed-phase chromatography may be coupled using the configuration shown in this figure. Multiple digested protein samples are loaded onto a SCX column and eluted onto a RP trap with volatile, ammonium acetate or formate, or nonvolatile salts, NaCl and KCl. A salt solution is then passed over the SCX column through the RP trap and to waste to move peptides from the SCX column to the RP trap. After washing the salts off the RP trap and into waste, the valves are reconfigured so that an RP gradient from a nanoflow pump is able to move peptides from the RP trap to the RP analytical column for tandem mass spectrometry analysis.

the ‘-’ represents the uncoupling of SCX and RP. There are variations to each of these approaches that will be described in this review.

11.1 SCX-RP/MS/MS In an off-line configuration, a complex peptide mixture from a proteomic sample is loaded onto a SCX column and fractions collected (Fig. 11.1). After the collection of fractions, they are then loaded into an autosampler and analyzed via the traditional RP/ MS/MS approach. Using this system, a variety of buffers and elution conditions may be used (Table 11.1). For example, one may use a volatile salt such as ammonium formate (Adkins et al., 2002; Blonder et al., 2004; Fujii et al., 2004; Yu et al., 2004; Qian et al., 2005a and b) or ammonium acetate (Cutillas et al., 2003; Coldham and Woodward, 2004), collect SCX fractions, lyophilize, resuspend in low acetonitrile and acid, and then directly analyze via RP/MS/MS. In most of the cases, when ammonium acetate or ammonium formate are used, a 20-minute wash period is used to remove the ammonium acetate or ammonium formate prior to the reversed-phase gradient (Table 11.1). However, because fractions are collected and can be buffer exchanged,

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Biphasic column SCX

RP

Triphasic column RP

Sample loading in pressurization vessel

SCX

RP

Split-triphasic column RP

SCX

RP M-520 Inline filter assembly

Low ACN

High ACN

Low ACN and salt

Column connection

HPLC 100 µL/min flow rate

ESI into MS Waste

RP

SCX

RP

200–300 nL/min flow rate kV

FIGURE 11.3 Biphasic and triphasic MudPIT configuration. In the MudPIT system, a biphasic or triphasic microcapillary column with both SCX and RP packing materials is prepared and loaded off-line in a pressurization vessel. The split-triphasic column uses larger diameter-fused silica for the RP/SCX section in front of an Upchurch Scientific (Oak Harbor, WA) M-520 Inline filter assembly. Columns are prepared, samples loaded, and loaded columns are washed off-line in pressurization vessels. Using a triphasic column, the first step of a MudPIT analysis is a reversed-phase gradient to move peptides from the RP to the SCX. Then the first salt bump is run moving peptides from the SCX to the RP followed by RP gradients that elute peptides into the mass spectrometer. In this approach, a volatile salt, such as ammonium acetate or ammonium formate, must be used.

NaCl or KCl (Peng et al., 2003; Ballif et al., 2004; Beausoleil et al., 2004; Wilmarth et al., 2004; DeSouza et al., 2005; Vitali et al., 2005) may be used for the SCX fractionation, in spite of the incompatibility of these salts with mass spectrometers. When using KCl, for example, the sample must be desalted off-line (Ballif et al., 2004; Beausoleil et al., 2004), on the RP column before MS/MS acquisition (DeSouza et al., 2005; Vitali et al., 2005), with a vented column (Peng et al., 2003), or with a RP-trap (Vollmer et al., 2004; Wilmarth et al., 2004). The configuration with a RP-trap is shown in Fig. 11.1, and in this case, a flow splitter is used to reduce the flow rate from hundreds of microliters per minute to hundreds of nanoliters per minute. However, HPLC pumps of lower flow rate are now available and could eliminate the need for a flow splitter. Examples using KCl that require desalting include the identification of the human saliva proteome (Wilmarth et al., 2004), the B. infantis proteome (Vitali et al., 2005), phosphoproteins from HeLa cells (Beausoleil et al., 2004), and phosphoproteins from mouse brain (Ballif et al., 2004). One of the important points to consider is that

247

Ammonium formate

Ammonium formate

Ammonium formate

Ammonium acetate

Ammonium acetate Ammonium formate Phosphate/KCl Phosphate/KCl Phosphate/KCl Phosphate/KCl Phosphate/KCl

Phosphate/KCl

NaCL, no buffer

Mouse cortical neuron culture

Mouse natural killer cells

Human plasma, mammary epithelial, and hepatocyte cells Salmonella typhimurium

Human urinary peptides Human plasma Yeast lysate HeLa phosphoproteins Mouse brain phosphoproteins Human saliva Bifidobascterium infantis

Cancer markers

Escherichia coli

Drying RP trap Vented column Off-line Off-line RP trap On analytical column On analytical column RP trap

On analytical column On analytical column On analytical column On analytical column No

MSD Trap XCT

QSTAR

Q-TOF LCQ DECA XP LCQ DECA XP LCQ DECA XP LCQ DECA XP LCQ Classic Q-TOF

LCQ

LCQ DECA XP

LCQ DECA XP

LCQ DECA XP

LCQ DECA XP

ProQUANT/ ProICAT Spectrum Mill

MASCOT MASCOT SEQUEST SEQUEST SEQUEST SEQUEST SEQUEST

SEQUEST

SEQUEST

SEQUEST

SEQUEST

SEQUEST

Database search engine*

(Vollmer et al., 2004)

(DeSouza et al., 2005)

(Qian et al., 2005a; Qian et al., 2005b) (Coldham and Woodward, 2004) (Cutillas et al., 2003) (Fujii et al., 2004) (Peng et al., 2003) (Beausoleil et al., 2004) (Ballif et al., 2004) (Wilmarth et al., 2004) (Vitali et al., 2005)

(Blonder et al., 2004)

(Yu et al., 2004)

(Adkins et al., 2002)

Reference

*The LCQ and LCQ DECA are products of Thermo Electron Corporation (San Jose, CA); the Q-TOF is a product of Waters (Beverly, MA); the QSTAR, ProQUANT, and ProICAT are products of Applied Biosystems (Foster City, CA); and Spectrum Mill and the MSD TRAP XCT are products of Agilent Technologies (Palo Alto, CA).

Ammonium formate

Human blood serum

Mass spectrometer*

Buffer/Salt

Applications

Desalting

Examples of Off-line Strong Cation Exchange On-line RP/MS/MS

TABLE 11.1

248


phosphopeptides tend to be enriched in early fractions (Beausoleil et al., 2004). As an example of the off-line approach with KCl, in the analysis of the S. cerevisiae proteome by Peng et al. (2003), 1 mg of yeast protein lysate was digested and separated into 80 one-minute fractions during an 80 min SCX gradient. Each fraction was then analyzed via an autosampler by RP/MS/MS with a vented column using 60 min, 90 min, 120 min, or 150 min RP gradients, depending on the complexity of each fraction, for a total of 135 h of RP/MS/MS time (Peng et al., 2003). In the end, 7537 unique peptides and 1504 proteins were identified (Peng et al., 2003). Recent applications using ammonium formate and no desalting include an analysis of a human blood serum proteome (Adkins et al., 2002), human blood plasma proteomes (Fujii et al., 2004; Qian et al., 2005), and a mouse cortical neuron proteome (Yu et al., 2004). The flexibility of the off-line approach allows for the incorporation of additional separation techniques to undertake, in effect, three-dimensional analyses by using size exclusion chromatography of proteins, collecting fractions, digesting proteins, separating peptides by off-line SCX followed by fraction collection, and analyzing each fraction by RP/MS/MS (Jacobs et al., 2004). The main limitation of the off-line SCX—online RP/MS/MS approach is the need to transfer each SCX fraction to an autosampler for RP/MS/MS, which can be a time consuming process. An advantage of this system is the ability to carry out salt gradients, rather than pulses or bumps, and to use phosphate buffers with KCl or NaCl, which are well-characterized SCX elution buffers. In addition, with the off-line SCX, large SCX columns with large sample capacities may be used, and organic solvent in the SCX buffer may be used to improve separations. Lastly, many laboratories have been set up to use RP/MS/MS with peptides from digested gel slices using an autosampler. It has been easier to simply do the off-line SCX on an HPLC and collect fractions that can then be placed into an autosampler and RP/MS/MS system already up and running in a lab.

11.2 SCX/RP/MS/MS In the first of the two general online configurations, the SCX and the RP/MS/MS are not directly coupled with any fractions collected from the SCX. The first description of complex peptide mixture analysis via SCX/RP/MS/MS used this approach and KCl to analyze S. cerevisiae ribosomes (Link et al., 1999). In an arrangement of this approach, multiple digested samples could be placed into an autosampler for SCX fractionation onto a RP trap followed by elution of the RP trap onto a RP microcolumn coupled directly to MS/MS (Fig. 11.2). The use of the RP trap allows for the use of phosphate and KCl/NaCl for the SCX analysis, as shown in a C. trachomatis proteome analysis (Skipp et al., 2005), or ammonium chloride, as shown in a S. cerevisiae analysis (Li et al., 2005) (Table 11.2). Ammonium formate (Nagele et al., 2003; Vollmer et al., 2003; Xiang et al., 2004) and ammonium acetate (Gu et al., 2004; Tyan et al., 2005a and b) may also be used with this configuration (Table 11.2). The configuration in Fig. 11.2 is one that is a generally available from many high-performance liquid chromatography (HPLC) and mass spectrometry manufacturers. Advantages of this system are that it is flexible with respect to the SCX elution conditions that can be used and no handling of fractions is needed, meaning it is a more automated approach than the one described in

249

Ammonium formate Ammonium acetate Ammonium acetate Ammonium acetate Ammonium chloride Phosphate/KCl Phosphate/sodium acetate No buffer/NaCl

Human cancer cell lines Human erythrocytes Human pleural effusion PUMA-induced apoptosis S. cerevisiae Chlamydia trachomatis Human lung fibroblasts and gliomas S. cerevisiae

LCQ DECA LCQ DECA LCQ DECA QSTAR XL LCQ DECA QTOF Global Ultima LCQ MSD Ion Trap XCT

2 RP traps

LCQ MSD Trap SL

Mass spectrometer*

On-column RP trap RP trap RP trap RP trap RP trap RP trap

On-column RP trap

Desalting

Spectrum Mill

SEQUEST SEQUEST SEQUEST MASCOT SEQUEST MassLynx Proprietary

SEQUEST MASCOT

Database search engine*

(Nagele et al., 2004)

(Link et al., 1999) (Nagele et al., 2003; Vollmer et al., 2003) (Xiang et al., 2004) (Tyan et al., 2005a) (Tyan et al., 2005b) (Gu et al., 2004) (Li et al., 2005) (Skipp et al., 2005) (Davis et al., 2001)

Reference

The LCQ and LCQ DECA are products of Thermo Electron Corporation (San Jose, CA); the Q-TOF and MassLyx are products of Waters Corporation (Beverly, MA); the QSTAR XL is a product of Applied Biosystems (Foster City, CA); and Spectrum Mill and the MSD TRAP XCT is a product of Agilent Technologies (Palo Alto, CA).

*

KCl Ammonium formate

Buffer/Salt

Examples of On-line Strong Cation Exchange RP/MS/MS

S. cerevisiae ribosome E. coli

Applications

TABLE 11.2

250


MudPIT

251

Fig. 11.1. In addition, one area that has yet to be characterized is the use of sequential RP columns with large dead volumes. It seems unlikely that peptides eluting from the RP trap would reconcentrate on the RP column directly in front of the mass spectrometer diminishing the value of the second RP column. This needs further analytical investigation. 11.3 MudPIT The MudPIT approach has arguably driven the field of proteomics to adopt SCX/RP/ MS/MS-based approaches. MudPIT is a shotgun proteomics approach that incorporates SCX/RP/MS/MS in a fully online fashion (Fig. 11.3) (Link et al., 1999; Washburn et al., 2001; Wolters et al., 2001). In this approach, first a bi/triphasic microcapillary column packed with RP and SCX HPLC grade materials is loaded with a complex peptide mixture generated from a biological sample (Fig. 11.3). Typically, this column is made of 100 mm inner diameter and 365 mm outer diameter fused silica. Next, the packed and loaded column is interfaced with a quaternary HPLC pump that acts as the ion source for a tandem mass spectrometer. In each chromatographic step, peptides are directly eluted from the biphasic microcapillary column, ionized, and then analyzed in the tandem mass spectrometer. The biphasic SCX/RP column that directly elutes into a tandem mass spectrometer was first described for the analysis of the S. cerevisiae proteome using a phosphate buffer and KCl (Link et al., 1999). A series of methodological improvements and the use of ammonium acetate (Washburn et al., 2001; Wolters et al., 2001) led to a large-scale analysis of the S. cerevisiae proteome that detected and identified 5540 peptides and 1484 proteins from three different fractions of the yeast proteome run on three different MudPIT columns for a total of 83 h of SCX/RP/MS/MS

3 FIGURE 11.4 MudPIT analsysis of yeast. The chromatograms of a 15-cycle MudPIT analysis of a heavily washed insoluble fraction from the S. cerevisiae, prepared as described in the materials and methods. The four buffer solutions used for the chromatography were 5% ACN/0.02% HFBA (buffer A), 80% ACN/0.02% HFBA (buffer B), 250 mM ammonium acetate/5% ACN/0.02% HFBA (buffer C), and 500 mM ammonium acetate/5% ACN/0.02% HFBA (buffer D). Cycle 1 (Fig. 11.4a) consisted of a 70 min gradient from 0% to 80% buffer B and a 10 min hold at 80% buffer B. Each of the next 12 cycles were 110 min with the following profile: 5 min of 100% buffer A, 2 min of X% buffer C, 3 min of 100% buffer A, a 10 min gradient from 0% to 10% buffer B, and a 90 min gradient from 10% to 45% buffer B. The 2 min buffer C in cycles 2–13 were as follows: Cycle 2—10% (Fig. 11.4b), Cycle 3—20% (Fig. 11.4c), Cycle 4—30% (Fig. 11.4d), Cycle 5—40% (Fig. 11.4e), Cycle 6—50% (Fig. 11.4f), Cycle 7— 60% (Fig. 11.4g), Cycle 8—70% (Fig. 11.4h), Cycle 9—80% (Fig. 11.4i), Cycle 10—90% (Fig. 11.4j), Cycle 11—90% (Fig. 11.4k), Cycle 12—100% (Fig. 11.4l), and Cycle 13—100% (Fig. 11.4m). Cycle 14 (Fig. 11.4n) consisted of a 5 min 100% buffer Awash followed by a 20 min 100% buffer C wash, a 5 min 100% buffer Awash, a 10 min gradient from 0—10% buffer B, and a 90 min gradient from 10—45% buffer B. Cycle 15 (Fig. 11.4o) was identical to Cycle 14 (Fig. 11.4n), except that the 20 min salt wash was with 100% buffer D. The chromatograms shown are representative of those obtained from samples of comparable complexity to the one described (reprinted with permission from Wolters et al., 2001). Copyright 2001 American Chemical Society.

252


600 A

Hits in cycle

500 400 300 200 100 0 1

2

3

4

5

6

7

8

9

10 11 12 13 14 15

9

10 11 12 13 14 15

Cycle 400

New unique hits in cycle

350

B

300 250 200 150 100 50 0 1

2

3

4

5

6

7

8 Cycle

FIGURE 11.5 The number of peptide identifications from each of the 15 cycles from the data displayed in Figure 11.4 is shown. In Figure 11.5a and 11.5b, Cycle 1 corresponds to Figure 11.4a, Cycle 2 corresponds to Figure 11.4b,. . ., and Cycle 15 corresponds to Figure 11.4o. Figure 11.5a displays the total number of peptide identifications in each cycle. These peptide identifications are not necessarily unique because a large number of peptides are identified multiple times during a typical MudPIT analysis. The total number of peptides identified in this sample was 5738. Figure 11.5b displays the total number of new unique peptide identifications from each cycle in the sample. The total number of unique peptides identified in this sample was 2114 (reprinted with permission from Wolters et al., 2001). Copyright 2001 American Chemical Society.

time (Washburn et al., 2001). An example of these fractions is a heavily washed insoluble fraction, as shown in Fig. 11.4. Each of these fractions contained at least 200 identifiable peptides (Fig. 11.5a) that provided new unique peptides to the running tally of 2114 peptides detected and identified from the analysis of the heavily washed insoluble fraction (Fig. 11.5b). Ammonium acetate is the salt predominantly used in MudPIT applications (Table 11.3), but ammonium formate could also be used. Examples (Table 11.3) of the analysis of proteomes via MudPITand a biphasic column

253

*

Biphasic/triphasic Triphasic Triphasic Triphasic Triphasic

Ammonium acetate

Ammonium acetate

Biphasic Biphasic Biphasic Biphasic Biphasic Biphasic Biphasic Biphasic Biphasic

Phosphate/KCl Ammonium acetate Ammonium acetate Ammonium acetate Ammonium acetate Ammonium acetate Ammonium acetate Ammonium acetate Ammonium bicarbonate Ammonium acetate Ammonium acetate Ammonium acetate

LCQ DECA/LTQ

LCQ DECA

LCQ LCQ DECA LCQ DECA

LCQ LCQ LCQ DECA LCQ DECA LCQ DECA LCQ DECA QSTAR LCQ DECA/LTQ LCQ DECA

Mass spectrometer*

SEQUEST

SEQUEST

SEQUEST SEQUEST SEQUEST

SEQUEST SEQUEST SEQUEST SEQUEST SEQUEST SEQUEST SONAR SEQUEST SEQUEST

Database search engine

(Ram et al., 2005)

(Cai et al., 2005)

(McDonald et al., 2002) (Graumann et al., 2004) (Mayor et al., 2005)

(Link et al., 1999) (Washburn et al., 2001) (Florens et al., 2002) (Kislinger et al., 2003) (Pan et al., 2004) (Wenner et al., 2004) (Gaucher et al., 2004) (Sandhu et al., 2005) (Breci et al., 2005)

Reference

The LCQ, LCQ DECA, and LTQ are products of Thermo Electron corporation (San Jose, CA), and the QSTAR is a product of Applied Biosystems (Foster City, CA).

Bovine microtubule S. cerevisiae protein complexes S. cerevisiae poylubiquitin conjugates HeLa histone acetyltransferase complex Microbial Biofilm

S. cerevisiae proteome S. cerevisiae proteome P. falciparum proteome Mouse organs Mouse heart Human cerebrospinal fluid Human heart mitochondria Breast cancer cells S. cerevisiae proteome

Buffer

Application

Bi/Triphasic

Examples of Biphasic and Triphasic MudPIT Analyses

TABLE 11.3

254


include the P. falciparum (Florens et al., 2002), mouse hearts (Pan et al., 2004), human cerebrospinal fluid (Wenner et al., 2004), and breast cancer cells (Sandhu et al., 2005). One of the problems with the biphasic MudPIT column is that digested peptide mixtures typically contain salts and urea, which requires off-line desalting prior to loading onto a biphasic column. This is an additional sample handling step that likely leads to sample loss and increases the time of analysis. As a result, the triphasic column using RP/SCX/RP was developed to carry out online desalting in the first dimension (McDonald et al., 2002). The triphasic column or the split-three phase column (Fig. 11.3) is gaining in popularity because it reduces sample handling. The triphasic column appears to be particularly useful for analyzing protein complexes (Graumann et al., 2004; Cai et al., 2005; Mayor et al., 2005). The split-three phase column allows for the use of larger inner diameter capillaries (250 mm inner diameter and 365 mm outer diameter fused silica) that allow for more RP/SCX material, and thus permits large sample quantities to be loaded. This proved useful in a recent analysis of a microbial biofilm (Ram et al., 2005). In my laboratory, we exclusively use the triphasic columns and split-three phase columns for proteomic analysis. Although the MudPIT approach requires the use of salt pulses or bumps and is not compatible with SCX gradients using phosphate buffers and KCl/NaCl, it has proved an effective approach for biological discovery.

11.4 ALTERNATIVE FIRST DIMENSION APPROACHES The use of two-dimensional chromatography coupled to MS/MS with SCX and RP as the chromatographic approaches has proven powerful and is gaining widespread acceptance. This has led researchers to investigate alternatives to SCX/RP/MS/MS for shotgun proteomics, which include coupling liquid chromatography and capillary electrophoresis (reviewed in Evans and Jorgenson, 2004, and described in Chapter 16 by Issaq). In one approach, anion exchange (AE) chromatography is used instead of SCX. Mawuenyega et al. (2003) performed a large-scale protein identification of C. elegans proteome using AE with a tris buffer and NaCl coupled to a RP trap followed by RP/MS/MS on a Q-TOF2, and used MASCOT for protein identification. This technological platform has also been used to analyze the Escherichia coli proteome (Taoka et al., 2004). Taking advantage of the ability of titanium oxide to selectively retain water soluble organic phosphates, Pinkse et al. (2004) used a titanium oxide column connected to a RP precolumn that eluted onto a RP analytical column in a specified application for phosphopeptide analysis. Another alternative with additional advantages uses isoelectric focusing of peptides followed by RP/MS/MS (Cargile et al., 2004a and b; Cargile and Stephenson, 2004; Chen et al., 2002; Chen et al., 2003a and b; Essader et al., 2005). When using isoelectric focusing one can couple capillary isoelectric focusing to RP/MS/MS for proteomic analysis (Chen et al., 2002; Chen et al., 2003a and b), or one can use immobilized pH gradient strips to separate peptides followed by RP/MS/MS analysis (Cargile et al., 2004a and b; Cargile and Stephenson, 2004; Essader et al., 2005). The

REFERENCES

255

potential additional advantage with this approach is the use of the pI of peptides in strengthening protein identification (Cargile et al., 2004; Cargile and Stephenson, 2004). 11.5 CONCLUSION With the increasing popularity of multidimensional chromatography coupled to tandem mass spectrometry, more and more researchers will explore innovations in chromatography and mass spectrometry to improve proteome analysis via shotgun proteomics. On the chromatographic side, effort is underway to dramatically increase the peak capacity of liquid chromatography by using smaller particles and ultrahigh pressure, greater than 50,000 psi, liquid chromatography (Mellors and Jorgenson, 2004; Patel et al., 2004, also see Chapter 7). When ultrahigh pressure chromatography systems are used in place of current HPLC systems, it is believed that far more complex peptide mixtures will be able to be analyzed in a given time period with a shotgun proteomics system. In addition, the use of higher temperatures should facilitate faster two-dimensional chromatography (Stoll and Carr, 2005). However, if the faster chromatography exceeds the ability of mass spectrometers to acquire tandem mass spectra, valuable information will be lost. This requires innovations in mass spectrometry, which most instrumentation manufacturers constantly pursue. The reason for the need of all these innovations is that comprehensive proteome analysis remains elusive because of the huge dynamic ranges in protein abundance and the even larger analytical challenge of posttranslational modification analysis from complex protein mixtures. However, as newer innovative mass spectrometers become available and are coupled to SCX and RP for proteome analyses, comprehensive proteomic analysis may become more and more possible.

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12 DEVELOPMENT OF ORTHOGONAL 2DLC METHODS FOR SEPARATION OF PEPTIDES Martin Gilar, Petra Olivova, Amy E. Daly, and John C. Gebler Waters Corporation, Milford, MA 01757, USA

12.1 INTRODUCTION Identification and quantitation of proteins in proteome samples present a great challenge even for state-of-the-art liquid chromatography–tandem mass spectrometry (LC–MS/MS) (Aebersold and Mann, 2003; Peng et al., 2003; Von Haller et al., 2003). It is estimated that in the human proteome approximately 10–20 thousand proteins are expressed at any given time (Anderson and Anderson, 2002; Wehr, 2002). In the case of shotgun proteomics (Wolters et al., 2001), when protein samples are digested with specific proteolytic enzymes prior to analysis, the complexity may reach 100–200 thousands peptides or more. No separation technique is currently capable of resolving such a complex sample in a single analysis. Consequently, the sample eluting from the LC column and entering the MS instrument at any given point of analysis is a rich mixture of peptides, making a complete and comprehensive MS/MS analysis difficult. An additional challenge of proteome analysis is the dynamic range. For example, the range of protein concentration in serum spans eleven orders of magnitude (Anderson and Anderson, 2002), severely exceeding the current dynamic range of mass spectrometers. Various separation techniques have been combined to enhance the resolution of peptides (or proteins) and decrease the complexity of proteomic samples to more Multidimensional Liquid Chromatography: Theory and Applications in Industrial Chemistry and the Life Sciences, Edited by Steven A. Cohen and Mark R. Schure. Copyright 2008 John Wiley & Sons, Inc.

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manageable levels. Among other things, 2D LC methods have been utilized (Kachman et al., 2002; Peng et al., 2003; Vollmer et al., 2004; Wagner et al., 2002). The consensus is that an efficient 2DLC separation improves the prospects for LC–MS/MS analysis of highly complex peptide samples over a simpler (less efficient) 1DLC setup (Essader et al., 2005; Liu et al., 2004; Man et al., 2005; Von Haller et al., 2003; Wehr, 2002). However, due to the nature of analysis and the lack of systematic studies utilizing comparable samples, it is difficult to judge the level of improvement, measured as the number of identified peptides/proteins, and the achievable limit of detection. Separation performance of gradient chromatography can be described by peak capacity (P), which is the maximum number of peaks that can be theoretically resolved on a column in a given gradient time (Dong and Tran, 1990; Ghrist et al., 1987; Neue et al., 2001; Snyder and Stadalius, 1986; Stadalius et al., 1987). Recent reports evaluated the peptide separation efficiency using both conventional and custom-made columns. It appears that a maximum peak capacity of 1DLC is somewhere between 1000–1600 peptides in a single run (Gilar et al., 2004; Shen et al., 2005). Extending the gradient time (using shallower gradients) and utilizing longer columns has diminished peak capacity gains, and therefore it is not economical. 2DLC seems to offer a larger separation power. Its peak capacity is defined as a multiplication of peak capacities of two chosen separation dimensions (Giddings, 1987). For example, when combining SCX-HPLC (typical peak capacity is 50 for a 50 min analysis) with RP-HPLC (peak capacity may be 100 for a 50 min analysis), the total 2D peak capacity is 5000. In reality, this value also depends on the orthogonality of separation (Gilar et al., 2005b; Liu et al., 1995; Slonecker et al., 1996). To date little research effort has been focused on the investigation of orthogonality of LC modes for peptides, even for the most common 2DLC based on strong cation exchange (SCX) and RP modes (Gilar et al., 2005; Peng et al., 2003). Therefore, the peak capacity of 2DLC for the separation of peptides can only be approximately estimated. Combining 2DLC with MS for proteomic research translates into a 3D separation technique with peak capacity exceeding the chromatographic separation space. Although MS can be viewed as another separation technique, the peak capacity estimates usually take into account only chromatographic resolution. This is understandable since it is not trivial to discern the ultimate LC–MS/MS separation performance due to the impact of dynamic range and degree of component overlap. Parameters, such as the speed of MS/MS acquisition, must be also considered. The effect of MS/MS duty cycle limitations on the overall number of identified peptides has only recently been systematically studied (Liu et al., 2004). The overlap of eluting peptides significantly affects the data dependent MS/MS acquisition (DDA) and creates a bias for more abundant components in the mixture. Because of the nature of DDA, MS/MS becomes more reproducible for less complex (sufficiently resolved) samples. The aim of this chapter is to evaluate the orthogonality of selected 2DLC systems for the separation of peptides. The orthogonality of different chromatographic modes was quantitatively characterized using a novel geometric approach. Practical peak capacity was calculated from the theoretical peak capacity and the knowledge of

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orthogonality, better defining the achievable separation performance of 2DLC systems. Finally, the peak capacity concept was extended into 2DLC–MS, where the duty cycle of MS/MS analysis was taken into account and used to define the overall system peak capacity.

12.2 PREVIOUS WORK In our earlier report (Gilar et al., 2004), we studied the peak capacity of several 1DLC systems using three approaches: (i) decreasing the gradient slope (tg) extending the gradient time, while keeping the column length fixed, (ii) increasing the column length (L) with proportional increase in gradient time, and (iii) employing columns packed with smaller sorbent particles. Because the gains in peak capacity with the increase of tg and L are not linear, the first two strategies have diminishing returns. The third strategy is limited by the operational pressure. A predicted maximum achievable peak capacity in single-dimensional (1D) RP-LC is within the range of 1400–1600 (Gilar et al., 2004). Other authors developed ultrahigh performance LC methods using the abovementioned strategies and dedicated pumps capable of achieving high operational pressures (100–500 MPa) (MacNair et al., 1999; Shen et al., 2002; Tolley et al., 2001). A peak capacity of 1500 was reported when using a 200 cm column and 33 h gradient (Shen et al., 2005). These papers clearly demonstrate both the strength and the limitations of 1DLC. Although impressive separations have been produced, further extensions in column and gradient length do not yield significantly improved separations. The observed trends are in a good agreement with a peak capacity prediction model based on gradient theory (Gilar et al., 2004; Neue et al., 2001). It is difficult to conceive 1DLC techniques capable of the separation of tens or hundreds of thousands of components desirable for proteome research. In general, 2DLC is expected to provide a greater peak capacity (P2D) than single-dimensional LC. The P2D can be calculated theoretically by multiplying the peak capacity values of the first (P1) and second (P2) LC dimensions (Eq. 1) (Giddings, 1987). P2D ¼ P1 P2

ð12:1Þ

This concept assumes that each fraction (peak) collected in the first dimension further separates in the second dimension with regular spacing and that the entire 2D separation space is evenly covered by eluting peaks. More realistically, the peaks would be distributed randomly: over the 2D separation space some peaks are likely to coelute, while some area will remain vacant of peaks. Therefore, Equation12.1 represents an idealized peak capacity estimate although the real number of resolved peaks is lower. Most importantly, the peak capacity proposed by Equation 12.1 is achievable when the chromatographic modes used for separation are completely orthogonal. The orthogonality of common LC modes for peptide separation is not well known, but in most cases it is not ideal. Several reports suggest that even dissimilar modes such as SCX and RP do not separate peptides in an orthogonal fashion, and some peak

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clustering is observed in the 2DLC separation space (Gilar et al., 2005; Peng et al., 2003; Wagner et al., 2002). Similar situations have been observed for comprehensive 2DLC systems described for the separation of small molecules. A limited orthogonality is often achieved, despite the considerable effort to identify the most promising separation modes for the first and second LC dimensions (Tanaka et al., 2004; van der Horst and Schoenmakers, 2003; Venkatramani and Zelechonok, 2003). The consequences of limited orthogonality in 2DLC on peak capacity have not been extensively investigated; however, it is clear that the achievable peak capacity will be lower than that proposed by Equation 12.1. In contrast to comprehensive 2DLC (Murphy et al., 1998; Tanaka et al., 2004; van der Horst and Schoenmakers, 2003; Venkatramani and Zelechonok, 2003), a typical experiment in proteome research involves the analysis of only a limited number of fractions, further sacrificing the achievable peak capacity of the chosen 2DLC system. For example, when collecting 10 fractions in the first LC dimension, its peak capacity is reduced to 10, even if the theoretical column peak capacity is 100. While fraction oversampling is important to maintain the component resolution in the 2D chromatogram (Murphy et al., 1998), it also splits the peptides into several consecutive fractions, reduces their signal, and decreases the number of MS (MS/MS) identifiable peptides. A frequent fractionation, in conjunction with the time-consuming second-dimension LC–MS/MS analyses, also increases the overall length of 2DLC analysis. Since the detection limit and sample throughput are two primary concerns in proteomic analysis, the fraction collection frequency is often limited to 1–5 peak widths (Gilar et al., 2005b). Fractionation frequency has to be taken into account when estimating the 2DLC peak capacity. Our recent study investigated the impact of orthogonality on practical 2DLC peak capacity (Gilar et al., 2005a). Selected LC modes were chosen for separation of tryptic peptides and their selectivity was correlated in 2D separation space. Several mathematical methods have been proposed in literature (Liu et al., 1995; Slonecker et al., 1996), employing complementary descriptors, such as informational similarity, percentage of synentropy, peak spreading angle, and practical peak capacity. However, these approaches are rather complex and not suitable for the description of 2DLC systems with apparent peak clustering. Therefore, we have developed a simpler geometric approach utilizing a single descriptor for description of 2DLC orthogonality (percent of orthogonality). This geometric approach has been employed to evaluate the potential of selected 2DLC systems (Gilar et al., 2005a).

12.3 DEVELOPING ORTHOGONAL 2DLC METHODS 12.3.1

LC Selectivity for Peptides: Experimental Design

In an effort to identify promising LC modes for 2DLC separation of tryptic peptides several traditional, as well as novel, LC modes have been evaluated, as earlier reported

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(Gilar et al., 2005a). Chromatographic modes included reversed-phase (RP) chromatography, size exclusion chromatography (SEC), hydrophilic interaction chromatography (HILIC), and strong cation-exchange (SCX) chromatography. The experiment was designed as a single-dimensional LC experiment with direct MS detection. Narrow bore 150 2.1 mm columns (with the exception of SEC) at a flow rate 0.2 mL/min were used. Five different mixtures of peptides were prepared by digesting following proteins with trypsin: yeast enolase (ENOL), bovine hemoglobin (BH), bovine serum albumin (BSA), rabbit phosphorylase b (PHOSP), and yeast alcohol dehydrogenase 1 (ADH). These five digestion standards were sequentially injected on LC–MS. Tryptic peptides of interest were identified (according to their unique mass), their chromatographic retention was recorded, and the retention data for different LC modes were vizualized in 2D plots. The sample is described in more detail in an earlier published report (Gilar et al., 2005a). The 2D retention maps were constructed for promising LC modes simulating 2DLC selectivity (Gilar et al., 2005a, b). Although the data were acquired in a singledimensional LC setup, the 2D retention maps (Fig. 12.2) are identical and interchangeable with those acquired in a 2DLC setup using a frequent fraction collection in the first LC dimension and reinjection of the fraction in a second LC–MS dimension. The simpler and less time-consuming data generation in 1DLC is useful as long as the peptide retention can be confirmed with direct MS detection. The LC modes and separation conditions evaluated are described in Table 12.1. Analysis using reversed-phase chromatography at low pH was carried out under conditions typically used for 2DLC–MS/MS analysis of peptides. The mobile phases for other chromatographic conditions were also chosen to be compatible with MS detection, including SCX LC, where the peptides were eluted with volatile ammonium formate buffer. An example of chromatograms illustrating the separation of phosphorylase b tryptic digest (90 tryptic peptides) for selected LC modes is shown in Fig. 12.3. The sequential analysis of all five tryptic digests (only peptides larger than four amino acids were included in the study) yielded a dataset of 196 peptides common to all 2DLC experiments (Gilar et al., 2005a). Retention data were then normalized according to Equation 12.2, where RTmin and RTmax represent the retention times of the first and last eluting peptides in the set. RTiðnormÞ ¼

RTi RTmin RTmax RTmin

ð12:2Þ

The retention times RTi are converted to normalized RTi(norm); the values of RTi(norm) range from 0 to 1. The normalization serves two purposes. First, it allows for a comparison of different chromatographic data in a uniform 2D retention space, regardless of absolute retention time values. Second, it removes the empty space in the 2D separation plot, where no peaks elute. The voids can be caused by the LC system gradient delay, column void volume, or gradient spanning outside the useful range (for example, a gradient of 0–100% acetonitrile in RPLC, while practically all tryptic peptides elute within 0–50% acetonitrile).

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TABLE 12.1 Chromatographic Modes and Conditions Used for LC–MS Study of Tryptic Peptides Separation Selectivity LC mode

Column type

Column L i.d; dp. (mm mm; mm)

RP C18

Atlantis dC18

150 2.1; 3

HILIC

Atlantis HILIC

150 2.1; 3

SEC

YMC diol, 60 A

SCX

PolySULFO A, 300 A

Other RP stationary embedded polar group (EPG) phenyl (PH), pentafluorophenyl (PFP)

RP C18, high pH

a

phases: Symmetry Shield RP18 XTerra Phenyl Prototype sorbent

XTerra MS C18, or XBridge MS C18

750 4.6; 5 (3 columns in series) 150 2.1; 5

Elution conditionsa 0.2% Formic acid, 0–42% acetonitrile in 50 min 10 mM ammonium formate, pH 4.5, 90–48% acetonitrile in 50 min 40 mM ammonium formate, pH 4.5, 20% acetonitrile 25% acetonitrile, gradient 40–300 mM of ammonium formate in 40 min

150 2.1; 3.5

Same as RP C18

150 2.1; 3.5 150 2.1; 5

Same as RP C18 10 mM ammonium formate, pH 3.25, 0–42% acetonitrile in 50 min 20 mM ammonium formate, pH 10, 0–42% acetonitrile in 50 min

150 2.1; 3.5

Flow rate was 0.2 mL/min, and separation temperature was 40 C for all experiments.

12.3.2

Investigation of 2DLC Orthogonality for Separation of Peptides

Separation selectivity in LC depends on various factors. The most important is the choice of the stationary and mobile phases (Chen et al., 2004; Guo et al., 1987). In addition, the separation temperature (Hancock et al., 1994) and gradient slope (Chloupek et al., 1994) have also been shown to have a moderate impact on LC selectivity. We have evaluated a set of different LC stationary phases utilizing different separation modes, as listed in Table 12.1. While the choice of a dissimilar separation mode has a primary impact on LC selectivity, it is not a guarantee of separation orthogonality (Tanaka et al., 2004; van der Horst and Schoenmakers, 2003; Venkatramani and Zelechonok, 2003). The guidelines for the selection of highly orthogonal LC modes have not been satisfactorily specified in the scientific literature (at least not for the separation of peptides). The problem is highlighted in the following example. Although SEC resolves peptides by their size and


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RP retains molecules according to their hydrophobicity, some correlation clearly exists between these two LC modes. Apparently, the peptide size loosely correlates with hydrophobicity; in general, the bigger peptides elute earlier in SEC and later in RP. Similarly, HILIC and RP are also likely to exhibit some selectivity correlation, as their retention mechanism is related to hydrophilicity (HILIC) or lack of hydrophilicity (RP). A second avenue toward the orthogonal 2DLC separation relies on the choice of the mobile phase as another principal factor affecting the separation selectivity (Chen et al., 2004; Guo et al., 1987; Young and Wheat, 1990). We have evaluated the impact of different ion-pairing agents and the mobile phase pH. The pH appears to be the most promising tool to generate alternative selectivity in RP (Gilar et al., 2005). Additional separation parameters, such as temperature and gradient slope, were previously investigated by other authors (Chloupek et al., 1994; Hancock et al., 1994). While noticeable changes in the separation of critical pairs of peptides have been reported (Chen et al., 2004; Chloupek et al., 1994; Guo et al., 1987; Hancock et al., 1994; Young and Wheat, 1990), the overall impact on separation selectivity is not dramatic (Gilar et al., 2005a; Gilar et al., 2005b). The gradient slope and temperature are not likely to generate a degree of orthogonality useful for 2DLC applications. A summary of the 2DLC orthogonality study is outlined in Fig. 12.2. Normalized 2D plots constructed for an identical set of 196 tryptic peptides are used to compare the investigated 2DLC systems. The x-axis retention data are common for all retention maps; the data were acquired using a RP C18 column at conditions typically used for second-dimension LC–MS analysis. The y-axis data vary according to the LC mode representing the first separation dimension. In the case of ideal (orthogonal) separation, one should observe that the entire 2D separation space randomly covered with eluting peptides (Fig. 12.1). This is clearly not the case in any of the investigated scenarios shown in Fig. 12.2. These results lead to several conclusions. As discussed in more detail in a previous study (Gilar et al., 2005b), altering the ionpairing agents (e.g., using trifluoroacetic acid instead of formic acid) and the RP stationary phase (C8, C18, embedded polar group RP18 or phenyl type of sorbent) has some impact on separation selectivity, but the overall impact on 2DLC orthogonality is minor. An example can be seen in Figure 12.2a for the C18 versus phenyl column. Most of the peptides are distributed along the diagonal of the 2D plot, as expected for a 2DLC system with low orthogonality. Out of the different RP modes, only the pentafluorophenyl stationary phase showed larger differences of selectivity compared to a C18 sorbent (Fig. 12.2b). Nevertheless, the orthogonality of separation is still rather weak and not promising for 2DLC applications. A careful inspection of Fig. 12.2 reveals that none of the investigated 2DLC systems are in fact completely orthogonal. The data points do not randomly cover the entire separation surface and large parts of the separation space remain unpopulated by peptides. Some degree of clustering is evident even for completely dissimilar separation modes. Therefore, the assumption of ideal 2DLC orthogonality, made by many researchers, is not valid and the peak capacity of commonly used 2DLC combinations is overestimated.

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(b)

Normalized RT

Second LC dimension

(a)

First LC dimension Normalized RT

FIGURE 12.1 Geometric approach to orthogonality. Normalized separation space is divided into the number of bins equal to the number of separated compounds (10 10 bins, peak capacity ¼ 100). Part (a) represents a nonorthogonal 2DLC system; surface coverage is 0.1, and orthogonality is 0%. Part (b) represents ideally orthogonal separation (random data); surface coverage is in average 0.63, and orthogonality is 100%.

The lack of orthogonality in 2DLC is not surprising. The tryptic peptides have some common physicochemical properties such as average length, number of charges, hydrophobicity, among others. Fig. 12.2 shows an interesting insight into the separation selectivity of chosen LC modes and deserves further discussion. SEC-RP 2DLC method has been proposed (Opiteck et al., 1997) as an alternative to SCX-RP for achieving an orthogonal 2DLC separation of peptides. Our study of SECRP selectivity. (Fig. 12.2d) revealed some correlation between size (SEC elution order) and hydrophobicity of peptides (C18 retention). Larger peptides eluting earlier in SEC are more retained in RP mode, and vice versa. Additional, nonspecific interaction with the sorbent seems to play some role in separation. We observed anomalous retention for some hydrophobic peptides, eluting outside of the expected time window or even outside of the inclusion/exclusion window. Although the secondary interaction improved the overall 2DLC orthogonality (causing the larger spread in data points in Fig. 12.2d), some peptides were incompletely recovered from the column. The addition of acetonitrile and ammonium formate buffer into the SEC mobile phase was necessary to control the peptides recovery and carryover. Fig. 12.2e illustrates a promising orthogonality of the HILIC-RP combination in 2DLC. At first this could be surprising, but a more judicious look reveals a pattern in the HILIC retention behavior. It appears that the separation is based, at least in part, on the peptide charge. As the HILIC experiment was carried out on a bare silica column, this could be explained by the role of negatively charged silanols, which can interact with positively charged peptides. Because the peptides with a higher charge (3þ, 4þ, 5þ . . . )


269

FIGURE 12.2 Normalized retention time plots for investigated 2DLC systems. The area used for separation is highlighted in gray. Peak capacity of 2D space is 14 14 (196 bins); normalized retention of 196 tryptic peptides is recorded. The charge or pI of peptides is shown in figure legends, if appropriate. (See color plate.)

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are better retained onthesorbent than the less charged (1þ, 2þ) ones, the retention relies, in part, on the ion-exchange chromatography mechanism. The increase in buffer concentration in the mobile phase counteracted this additional charge-tocharge retention mechanism to a certain extent and lowered the apparent orthogonality (data not shown). Therefore, it appears that the combination of two separation mechanisms is responsible for the high orthogonality observed for the chosen HILIC-RP 2DLC system. Currently, the most common 2DLC approach is based on a combination of SCX-RP modes. The orthogonality of SCX-RP was investigated and is illustrated in Fig. 12.2f. The principal retention mechanism is the charge-based interaction of peptides with the SCX sorbent. In general, the peptides carrying a low charge (1þ) elute first, followed by charged species 2þ, 3þ, 4þ . . . (Fig. 12.2f). Because trypsin cleaves peptides at the C terminus of basic amino acids (arginine or lysine) and their N terminus contributes another charge (NH2 group), the majority of peptides have a 2þ charge. Singly charged peptides are rare. They are either C terminal peptides (without arginine or lysine at the C terminus), originating from a nontryptic cleavage, or their net charge is reduced by a posttranslational modification (e.g., phosphorylation) (Beausoleil et al., 2004). Peptides carrying a larger charge (3þ and higher) contain histidine(s) in their sequence, or they are so-called missed-cleaved peptides (having a non-cleavable sequence motif consisting of multiple arginines/ lysines). Only limited populations of tryptic peptides have a 4þ or higher charge. This narrow range of peptide charges presents the main limitation for SCX separation; the peptides tend to elute in clusters according to their charge. Figure 12.2f shows the orthogonality of a SCX-RP 2DLC method. As expected, the most abundant groups of 2 þ (66%) and 3 þ (28%) charged peptides form relatively tight clusters (Fig. 12.2f) and the 2D separation space is not uniformly covered. In an extreme case, all the peptides with the same charge would elute under a single peak. Fortunately this is not the case, the trends emerging from the plot suggest that peptide retention also depends on their length. While large peptides (more hydrophobic and better retained in RP) are relatively less retained in SCX chromatography, the short peptides of the same charge more strongly interact with the SCX sorbent. This behavior implies that charge density (which is greater for the shorter peptides) plays an important role in the retention mechanism (Gilar et al., 2005b). Figure 12.2d,e, and f illustrate the 2DLC methods that employ different modes in both LC dimensions. An alternative approach is to use similar (or identical) LC stationary phases while altering the mobile phase. We explored the impact of mobile phase pH using the same sorbent (C18) in both LC dimensions (Gilar et al., 2005; Toll et al., 2005). As evident from Fig. 12.2c, the pH is a potent tool for altering the selectivity of separation. This is in agreement with the accepted theory of RP separation for charged solutes, nevertheless, the degree of orthogonality is rather surprising. The orthogonality is comparable to other 2DLC scenarios using dissimilar LC separation modes. To illustrate the separation mechanism in high/low pH RP-RP 2DLC (Fig. 12.2c), the peptides are divided into the three groups according to their pI. The pI values were calculated using the method of Shimura et al. (2002). As expected, the acidic peptides


271

are more retained at low pH when they are less charged (more hydrophobic), whereas basic peptides are more retained at high pH. The “neutral peptides” (pI 5.5–7.5) are affected by pH as well. The pI values are the sum of contributions of all ionizable amino acids; changing the pH from 10 to 3 alters the ionization of amino acid functional groups (pKa 3.6–12.5). The pH used in the first C18 separation dimension was rather high (pH 10), however, no peptide loses or carryover, due to on-column precipitation, were observed. Peak shape was comparable to peptide analysis at low pH. Modern stationary phases, based on hybrid silica and stable alkyl bonding chemistry, are well suited for chromatography at extreme pH without compromising column lifetime or analysis-to-analysis reproducibility (Wyndham et al., 2003). 12.3.3

Geometric Approach to Orthogonality in 2DLC

The retention maps summarized in Fig. 12.2 allow one to visually compare the degree of separation orthogonality. Several promising 2DLC setups for the separation of tryptic peptides have been identified. However, to quantitatively compare the data orthogonality and estimate an achievable 2DLC peak capacity, more rigorous mathematical tools are needed. The description of the degree of retention data correlation is more complicated than it appears. For example, the 2D retention maps cannot be characterized by a simple correlation coefficient (Slonecker et al., 1996) since it fails to describe the datasets with apparent clustering (Fig. 12.2f). Several mathematical approaches have been developed to define the data spread in 2D separation space (Gray et al., 2002; Liu et al., 1995; Slonecker et al., 1996), but they are nonintuitive, complex, and use multiple descriptors to define the degree of orthogonality. We have recently developed an alternative geometric approach suitable for the description of 2DLC orthogonality (Gilar et al., 2005a). The approach uses a single descriptor (percent of orthogonality) and is based on the concept of area covered by eluting peaks in the normalized 2D separation space. Briefly, the normalized separation space is divided into rectangular bins corresponding to the number of separated components (in this fashion the datasets of different sizes can be compared). Each bin essentially corresponds to a peak area. Normalized separation space is superimposed with the dataset and the bins containing peak(s) are summed. The degree of area coverage describes the orthogonality of an interrogated 2D system. The greater the use of separation space, the greater the orthogonality and the practical peak capacity (Gilar et al., 2005a). In their seminal work from 1983, Davis and Giddings used a statistical theory to define the number of peaks observable in 1DLC separation upon the injection of a sample of different complexity on a column of a given peak capacity (Davis and Giddings, 1983). The theory was later extended into 2D separation space (Davis, 2005; Shi and Davis, 1993), also discussed in Chapter 2 of this book. The theory implies that when the 1D or 2D separation space is randomly covered with the number of peaks equal to the separation space peak capacity (area), the normalized surface coverage is

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an average of 0.63 (due to partial peak overlap) (Davis, 2005). Remaining space is unused for the separation and remains devoid of peaks. Similar mathematical solution can be derived from a Poisson distribution of random events in 2D space. The probability that 2D separation space will be covered by peaks in ideally orthogonal separation is analogical to an example where balls are randomly thrown in 2D space divided into uniform bins. The general relationship between the number of events K (number of balls, peaks, etc.) and the number of bins occupied F (bins containing one or more balls, peaks, etc.) is described by Equation 12.3, where N is the number of available bins (peak capacity in 2DLC). 1 K ð12:3Þ F ¼ NN 1 N In the special case considered here, where the number of events K is equal to number of bins (N ¼ K; peak capacity is equal to the number of separated components), this formula can be rearranged into Equation 12.3. F 1 ¼ 1 1 ð12:4Þ N N The ratio F/N is the degree of area coverage; for a N approaching infinity, the F/N value is 0.63. The separation space coverage of 0.63, therefore, represents an ideally orthogonal separation. The degree of orthogonality for such coverage is regarded to be 100%. Both above-mentioned theoretical solutions are useful for peak capacity estimation of ideally orthogonal 2DLC. However; they fail to describe the incompletely orthogonal systems. Therefore, we developed the geometric orthogonality concept (Gilar et al., 2005a), outlined in Fig. 12.1. The surface coverage with peaks (data points) is calculated as a sum of bins containing data points (grayed out). Fig. 12.1b shows an example of a randomized dataset; 100 normalized retention data points were inserted into the separation space with a peak capacity of 100 (number of bins). The randomized datasets were generated in silico using a random number generator function (MS Excel). The average area used for the separation obtained in repeated simulations matched the stochastic prediction (0.63) (Davis, 2005). The opposite case, completely nonorthogonal separation, is presented in Fig. 12.1a. In this scenario, both separation dimensions are identical (nonorthogonal); the data are aligned along the diagonal of the separation space. The area coverage of 2D space averaged 0.1; the assigned orthogonality value is 0%. Fig. 12.1a and b, with area coverage 0.1–0.63, represents the achievable ranges of orthogonality (0–100%). The orthogonality % for any 2D separation scenario could then be calculated from Equation 12.5, P pffiffiffiffiffiffiffiffiffiffi bins Pmax O% ¼ 100 ð12:5Þ 0:63 Pmax P where bins is the number of bins in a 2D plot containing data points, and Pmax is the total peak capacity obtained as a sum of all the bins. For the rectangular separation space (peak capacity is the same in both dimension, P1 ¼ P2), the Pmax can be

273

Retention time, min

Retention time, min

TIC 45

SEC P = 14

TIC 50

C18, pH 2.6 P = 115

10

(e)

0

(b)

Retention time, min

Retention time, min

TIC 60

HILIC P = 79

60

TIC

PFP P = 115

0

(f)

0

(c)

Retention time, min

Retention time, min

TIC 45

SCX P = 51

TIC 50

P = 115

C18, pH 10

FIGURE 12.3 Examples of LC–MS analysis of phosphorylase b tryptic digest (approximately 100 peptides). The LC modes used for analysis were (a) C18, pH 2.6, (b) PFP reversed phase, (c) C18, pH 10, (d) SEC, (e) HILIC, and (f) SCX. For details see Table12.1.

30

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TABLE 12.2 Theoretical Peak Capacity, Orthogonality, and Practical Peak Capacity of Investigated 2DLC Setups. Second Dimension was in All Cases Carried Out Using C18 Column and Typical LCMS Compatible Elution Conditions First LC dimension

Second LC dimension C18; pH2:6; P ¼ 115

1DLC peak capacitya 2D number of binsb 2D normalized area fractionc 2DLC orthogonality %d 2DLC theoretical peak capacity P2De 2DLC practical peak capacity Npf 2DLC practical peak capacity Npg (10 fractions collected in 1st D)

Phenyl 115

PFP 115

C18 pH 10 115

SEC 14

HILIC 79

SCX 51

30

52

80

86

100

81

0.15

0.27

0.41

0.44

0.51

0.41

13 13225

31 13225

53 13225

58 1610

69 9085

54 5865

1984

3571

5422

708

4633

2405

172

311

472

506

587

472

a

Calculated according to (Gilar et al., 2004). See Fig. 12.2, used bins are highlighted in gray. c Number of bins used for separation divided by total number of bins (196). d See Equation 12.5. e Calculated from Equation 12.1. f See Equation 12.6. g Equation 12.6, P1 is equal to number of collected fractions (P1 ¼ 10). b

pffiffiffiffiffiffiffiffiffiffi calculated as P2; thus, the Pmax value is equal to the number of bins intersected by a diagonal line (e.g., Fig. 12.3a). Using Equation 12.5 one can describe the orthogonality of different 2DLC data shown in Fig. 12.2 with quantitative values. The data points of 196 tryptic peptides were projected in the normalized space divided into 14 14 bins (196 bins in total). Please note that regardless of the number of bins in the normalized separation space, the area is always one. The rectangular bins used for the 2DLC separation surface coverage calculation are highlighted in gray in Fig. 12.2. Number of bins, surface coverage, and % or orthogonality for different 2DLC scenarios are also shown in Table 12.2. The highest orthogonality was observed for HILIC-RP scenario (69%), followed by SEC-RP (58%). The most common 2DLC approach used in proteome research based on SCXRP combination (Fig. 12.2f) is 54% orthogonal, which is comparable to RP-RP separation shown in Fig. 12.2c (53%). When comparing the orthogonality values listed in Table 12.2, one may assume that the HILIC-RP combination is the most efficient 2DLC setup for the separation of


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peptides. However, besides the orthogonality, the peak capacities in both separation dimensions have an impact on the overall achievable 2DLC peak capacity. This is reflected by Equation 12.6, which defines the 2D practical peak capacity Np by taking into account an impact of surface coverage (orthogonality) and peak capacities P1, P2. P bins ð12:6Þ Np ¼ P1 P2 Pmax Table 12.2 summarizes the practical peak capacities of all 2DLC systems investigated in Fig. 12.2. It is apparent that despite the relatively large orthogonality of the SEC-RP setup, the overall practical 2DLC peak capacity value is reduced by a low separation efficiency of SEC. The importance of high peak capacity in both separation dimensions is highlighted in the case of RP-RP 2DLC (employing different pH). It provides the highest achievable Np of all systems, despite the fact that its orthogonality falls behind HILIC-RP and SEC-RP 2DLC methods. SCX-RP 2DLC has comparable orthogonality to the RP-RP setup, but due to the lower peak capacity of the SCX column, its achievable practical peak capacity Np is lower. 12.3.4

Practical 2DLC Considerations in Proteome Research

Although comprehensive 2DLC is the preferable setup from the point of achievable peak capacity (Gilar et al., 2004; Murphy et al., 1998) and is often used for the analysis of small molecules (Gray et al., 2004; Schoenmakers et al., 2005; Tanaka et al., 2004), the 2DLC analysis in proteome research usually consists of only 10–20 fractions and employs time-consuming analyses in the second LC dimension. The differences in this approach are forced by several factors. First, the comprehensive analysis is difficult to design in the nano- and capillary-LC scale, especially when both separation dimensions employ a gradient elution. Second, the duty cycle of MS/MS peptide analysis in the second dimension has to be taken into consideration. Therefore, further discussion will be carried out from the perspective of 2DLC–MS, as is typically done for proteomic applications. 2DLC can be practiced either in an online or an off-line setup. Both approaches have their distinct advantages and disadvantages. Some researchers favor an online approach because of the ease of use and minimal sample manipulation (Wagner et al., 2002; Washburn et al., 2001; Wolters et al., 2001). The necessary requirements are (i) mobile phase composition compatibility between the first and the second LC dimension and (ii) volume compatibility between fractions submitted for analysis and the scale of the second LC dimension. Although these requirements seem trivial, only two online 2DLC–MS approaches for separation of peptides have been described in the literature: SEC-RP (Opiteck et al., 1997) and SCX-RP 2DLC systems (Bushey and Jorgenson, 1990; Wagner et al., 2002; Washburn et al., 2001; Wolters et al., 2001). Both SEC and SCX modes operate in principle with aqueous mobile phases that could be transferred directly to the second RP dimension. However, nonspecific interactions of peptide with the sorbent makes it advisable to include organic solvents in the mobile phase (typically 5–25% acetonitrile) (Alpert and Andrews, 1988; Burke et al., 1989; Gilar et al., 2005; Peng et al., 2003; Winther and Reubsaet, 2005). This effectively

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eliminates the focusing effect of the second RP dimension and leads to a considerable peptide loss (Masuda et al., 2005). An off-line 2DLC setup (Gilar et al., 2005b; Peng et al., 2003) eases the restrictions of mobile phase compatibility. For example, the addition of 25% acetonitrile in SCX mobile phases is acceptable. The collected fractions can be either evaporated or diluted to minimize the negative effect of organic solvents on second RPLC dimension. The flexibility of the off-line 2DLC setup was especially useful in the presented study, since a majority of the investigated LC modes use solvents that cannot be injected directly on a second (RPLC) separation dimension. While the choice of a 2DLC method has a great impact on practical peak capacity, it is important to notice that the Np values shown in Table 12.2 are based on the assumption of frequent fraction collection. When the number of collected fractions transferred from the first to second dimension is limited to the level typical in 2DLC proteomic analysis (e.g., 10), the practical peak capacity Np (Equation 12.6) decreases. The importance of peak capacity in the first dimension is eliminated (P1 is equal to the number of fraction), and the Np largely depends on orthogonality. This scenario is illustrated in Table 12.2; the practical peak capacity for the most efficient 2DLC system is between 500–600. Interestingly, the SEC-RP approach becomes a viable choice for 2DLC. The only advantage then of using an efficient LC mode in the first dimension is the lower degree of fraction overlap. When the peaks are significantly narrower than the fraction collection window, they are less likely to be divided between consecutive fractions (Gilar et al., 2005b). As discussed above, in a typical proteomic experiment with a limited number of fractions collected, the practical peak capacity of 2DLC is well below 1000. This resolution is dramatically lower than the values considered in literature. 12.3.5

Evaluation of Selected 2DLC MS/MS Systems

We have successfully used partial evaporation of the fractions collected from the first LC dimension for an off-line 2DLC-MS/MS analysis of 17 digested proteins (Olivova et al., 2005). Three different modes were applied as a first dimension (150 1 mm or 150 2.1 mm columns): high pH RP, HILIC–LC, and SCX (with 25% acetonitrile in mobile phase). After the organic content was reduced and concentrated, the fractions were injected on the capillary RPLC–MS/MS system. The concentration of proteins varied between 30 fmole and 10 pmole; the lower concentration represented the MS instrument limit of detection for peptides. The amount of the 17-protein digest sample injected was identical for all three 2DLC experiments. An alternate low energy MS and high energy MS/MS scanning was used for MS/MS analysis (2.2 s per scan) (Silva et al., 2005). MS chromatograms of collected fractions are shown in Fig. 12.4. No peak distortion or peptide breakthrough was observed. All three 2DLC approaches are promising alternatives for proteomic research and show useful orthogonality. The highest number of peptides/proteins (221/15) was identified by RP-RP, followed by HILIC-RP (164/13) and SCX-RP (146/12) in 2DLC–MS/MS. The greater amount of peptide/protein identification in the RP-RP experiment is likely related to higher peptide recovery from the first LC dimension. In addition, a

277

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7

1st D separation, C18 column, pH 10

0

Minutes

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6

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5

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2nd D LC–MS, C18 column, pH 2.6

FIGURE 12.4 Comparisons of three off-line 2DLC methods for the separation of 17 protein tryptic digest. (a) RP-RP 2DLC separation based on pH differences in both separation dimensions, (b) HILIC-RP 2DLC, (c) SCX-RP 2DLC. The large peak eluting close to the column void volume (in all three 1st D LC analyses) is a UV signal of alkylating and reducting agents. The first fractions in HILIC and SCX contained no peptides; their LC–MS chromatogram is not shown. First RP-LC fraction contains mostly small hydrophilic peptides.

0

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278

FIGURE 12.4

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(b)

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FIGURE 12.4

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more efficient separation (less peak tailing) and lower fraction overlap in the RP-RP mode reduces the dilution of peptides, leading to the loss of identification of less abundant peptides (Gilar et al., 2005b) as their concentration falls below the MS limit of detection. In our experience, the RP-RP 2DLC method is a robust and efficient approach for the separation of complex peptide samples, which can supplement the most common current 2DLC technique based on SCX-RP chromatographic modes.

12.3.6

Peak Capacity in 2DLC-MS/MS

12.3.6.1 Chromatographic Versus MS/MS Peak Capacity In an earlier publication, we employed the RPLC gradient theory to predict peak capacity for peptide separations (Gilar et al., 2004). Different column length/efficiency and gradient slopes were considered. It appears that the theory reliably described the peptide behavior; it was utilized to model the separation productivity in 2DLC. The model predicts that the best productivity (defined as the number of separated peptides per unit of time) is achieved when using a frequent fraction collection in the first dimension (Murphy et al., 1998) in conjunction with a comprehensive and efficient analysis in the second LC dimension. The earlier study concluded that it is feasible to separate approximately 10,000–15,000 peaks within 8 h when using 6 min long analysis in the second dimension (Gilar et al., 2004). The proposed estimate has several limitations. When taking into account the limited orthogonality of investigated 2DLC modes, the practical peak capacity is reduced approximately to half. It needs to be also emphasized that a full separation power of the first LC dimension is realized only when the number of collected fractions exceeds its peak capacity (Murphy et al., 1998). If the number of fractions analyzed is low, the achievable chromatographic peak capacity suffers. A comparison of theoretical and practical peak capacity values, summarized in Table 12.2, leads to a conclusion that even the most promising 2DLC setups do not provide for the peak capacity needed to fully resolve a complex proteomic sample. As a result, the eluent entering the MS source typically contains multiple coeluting peptides. While chromatographic peak capacity is not adequate to resolve hundreds of thousands of components, many researchers argue that MS itself is an additional separation dimension with an orthogonal selectivity (separation is based on mass-tocharge ratio). Therefore, the combined resolution of LC and MS is greater than the chromatographically defined peak capacity. The question therefore stands: What is the achievable peak capacity of the 2DLC-MS/MS system? 12.3.6.2 MS/MS Duty Cycle Typical MS/MS analysis is a serial process, relying on the selection of precursors (peptides) in MS mode, followed by high-energy fragmentation in MS/MS. This process is termed data dependent acquisition (DDA). The duty cycle for the completion of MS and MS/MS cycles (the time necessary for MS/MS spectrum acquisition) is of primary importance. When the separation performance is viewed from the mass spectrometry perspective, the peak capacity can be characterized by the number of MS/MS scans, yielding successful


281

identification of peptides. In this scenario, the peak capacity of the LC–MS/MS system is given by the DDA duty cycle, which is virtually independent of LC performance. Although this approach is simplistic, it is useful for evaluating the overall peak capacity of a 2DLC–MS/MS system. Over the years, MS/MS duty cycle of modern MS instruments has constantly been improving, but for simplicity we assume it is equal to 1 s. Considering this it is possible to identify up to 60 peptides per minute and up to 3600 peptides in a LC-MS/MS analysis of 1 h. It is important to mention that only a small percentage of MS/MS scans typically yield a spectrum of sufficient quality that can be matched against a protein database and can result in peptide identification. Extending the concept of peak capacity defined by MS/MS, one may arrive to the conclusion that it is the overall LC analysis time that decides the number of identified peptides regardless of whether the analysis is practiced in a 1D- or 2DLC setup. As the time of analysis increases, more MS/MS scans can be accomplished during the experiment. In other words, a 10-h long 1DLC should be equivalent to a 10 h long 2DLC experiment (10 fractions collected in the first dimension and analyzed in 1 h long LC–MS run). This assumption correlates well with the practical experience in many proteomic laboratories. However, shallow gradients in 1DLC runs will result in wider peaks (Shen et al., 2005), and the peptides will be more dilute than in a typical 2DLC run, where the second dimension analysis is short, producing sharp (intense) peaks (Gilar et al., 2004). Thus, the favorable detection limit of peptides is expected in the latter case. 12.3.6.3 Achievable MS/MS Peak Capacity Proteomic samples are often more complex than the peak capacity defined by a MS/MS duty cycle. As a consequence, data-dependent MS/MS analysis undersamples the mixture and provides results that do not represent a full complement of peptides in the mixture. As the automated DDA acquires MS/MS spectra based on the MS precursor intensity, the protein identification is generally biased toward the most abundant ones. Understandably, the low abundant peptides are often not selected for DDA although they may be present in detectable quantities. In addition, the precursors selected for MS/MS from a complex sample may be polluted with other ions of similar masses. Therefore, the MS/MS spectra may contain foreign fragments that may lead to false positive/negative identification via the database search. Typically, only a small fraction of MS/MS scans (10–25%) yield a useful identification of peptides (Liu et al., 2004; Peng et al., 2003). The maximum LC–MS peak capacity calculated for a DDA duty cycle of 1 s is shown in Table 12.3. The number of MS/MS scans exceeds 100,000 for 10 h long 1D/2DLC experiment, but the number of identified peptides is typically lower. When considering the 25% success rate of a database search and the limited 2DLC orthogonality, the number of identified peptides is not more than 4500 in a 10 h experiment. The proteins of high abundance are often identified by multiple peptides and high statistical confidence. On the contrary, many medium and low abundant proteins are typically identified by only a single peptide. This is a direct result of the semirandom nature of the DDA algorithm (Liu et al., 2004), as discussed above. In addition, these peptide hits often cannot be confirmed by subsequent DDA experiments conducted

282


TABLE 12.3 The Peak Capacity Estimate of 2DLC–MS/MS Based on the MS/MS Duty Cycle (1 s per MS/MS spectra acquisition)

1DLC (60 min analysis) 2DLC 10 fractions 60 min 2DLC 20 fractions 120 min

MS maximum peak capacity, Number of MS/MS spectra (one precursor per second)

MS practical peak capacity, Number of identified peptides (25% success rate, 63% orthogonality in 2DLCa)

3600

900

36,000

4500

144,000

18,000

a

The 63% orthogonality represents a situation in which only half of the 2DLC separation space was covered with eluting peaks (see Eq. 12.6). The practical MS peak capacity in 2DLC is reduced to 50% (in addition to 25% MS/MS success rate), since the chromatogram in the second dimension is incompletely covered with eluting peaks (e.g., Figures 12.4(a) and 12.5).

even in the same laboratory and on the same day (Cargile et al., 2004; Maynard et al., 2004; Omenn et al., 2005; Von Haller et al., 2003). A parallel MS and MS/MS data acquisition approach introduced recently is a promising alternative to DDA, alleviating some of the inherent experimental drawbacks, such as undersampling, and providing for qualitative and quantitative protein analysis at the same time (Silva et al., 2005, 2006). 12.3.7

Considerations of Concentration Dynamic Range

The enormous dynamic range of proteins in the sample represents an additional difficulty in proteome analysis. The best example is serum with a protein abundance ranging over eleven orders of magnitude (Anderson and Anderson, 2002). To detect the low abundant species, one has to load a sufficient amount of digest on a column to meet the limit of detection (LOD) of the MS instrument. Some reports published used up to 2.5 L of plasma with an extensive fractionation of intact proteins prior to LC–MS analysis on the peptide level (Rose et al., 2004). One benefit of off-line 2DLC is the flexibility to operate the first LC dimension at a desirable scale with larger columns, providing a sufficient mass load capacity (Peng et al., 2003). The second dimension is typically carried out on nano-LC scale. One can, in principal, process more material in the 2DLC setup than in 1DLC, thereby improving the peptide LOD and overall MS signal. However, this approach is still limited by column mass load capacity, since the peptides deriving from both high abundant and low abundant proteins are present in the same sample. Consequently, the capillary or nanocolumns in second dimension may still be severely overloaded before the desirable concentration of low abundant peptides is reached. Figure 12.5 illustrates the problem of using human serum digest analysis. The digest was fractionated using a 2.1 150 mm C18 column at pH 10 (50 mL serum


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5–10mm

30–35mm

10–15mm

35–40mm

15–20mm

40–45mm

20–25mm

Fr10 45-50mm

70 0

Minutes

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FIGURE 12.5 Human serum tryptic digest analysis. Fractionation in the first LC dimension was performed using a C18 column at pH 10. Fractions were analyzed using NanoEase 0.3 150 mm Atlantis d18 column. Approximately 66 mg (400 pmole of serum albumin peptides) was injected on column. Arrow points to a selected albumin peptide illustrating a local column mass overloading. Ten-5mm wide fractions were collected in Ist LC dimension.

volume equivalent was injected on first dimension LC, total peptide content 3.3 mg, data not shown), and 1 mL of serum volume equivalent (66 mg of peptides) was injected on a 0.3 150 mm C18 column. MS chromatograms of collected fractions (Fig. 12.5) reveal that lower abundant peptides elute as narrow peaks, whereas the albumin peptides (400 pmole per peptide injected on column) give rise to 5 min broad peaks. This has a negative impact on achievable chromatographic peak capacity, while also decreasing MS accuracy due to MS signal saturation. Loading even more sample on the second dimension column would result in a complete deterioration of chromatographic separation and sample breakthrough. Figure 12.5 illustrates a typical problem of analysis of minor components present in a matrix of highly abundant ones. Despite the availability of large LC–MS peak capacity (Table 12.3), the number of peptides detected in a serum/plasma digest does not exceed several hundreds (Kapp et al., 2005). These peptides typically match 30–50 high abundant proteins. We believe that the majority of remaining proteins/peptides in the sample are present at concentrations well below the LOD of MS instrument. Depleting albumin and other major proteins from the sample combined with 2DLC–MS/MS analysis offer a promising solution (Bjorhall et al., 2005; Zolotarjova

284


et al., 2005) and can extend the limit of detection by one or two orders of magnitude; however, serum remains the most challenging sample for proteomic research.

12.4 CONCLUSIONS The consensus among proteome researchers is that separation is an essential part of complex protein analysis methods. A simplification of complex samples using 2DLC is beneficial for state-of-the-art MS/MS analysis. While 2DLC potentially provides a higher peak capacity than 1DLC, the orthogonality of separation has to be taken into consideration. We have evaluated several 2DLC methods for the separation of peptides and constructed 2D retention maps suggesting that it is difficult to find any two modes with complete orthogonality. The geometric approach for the quantification of orthogonality and practical peak capacity estimates indicate that the most suitable 2DLC approaches are based on a combination of SCX-RP and HILIC-RP chromatographic modes. In addition, we found that RP-RP 2DLC based on widely different pHs, in both separation dimensions, may also be a promising approach for proteomic research. It has been argued that in a typical 2DLC proteomic experiment, with only a limited number of fractions submitted for analysis in the second LC dimension, chromatographic peak capacity is less than 1000. This value is considerably lower than the expected sample complexity. Additional resolution is offered by MS, which represents another separation dimension. With the peak capacity defined as the number of MS/MS scans (peptide identifications) accomplished within the LC analysis time, the MS-derived peak capacity was estimated to be in an order of tens of thousands. While the MS peak capacity is virtually independent of LC separation performance, the complexity of the sample entering the MS instrument still defines the quality of MS/MS data acquisition. The primary goal of 2DLC separation is to reduce the complexity of the sample (and concentrate it, if possible) to a level acceptable for MS/MS analysis. What is the “acceptable” level of complexity to maintain the reliability and the repeatability of DDA experiments remains to be seen.

ACKNOWLEDGMENT The authors thank Aleksander Jaworski and Jessica Fridrich for helpful suggestions to the manuscript.

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13 MULTIDIMENSIONAL SEPARATION OF PROTEINS WITH ONLINE ELECTROSPRAY TIME-OF-FLIGHT MASS SPECTROMETRIC DETECTION Steven A. Cohen and Scott J. Berger Life Sciences R&D, Waters Corporation, Milford, MA 01757, USA

13.1 INTRODUCTION Multidimensional chromatography is being more widely used as scientists study biological systems of ever-increasing complexity. The emerging field of proteomics, where the goal is to qualitatively and/or quantitatively describe the entire protein complement of a system, presents some of the most challenging separation problems (e.g., Anderson and Anderson, 2001; Wang et al., 2005; Wang and Hanash, 2005). A typical sample from a cellular extract, tissue homogenate, or biological fluid may comprise thousands of proteins, with a tremendous variation in physical properties. Protein size can range from small soluble proteins with molecular weights of 103 Da to large protein complexes of 106 Da (Peng and Gygi, 2001). Solubility and charge characteristics vary widely, and can influence protein chromatographic properties and recovery during fractionation. Membrane proteins are often poorly soluble in common aqueous buffers, and as a consequence present exceptionally challenging issues with recovery and chromatographic efficiency. At the other extreme, the small soluble proteins may be highly charged and present recovery difficulties for ion-exchange (IEX) separations.


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Separation difficulties are further exacerbated by the wide variation in protein concentrations present in most samples (Gygi et al., 2000; Anderson and Anderson, 2001; Wang et al., 2005; Wang and Hanash, 2005). Typical protein concentrations in cells vary by 6 orders of magnitude, and the variation in plasma can be as high as 1011. Analysis of low concentration analytes is often masked by a single protein or group of proteins that dominate the sample. For example, in serum or plasma, albumin is the predominant protein, comprising approximately 60% of the total protein mass, and the 10 most abundant proteins comprise roughly 90% of the total mass present (Anderson and Anderson, 2002). Sample complexity is also greatly increased because of the presence of multiple forms of many proteins arising from alternative transcriptional editing, cotranslational protein processing, and posttranslational modifications (Anderson and Anderson, 2001; Brunet et al., 2003; Jensen, 2004; Wang and Hanash, 2005). These modified forms can also be present over a significant dynamic range, and minor variations in structure often lead to very similar retention characteristics such that resolving the various forms is not an easy task. The limitations of using one separation mode for purifying a single protein from a crude biological sample have been apparent to biochemists for decades, and virtually all protein purification schemes, except for some affinity purifications, employ multiple steps to produce a purified sample. The goal of proteomic studies is often to create a comprehensive picture of proteins in a sample, and multiple steps of fractionation can be critical to achieving this aim. This is particularly true in those studies that seek to combine information at the intact protein level (top-down proteomics) (Nemeth-Cawley et al., 2003; Kelleher, 2004; Whitelegge et al., 2002) with identification based on peptide tandem mass spectrometry (bottom-up proteomics) (Ducret et al., 1998; Hamler et al., 2004). No single chromatographic mode possesses the resolving power to separate the hundreds to thousands of protein species present in typical samples. This has rekindled an interest in multidimensional protein separations, an area pioneered by Jorgenson and coworkers (Bushey and Jorgenson, 1990; Opiteck et al., 1997, 1998), and now finding great utility for proteomics. In addition, there are several recent reviews covering protein MDLC (Apffel, 2004; Evans and Jorgenson, 2004; and Chapter 8 by Evans and Jorgenson) that describe practical aspects of linking various orthogonal chromatographic modes. Multidimensional separations are most useful when the different separation modes are orthogonal in nature, with true orthogonality (defined as lack of correlation between analyte retention in the two modes) (Slonecker et al., 1996; also see Chapters 3 and 12 by Davis and Gilar et al., respectively) yielding systems with peak capacities that are the product of the individual separation dimensions (Giddings, 1984, 1987; Bushey and Jorgenson, 1990; Liu et al., 1995). Thus, the choice of chromatographic modes, such as IEX or reversed-phase (RP), is one of the essential elements for optimizing the resolving power of a MDLC system. Fortunately, several potential separation modes rely on completely different retention mechanisms that, when coupled in a 2D system, provide excellent orthogonality. For example, researchers have successfully coupled first dimension separations by size exclusion

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chromatography (SEC) (Opiteck et al., 1997, 1998), IEX chromatography (Bushey and Jorgenson, 1990; Liu et al., 2002; Millea et al., 2005, 2006), affinity chromatography (Geng et al., 2001) and chromatofocusing (Chong et al., 2001) with a second dimension reversed-phase chromatographic (RPLC) step. Several other chapters in this book provide detailed descriptions of various 2D protein separations, including those coupling LC with CE (Chapter 16), 2D capillary electrophoresis (Chapter 15), and chromatofocusing with RPLC (Chapter 10). In addition, Evans and Jorgenson (Chapter 8) provide a broad overview of 2D-LC protein separations. This chapter will focus specifically on our research using an IEX–RP configuration in conjunction with electrospray-time-of-flight mass spectrometry (ESI–TOF MS), which provides an excellent combination of chromatographic resolution, orthogonal separation, and compatibility with the online protein mass determination.

13.2 CHROMATOGRAPHIC PARAMETERS In the first section of the chapter, we will discuss the advantages and disadvantages of various operating parameters of the first dimension IEX operation, such as anion- and cation-exchange separations, as well as a comparison of step versus linear gradient elution for the initial dimension. Configuration issues, such as coupling the two dimensions, are presented in the second section. Because the buffers used for IEX are typically weak eluents in RPLC, concentration of the eluting components on RP columns is readily accomplished, but different configurations for this transfer step have their own beneficial consequences. The RP step itself employs MS-compatible mobile phases, such as dilute formic acid-acetonitrile gradient systems, and meets the requirement for introducing a salt-free sample into the MS interface. However, residual salt from the IEX step will interfere with the analysis. Approaches to removing the salt are discussed. Downstream post-run processing of the 2D separation, including the generation of peptides for further analysis of the eluting proteins, is also an important aspect for proteomics analysis, and is taken into consideration.

13.3 ANALYTE DETECTION AND SUBSEQUENT ANALYSIS A 2D system coupled with a TOF-MS detector provides not only resolution for a large number of protein components, but also yields accurate intact molecular weight information (e.g., Opiteck et al., 1997; Liu et al., 2002; Millea et al., 2005). Moreover, by splitting the effluent just prior to the MS interface, a small portion can be diverted for MS analysis, whereas the bulk of the sample can be collected for subsequent analysis, following enzymatic digestion, to provide positive identification and characterization of the proteins present in the fraction. The ESI–MS of an intact protein yields a series of ions with m/z values corresponding to sequentially charged species (Fenn et al., 1989). Algorithms and software for the deconvolution of these peaks into a single neutral mass have been available for many

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years, but the enormous number of spectra produced by a comprehensive 2D protein, poses new challenges for data reduction. For simple systems, a total ion chromatogram (TIC) generated from individual RPLC steps can be sufficient to permit automated peak identification (Liu et al., 2002). This scheme often fails with highly complex mixtures, such as whole cell extracts that exhibit overlapping peaks and a much higher dynamic range. The approach taken for these complicated samples has been to apply a time-based segmentation of the data to automate spectral deconvolution of resulting LC–MS analysis (Millea et al., 2006). This preserves chromatographic resolution, and provides a means to evaluate the distribution of a protein among multiple fractions. The combination of this top-down proteomics approach, which generates information on the structure of the intact protein, with a bottom-up approach for protein identification (using MS/MS data of tryptic peptides from the collected fractions) has been particularly useful for identifying posttranslational modifications, cotranslational processing, and proteolytic modifications in a number of proteins. Examples from our work will be shown to illustrate this hybrid methodology for proteomics analysis.

13.4 BUILDING A MULTIDIMENSIONAL PROTEIN SEPARATION Multidimensional separations in proteomics have most often been targeted toward peptide-level (or bottom-up) analysis. This has been effective for simplifying samples for subsequent mass-spectrometric analysis, and addressing the limitations of mass spectrometry to acquire MS/MS data from online separations. Peptide-level separations do not significantly address issues of the fundamental discontinuities between proteomic sample dynamic range (106–1012) and the dynamic range of detection in modern mass spectrometers (typically 103–104). The peptides from abundant proteins are distributed throughout a fractionation, and thus the dynamic range of individual fractions closely resembles that of the original sample. In the end, the largest improvements in such peptide-level separations can be seen when fractions (of lower sample mass than the original sample) are overloaded with respect to these abundant components to facilitate the identification of lower abundance components. Separations at the intact protein level can effectively resolve abundant proteins from those of lesser abundance, but introduce the additional complications of keeping proteins soluble and maintaining or preventing protein interactions. It can also be challenging to maintain the consistency of protein–sorbent interactions over multiple chromatographic runs, which is potentially compromised by incomplete desorption of strongly retained or poorly solubilized sample components, such as lipids or membrane proteins. Whether the separation is used for fractionation or for direct analysis of a sample, separation methodologies must balance the benefits of more extensive fractionation with the capability of extracting quantitative information from the data. Each processing or separation step demonstrates some variation for overall analyte recovery, and distribution, and these effects are multiplied with each additional dimension of sample processing. In the following sections, we will detail various aspects of several IEX/reversed-phase multidimensional chromatographic separation

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methodologies we have developed for the resolution and analysis of complicated protein mixtures. 13.4.1 Selection of an Ion-Exchange–Reversed-Phase Separation System for Protein-Level Separations The IEX chromatography possesses several attractive features as a first-dimension separation mode in an MDLC scheme. These features include the following: (1) The capability to concentrate dilute biological samples prior to elution. (2) The ability to generate separation peak capacities on the order of 10–100 peaks. (3) High orthogonality with reversed-phase chromatography. (4) Compatibility with many of the additives used to prevent protein interactions, and maintain sample stability and solubility (detergents, denaturants, and reductants, and protease inhibitors). (5) Flexibility to address proteins over the wide range of size and pI. (6) Commercial availability of high binding capacity sorbents with moderate to high pressure tolerance. On the downside, eluent pH, ionic strength, and the presence of additives are often incompatible with downstream analysis methods, particularly those involving massspectrometric analysis. Thus, the pairing of IEX methods with a second-dimension reversed-phase separation can accomplish not only the goals of orthogonal separation and increased peak capacity, but also the practical objectives of sample cleanup and removal of interfering substances prior to analysis. The practical implementation of this chromatographic pairing will be discussed in detail in subsequent sections of this chapter. 13.4.2

Chromatographic Sorbent Considerations

Anion-exchange chromatography appears, in the literature, to be the preferred separation mechanism for complex protein separations. In most of the organisms studied, there is an asymmetrical distribution of protein pI, with 70% of typical cellular proteins having pI below 7 (Gianazza and Righetti, 1980). Thus, anionexchange chromatography, at or near neutral pH, would demonstrate superior utility for the separation of greater numbers of proteins. Notable exceptions to this rule would be classes of basic proteins dominated by proteins that interact with nucleic acids via electrostatic interactions (e.g., ribosomal proteins and histones). For these protein classes, cation-exchange chromatography has been demonstrated (e.g., Threadgill et al., 1987) to be a highly efficient mode of separation. Both large-pore and nonporous sorbents have been successfully applied for large biomolecule separations. The fundamental distinction between these two particle types is the balance between efficiency of mass transfer and loading capacity. Porous

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particles possess significantly greater surface area (approaching 50x) than comparable nonporous particles, which is typically manifested as higher analyte loading capacity. This extra binding capacity is achieved while sacrificing both separation efficiency and pressure resistance. Pressure limitations can be particularly notable when using porous polymeric-based ion-exchangers, and can significantly limit potential choices for the second-dimension RP column configurations. Capacity limitations are typically more acute with nonporous RP sorbents than IEX sorbents, and column overloading can manifest during a multidimensional separation when protein content varies significantly between steps or segments of an IEX first dimension. 13.4.3

Chromatographic Behavior of Proteins

Proteins are all “large molecules’’ in chromatographic terms, and typically produce multisite interactions with a chromatographic sorbent. Regnier described the cooperative nature of such interactions in the 1980s as part of the stoichiometric displacement model (Geng and Regnier, 1984; Drager and Regnier, 1986). The model argues that an ideal protein achieves significant chromatographic linear velocity only when a critical elution strength, necessary to break all interactions between protein and sorbent, has been reached. This translates into the expectation that proteins can be maintained on a column at subcritical eluent strength for extended periods without degrading separation quality once the critical elution strength is achieved. Thus, the use of both step and linear gradients in the first-dimension IEX separation should prove useful for protein separations. In reality, many proteins demonstrate mixed mode interactions (e.g., additional hydrophobic or silanol interactions) with a column, or multiple structural conformations that differentially interact with the sorbent. These nonideal interactions may distribute a component over multiple gradient steps, or over a wide elution range with a linear gradient. These behaviors may be mitigated by the addition of mobile phase modifiers (e.g., organic solvent, surfactants, and denaturants), and optimization (temperature, salt, pH, sample load) of separation conditions.

13.5 COMPREHENSIVE MULTIDIMENSIONAL CHROMATOGRAPHIC SYSTEMS In general, a comprehensive separation strategy implies the desire to resolve/analyze all components within a sample. In the specific context of a multidimensional chromatographic method, the term is more narrowly applied to indicate that all analytes introduced to the first-dimension separation are also subjected to a seconddimension separation. There are two basic configurations used by our laboratory to carry out comprehensive multidimensional (IEX/RP) protein separations—IEX— Dual Column RP system and IEX—Dual Trap RP system (Figs. 13.1 and 13.2), respectively. The IEX—Dual Column RP system configuration shown (Fig. 13.1) utilizes independent pumping systems for each separation dimension. One pumping system

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has responsibility for sample injection onto the first-dimension column, and for generation of the first-dimension gradient separation. The effluent from the firstdimension column is directed into a 10-port two-position valve, where flow is delivered to one of the two reversed-phase columns plumbed into the valve, and on to waste. Flow from the second-dimension pumping system is simultaneously directed through the valve and the second reversed-phase column to fraction collection and/or mass analysis. The actuation of this “ column selection’’valve (V1) alternates the roles of the two reversed-phase columns between capture of the first-dimension effluent and RP analysis of the previous first-dimension IEX sampling. The second-dimension effluent in Fig. 13.1 is typically directed through a second two-position valve (V2) to divert salts and other hydrophilic substances that do not retain on a second-dimension column. This is necessary when separations are coupled with online ESI—MS analysis. The valve is initially switched to the divert position when a second-dimension column is switched in-line, and maintained in this position until residual salts and hydrophilic components are washed from the column. During this period of desalting/cleanup, the second-dimension pumping system is held at initial gradient conditions. Following the desalting step, the valve is actuated, the RP gradient initiated, and the second-dimension flow is returned to mass detection and/or fraction collection. The IEX—Dual Trap RP system configuration (Fig. 13.2) directs first-dimension IEX effluent to two alternating reversed-phase trap columns (typically 2.1 10 mm

FIGURE 13.1 Schematic of a comprehensive 2DLC (IEX/RP) configuration with alternating second-dimension analytical RP column sampling of first-dimension eluent. A salt diversion valve is present to divert salts from the IEX dimension to waste, and prevent contamination of downstream collected fractions or mass analyzer.

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FIGURE 13.2 Schematic of a comprehensive 2DLC (IEX/RP) configuration with alternating sampling of first-dimension IEX eluent by two RP trap columns, and a single downstream analytical RP column. A salt diversion valve is present to divert salts from the IEX dimension to waste, and prevent contamination of downstream collected fractions or mass analyzer.

cartridges) rather than the higher resolution analytical columns employed for the previous configuration. A single analytical reversed-phase column is placed downstream of the salt-divert valve. To prevent rapid pressure changes in the system, the salt-divert waste line now contains a pressure restrictor roughly equal in backpressure to the analytical column. This configuration is typically employed when the firstdimension IEX column has limited pressure tolerance, as is the case with most porous polymeric IEX phases, and cannot withstand the high backpressure of a modern small particle RP typically used in the second-dimension. This configuration also permits rapid desalting/washing of the reversed-phase trap column, which can be useful for protecting silica-based reversed-phase chemistries from attack by alkaline eluent employed for anion-exchange chromatography. We have found that the dual-trap configuration is not significantly more difficult to execute, nor appreciably less efficient than the dual-column format. The most common failure mode appears to be fouling of one of the trap columns. We have observed that the trap column that receives components in the flow through IEX fraction is usually the first to show performance degradation with complicated proteomic samples. This is the initial fraction containing those components (including likely nonproteinaceous components such as lipids) that are not retained on the IEX column under loading conditions. Performance degradation of a trap column is often

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diagnosed by observing increased system backpressure when the affected column is in line with the first-dimension IEX column. To maintain parallel equivalent function, the trap columns are typically replaced as pairs. In contrast to the two previous multidimensional reversed-phase LC systems, a third online configuration (IEX–Multiple RP) has been described in the literature (Masuda et al., 2005), where each first-dimension elution step or first-dimension gradient segment is captured by a unique second-dimension column. This “Gattling Gun’’ configuration effectively decouples the two separation dimensions, and could more aptly be described as an approach for “online fraction collection.’’ Two functional distinctions arise from this third methodology: A single chromatographic system could potentially support both separation dimensions, and that the maximum peak capacity for the first-dimension is directly limited by the number of seconddimension columns in the system. Although the fluidics of such a system is rather straightforward (sets of columns are plumbed between two synchronized multiposition valves, e.g., a seven-port six-position valve would support six RP columns), we have not explored this configuration because of the lack of scalability inherent in this approach.

Q4

13.6 COUPLING 2DLC WITH ONLINE ESI–MS DETECTION Mass spectrometry provides an information-rich and concentration-sensitive detection scheme that can yield component-level characterization of complex protein mixtures. Interfacing multidimensional separations to mass spectrometry provides a significant increase in analytical capability over UV detection, permitting the tracking of individual proteins throughout a separation scheme, and resolving data from proteins with overlapping elution profiles. This ability to simultaneously characterize multiple analytes provides an extra analytical dimension to the separation, increasing peak capacity by fivefold or greater in most systems. Subsequent figures will provide examples where multiple coeluting or partially coeluting proteins are resolved during a 2DLC/MS analysis. The use of the salt-divert valve (Figs. 13.1 and 13.2; V2) is critical to applications involving online MS analysis, as non-volatile salts can be strongly bound to proteins during ionization, producing salt adduct peaks in resulting mass spectra. These adducts complicate qualitative characterization of a protein mixture, and can significantly degrade signal response of the nonadducted protein. Figure. 13.3 contains two spectra obtained during 2DLC(SCX/RP)/MS analysis of yeast ribosomal proteins, where insufficient removal of nonvolatile salts (7 column volumes under initial RP gradient conditions) resulted in a series of detected salt adducts (þ22 Da mass differences, consistent with a series of sodium adducts) dominating the deconvoluted spectrum for RPL17 protein (Fig. 13.3a). More extensive salt removal (14 column volumes) significantly eliminated the adduct signals, and the resulting deconvoluted mass spectrum is dominated by the signal of the intact protein (Fig. 13.3b). Organic solvents and mobile phase modifiers present in the chromatographic eluents can also be a source of adducts formed during LC/MS analysis.

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FIGURE 13.3 Raw and deconvoluted mass spectra of a yeast ribosomal protein (L16) from a 2DLC(SCX/RP)/MS experiment were obtained, where mass spectral adducts were observed because of insufficient washing of the second-dimension RP column (Panel a, 7 column volumes of wash). Panel b shows mass spectra for the same protein from an experiment with sufficient second-dimension wash volumes (Panel b, 14 column volumes of wash).

Employing mass spectrometry as a detector may ultimately require a tradeoff between optimizing separation peak capacity and MS sensitivity. In particular, trifluoroacetic acid (TFA), a strong reversed-phase ion pairing agent, will typically produce very narrow (concentrated) protein peaks during reversed-phase chromatography, but will also strongly suppress electrospray ionization of biomolecules (Apffel et al., 1995). Formic acid, a weaker ion-pairing agent, typically produces inferior chromatography, but up to 30-fold greater MS response than TFA (Huber and Premstaller, 1999). When these effects were compared by our group for the LC/MS analysis of yeast ribosomal proteins, shown in Figure 13.4, we observed that the TIC signal was suppressed threefold in 0.1% TFA versus 2% formic acid even when chromatographic peak widths were substantially reduced (Liu et al., 2002). The effect of acidic modifier choice and concentration on chromatographic performance is sorbent dependent, and may be one additional parameter to consider during sorbent selection.


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FIGURE 13.4 Total ion chromatograms from the 1D LC/MS analysis of a yeast ribosomal protein fraction separated using 0.1% TFA (Panel a) and 0.1% formic acid (Panel b) as mobile phase modifiers. TFA produced narrower, more concentrated, peaks for mass analysis that did not overcome the significant electrospray ionization suppression associated with using this modifier for LC/MS studies, resulting in an overall reduction in component intensities.

Data files obtained from the LC/MS and 2DLC/MS analysis of intact protein mixtures can often reach gigabyte size, even when modern data compression routines are employed. Large datasets can be challenging to deal with from both a processing time and storage requirement. The end goal of the data processing workflow is to reduce voluminous retention time, m/z, intensity data down to a set of protein components with characteristic retention time and intensities. The central engine of this data workflow is the process of spectral deconvolution. During spectral deconvolution, sets of multiply charged ions associated with particular proteins are reduced to a simplified spectrum representing the neutral mass forms of those proteins. Our laboratory makes use of a maximum entropybased approach to spectral deconvolution (Ferrige et al., 1992a and b) that attempts to identify the most likely distribution of neutral masses that accounts for all data within the m/z mass spectrum. With this approach, quantitative peak intensity information is retained from the source spectrum, and meaningful intensity differences can be obtained by comparison of LC/MS runs acquired and processed under similar conditions. To process the LC/MS data more efficiently, we have automated this deconvolution functionality using a Visual Basic macro (termed AutoME or Automated Maximum

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FIGURE 13.5 The total ion chromatogram and deconvoluted protein mass map for a 1D LC/MS analysis of yeast ribosomal proteins. The bubble size is proportional to component intensity.

Entropy) operating within the MassLynx 4.X data processing software (Waters). Chromatographic runs are time-segmented, and the summed mass spectra for each segment are deconvoluted, centroided, and thresholded to produce a set of deconvoluted neutral mass—intensity pairs for that processing segment. When the width of processing segments is a fraction of a chromatographic peak width, protein components can be recognized as a set of mass/intensity pairs present over adjacent processed segments. The resulting datasets are most commonly visualized as a virtual 2D protein mass map with retention time as the independent variable, deconvoluted mass as the dependent variable, and bubble size equal to component intensity. This plot was generated using the Microsoft Excel bubble plot display. This process was used to produce a 2D protein mass map for a single-dimension LC/MS analysis of yeast ribosomal proteins as shown in Figure 13.5, along with the associated TIC plot displayed above. The correspondence between TIC signal and component intensities is clearly indicated. However, the map reveals that samples of moderate complexity can contain regions where components are not sufficiently resolved to permit TIC or UV chromatographic data to reveal component-level detail, tracking, and quantitation between samples. Deconvoluted mass spectral data can provide the additional selectivity needed to distinguish coeluting analytes, and


303

FIGURE 13.6 The total ion chromatogram and deconvoluted protein mass map for a 2DLC (SCX/RP)/MS analysis of yeast ribosomal proteins. The bubble size is proportional to component intensity.

monitor complex samples at the component level. The data were processed by summing mass spectral time segments that spanned a fraction of chromatographic peak width, and revealed the underlying chromatographic profiles for each of the components detected during the analysis (e.g., Fig. 13.5, inset). Processing LC/MS data by this approach naturally extends to the characterization of multidimensional LC/MS data. The protein mass map of a 2D(SCX/RP) LC/MS analysis of the same ribosomal protein fraction is displayed in Figure 13.6. The resulting orderly series of reversed-phase cycles, corresponding to individual SCX elution steps, can be visualized on the associated TIC plot. It can be seen that the shorter, steeper RP gradients produce not only narrower, more concentrated peaks than with the 1D separation of equivalent run length, but also considerable periods of time where no protein data are collected. These periods where no data are collected occur as the column is being regenerated in a low percentage organic mobile-phase before it can again capture components from the first-dimension separation. The resulting “overhead’’ from column regeneration reduces the practical peak capacity below what the narrow peaks could theoretically yield. A system could be configured with an additional isocratic LC pump and valving to produce rapid off-line regeneration of a RP column at the cost of significantly increased system complexity. It should

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be noted that the percentage of the total cycle time utilized for system overhead is directly related to the ratio of system regeneration time to second-dimension analysis time, and can limit the practical lower limits for the rate of second-dimension sampling of the first-dimension. The capability to analyze 2DLC data by the automated process, and the ability to resolve component-level behaviors within complex protein separations enabled us to undertake a subsequent series of comparative experiments where protein behaviors within a cellular lysate were analyzed using both step and linear gradients in a 2DLC/MS experiment. This will be described in the following sections of this chapter. 13.6.1 Interactions between the Two Dimensions of Chromatography (Step Vs. Linear) The choice between a step and linear first-dimension gradient mode fundamentally changes the interaction between the first-dimension IEX and second-dimension RP separations. Assuming the ideal chromatographic behavior of proteins according to the stoichiometric displacement model (see above), it is expected that proteins will either elute or retain on the IEX column during an isocratic salt elution step, independent of the length for which that step is maintained. During step gradient chromatography, the two separation dimensions would be effectively decoupled, and extended high-resolution separations in the second-dimension could be applied. In this mode, the maximum peak capacity of the first-dimension IEX separation is limited to the number of gradient steps applied, and the bulk of the system separation capacity would be obtained from the second-dimension RP separation. In contrast, with a system using a linear gradient for the IEX separation, the first and second dimensions are temporally coupled, in that the ratio of gradient lengths for the first and second dimension determines the effective peak capacity of a first-dimension separation. A more rapid second-dimension RP analysis cycle can sample the IEX separation with greater frequency, and make more efficient use of first-dimension peak capacity. Thus, the use of a linear gradient for the first-dimension IEX separation will provide peak capacities limited by three factors, the IEX column peak capacity, the gradient slope, and the rate at which the first-dimension effluent is sampled. As with all 2D separations, frequent sampling of the first-dimension is highly desirable (Murphy et al., 1998), but rapid RP analysis can cause a significant reduction in seconddimension peak capacity because of the steep gradient employed to increase the firstdimension sampling rate. Oversampling of the first dimension distributes components over several second-dimension cycles, thus diluting the concentration of an individual component in a specific second-dimension cycle, effectively increasing detection limits and potentially limiting the dynamic range of analysis. Secondarily, this requires combining data from multiple cycles, with a possible consequence of reducing the quantitative capacity of the analysis. In addition, oversampling in combination with online MS detection may generate an incredibly large dataset for postrun analysis that significantly increases the required processing time. Maintaining a suitable balance between the desire to maximize resolution and the practicality of


305

analyzing the resulting dataset in a timely fashion can have a significant effect on the choice of first-dimension fraction size and total peak capacity achieved. Analysis of a purified ribosomal protein extract by strong cation exchange (denaturing conditions)-reversed-phase 2D LC/MS analysis (nine step IEX gradient, dual column configuration) under denaturing conditions showed that 67 of 91 (74%) identified ribosomal proteins were observed in a single reversed-phase cycle. In this study (Liu et al., 2002), the occurrence of a protein being distributed between adjacent IEX fractions was roughly twice as likely as second-dimension RP carryover distributing a component into multiple reversed-phase cycles. Analysis of all detected components revealed that only 6 of 120 (5%) proteins were observed in more than two RP cycles. Recent studies by our group have found that up to half of the proteins in a more complex and heterogeneous sample, Escherichia coli cytosol, exhibited nonideal behaviors in one or both chromatographic dimensions of a nondenaturing SAX-RP (dual trap/analytical) MDLC system (Millea et al., 2005). These include the splitting of an analyte between two consecutive second-dimension cycles, where a protein elution band is split by the activation of valve 1, and analyte carryover in the RP dimension, which results in a protein being observed in two nonadjacent (cycle n and n þ 2) cycles, or a combination of these two phenomena. In this study, the separation behavior of the top 100 abundant proteins (by LC/MS response) were followed over the course of triplicate analyses, using either an eight-step gradient or a corresponding linear gradient for the SAX first dimension. Replicate experiments showed that protein mixtures exhibit reproducible chromatographic elution patterns using both modes of multidimensional separation. Overlaid TIC data from triplicate analyses of a 2D (Step) LC/MS experiment are presented in Figure 13.7, and demonstrates the overall consistency of elution profiles, with only minor variability between replicates of the same salt step. There was, however, variation noted with respect to the relative intensity distribution of components that distributed across multiple RP cycles. Figure. 13.8 contains 2D protein map data from a single RP cycle of the linear (Fig. 13.8a) and step (Fig. 13.8b) 2DLC/MS analysis of the E. coli cytosol. In this example, the step gradient corresponds to the beginning and end salt concentrations for the linear segment. In Fig. 13.8, the deconvoluted protein data from replicate runs are represented by red- and green-colored spots, where data in intersection are visible in orange. Within each experimental type, the same components are typically observed in the replicate analysis, with some minor variation in component intensity. Although many of these components are common to the same step and linear cycle, many more differences are evident. Under the conditions used for this study, 70 of the top 100 proteins resolved by a linear 2D SAX/RP separation appeared in a single RP cycle, whereas only 51 of the top 100 proteins in the step-gradient separations are well-behaved. Surprisingly, the negative effects of the step-gradient separation were observed as both individual firstdimension SAX peak-splitting, and second-dimension RP carryover events, rather than peak-splitting effects alone. The 28 proteins that showed nonideal separation behaviors under both gradient modes exhibited roughly equal contributions of peaksplitting in the SAX dimension and carryover in the RP dimension. Such results likely

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FIGURE 13.7 Total ion chromatogram reproducibility for three 2DLC (SAX-Step Gradient/ RP)/MS analyses of an E. coli cytosolic fraction.

indicate the need for more denaturing separation conditions, or the need for additional solubilizing agents to promote more uniform column—protein interactions. 13.6.2

Recognizing Increased Selectivity in 2DLC Separations

Total theoretical peak capacity for the 1D and 2D LC/MS analyses of the yeast ribosomal protein sample was calculated as 240 and 700, respectively. Individual separation peak capacities were calculated by dividing the total separation time by the average peak width at baseline, and the 2D peak capacity determined as the product of the peak capacity of the two dimensions. These theoretical calculations rely on optimal use of the two-dimensional separation space, which in turn is dependent upon the lack of correlation between the component retention times in the two separation modes. Thus, the maximum use of the theoretical peak capacity is not only dependent on the selection of chromatographic modes based on different physicochemical


307

FIGURE 13.8 Duplicate 2DLC(SAX/RP)/MS analyses of an E. coli cytosolic fraction were conducted using either a linear (Panel a) or step gradient (Panel b) IEX gradient first-dimension. Resulting protein mass maps show reproducibility of components over duplicate runs for one mode (merged image within each panel), and between gradient modes (Panel a vs. Panel b) from a single RP cycle corresponding to a common salt range in the first-dimension). In each panel the first run is displayed in red, the second in green, and overlapping data in orange. (See color plate.)

characteristics of the analytes, but also relies on the mixture of analytes itself possessing a sufficiently broad range of those physicochemical properties. Despite this threefold increase in peak capacity, the observed number of resolved protein peaks only rose from 53 to 80, even though a total of approximately 125 distinct proteins were detected in both experiments. This can be attributed at least in part to the similar chemical properties of the ribosomal proteins themselves, which are nearly all strongly basic molecules with a smaller range of both ionic character and hydrophobicity compared

308


to a more diverse set of proteins such as would be expected in a more complex total cellular extract. The narrow pI range of ribosomal proteins is in contrast to cellular extracts that consist of many more acidic and neutral pI proteins, and these extracts are more effectively fractionated by anion-exchange than by cation-exchange columns. A subsequent section in this chapter details such an analysis of an E. coli extract.

13.7 EXPANDING MULTIDIMENSIONAL SEPARATIONS INTO A “MIDDLE-OUT” APPROACH TO PROTEOMIC ANALYSIS

Q5

The ability to resolve and characterize complicated protein mixtures by the combination of 2DLC and online mass spectrometry permits the combination of sample fractionation/simplification, top-down protein mass information, and bottom-up peptide level studies. In our lab, the simplified fractions generated by 2D(IEX–RP)LC are digested and analyzed using common peptide-level analysis approaches, including peptide mass fingerprinting (Henzel et al., 1993; Mann et al., 1993), matrix-assisted laser desorption/ionization (MALDI) QTOF MS/MS (Millea et al., 2006), and various capillary LC/MS/MS methodologies (e.g., Ducret et al., 1998). The intact mass of a protein represents the contribution of all modifications to the primary protein structure, but the combinatorial nature of the potential modifications limits the utility of intact mass to conclusively produce an initial protein identification. Although some labs have started to develop top-down intact protein MS/MS approaches to overcome this obstacle (Nemeth-Cawley et al., 2003; Kelleher, 2004), we have chosen to use a supplemental peptide level analysis as the primary tool for identifying proteins within a complex sample. Intact masses corresponding to a collected 2DLC fraction are compared to the theoretical mass of identified proteins to assign likely modification(s). These, of course, can be validated by targeted analysis of the digested 2DLC fraction that produced the initial identification. This hybrid approach was first applied to an enriched fraction of yeast ribosomal proteins using SCX/RP 2DLC proteins fractionation where roughly 10% of the effluent was directed to an ESI-TOF mass spectrometer for intact mass analysis, and the remainder collected for further analysis (Liu et al., 2002). The collected fractions were digested and subjected to MALDI peptide mass fingerprint (PMF) analysis. The simplicity of the sample coupled with the low dynamic range of ribosomal proteins in the purified sample meant that fractions on the order of 5–6 peak widths could be collected without exceeding the capabilities of PMF to analyze the resulting fractions as they only contained at most six proteins. Figure 13.9 is a demonstration of the synergy obtained by combining intact protein analysis with the peptide-level analysis of corresponding digested fractions. The data were obtained by diverting 90% of the column effluent of a 2DLC/ MS yeast ribosomal protein separation to fraction collection for digestion and protein mass fingerprint analysis, and directing the remaining 10% of eluent for ESI-TOF MS intact protein mass analysis. Fraction 35 was collected over 2 min of the RP gradient corresponding to the third SCX step. During this elution window,

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FIGURE 13.9 Eluent was collected (Fraction 35) over a 2 min segment of a reversed-phased LC separation corresponding to the third salt step of a 2DLC(SCX/RP)/MS analysis of yeast ribosomal proteins. The total ion chromatogram for this step is shown in Panel a. Deconvolution of the MS data acquired over this period reveals six significant components (Panel b) that can be assigned to known yeast ribosomal protein using the intact protein mass data, and supplementary protein MALDI mass fingerprint data (Panel c) obtained from the tryptic digest the collected fraction.

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FIGURE 13.9

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only four TIC peaks were observed (Fig.13.9a) that revealed five ribosomal proteins (rpL38, rpL31A, rpL31B, rpL33A, and rpL33B) following mass spectral deconvolution (Fig. 13.9b). A sixth component (20307.0 Da) was also observed that did not correspond to any known modified or unmodified ribosomal proteins. MALDI PMF analysis (Fig. 13.9c) confirmed the presence of both rpL31 and rpL33 isoforms, but not of the smaller and more basic (9 kD, pI 11) rpL38 protein. Arrows point to the peptides that contained protein isoform differences. In each case, the intact mass was able to demonstrate the proteolytic processing of the N-terminal methionine (-Met) and the lack of acetylation on the penultimate alanine of both subunits. The PMF data also demonstrated high sequence coverage (59%) for rpL20A/B, for which no intact mass data had been assigned. This prompted us to further examine the sequence of rpL20 to conclude that the unidentified mass corresponds to a truncated form of rpL20 lacking three (MYL, rpL20B) or nine (MKILVILSV, rpL20A) amino acids from the N-terminus. Surprisingly, these regions contain all differences between these two isoforms, and the “processed’’ fragment is identical for both isoforms. In a more recent paper (Millea et al., 2006) we have applied this combined analytical approach to a more complicated cellular protein mixture. In this study the soluble protein fraction of E. coli was resolved using a step-gradient SAX/RP 2DLC fractionation with 80% of flow diverted for online ESI–TOF MS analysis. Collected fractions were digested and analyzed using MALDI QTOF MS and data-dependent MS/MS analysis. The increased complexity of the sample coupled with increased dynamic range of E. coli cytosolic proteins required collecting fractions at approximately the same volume as the chromatographic peaks to analyze the resulting fractions. Using this approach, it was possible to identify N-terminal protein processing, proteolytic maturation of proteins, and posttranslational protein modifications, and to distinguish between closely related protein isoforms. In this study, the bias towards detection of lower than average molecular weight proteins (

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