1 INTRODUCTION Jerzy Silberring*, † and Pawel Ciborowski‡ * AGH University of Science and Technology, Krakow, Poland Centre of Polymer and Carbon Materials, Polish Academy of Sciences, Zabrze, Poland z University of Nebraska Medical Center, Omaha, Nebraska y CHAPTER OUTLINE 1.1 Why Analytics Matter? 1.2 Expectations: Who and What? 1.3 What Is Next and Where Are We Going? 1.1 Why Analytics Matter? The sum of the optimal steps in analytical and proteomic analysis (process) is not equal to the optimal process in its entirety! Proteomic studies are multistep tasks involving a variety of methods, each governed by its own strengths and limitations As much as it is a trivial statement, with which all or at least most of us agree, it has not been fully appreciated, despite having a profound impact on the success of laborious, expensive, and, in many instances, lengthy projects The concept of a proteomic study can be depicted in many ways Figure 1.1 shows schematic representation of a proteomic study In this model, analytical components/phases are highlighted intentionally because the same rules of analytical chemistry/biochemistry apply to discovery, as well as to validation experiments Experimental design will be governed by a set of different rules, which does not include instrumentation, but has biology heavily involved Bioinformatics will be governed by its own set of rules, Proteomic Profiling and Analytical Chemistry http://dx.doi.org/10.1016/B978-0-444-59378-8.00001-3 Ó 2013 Elsevier B.V All rights reserved Chapter INTRODUCTION PROTEOMICS Experimental design phase Analytical phase I Performance of instrumentation Detection level Your question Reproducibility Sample loss New experiments New questions Knowledge Data analysis phase Bioinformatics Data Analytical phase II Validation Information Figure 1.1 Schematic representation of a proteomic study which are applied to a validation of algorithms Nevertheless, looking at constituents of a proteomic study, it must be realized that the scientist conducting such experiments must grasp the overview of not only how biological systems work, but also analytical boundaries for sample preparation, fractionation, and measurements; tools for database searches; bioinformatic tools for data analysis; and, of course, statistics Because of such complexity, proteomic studies should be conducted by a team of experts Still, a lack of uniformed quality criteria accepted by all will cause disconnectedness of individual components in a proteomic study For instance, currently there is no consensus on how a “good” twodimensional (2D) gel should look like or how good (efficient) should, for example, 2D liquid chromatography analysis be Such criteria exist in analytical/ clinical chemistry, where quality control is an integral part of each analysis on a daily basis, but proteomics falls short in this area Therefore, this book attempts to highlight, in a short but comprehensive manner, the impact of analytical chemistry/ biochemistry basic principles on the final success of a proteomic experiment It is hoped that this point of view will help all, biologists and chemists, in better understanding of all components of a complex proteomic study Chapter 1.2 Expectations: Who and What? If two scientists, a biologist and a chemist, sit at a table and discuss proteomic methodology, they likely will emphasize different aspects of the same study, which, in each viewpoint, is critical for the outcome Nevertheless, they very often speak in technical language that is not fully understood by the other This is because chemists are focused on the sensitivity and accuracy of analytical measurements, whereas biologists pay attention to explaining biological/pathological effects and are less concerned with the exact quantitation of analytes This resembles the famous poem by John G Saxe “The Blind Men and the Elephant,” where everyone tries to identify the part they are touching (i.e., biologist/ chemist) but nobody can get a sense of the whole system (i.e., proteomic study) Biologists are willing to accept a high range of responses resulting in high standard deviations showing or indicating “trends” in data behavior that support their hypothesis Chemists, however, expect data to be expressed by numerical values with high precision, accuracy, reproducibility, and low standard deviation Indeed, as much as the precision of analytical measurements is important, in many instances such efforts will not improve the overall output discriminating between true and false mostly because an exact correlation between quantitative change and biological effect is not defined very often For example, how important is it to measure a difference between levels of protein expression above a 10-fold change when the response of a biological system is already saturated by the 5-fold change of this protein? A similar question may arise from enzymology, where the most important factor is enzymatic activitydnot the protein expression measured by a typical proteomic approach If statisticians and bioinformaticians are brought to the very same table of biologist and chemist, which happens very often, the discussion becomes even more complicated So our question is what we see on the other side of the wall of our office when we look into the office space of our fellow colleagues with their expertise? We tried to illustrate this in Figure 1.2 Therefore, it is critical for each of us INTRODUCTION Chapter INTRODUCTION BIOLOGISTS Chemists and Mass Spectrometrists Biostatisticians and Bioinformaticians Figure 1.2 What we see on the other side of the wall of our office when we look into the office space of our fellow colleagues with their expertise to peer outside of our walls that confine us and look into the world of those who surround us 1.3 What Is Next and Where Are We Going? Since proteomics moved from qualitative to quantitative profiling using liquid phase-based methods of sample fractionation, it fully entered a domain of analytical chemistry As much as it is very beneficial for proteomics due to the wide range of well-established analytical methods, the complexity of proteomic profiling creates multiple technical issues First, classical analytical chemistry focuses on high accuracy measurements of single or few compounds at the same time It allows adjusting methods of sample preparation and analytical parameters with a specific objective(s) scarifying measurements of other compounds that are contaminants rather than analytes Importantly, analytical chemistry exploits specific characteristics of analyzed compounds, and this concept fulfills its purpose In contrast, proteomics attempts measurements of hundreds and thousands of molecules at the same time, which can have Chapter a wide range of chemical characteristics (e.g., posttranslational modifications of proteins and peptides) and which have a wide dynamic range of concentrations, such as the circumstance with plasma or serum One good example is the use of isobaric tags for relative and absolute quantitation (iTRAQ) as a means of quantitation, which is, to some extent, separate from peptide identification We have observed nicely quantitated species that otherwise generated very poorly fragmented spectra, making confident identification nearly impossible In Figure 1.1, all three steps of a proteomic study are equally important It would have been a trivial statement if we looked at each step separately Caveats arise from connection of these three steps as a “well-oiled logically working machine.” In summation, the main goals of this book are to highlight points of junction between proteomics and analytical chemistry and to link experimental design with analytical measurements, data analysis, and quality control It also provides “a list” of points to consider for those who are planning or entering the field of proteomics and have minimal experience INTRODUCTION BIOMOLECULES Pawel Olszowy,1, Ariel Burns1 and Pawel Ciborowski1 University of Nebraska Medical Center, Omaha, Nebraska Nicolaus Copernicus University, Torun, Poland CHAPTER OUTLINE 2.1 Major Features and Characteristics of Proteins and Peptides 2.2 Hydrophilicity and Hydrophobicity 2.3 Effect of Protein Fragmentation 10 2.4 Effect of Post-translational Modifications 14 2.5 Amino Acid Sequence and Separating Conditions 15 2.6 Cysteine and Methionine; Amino Acids Containing Sulfur 16 2.7 Protein Identification and Characterization 19 2.8 StructureeFunction Relationship and Its Significance in Systems Biology Function 19 2.9 Protein Folding and ProteineProtein Interactions 21 2.10 Moonlighting of Proteins 22 References 23 2.1 Major Features and Characteristics of Proteins and Peptides Proteins are very, if not the most, diverse naturally occurring heteropolymers This is because they consist of 20 different monomers (amino acids), vary in length, and carry multiple modifications Amino acids, monomers of proteins, and peptides range in their properties from hydrophilic (acidic or basic) to hydrophobic in nature Therefore, a combination of amino acid composition and combination (sequence) will have a big impact on the overall physicochemical Proteomic Profiling and Analytical Chemistry http://dx.doi.org/10.1016/B978-0-444-59378-8.00002-5 Ó 2013 Elsevier B.V All rights reserved Chapter BIOMOLECULES properties of proteins or peptides Moreover, proteins may have regions of quite opposite hydrophobic/ hydrophilic properties For example, membrane proteins have very hydrophobic transmembrane domains and hydrophilic extra- and intracellular domains that have to interact with ligands This being said, fractionation of proteins, which is an essential step in any proteomic profiling experiment, is challenging One approach to making this step easier is to fragment proteins into short peptides by various chemical and enzymatic methods The resulting pool of peptides will still form a wide spectrum of molecules, ranging from hydrophobic to hydrophilic; however, each peptide will have a much more narrow range of chemical characteristics and will be easier to be separated as a single, narrow peak in liquid chromatography Although true in many instances, many peptides still carry post-translational modifications influencing overall chemical properties 2.2 Hydrophilicity and Hydrophobicity Amino acids range from hydrophilic or hydrophobic depending on their chemical nature of their side chains This feature was used by Jack Kyte and Russell Doolittle who calculated the hydropathy index1 based on a measurement of how the R group (side chain) interacts with water Their calculations are based on the free energy of transfer (DG trans) of the solute amino acid between water and condensed vapor phase A negative DG trans indicates a strong preference for the R group to water, whereas a positive value indicates the opposite The hydropathy index is applied to proteins as well Starting at the N terminus, individual hydropathy indexes are summed over an arbitrary length of the protein, usually 7, 9, 11, or 13 residues Then a sliding window shifts one amino acid and sums the hydropathy index of those amino acids and will be continued until the end of the protein The hydropathic index versus the position of the amino acid gives a graphic representation of which parts of the protein have transmembrane domains Chapter BIOMOLECULES It is thermodynamically favorable for water to minimize the interaction with nonpolar, hydrophobic moieties This causes nonpolar molecules to accumulate with each other and form a clathrate structure A clathrate structure is a cage-like network of water surrounding all the hydrophobic interactions of the nonpolar molecules.2 Reversed-phase chromatography (RPC) is an important tool that uses hydrophobicity to purify peptides and proteins RPC has nonpolar, stationary phases (C-4, C-8, or C-18 consisting of aliphatic chains containing 4, 8, or 18 carbon atoms, respectively) linked covalently to a silica support and a moderately polar mobile phase The ability of the sample to bind to the stationary phase is proportional to the contact surface area around the nonpolar stationary phase For example, a peptide consisting of only amino acids has less surface area and hence, less hydrophobic amino acids to come in contact with the stationary phase than a peptide with 16 amino acids These conditions, among others, will determine which column is best and how good the separation will be The KyteeDoolittle analysis1 can be used to determine the hydrophobicity of peptides and whether they elute in the beginning or the end of the chromatography run Keeping with the amino acid peptide, every addition of an amino acid will cause a secondary structure to arise The secondary structure could hurt the ability to bind to the matrix due to shielding of the hydrophobic R groups As the polypeptide chain grows even more, the protein will fold spontaneously to the most thermodynamically stable, tertiary structure by confining the most hydrophobic regions to the interior to minimize the interaction with water.3 The capacity of a RPC column to purify a peptide is related to the amount of surface area that can bind to the nonpolar stationary phase, as mentioned previously Because a large polypeptide or protein has more surface area, a RP C-18 column is not as efficient in separating In peptide sequencing using mass spectrometry (MS), precursor ions used for consideration have to be larger than 700 Da (m/z > 700ỵ1 or 350ỵ2) Assuming that the average mass of the amino acid is z110 Da, peptides to be considered as having 10 Chapter BIOMOLECULES a sequence unique for any given protein have to consist of six or more amino acids Peptides of such length have a limited surface area to interact with the stationary phase used for separation The addition of each amino acid will gradually lead to formation of a secondary structure(s) and will impact elution time under any given chromatographic condition Longer peptides will tend to bury hydrophobic side chains and prevent them from interactions with the stationary phase As much as this property is exploited in the separation of peptides of various lengths, peptides carrying mutations may have quite different physicochemical properties The effect will depend on the amino acid change, its position, and the overall length of the peptide 2.3 Effect of Protein Fragmentation As pointed out earlier, protein fragmentation will generate a set of peptides This set of peptides can be unique if the method used for protein fragmentation has high enough specificity This feature was used to develop protein identification based on peptide fingerprinting This method exploits the specificity of molecular masses of peptide fragments generated by a specific method Tables 2.1A and 2.1B show characteristics of peptide sets generated by pepsin and trypsin digestion of insulin-like growth factor II (IGF2) Tables 2.1A and 2.1B show an example of differences of physicochemical properties of peptides derived from the same IGF2 protein fragmented in silico by trypsin and pepsin It is important to note that trypsin derived set of peptides that are either acidic with pI points below 4.33 or basic with pI points above 10.34 If such a digest is fractionated further based on isoelectric focusing, for example OFFGEL, we expect them to be on opposite ends of the fractionation spectrum, and most of these peptides will be in one or two fractions In contrast, complete pepsin digestion will generate only four peptides suitable for mass spectrometry-based protein identification due to the length of the resulting fragments (m/z bigger than 300 for doubly Table 2.1 A Composition and properties of fragments from in silico trypsin digestion of IGF2 Fragment number Isoelectric pointa Hydrophobicityb Molecular mass (Da) Amino acid residues Amino acid sequencec 4.13 11.01 10.34 10.34 10.34 4.33 4.17 3.67 À3.4 À6.5 À1.1 À4.5 À5.3 4.6 5.8 À4.3 2761 1187 360 174 261 1055 1699 234 24 10 16 AYRPSETLCGGELVDTLQFVCGDR GFYFSRPASR VSR R SR GIVEECCFR SCDLALLETYCATPAK SE B Composition and properties of fragments from in silico pepsin digestion of IGF2 Fragment number Isoelectric pointa 6.38 5.79 3.67 5.79 3.49 5.79 Hydrophobicityb À10.6 3.8 À1.8 3.8 0.0 3.8 Molecular mass (Da) 823 131 364 131 333 131 Amino acid residues Amino acid sequencec AYRPSET L CGGE L VDT L Continued PROTEOMIC PROFILING AND ANALYTICAL CHEMISTRY The Crossroads PAWEL CIBOROWSKI Department of Pharmacology and Experimental Neuroscience, University of Nebraska Medical Center, Omaha, NE, USA JERZY SILBERRING Department of Biochemistry and Neurobiology, AGH University of Science and Technology, Krakow, Poland AMSTERDAM • BOSTON • HEIDELBERG • LONDON NEW YORK • OXFORD • PARIS • SAN DIEGO SAN FRANCISCO • SINGAPORE • SYDNEY • TOKYO Elsevier 225, Wyman Street, Waltham, MA 02451, USA The Boulevard, Langford Lane, Kidlington, Oxford OX5 1GB, UK Radarweg 29, PO Box 211, 1000 AE Amsterdam, The Netherlands Ó 2013 Elsevier B.V All rights reserved 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 or otherwise without the prior written permission of the publisher Permissions may be sought directly from Elsevier’s Science & Technology Rights Department in Oxford, UK: phone (+44) (0) 1865 843830; fax (+44) (0) 1865 853333; email: permissions@elsevier.com Alternatively you can submit your request online by visiting the Elsevier web site at http://elsevier.com/locate/permissions, and selecting Obtaining permission to use Elsevier material Notice No responsibility is assumed by the publisher for any injury and/or damage to persons or property as a matter of products liability, negligence or otherwise, or from any use or operation of any methods, products, instructions or ideas contained in the material herein Because of rapid advances in the medical sciences, in particular, independent verification of diagnoses and drug dosages should be made British Library Cataloguing in Publication Data A catalogue record for this book is available from the British Library Library of Congress Cataloging-in-Publication Data A catalog record for this book is available from the Library of Congress ISBN: 978-0-444-59378-8 For information on all Elsevier publications visit our web site at store.eslevier.com Printed and bound in Great Britain 13 14 15 10 PREFACE The term “proteomics” was coined in the mid-1990s, however the history of proteomics dates back to the mid-1950s if we consider first scientific report on 2-dimensional electrophoresis (“Two-dimensional electrophoresis of serum proteins.” Smithies, O and Poulik MD Nature 1956, 177(4518):1033 PMID: 13322019) Many laboratories used 1- and 2-dimensional electrophoresis for protein analyses, and even though it was not termed “profiling”, it was very similar to what we now use in proteomic research More recently, soft ionization and development of mass spectrometry sequencing of peptides and even intact proteins, widely opened the possibilities for global protein analysis Suddenly, we found ourselves in the middle of something, which was growing rapidly and extremely attractive to pursue scientifically Our enthusiasm for proteomics is still growing as we enter new frontiers with the development of analytical instrumentation (mass spectrometers, Ultra High Pressure Liquid Chromatography, instruments for nano-flow analyses etc.) and computational capabilities of data analysis We strongly believe that a holistic approach will reveal much knowledge, which is yet not known We have learned that proteomics is a highly interdisciplinary approach but carries a risk of false positive results if not properly controlled at the analytical level Hence we learned that proteomics is still short of many standards and widely accepted quality controls Such standards and quality control measures will be built because of our collective experience and to some extent based on “trial and error” experiments The field of proteomics is very dynamic technologically, with new tools for sample preparation, sample analyses and data processing being announced almost every day Tools that we use today might be easily replaced tomorrow by new and greatly improved ones It is not an easy task to prepare a yet another book on proteomics but we hope that the content of our xi xii PREFACE book will stimulate the Readers and their interest to use proteomics approach with caution, for the benefit of expansion of our knowledge Our book is aimed at those researchers who are looking for a relatively compact guide that can walk them through major points of proteomic studies without great detail for each and every step but with a focus on quality control elements, frequently overlooked during daily work maintaining basic concepts and principles of proteomic studies Therefore, “Proteomics Profiling and Analytical Chemistry: The Crossroads“ is written for an audience at various levels: technologists/ technicians, undergraduate and graduate students, post-doctoral fellows, scientists as well as principal investigators to highlight key points ranging from experimental design and biology of systems in question to analytical requirements and limitations We are indebted to all our colleagues, coworkers, and students for their excellent contributions to this book This book could not have been prepared without extensive editorial work of Elsevier Thank you all for your efforts and also for pushing us to complete materials for printing As always, we have to say that nobody’s perfect and we would be grateful for any comments and suggestions that may lead to the improvement of future editions Pawel Ciborowski and Jerzy Silberring INDEX Note: Page numbers with “f” denote figures; “t” tables A Absolute quantitation (AQUA), 136e137 Accuracy defined, 219e220 validation, 207e208, 219e221, 221f See also Precision Acetone precipitation, 36, 87e88 Acetylation, 14 Acetylcholinesterase (AChE) microarrays, 172 Acrylamide, 110e113 concentration correlation with separated species molecular weight, 109t Agarose gel electrophoresis, 75, 110e111, 111f, 117e118 molecular separation range, 109t AKTA liquid chromatography, 94e95 Albumin, 91e92, 94e95 Albuminome, 97e104 Alkylation of proteins, 123f American Association of Pharmaceutical Scientists (AAPS), 218e219 Amino acids, 7e8 -containing peptides, labeling of, 142e143 advantages an disadvantages, 147t -containing sulfur See Cysteine; Methionine fluorescent, 169e170 chemical structures of, 169f hydrophilicity and hydrophobicity, 8e10 sequence and separating conditions, 15e16 4-(2-Aminoethyl)benzenesulfonyl fluoride (AEBSF), 81e82 Ammonium persulfate (APS), 74e75 Ammonium sulfate, 85e86 Analyses errors, in analytical methods, 210e212 Analysis of variance (ANOVA) method, 195e197 Analytes, 224e225 recovery, 227 stability of, 209e210 Analytical quantification, 151e164 Analytical validation, 205e216, 223e224 aberrant results and analyses errors, 210e212 accuracy, 207e208 calibration curve, linearity, and sensitivity, 208e209 defined, 205e206 further developments of, 214 HPLC methods, 206e207 precision, 208 quantitative Western blot analysis and ELISA, 212e214 recovery, 207 selectivity and specificity, 209 stability, 209e210 See also Validation Analytics, importance of, 1e2 Anion exchangers, 60e61 Anion-exchange columns, 52 Antipain, 81e82 APOBEC3F, 22 APOBEC3G, 22 Aprotinin, 81e82 235 236 INDEX Arrays, defined, 165e166 See also Microarrays Association of Biomolecular Research Facilities (ABRF), 218e219 Aurum Affi-Gel Blue, 91e92 Aurum serum protein minikits, 91e92 Averaging, 211 B Balanced incomplete block design, 186f, 188 Ballast proteins, 87e88 Bayesian methods, 197 Bayesian network (BN), 200e201 BenjaminieeHochberg (BH) method, 198e199 Benzyldimethyl-n-hexadecyl ammonium chloride (16-BAC), 126 Bias, label, 187 Bioinformatics, 1e2, 229e231 Biological material, for validation, 222e223 Biological replicates, 183e184 Biological variability, 95e96 Biomolecules, 7e24 proteins and peptides, major features and characteristics of, 7e8 Biopharming, 231e232 Bio-Rad, 73e74 BioWorks, 154e155 Blue native electrophoresis (BNE), 113e114 Blue Sepharose Fast Flow affinity chromatography, 94e95 BODIPY (boron-dipyrromethene), 172 Bound protein fractions, 98t protein identification of, 98t Bovine serum albumin (BSA), 30, 228e229 C 12 C isotopes, 138e139, 142e143 C isotopes, 138e139, 142e143, 224e225 Calibration curve, 208e209, 227 Capillary columns, 46 13 conventional capillary columns, 47e48 monolithic columns, 48e54 methacrylate-based monolithic columns, 52 organic-based monolithic columns, 51 silica-based monolithic columns, 49e51 styrene-based monolithic columns, 52e54 Capillary isoelectric focusing (CIEF), 75e76 Carboxyl termini, 141e142 Cation exchangers, 60e61 Cell cultures, 30e32 Cell lysates, 36, 225e226 Centrifugation in a Percoll R method, 32e34 Cerebrospinal fluid(CSF) immunodepletion of, 93e94 validation of, 221e223 Chaperons, 21e22 Chemical immobilization, 167 Chemical labeling, 144e145, 181e182 Chloroform/methanol extraction, 82 Chromatographic methods, proteomic sample fractionation, 39e41 Chymostatin, 81e82 Clathrate structure, Clear native electrophoresis (CNE), 114 Clustering, 199e200 Continuous density gradient, 35 Coomassie Brilliant Blue (CBB), 113e115, 119e120, 124 Counterions, 60e61 Cross-validation, 206e207 Crude protein extract, 36e38 C-terminal labeling, 141e142 advantages an disadvantages, 147t Cul5, 22 Cyanine dyes, 131e132, 170e172 Cy2, 131 Cy3, 131, 170e172 Cy5, 131, 170e172 Cysteine, 16e19 residues, 16e17, 131e132 alkylation of, 162 INDEX D Data Data Data Data cleaning, 230e231 filtering, 193 preprocessing, 193 storage, gel electrophoresis, 117e118 Databases See Protein databases; Search; Search engines DecyderMS, 146 Denaturation of proteins, 120, 123 Density markers, 35e36 Deoxycholate (DOC), 88e89 Dependencies between proteins, 199 Design See Experimental design Detection levels, validation of, 224e226 Detergents, 89 Dextrans, 86 Difference gel electrophoresis (DIGE), 130f, 137e138, 129e130 advantages and disadvantages of, 132 dyes used in, 131t fluorescent dyes, 130e131 internal standard, 131e132 Differentially expressed proteins, comparison of, 195e197 Digestion, tryptic, 141e144, 161 Direct fluorescence labeling, 170e172 Directed acyclic graphs (DAGs), 200e201 Dithiothreitol (DTT), 123 Dyes fluorescent, 170e174 application to biological systems, 170e174 chemical structure of, 171f used in DIGE, 130e131 used in 2DE gel staining, 124t E Eight-plex iTRAQ, 140e141, 184e185 Electrophoresis-based experiments instruments, validation of, 229 See also Gel electrophoresis Electrospray ionization (ESI), 139e140, 160 instruments, validation of, 228e229 ELISA, 212e214 237 EloB, 22 EloC, 22 Elucidator, 146 Enzymes, 80e82 inhibitors of, 80e82 Enzymology, 3e4 Errors, in analytical methods, 210e212 Escherichia coli, 21e22 Ethanol (EtOH) precipitation, 36e37, 87e88 Ethylenediaminotetraacetic acid, 81e82 European Bioinfomatics Institute (EBI), 158 European Medicine Agency (EMA), 218e219 Expectations, 3e4 Experimental design, 1e2 mass spectrometry, issues and statistical consideration on, 182e189 balanced incomplete block design, 188 experimental layout and label assignment, 184e185 label-free experiment, 185e187 Latin square design, 187e188 loop/cyclic design, 189 randomization, 183 reference design, 188e189 stable isotope labeling, 187 technical/biological replicates, 183e184 validation of, 221e223 Experimental layout, mass spectrometry, 184e185, 186f Extraction See Protein extraction Extraction efficiency, 207 See also Protein extraction F False discovery rate (FDR), 191e192, 198 Family-wise error rate (FWER), 198 FicollÔ , 34 Fixed post-translational modification, 162 Flow cytometry, 30e32 238 INDEX Fluorescence labeling, 168e175 fluorescent dyes and their application to biological systems, 170e174 fluorophore, 168e170 quantum dots, 174e175 Fluorescence resonance energy transfer (FRET), 174e175 Fluorescence staining, 117, 125 Fluorescent amino acids, 169e170 chemical structures of, 169f Fluorescentdyes,usedinDIGE,130e131 Fluorophore, 168e170 Free energy of transfer (DG trans), Future directions, of proteomics, 4e5 G Gel electrophoresis, 46, 107e134 advantages and disadvantages of, 116t agarose gel electrophoresis, 110e111, 111f conditions, 110 denaturating, 114 data storage, 117e118 difference gel electrophoresis, 129e130 advantages and disadvantages of, 132 fluorescent dyes used in, 130e131 internal standard, 131e132 isotope labeling, 117 molecular separation range, 109t native polyacrylamide gel electrophoresis, 112e114 obstacles during, 116t sample preparation, 111 prior to SDS-PAGE, 115 separation conditions, 111e114 staining techniques, 115e117 2D gel electrophoresis, 125e127 advantages and disadvantages of, 125e127 gel staining, 124e125 isoelectric point, 120e121 molecular weight, 121e124 quantitation of protein using 2D gels, 127e129 Gel staining, 115e117 fluorescence staining, 117, 125 2DE, 124e125 dyes used in, 124t Gel-based quantitative proteomics, 137e138 advantages an disadvantages, 147t Gel-free-based quantitative proteomics, 138e139 Gene therapy, 232 Genetically Modified Organism Compass, 231e232 Glass beads shaking/crushing, 31e32t b-glycerophosphate, 82 Glycidyl methacrylate (GMA), 52 Glycoprotein analysis, 87e88 Graphical Gaussian models (GGMs), 200e201 Green fluorescent protein (GFP), 168e169 H Hemoglobin, 91e92 Hierarchical clustering, 199e200 High-abundant proteins immunoaffinity depletion of, sample preparation, 91e106 High-performance liquid chromatographic (HPLC) methods vs UPLC, 49t validation of, 206e207 See also Analytical validation; Liquid chromatography (LC) High resolution clear electrophoresis (hrCNE), 114 Histones, 19, 161e162 HistopaqueÔ , 34 HIV-1 Vif protein, 22 Human Proteome Organization (HUPO), 218e219 Plasma Proteome Project (HPPP), 91e92 Hydrochloric acid, 86e87 Hydropathy index, Hydrophobic proteins, 126, 132 extraction of, 82 Hypothesis testing, 195e198 I Identification See Protein identification IgG, 91e92 INDEX Image analysis, 127e129, 128f Image warping, 127e128 Immobilization methods, protein arrays, 167 Immobilized pH gradient (IPG) strips, 119e121, 123e124, 126e127 Immunodepletion, 15e16 of high-abundant proteins for sample preparation, 91e106 albuminome, 97e104 capacity of immunodepletion columns and other devices, 93e95 quality control of, 96e97, 97f reproducibility, 95e96 methods, 40 Immuno-RCA method, 173e174 In silico digestion, 10e14 Insulin-like growth factor II (IGF2), composition and properties of fragments from in silico trypsin digestion of, 11te13t pepsin digestion of, 11te13t in silico digestion of, 11te13t In vitro labeling, 138e139, 146e148 In vivo-labeling, 143e144, 146e148 Indirect fluorescence labeling, 170e173 Inhibitors, of proteolytic and other enzymes, 80e82 Inter-assay precision, 226 See also Precision Intermediate precision, 208 Internal standard, 30, 115, 136e137, 142e143, 207, 227 DIGE, 132 stability, 209e210 International Conference on Harmonization (Q2R1), 208 International Organization for Standardization, 208 International Protein Index (IPI), 91e92, 157e158 International Union of Pure and Applied Chemistry (IUPAC), 218e219, 223e224 239 Intra-assay precision, 226 See also Precision Invitrogen, 73e74 Iodoacetamide, 123 Ion-exchange chromatography buffers in, 65e66 choice of anion vs cation, 63e64 strong vs weak IEC, 64e65 historical perspective, 58 principle of, 58e60 in proteomic studies, 66e68 stationary phases, 60e62 See also Liquid chromatography (LC) Ionization, 160 Isobaric tags for relative and absolute quantitation (iTRAQ), 4e5, 135e136, 140e141, 146e148, 181e182, 193e197 structure of, 140f See also Quantitative measurements Isoelectric focusing (IEF), 120, 121f capillary IEF (CIEF), 75e76 immobilized pH gradient, 74e75 in liquid state, 73e74 in living organisms, 76 principles of, 69e72 sample preparation prior to, 72e73 Isoelectric point (pI), 63 precipitation, 86e87 Isotope-coded affinity tagging (ICAT), 18, 138e139, 181e182 advantages an disadvantages, 147t “labeled” and not “labeled”, 139f Isotope labeling, gel electrophoresis, 117 K KyteeDoolittle analysis, L Label swapping, 187, 191e192 Label-free quantification, 144e146, 182 advantages an disadvantages, 147t MS, 184e185 Labeling of amino acid-containing peptides, 142e143 240 INDEX Labeling (Continued ) C-terminal labeling, 141e142 chemical labeling, 144e145 fluorescence labeling, 168e175 in vitro labeling, 138e139, 146e148 in vivo-labeling, 143e144, 146e148 metabolic, 143e144, 181e182 MS, 184e185 N-terminal labeling, 139e141 18 O labeling, 142f SILAC, 143e144, 144f, 181e182 stable isotope labeling, 181e182, 187 Latin square design, 186f, 187e188 Leupeptin, 81e82 Light emission (luminescence) method, 173 Linearity, 208e209 Lipids, 89 Liquid chromatography (LC), 46, 91e92, 145e146, 206e207 multidimensional (2D) MS/MS approaches, IEC in, 60 progenesis LC-MS, 146 validation of, 228 See also Ion-exchange chromatography Liquid nitrogen crushing, 31te32t Log transformation, 193 Long terminal repeat (LTR), 165e166 Loop/cyclic design, 186f, 189 Lower limit of quantitation (LLOQ), 208e209 Lysis buffers, 31te32t, 34 M ManneeWhitney test, 195e197 MASCOT, 153e154 Mass spectrometry (MS)-based quantitative proteomics, 179e204, 225 experimental design, issues and statistical consideration on, 182e189 label-free quantification, 182 platform, 160 progenesis LC-MS, 146 protein expressions between groups, comparison of, 195e201 sample size calculation, 189e192 stable isotope labeling, 181e182 statistical analysis, 192e195 2D LC-MS/MS approaches, IEC in, 60 validation of, 227e228 Mass tolerance, 160e161 Mass-coded abundance tagging (MCAT), 142e143 Matrix-assisted laser desorption ionization (MALDI), 139e141, 160 TOF instruments, 152e153, 227e228 MaxQuant, 146, 193e195 MES (2-(N-morpholino) ethanesulfonic acid), 74 Metabolic labeling, 143e144, 181e182 advantages an disadvantages, 147t Methacrylate-based monolithic columns, 52 Methanol precipitation, 87e88 Methanosarcina acetivorans, 53e54 Methionine, 16e19 Method, validation of, 223e224 Microarrays, 165e178 defined, 165e166 historical perspective, 165e166 protein arrays, 166e168 Microisolation methods, 32e34 Migration ratio, 108e109 Mineral acids, in protein precipitation, 86e87 Minimal labeling, 131e132 Missing value imputation, 195 Molecular farming, 231e232 Molecular weight (MW), twodimensional gel electrophoresis, 121e124 Molecular Weight Search (MOWSE), 152 Monolithic columns, 48e54 methacrylate-based monolithic columns, 52 organic-based monolithic columns, 51 silica-based monolithic columns, 49e51 styrene-based monolithic columns, 52e54 INDEX Monomers of proteins, 7e8 Moonlighting phenomenon, 19e20, 22e23 MPOS (3-(N-morpholino) propanesulfonic acid), 74 MudPIT proteomic profiling, 14 Multidimensional (2D) liquid chromatography (LC), MS/MS approaches, IEC in, 60 Multiple proteins, comparison of, 198e199 Multiple reaction monitoring (MRM), selection of peptides for, 10e14 N 15 N isotopes, 143e144, 224e225 Nanoelectrospray emitters, 50e51 Nanoflow systems, validation of, 228 National Center for Biotechnology Information (NCBI), nr protein database, 157e159 National Institutes of Health, 158 Native polyacrylamide gel electrophoresis, 112e114 Networks See Protein networks 2-nitrobenzenesulfenyl chloride (NBSCI), 142e143 Nonidet P-40, 88e89 Nonionogenic detergent, 88e89 “Nonredundant” database, 156e157 Normalization of spot quantities, 128e129 Normalization, 194e195 N-terminal isotope-encoded tagging (NIT), 139e140 N-terminal labeling, 139e141 advantages an disadvantages, 147t Nucleic acid hybridization, 165e166 O 18 O labeling, 142f OFFGEL, 10, 99, 229 Offline SCX separation, 67 One-dimensional (1D) SDSePAGE, 36 One-dimensional gel electrophoresis (1DE), 96e97, 98t, 119e120 validation of, 229 See also Gel electrophoresis Online SCX separation, 67 241 Orbitrap, 152e153, 227e228 Organellar proteomics, 34e36 Organic solvent-driven precipitation, 87e88 Organic-based monolithic columns, 51 Outliers, 211 P 32 P radioisotope, 170e172 Paragon, 153, 155e156 Partial validation, 206e207 Pepsin, 10 Pepstatin A, 81e82 Peptide-Cutter, 10e14 Peptides, 7e8 amino acid-containing, labeling of, 142e143 post-translational modifications, 14e15 purification, proteomic sample fractionation, 41e43 sequencing using mass spectrometry, 9e10 PercollÔ , 34 Performance instruments, validation of, 227e229 Permutation t test, 198 Phenol extraction, 82 Phenylmethanesulfonyl fluoride (PMSF), 81e82 Phosphorylation, 41 Planetary discs blending, 31te32t Plasma gelsolin (pGSN), quantitative Western blot analysis of, 213f Plasma human proteins coimmunodepleted with IgY14 column, 100te103t quantitative analysis of gelsolin in, 212e214, 213f validation of, 221e223 Polyacrylamide gel (PAGE), 75, 112e114, 113f, 119e122, 122f, 129, 229 See also Gel electrophoresis Polyethylene glycols, 86 Polymers, in protein precipitation, 86 POROS affinity depletion cartridges, 91e92 242 INDEX Porous-layer open-tubular (PLOT) columns, 53e54 Post-translational modifications (PTMs), 7e8, 19e20, 41, 74e76, 125e126, 167 searches for, 161e162 Potter type (PTFE-glass or PTFE-PTFE crushers), 31t Precipitation See Protein precipitation Precision defined, 219e220 validation, 208, 219e221, 221f See also Accuracy Precursor ions, 152e153 Pressure, homogenization method, 31te32t PROC itraqnorm, 194e195 Pro-Q dyes, 117 Progenesis LC-mass spectrometry, 146 Proteases inhibitors, 81e82 Protein arrays, 166e168 Protein characterization, 19 Protein chip, 166e168 Protein databases, 156e160 International Protein Index, 159e160 NCBI nr, 159 rationale for choosing, 157e158 Swiss-Prot, 158 UniProt, 158 UniRef, 158e159 See also Search; Search engines Protein expressions between groups in mass spectrometry, comparison of, 195e201 clustering, 199e200 dependencies between proteins, 199 differentially expressed proteins, 195e197 multiple comparisons, 198e199 protein networks, 200e201 time-dependent proteins, 197 Protein extraction, 79e80 hydrophobic protein, 82 inhibitors of proteolytic and other enzymes, 80e82 Protein folding, 21e22 Protein fragmentation, effect of, 10e14 Protein identification, 19, 135e136 of bound protein fractions, 98t Protein Information Resource (PIR), 158 Protein moonlighting, 19e20, 22e23 Protein networks, 200e201 Protein precipitation, 79e80, 83e85 isoelectric point, 86e87 mechanism, principle of, 84f organic solvent-driven, 87e88 salting-out process, 85e86 trichloroacetic acid, 88e89 Protein profiling, 166e167 Protein Prospector, 153, 156 Protein quantitation See Quantitative measurements Protein solvation, 83e84 See also Protein precipitation Protein standard, for absolute quantification, 137 ProteinLynx, 146 ProteinPilot, 135e136, 155e156, 193 Proteineprotein interactions, 21e22 Proteins major features and characteristics of, 7e8 post-translational modifications, 14e15 reduction and alkylation of, 123f ProteoExtract albumin/IgG removal kit, 91e92 Proteolytic enzymes, 80e82 inhibitors of, 80e82 Proteome Discoverer, 154e155 Proteome map, 128 Proteomic profiling experiments, functional information, 19e20 Proteomic sample fractionation cells as biological material source, 30e34 chromatographic methods, 39e41 crude protein extract, 36e38 homogenization, 29e30 inhibition of protease activity, 28e29 peptide purification, 41e43 by size-exclusion filters, 38e39 subcellular compartments, 34e36 Proteomic studies, defined, 1, 2f ProteOn (Bio-Rad Laboratories), 166e167 INDEX ProtoArray (Invitrogen), 166e167 pSILAC experiment, 184e185, 194e195 Q QconCAT, 137 Quality control, of immunodepletion, 96e97, 97f Quantification, analytical, 151e164 Quantitative measurements, 135e150 absolute quantitation, 136e137 relative quantitation, 137e146 techniques, summary of, 147t Quantitative Western blot analysis, 212e214 of plasma gelsolin (pGSN), 213f Quantum dots (Qdots), 174e175 R Radioisotope, 170e172 Randomization, mass spectrometry, 183 Randomized complete block designs (RCBD), 185, 186f Rbx2, 22 Recovery, 207 Reduction of proteins, 123f Reference design, 186f, 188e189 Regulatory affairs, 231e232 Relative quantitation, 137e146 C-terminal labeling, 141e142 gel-based quantitative proteomics, 137e138 isotope-coded affinity tagging (ICAT), 138e139 label-free techniques, 144e146 labeling of definite amino acidcontaining peptides, 142e143 metabolic labeling, 143e144 N-terminal labeling, 139e141 Relevance networks (RN), 200e201 Repeatability, 208 Replicate analysis, 207e208, 222e223 Replicates, 145e146, 189 technical/biological, 183e184 Reproducibility, 208 in immunodepletion, 95e96, 97f validation of, 226e227 Results, aberrant 243 in analytical methods, 210e212 Reversed-phase (RP) columns, 39e40, 52 Reversed-phase chromatography (RPC), Reversed-phase high-performance liquid chromatography (RPHPLC), 119e120 Riboflavin, 74e75 Rotor-stator homogenization method, 31t S S-Adenosyl-l-methionine, 18e19 Salting-out process, 85e86 Sample size calculation mass spectrometry, 189e192 Samples fractionation See Proteomic sample fractionation loss, validation of, 226e227 preparation gel electrophoresis, 111, 115 immunoaffinity depletion of highabundant proteins for, 91e106 selection, mass spectrometry, 183 stability of, 209e210 Sampling uncertainty, 223e224 SAS/STAT, 194e195 Saturation labeling, 131e132 Search parameters, 160e161 for post-translational modifications, 161e162 Search engines, 153e156 Selectivity, 209 Sensitivity, 208e209 of performance intruments, 228e229 Sephadex, 61e62, 142e143 Sepharose, 61e62 Seppro IgY12 column, 92e93 Seppro IgY14 column, 92e94 human plasma proteins coimmunodepleted with, 100t SEQUEST, 99, 153e155 Serum, 37e38 validation of, 221e223 SIEVE, 146 Signal-to- noise (S/N) ratio, 225 244 INDEX Silica-based monolithic columns, 49e51 Silver staining, 116e117, 125 Size-exclusion filters, proteomic sample fractionation by, 38e39 Size exclusion high-performance liquid chromatography (SEHPLC), 119e120 Sodium deoxycholate, 36e37 Sodium dodecyl sulfate (SDS), 112, 119e120, 123 SDSePAGE, 36e38, 74e75, 94e95, 98t, 114e115, 126 Sodium fluoride, 82 Sodium orthovanadate, 82 Sodium pyrophosphate, 82 Software for database search, 152 validation, 229e230 See also specific entries Software analysis of 2D gels, 127e128, 128f Solid-phase extraction (SPE) microcolumns, 39e40 Solvation See Protein solvation Sonication, 31t Specificity, 209 Spectral counting, 145e146, 182 Spectrometric signal intensity, 182 Spin column devices, 94e95 Spot matching, 128e129 Stability, 209e210 Stable isotope labeling, 181e182, 187 label swapping, 187 Stable isotope labeling with amino acids in cell culture (SILAC), 143e144, 144f, 181e182 Staining See Gel staining Statistical analysis, mass spectrometry, 192e195 data preparation and filtering, 193 data preprocessing, 193 missing value imputation, 195 normalization, 194e195 transformation, 193 Stepwise gradient, 35 Stock solution, stability in, 209e210 Strong cation exchange (SCX), 39e40, 67 columns, 52 offline separation, 67 online separation, 67 Styrene-based monolithic columns, 52e54 Sucrose, 34 Sulfuric acid, 86e87 SuperMix column, 91e93 Surface plasmon resonance, 167e168 Swiss Institute of Bioinformatics (SIB), 158 Swiss-Prot, 158e159 SYPRO Ruby, 117, 119e120, 124e125 T Technical replicates, 183e184 Technical variability, 95 Tertramethylenediamine (TEMED), 74e75 Thiol ether, 16e17 Thiolate anion, 16e17 Time-dependent proteins, comparison of, 197 Transformation, of protein expression, 193 TrEMBL, 99, 158 Trichloroacetic acid (TCA) and ethanol (EtOH) mixture, 36e37 precipitation, 88e89 and sodium deoxycholate mixture, 36e37 Trifluoroacetic acid (TFA), 53e54 Triton X-100, 88e89 Trypsin, 10e14, 141e142, 161 Tryptophan-containing peptides, 142e143 Two-dimensional gel electrophoresis (2DE), 137e138, 212, 119 advantages and disadvantages of, 125e127 isoelectric point, 120e121 molecular weight, 121e124 2D gels, quantitation of protein using, 127e129 validation of, 229 See also Gel electrophoresis Type I error, 189e192, 198 Type II error, 190e191, 198 INDEX U Ultra performance liquid chromatography (UPLC), 47e48 vs HPLC, 49t UniProt, 99, 158 UniRef, 99, 157e159 United States Food and Drug Administration (FDA), 207e208, 211, 218e219 Universal Protein Resource Consortium, 158e159 Upper limit of quantitation (ULOQ), 208e209 V Validation, 217e234 accuracy and precision, 219e221 battle of, 217e219, 220f bioinformatics, 229e231 detection levels, 224e226 experimental design, 221e223 master plan, 205e206 method, 223e224 performance instruments, 227e229 reproducibility and sample loss, 226e227 See also Analytical validation Variability, 95 Variable post-translational modification, 162 Variance, 191e192 W Water molecules, 83e84 Western blot analysis, 36 See also Quantitative Western blot analysis X !XTandem, 153 Y Yellow fluorescent protein (YFP), 168e169 Z Zoom gels, 127 245 ... to highlight points of junction between proteomics and analytical chemistry and to link experimental design with analytical measurements, data analysis, and quality control It also provides “a... entered a domain of analytical chemistry As much as it is very beneficial for proteomics due to the wide range of well-established analytical methods, the complexity of proteomic profiling creates... Importantly, analytical chemistry exploits specific characteristics of analyzed compounds, and this concept fulfills its purpose In contrast, proteomics attempts measurements of hundreds and thousands