Proteomics for Biological Discovery E-Book
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- Herausgeber: John Wiley & Sons
- Kategorie: Wissenschaft und neue Technologien
- Sprache: Englisch
An update to the popular guide to proteomics technology applications in biomedical research Building on the strength of the original edition, this book presents the state of the art in the field of proteomics and offers students and scientists new tools and techniques to advance their own research. Written by leading experts in the field, it provides readers with an understanding of new and emerging directions for proteomics research and applications. Proteomics for Biological Discovery begins by discussing the emergence of proteomics technologies and summarizing the potential insights to be gained from proteome-level research. The tools of proteomics, from conventional to novel techniques, are thoroughly covered, from underlying concepts to limitations and future directions. Later chapters provide an overview of the current developments in post-translational modification studies, structural proteomics, biochemical proteomics, applied proteomics, and bioinformatics relevant to proteomics. Chapters cover: Quantitative Proteomics for Differential Protein Expression Profiling; Protein Microarrays; Protein Biomarker Discovery; Biomarker Discovery using Mass Spectrometry Imaging; Protein-Protein Interactions; Mass Spectrometry Of Intact Protein Complexes; Crosslinking Applications in Structural Proteomics; Functional Proteomics; High Resolution Interrogation of Biological Systems via Mass Cytometry; Characterization of Drug-Protein Interactions by Chemoproteomics; Phosphorylation; Large-Scale Phosphoproteomics; and Probing Glycoforms of Individual Proteins Using Antibody-Lectin Sandwich Arrays. * Presents a comprehensive and coherent review of the major issues in proteomic technology development, bioinformatics, strategic approaches, and applications * Chapters offer a rigorous overview with summary of limitations, emerging approaches, questions, and realistic future industry and basic science applications * Features new coverage of mass spectrometry for high throughput proteomic measurements, and novel quantitation strategies such as spectral counting and stable isotope labeling * Discusses higher level integrative aspects, including technical challenges and applications for drug discovery * Offers new chapters on biomarker discovery, global phosphorylation analysis, proteomic profiling using antibodies, and single cell mass spectrometry Proteomics for Biological Discovery is an excellent advanced resource for graduate students, postdoctoral fellows, and scientists across all the major fields of biomedical science.
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Table of Contents
Cover
Foreword
WHAT IS PROTEOMICS?
HOW IS THIS DRIVING BIOLOGICAL RESEARCH?
List of Contributors
1 Quantitative Proteomics for Differential Protein Expression Profiling
1.1 INTRODUCTION
1.2 QUANTIFICATION APPROACHES
1.3 DATA ANALYSIS IN DIFFERENTIAL QUANTITATIVE PROTEOMICS
1.4 COMPARISON OF DIFFERENT LABELING STRATEGIES
1.5 CONSIDERATIONS FOR SELECTING THE MOST APPROPRIATE QUANTIFICATION STRATEGY
REFERENCES
2 Protein Microarrays
2.1 INTRODUCTION
2.2 PROTEIN MICROARRAY FABRICATION: POLYPEPTIDE IMMOBILIZATION ON SOLID MATRICES
2.3 SIGNAL DETECTION
2.4 PROTEIN MICROARRAY PLATFORMS
2.5 PROTEIN‐DETECTING MICROARRAYS OR ANALYTICAL MICROARRAYS
2.6 FUNCTIONAL PROTEIN MICROARRAYS
2.7 PROTEIN MICROARRAY QUALITY CONTROL
2.8 DATA NORMALIZATION AND ANALYSIS
2.9 DATA VALIDATION
2.10 FINAL CONSIDERATIONS
ACKNOWLEDGMENTS
REFERENCES
3 Protein Biomarker Discovery
3.1 INTRODUCTION
3.2 ADDRESSING UNMET CLINICAL NEEDS WITH PROTEIN BIOMARKERS
3.3 BIOMARKER DISCOVERY PIPELINE
3.4 BIOLOGICAL SAMPLES FOR BIOMARKER DISCOVERY
3.5 INTEGRATION OF ‐OMICS APPROACHES TO SELECT BIOMARKER CANDIDATES
3.6 PROTEIN IDENTIFICATION BY MASS SPECTROMETRY
3.7 PROTEIN QUANTIFICATION BY MASS SPECTROMETRY
3.8 BIOMARKER VERIFICATION
3.9 BIOMARKER VALIDATION
3.10 DEVELOPMENT OF PROTEIN‐BASED CLINICAL ASSAYS
3.11 COMMON LIMITATIONS OF BIOMARKER DISCOVERY PROJECTS
3.12 CONCLUSIONS AND FUTURE OUTLOOK
REFERENCES
4 Biomarker Discovery with Mass Spectrometry Imaging and Profiling
4.1 INTRODUCTION
4.2 MASS SPECTROMETRY IMAGING
4.3 BIOMARKER DISCOVERY IN BIOMEDICAL RESEARCH
4.4 BIOMARKERS BEYOND BIOMEDICAL RESEARCH
4.5 CONCLUSION
ACKNOWLEDGMENTS
REFERENCES
5 Protein–Protein Interactions
5.1 INTRODUCTION
5.2 PROTEIN ENRICHMENT METHODS
5.3 YEAST TWO‐HYBRID
5.4 POSTGENOMIC METHODS
5.5 AFFINITY PURIFICATION MASS SPECTROMETRY
5.6 APPLICATIONS
5.7 CONCLUSION
REFERENCES
6 Mass Spectrometry of Intact Protein Complexes
6.1 INTRODUCTION
6.2 INSTRUMENTATION
6.3 ION MOBILITY
6.4 PROTEINS IN THE GAS PHASE
6.5 RIBOSOME
6.6 PROTEASOME
6.7 AMYLOID‐FORMING PROTEINS
6.8 HEAT SHOCK PROTEINS
6.9 VIRUSES
6.10 MEMBRANE PROTEINS
6.11 MASS SPECTROMETRY IN INTEGRATIVE STRUCTURAL BIOLOGY
6.12 FUTURE PROSPECTS
ACKNOWLEDGMENTS
REFERENCES
7 Cross‐linking Applications in Structural Proteomics
7.1 INTRODUCTION
7.2 THE CONCEPT BEHIND CROSS‐LINKING ANALYSIS
7.3 CROSS‐LINKING REACTIONS AND REAGENTS
7.4 METHODS FOR IMPROVED MASS SPECTROMETRy DETECTION OF CROSS‐LINKS
7.5 METHODS FOR MORE CONFIDENT IDENTIFICATION OF CROSS‐LINKS
7.6 ANALYSIS OF DATA FROM CROSS‐LINKING EXPERIMENTS
7.7 APPLICATION OF CROSS‐LINKING COMBINED WITH MASS SPECTROMETRY FOR STRUCTURAL ANALYSIS OF PROTEINS
7.8 FUTURE DIRECTIONS
7.9 CONCLUSION
ACKNOWLEDGEMENTS
REFERENCES
8 Functional Proteomics
8.1 INTRODUCTION
8.2 EXPERIMENTAL APPROACHES FOR GENOME‐SCALE MAPPING OF PROTEIN “INTERACTOMES”
8.3 AFFINITY PURIFICATION/MASS SPECTROMETRY ANALYSIS OF THE INTERACTOME OF YEAST AS A MODEL EUKARYOTE
8.4 DEFINING COMPLEX MEMBERSHIP BY NETWORK PARTITIONING
8.5 AFFINITY PURIFICATION/MASS SPECTROMETRY ANALYSIS OF MEMBRANE PROTEIN COMPLEXES IN YEAST
8.6 AFFINITY PURIFICATION/MASS SPECTROMETRY ANALYSIS OF THE INTERACTOME OF THE MODEL BACTERIUM
ESCHERICHIA COLI
8.7
FRACTIONOMICS
: CLASSIC PROTEIN BIOCHEMISTRY WITH A MODERN TWIST
8.8 ADVANTAGES AND LIMITATIONS OF “TAGLESS” PROTEIN COMPLEX RECONSTRUCTIONS
8.9
GUILT‐BY‐ASSOCIATION
: CHARACTERIZING PROTEIN FUNCTIONS USING INTERACTOME MAPS
8.10 CASE STUDY: ASSIGNING FUNCTIONS TO UNCHARACTERIZED
E. COLI
PROTEINS
8.11 CONCLUSIONS:
SURVIVING THE TRENCHES
REFERENCES
9 High‐Resolution Interrogation of Biological Systems via Mass Cytometry
9.1 INTRODUCTION
9.2 INSTRUMENTATION
9.3 SAMPLE PREPARATION
9.4 DATA ANALYSIS
9.5 APPLICATIONS
ACKNOWLEDGMENTS
REFERENCES
10 Characterization of Drug–Protein Interactions by Chemoproteomics
10.1 INTRODUCTION
10.2 SMALL MOLECULE AFFINITY CHROMATOGRAPHY IN CHEMOPROTEOMICS
10.3 NONAFFINITY CHROMATOGRAPHY‐BASED APPROACHES IN CHEMOPROTEOMICS
10.4 CHEMOPROTEOMICS AND DRUG DISCOVERY
10.5 CONCLUSION
REFERENCES
11 Phosphorylation
11.1 INTRODUCTION
11.2 IDENTIFICATION OF PHOSPHORYLATED RESIDUES
11.3 SAMPLE PREPARATION FOR ENRICHMENT OF PHOSPHOPEPTIDES
11.4 DATABASE SEARCHING
11.5 DISCOVERING BIOLOGICAL INSIGHT IN PHOSPHORYLATION STUDIES
11.6 CONCLUSION
REFERENCES
12 Large‐Scale Phosphoproteomics
12.1 INTRODUCTION
12.2 METHODS
12.3 ENRICHMENT METHODS
12.4 ION CHROMATOGRAPHY FE, GA IMAC
12.5 CONCLUSION
REFERENCES
13 Probing Glycoforms of Individual Proteins Using Antibody‐Lectin Sandwich Arrays
13.1 INTRODUCTION: THE NEED FOR PRECISE MEASUREMENT OF PROTEIN GLYCOFORMS
13.2 ANTIBODIES AND LECTINS FOR PROBING GLYCOSYLATION OF INDIVIDUAL PROTEINS
13.3 DEVELOPING AND VALIDATING CUSTOM ANTIBODY‐LECTIN ASSAYS
13.4 GLYCANS ASSOCIATED WITH PANCREATIC CANCER: FINDINGS FROM ALSA STUDIES
13.5 FUTURE CHALLENGES
13.6 CONCLUSION
REFERENCES
Index
End User License Agreement
List of Tables
Chapter 1
TABLE 1.1 A selection of quantitation software.
Chapter 9
TABLE 9.1 Brief summary of multidimensional data analysis algorithms applied to m...
Chapter 11
TABLE 11.1 Partial list of publicly available phosphoproteomic tools and database...
Chapter 13
TABLE 13.1 Comparison between adenocarcinomas and cystic neoplasms in mucin prote...
List of Illustrations
Chapter 1
Figure 1.1 Workflows in quantitative proteomics. Dashed lines highlight the ste
...
Figure 1.2 Chemical structures of the stable isotope labeled peptides in quanti
...
Figure 1.3 Peptide quantification on MS‐ and MSn‐level. (a) Typical chromatogra
...
Figure 1.4 Labeled and label‐free quantification methods. Graphical representat
...
Figure 1.5 Statistical analysis of quantification. (a) A bell‐shaped curve, ind
...
Figure 1.6 Comparison of SILAC, dimethyl and isobaric labeling in terms of prec
...
Chapter 2
Figure 2.1 Immobilization schemes for protein microarrays. Proteins and peptide
...
Figure 2.2 Detection scheme. Protein microarrays are probed with queries and th
...
Figure 2.3 Three main classes of protein microarray platforms. Protein microarr
...
Figure 2.4 Self‐assembling protein microarrays. Several platforms were describe
...
Chapter 3
Figure 3.1 Protein biomarker discovery pipeline. Integrated approaches for biom
...
Figure 3.2 Clinical samples available for biomarker discovery. The major charac
...
Figure 3.3 Integration of genomic, epigenetic, transcriptomic, and proteomic ap
...
Figure 3.4 Approaches for quantitative proteomics. Commonly used approaches inc
...
Chapter 4
Figure 4.1 MSI is performed on a spinal cord tissue section. (Left) MS acquisit
...
Figure 4.2 Common matrix application methods for MALDI‐MSI. (Top) Schematic sho
...
Figure 4.3 Histology correlation between H&E staining and MSI. (a) MSI image at
...
Figure 4.4 Localization and effect of OSI‐774 on histone and ubiquitin expressi
...
Figure 4.5 MALDI‐MS profiling and imaging at the cellular scale. (Left) A cultu
...
Chapter 5
Figure 5.1 Yeast two‐hybrid (Y2H) method. (a) The Y2H method uses molecular bio
...
Figure 5.2 Two of the most commonly used strategies to identify interacting pro
...
Figure 5.3 Protein cross‐linking can be used to identify protein interactions a
...
Figure 5.4 Protein complexes can be separated under liquid chromatography condi
...
Figure 5.5 A proximity assay is an experiment that measures physical proximity
...
Chapter 6
Figure 6.1 ESI‐MS spectra of the intact ribosome from Thermus thermophilus (a),
...
Figure 6.2 ESI‐MS of HSP18.1 and luciferase under elevated temperature (42 °C),
...
Figure 6.3 (a) The deconvolution of multiple coexisting species. The mass spect
...
Figure 6.4 A summary of how intact membrane protein complexes can be desolvated
...
Figure 6.5 ESI‐MS of intact rotary ATPases from both Thermus thermophilus (Tt)
...
Figure 6.6 A flow diagram which illustrates the process of generating and evalu
...
Chapter 7
Figure 7.1 Representation of a cross‐linking reagent possessing multiple featur
...
Figure 7.2 Examples of cross‐linkers with special features that are useful for
...
Figure 7.3 Map of the lysine‐lysine cross‐link pairs found in the middle module
...
Figure 7.4 Protein cross‐linking and Mediator complex architecture. (a) Overvie
...
Figure 7.5 Cross‐linking analysis of the prion proteins with CBDPS cross‐linker
...
Figure 7.6 Structure of the native form of the PrP 90–231 showing differential
...
Figure 7.7 Summary of the structural differences between PrPC and PrPβ, as reve
...
Figure 7.8 Protein structures solved by CL‐DMD. Calculated protein structures (
...
Chapter 8
Figure 8.1 Graphical representation of putative yeast membrane‐associated prote
...
Figure 8.2 Nuclear and cytoplasmic protein extracts were prepared from cultured
...
Figure 8.3 Network‐based protein function prediction in E. coli. We first mappe
...
Chapter 9
Figure 9.1 General overview of CyTOF hardware. After samples are introduced to
...
Figure 9.2 Consecutive mass spectra showing integrated signal for a single dete
...
Figure 9.3 Metal chelating polymers are made up of three main components: a pol
...
Figure 9.4 DNA metallointercalators usually contain three ligands bound to a tr
...
Figure 9.5 An essential characteristic of DNA intercalators is that they not in
...
Figure 9.6 Determination of cell viability via cisplatin content. Three incubat
...
Figure 9.7 The scheme for barcoding 96 samples using seven different lanthanide
...
Figure 9.8 A biofunctional probe integrating tellurium into the covalent scaffo
...
Figure 9.9 Normalization of mass cytometry data via calibration beads. The medi
...
Figure 9.10 Analysis of multidimensional cytometry data using the SPADE algorit
...
Figure 9.11 viSNE projects multidimensional data into two dimensions with high
...
Figure 9.12 viSNE can be used to detect “different‐from‐normal phenotypes.” Ami
...
Figure 9.13 Cells of vastly different developmental stages can be close to one
...
Figure 9.14 The Wanderlust algorithm plots cells in n‐dimensional space where n
...
Figure 9.15 Citrus identifies subpopulations of cells across multiple samples w
...
Chapter 10
Figure 10.1 Chemoproteomics can be described as the systematic analysis of the
...
Figure 10.2 Target identification using immobilized small molecules. Lysates of
...
Figure 10.3 Factors that govern the results of a target identification experime
...
Figure 10.4 Schematic representation of a typical selectivity profiling experim
...
Figure 10.5 Schematic representation of the ABPP technology. Lysates of tissues
...
Figure 10.6 Schematic representation of the DARTS technology. Here, proteins fr
...
Figure 10.7 Schematic representation of the TICC technology. In this approach,
...
Chapter 11
Figure 11.1 (a) Number of posttranslational modifications (PTMs) experimentally
...
Figure 11.2 The use of 2D‐PAGE and immunoblotting for the identification of pho
...
Figure 11.3 A comparison of tandem mass spectrometry (MS2) spectra of phosphory
...
Figure 11.4 Use of MS3 for identification of phosphorylated peptides. In the to
...
Figure 11.5 Experimental strategy used for the quantitative analysis of phospho
...
Figure 11.6 Diagram showing tyrosine phosphorylation sites identified in EGFRvI
...
Figure 11.7 Summary of experimental workflow for developing multiplexed immunoa
...
Chapter 12
Figure 12.1 Four methods to enrich phosphopeptides. (a) Precipitation with cati
...
Figure 12.2 Sequence logos show the preference of phosphopeptides for the bariu
...
Figure 12.3 Pre‐enrichment strategies for phosphopeptide analysis. (a) Strong c
...
Figure 12.4 Hybrid enrichment strategies to improve recovery of phosphopeptides
...
Chapter 13
Figure 13.1 Microarray formats for glycoproteomics research. The types of micro
...
Figure 13.2 Motif analysis of glycan array data. (a) Motif segregation for inte
...
Figure 13.3 Improved patient discrimination by detecting CA 19‐9 on individual
...
Figure 13.4 Cancer‐associated changes in protein abundance and glycosylation. T
...
Guide
Cover
Table of Contents
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Proteomics for Biological Discovery
SECOND EDITION
Edited by
Timothy D. Veenstra
Watertown, Wisconsin
John R. Yates III
Torrey Mesa, California
This Second edition first published 2019© 2019 John Wiley & Sons, Inc.
Edition HistoryJohn Wiley & Sons, Inc. (1e 2006)
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, except as permitted by law. Advice on how to obtain permission to reuse material from this title is available at http://www.wiley.com/go/permissions.
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Library of Congress Cataloging‐in‐Publication DataNames: Veenstra, Timothy Daniel, 1966– editor. | Yates III, John R., editor.Title: Proteomics for biological discovery / edited by Timothy D. Veenstra, John R. Yates III.Description: Second edition. | Hoboken, NJ : Wiley‐Blackwell, 2019. | Includes bibliographical references and index. |Identifiers: LCCN 2019015129 (print) | LCCN 2019015652 (ebook) | ISBN 9781119081692 (Adobe PDF) | ISBN 9781119081722 (ePub) | ISBN 9781118279243 (hardback)Subjects: | MESH: Proteomics | Computational Biology–methodsClassification: LCC QP551 (ebook) | LCC QP551 (print) | NLM QU 460 | DDC 612.3/98–dc23LC record available at https://lccn.loc.gov/2019015129
Cover Design: WileyCover Image: © Scala/Art Resource
Foreword
WHAT IS PROTEOMICS?
A critical advance in biology was the sequencing of the human genome approximately 15 years ago. A dedicated effort to advance technology has made it feasible and cost‐effective to sequence the entire genomes of individuals with a growing use in clinical diagnosis. The growing collection of DNA sequence data has provided a powerful resource for studies involving protein biochemistry, in particular to create a better understanding of how disease mechanisms manifest from genes to proteins. Advanced methods in large‐scale protein biochemistry or proteomics have broadened the types of experiments possible.
HOW IS THIS DRIVING BIOLOGICAL RESEARCH?
Understanding diseases requires discovering the mechanisms by which biological processes are disrupted. These mechanisms are often manifested through proteins and their functions. Proteomic methods are now able to measure protein expression, the composition of organelles, posttranslational modifications, and protein–protein interactions to determine how proteins are changed as a function of disease. A variety of methods make these measurements possible, including mass spectrometry and protein arrays. Protein arrays allow the study of large‐scale protein expression. They also allow scanning for circulating reactive antibodies that associate with disease. These advanced methods are increasingly used for studies to identify markers for disease. Increasingly, proteomic tools are being used in the development of therapeutic treatments.
In this second edition of Proteomics for Biological Discovery, chapters describe research meeting these needs.
Mohammed and Heck describe recent advances in quantitative proteomics using mass spectrometry. Veenstra describes proteome analysis of posttranslational modifications. Delahunty and Yates describe mass spectrometry‐based methods and applications to use affinity purification mass spectrometry for characterization of protein complexes. Diamandis and Drabovich cover the process of biomarker discovery. Yates discusses the large‐scale analysis of phosphorylation in biological systems. Robinson discusses the characterization of intact protein complexes using native mass spectrometry. Borchers describes the use of protein cross‐linking to characterize protein structures and protein–protein interactions. Emili describes the use of proteomics to understand protein function. Haab discusses the use of antibodies for proteomic profiling. LaBaer describes the use of protein arrays in proteomics. Sweedler describes the use of mass spectrometry imaging. An important new area of proteomics is single cell mass cytometry which is described by Edgar Arriaga. Kuster describes how to characterize drug–protein interactions.
List of Contributors
Timothy D. VeenstraDepartment of Applied ScienceMaranatha Baptist UniversityWatertown, WI, USA
John R. Yates IIIDepartments of Molecular Medicine and NeurobiologyThe Scripps Research Institute, LaJolla, CA, USA
Edgar A. ArriagaDepartment of Chemistry, University of MinnesotaMinneapolis, MN, USA
Marcus BantscheffCellzome, Heidelberg, Germany
Christoph H. BorchersUniversity of Victoria – Genome British Columbia Proteomics CentreVancouver Island Technology Park, Victoria, BC, Canada
Claire M. DelahuntyDepartments of Molecular Medicine and NeurobiologyThe Scripps Research Institute, LaJolla, CA, USA
Eleftherios P. DiamandisSamuel Lunenfeld Research Institute, Mount Sinai Hospital, Toronto, ON, Canada
Andrei P. DrabovichDepartment of Laboratory Medicine and Pathology, University of Alberta,Edmonton, AB, Canada
Sage J.B. DunhamDepartment of Chemistry and the Beckman Institute of Science and TechnologyUniversity of Illinois at Urbana–Champaign, Champaign, IL, USA
Andrew EmiliDonnelly Centre for Cellular and Biomolecular ResearchUniversity of Toronto, Toronto, ON, Canada
Fernanda FestaDepartments of Pediatrics and Biochemistry/Molecular BiologyCollege of Medicine, Penn State University, Hershey, PA, USA
Christian K. FreseBiomolecular Mass Spectrometry and ProteomicsBijvoet Center for Biomolecular Research and Utrecht Institute forPharmaceutical Sciences, Utrecht University, UtrechtThe Netherlands
Heather M. GrundhoferDepartment of Chemistry, University of MinnesotaMinneapolis, MN, USA
Brian B. HaabVan Andel Research Institute, Grand Rapids, MI, USA
Pierre C. HavugimanaDonnelly Centre for Cellular and Biomolecular Research, University of Toronto,Toronto, ON, Canada
Albert J.R. HeckBiomolecular Mass Spectrometry and Proteomics, Bijvoet Center for BiomolecularResearch and Utrecht Institute for Pharmaceutical Sciences, Utrecht University,Utrecht, The Netherlands
Jonathan T.S. HopperDepartment of Chemistry, Physical and Theoretical Chemistry Laboratory,University of Oxford, Oxford, UK
Pingzhao HuDepartment of Biochemistry and Medical Genetics, University of Manitoba,Winnipeg, MB, Canada
Michelle M. KuhnsDepartment of Chemistry, University of Minnesota, Minneapolis, MN, USA
Bernhard KusterCellzome, Heidelberg, Germany. Technical University Munich, F.reising,Germany
Joshua LaBaerVirginia G. Piper Center for Personalized Diagnostics, Biodesign Institute,Arizona State University, Tempe, AZ, USA
Eric J. LanniDepartment of Chemistry and the Beckman Institute of Science and Technology,University of Illinois at Urbana–Champaign, Champaign, IL, USA
Eduardo Martínez‐MorilloSamuel Lunenfeld Research Institute, Mount Sinai Hospital, Toronto, ON, Canada
Shabaz MohammedBiomolecular Mass Spectrometry and ProteomicsBijvoet Center for Biomolecular Research and Utrecht Institute forPharmaceutical Sciences, Utrecht University, UtrechtThe Netherlands
Elizabeth K. NeumannDepartment of Chemistry and the Beckman Institute of Science and TechnologyUniversity of Illinois at Urbana–Champaign, Champaign, IL, USA
Ta‐Hsuan OngDepartment of Chemistry and the Beckman Institute of Science and Technology,University of Illinois at Urbana–Champaign, Champaign, IL, USA
Evgeniy V. PetrotchenkoUniversity of Victoria – Genome British Columbia Proteomics CentreVancouver Island Technology Park, Victoria, BC, Canada
Carol V. RobinsonDepartment of Chemistry, Physical and Theoretical Chemistry LaboratoryUniversity of Oxford, Oxford, UK
Markus SchirleNovartis Institutes for BioMedical Research, Inc., Cambridge, MA, USA
Jason J. SerpaUniversity of Victoria – Genome British Columbia Proteomics CentreVancouver Island Technology Park, Victoria, BC, Canada
Jonathan V. SweedlerDepartment of Chemistry and the Beckman Institute of Science and TechnologyUniversity of Illinois at Urbana–Champaign, Champaign, IL, USA
Henk van den ToornBiomolecular Mass Spectrometry and Proteomics, Bijvoet Center forBiomolecular Research and Utrecht Institute for Pharmaceutical SciencesUtrecht University, Utrecht, The Netherlands
