About the Book
On the cutting edge of medical diagnostics, plasma proteomics promises to generate a new wave of technologies to help identify many different diseases and disease risks. Plasma and serum are the preferred non-invasive specimens to test normal individuals, at-risk groups, and patients for protein biomarkers discovered and validated to reflect physiological, pathological, and pharmacological phenotypes. These specimens present enormous challenges due to extreme complexity, huge dynamic range in protein concentrations, non-standardized methods of sample processing, and intra- and inter-individual variation from genetics, diet, smoking, hormones, and other sources. This book presents the major findings from the collaborative Plasma Proteome Project organized by the international Human Proteome Organization (HUPO). The chapters are drawn from a larger set of publications in the journal PROTEOMICS. This book provides a valuable foundation for development and applications of proteomics.
Table of Contents:
1 Overview of the HUPO Plasma Proteome Project: Results from the pilot phase with 35 collaborating laboratories and multiple analytical groups, generating a core dataset of 3020 proteins and a publicly-available database (Gilbert S. Omenn, David J. States, Marcin Adamski, Thomas W. Blackwell, Rajasree Menon, Henning Hermjakob, Rolf Apweiler, Brian B. Haab, Richard J. Simpson, James S. Eddes, Eugene A. Kapp, Robert L. Moritz, Daniel W. Chan, Alex J. Rai, Arie Admon, Ruedi Aebersold, Jimmy Eng, William S. Hancock, Stanley A. Hefta, Helmut Meyer, Young-Ki Paik, Jong-Shin Yoo, Peipei Ping, Joel Pounds, Joshua Adkins, Xiaohong Qian, Rong Wang, Valerie Wasinger, Chi Yue Wu, Xiaohang Zhao, Rong Zeng, Alexander Archakov, Akira Tsugita, Ilan Beer, Akhilesh Pandey, Michael Pisano, Philip Andrews, Harald Tammen, David W. Speicher and Samir M. Hanash). 1.1 Introduction. 1.2 PPP reference specimens. 1.3 Bioinformatics and technology platforms. 1.3.1 Constructing a PPP database for human plasma and serum proteins. 1.3.2 Analysis of confidence of protein identifications. 1.3.3 Quantitation of protein concentrations. 1.4 Comparing the specimens. 1.4.1 Choice of specimen and collection and handling variables. 1.4.2 Depletion of abundant proteins followed by fractionation of intact proteins. 1.4.3 Comparing technology platforms. 1.4.4 Alternative search algorithms for peptide and protein identification. 1.4.5 Independent analyses of raw spectra or peaklists. 1.4.6 Comparisons with published reports. 1.4.7 Direct MS (SELDI) analyses. 1.4.8 Annotation of the HUPO PPP core dataset(s). 1.4.9 Identification of novel peptides using whole genome ORF search. 1.4.10 Identification of microbial proteins in the circulation. 1.5 Discussion. 1.6 References. 2 Data management and preliminary data analysis in the pilot phase of the HUPO Plasma Proteome Project (Marcin Adamski, Thomas Blackwell, Rajasree Menon, Lennart Martens, Henning Hermjakob, Chris Taylor, Gilbert S. Omenn and David J. States). 2.1 Introduction. 2.2 Materials and methods. 2.2.1 Development of the data model. 2.2.2 Data submission process. 2.2.3 Design of the data repository. 2.2.4 Receipt of the data. 2.3 Inference from peptide level to protein level. 2.4 Summary of contributed data. 2.4.1 Cross-laboratory comparison, confidence of the identifications. 2.5 False-positive identifications. 2.6 Data dissemination. 2.7 Discussion. 2.8 Concluding remarks. 2.9 Computer technologies applied. 2.10 References. 3 HUPOPlasma Proteome Project specimen collection and handling: Towards the standardization of parameters for plasma proteome samples (Alex J. Rai, Craig A. Gelfand, Bruce C. Haywood, David J. Warunek, Jizu Yi, Mark D. Schuchard, Richard J. Mehigh, Steven L. Cockrill, Graham B. I. Scott, Harald Tammen, Peter Schulz-Knappe, David W. Speicher, Frank Vitzthum, Brian B. Haab, Gerard Siest and Daniel W. Chan). 3.1 Introduction. 3.2 Materials and methods. 3.2.1 HUPO reference sample collection protocol. 3.2.2 Differential peptide display. 3.2.3 Stability studies and SELDI analysis. 3.2.4 SDS-PAGE analysis for stability studies. 3.2.5 2-DE for stability studies. 3.2.6 SELDI-TOF analysis for protease inhibitor studies. 3.2.7 2-DE for plasma protease inhibition studies. 3.2.8 Tryptic digestion and protein identification for protease inhibition studies. 3.2.9 Antibody microarray analysis using two-color rolling circle amplification. 3.3 Results. 3.3.1 Comparisons of specimen types. 3.3.2 Evaluation of storage and handling conditions. 3.3.3 Evaluations of the use of protease inhibitors. 3.4 Discussion. 3.4.1 Other pre-analytical variables and control considerations. 3.4.2 Reference materials. 3.5 Concluding remarks. 3.6 References. 4 Immunoassay and antibody microarray analysis of the HUPO Plasma Proteome Project reference specimens: Systematic variation between sample types and calibration of mass spectrometry data (Brian B. Haab, Bernhard H. Geierstanger, GeorgeMichailidis, Frank Vitzthum, Sara Forrester, Ryan Okon, Petri Saviranta, Achim Brinker, Martin Sorette, Lorah Perlee, Shubha Suresh, Garry Drwal, Joshua N. Adkins and Gilbert S. Omenn). 4.1 Introduction. 4.2 Materials and methods. 4.2.1 Reference specimens. 4.2.2 DB immunoassays. 4.2.3 Antibody arrays at GNF. 4.2.4 Antibody microarrays at MSI. 4.2.5 Antibody microarrays at VARI. 4.2.6 Retrieval and matching of IPI numbers for the analytes. 4.3 Results. 4.3.1 Antibody-based measurements of the HUPO reference specimens. 4.3.2 Systematic variation between the preparation methods of the PPP reference specimens. 4.3.3 Consistent alterations in specific protein abundances. 4.3.4 Linkage of MS data and antibody-based measurements. 4.4 Discussion. 4.5 References. 5 Depletion of multiple high-abundance proteins improves protein profiling capacities of human serum and plasma (Lynn A. Echan, Hsin-Yao Tang, Nadeem Ali-Khan, KiBeom Lee and David W. Speicher). 5.1 Introduction. 5.2 Materials and methods. 5.2.1 Serum/plasma collection. 5.2.2 MARS. 5.2.3 Multiple affinity removal spin cartridge. 5.2.4 Microscale solution IEF (MicroSol IEF) (ZOOM-IEF) fractionation. 5.2.5 2-DE. 5.2.6 LC-MS/MS. 5.3 Results. 5.3.1 Depletion of major proteins to enhance detection of lower abundance proteins. 5.3.2 Evaluation of high-abundance protein removal using 2-DE. 5.3.3 Specificity of major protein depletion. 5.3.4 Impact of Top-6 protein depletion on detection of lower abundance proteins using 2-D gels. 5.3.5 Combining Top-6 protein depletion with microSol IEF prefractionation and narrow pH range gels. 5.3.6 Analysis of Top-6 depleted serum and plasma using protein array pixelation. 5.4 Discussion. 5.5 References. 6 A novel four-dimensional strategy combining protein and peptide separation methods enables detection of low-abundance proteins in human plasma and serum proteomes (Hsin-Yao Tang, Nadeem Ali-Khan, Lynn A. Echan, Natasha Levenkova, John J. Rux and David W. Speicher). 6.1 Introduction. 6.2 Materials and methods. 6.2.1 Materials. 6.2.2 Top six protein depletion. 6.2.3 MicroSol-IEF fractionation. 6.2.4 Protein array pixelation. 6.2.5 LC-ESI-MS/MS methods. 6.2.6 Data analysis. 6.3 Results and discussion. 6.3.1 Protein array pixelation strategy. 6.3.2 Optimization of protein array pixelation. 6.3.3 Total analysis time for protein array pixelation of human plasma proteome. 6.3.4 Systematic protein array pixelation of the human plasma proteome. 6.3.5 Systematic protein array pixelation of the human serum proteome. 6.3.6 Analyses of human plasma and serum proteomes using HUPO filter criteria. 6.4 Concluding remarks. 6.5 References. 7 A study of glycoproteins in human serum and plasma reference standards (HUPO) using multilectin affinity chromatography coupled with RPLC-MS/MS (Ziping Yang, William S. Hancock, Tori Richmond Chew and Leo Bonilla). 7.1 Introduction. 7.2 Materials and methods. 7.2.1 Materials. 7.2.2 Isolating glycoproteins using multilectin affinity columns. 7.2.3 Analysis of glycoproteins on LC-LCQ MS. 7.2.4 Analysis of glycoproteins on LC-LTQ MS. 7.2.5 Protein database search. 7.3 Results and discussion. 7.3.1 Protein IDs from the plasma and serum samples. 7.3.2 Comparison between serum and plasma glycoproteomes. 7.3.3 Comparison of the glycoproteins present in the samples collected from three ethnic groups. 7.4 Concluding remarks. 7.5 References. 8 Evaluation of prefractionation methods as a preparatory step for multidimensional based chromatography of serum proteins (Eilon Barnea, Raya Sorkin, Tamar Ziv, Ilan Beer and Arie Admon). 8.1 Introduction. 8.1.1 The HUPO Plasma Proteome Project (PPP) goals and the serum as a complex sample. 8.1.2 The scope of this manuscript. 8.2 Materials and methods. 8.2.1 Depletion from serum albumin and antibodies. 8.2.2 MudPIT and mass segmentation. 8.2.3 Protein separation by SDS-PAGE. 8.2.4 SCX separation of intact proteins followed by MudPIT. 8.2.5 Liquid-phase IEF followed by MudPIT. 8.2.6 Capillary RP-LC-MS/MS. 8.2.7 MS data processing and peptide/protein identifications. 8.3 Results. 8.3.1 Comparisons between the prefractionation methods. 8.3.2 Identification of different protein subsets. 8.3.3 Proteins identified by only one prefractionation method. 8.3.4 Different methods resulted in diverse peptide coverage. 8.4 Discussion. 8.4.1 Giving every peptide a chance. 8.4.2 How to identify more of the marginal proteins. 8.4.3 Clustering and comparing raw data. 8.4.4 High throughput and ruggedness versus high sensitivity. 8.4.5 The cost effectiveness of the different methods. 8.5 Concluding remarks. 8.6 References. 9 Efficient prefractionation of low-abundance proteins in human plasma and construction of a two-dimensional map (Sang Yun Cho, Eun-Young Lee, Joon Seok Lee, Hye-Young Kim, Jae Myun Park, Min-Seok Kwon, Young-Kew Park,Hyoung-Joo Lee, Min-Jung Kang, Jin Young Kim, Jong Shin Yoo, Sung Jin Park, JinWon Cho, Hyon-Suk Kimand Young-Ki Paik). 9.1 Introduction. 9.2 Materials and methods. 9.2.1 Plasma sample preparation. 9.2.2 Depletion of major abundance proteins with an immunoaffinity column. 9.2.3 2-DE. 9.2.4 Identification of proteins by MS. 9.2.5 Fractionation of the plasma samples by FFE. 9.2.6 LC-MS/MS. 9.2.7 Bioinformatics. 9.3 Results and discussion. 9.3.1 2-DE map of human plasma devoid of high-abundance proteins. 9.3.2 Expression of different anticoagulant-treated plasma. 9.3.3 FFE/1-DE/nanoLC-MS/MS and 2-DE/MALDI-TOF. 9.4 Concluding remarks. 9.5 References. 10 Comparison of alternative analytical techniques for the characterisation of the human serum proteome in HUPO Plasma Proteome Project (Xiaohai Li, Yan Gong, YingWang, Songfeng Wu, Yun Cai, Ping He, Zhuang Lu, Wantao Ying, Yangjun Zhang, Liyan Jiao, Hongzhi He, Zisen Zhang, Fuchu He, Xiaohang Zhao and Xiaohong Qian). 10.1 Introduction. 10.2 Materials and methods. 10.2.1 Materials. 10.2.2 Human serum samples. 10.2.3 Integrated strategy for characterising analytical approaches. 10.2.4 Depletion of the highly abundant serum proteins by MARS. 10.2.5 Desalting and concentrating the flow-through fractions by centrifugal ultrafiltration. 10.2.6 Fractionation of depleted serum samples by anion-exchange HPLC. 10.2.7 Protein fractionation by 2-D HPLC with nonporous RP-HPLC. 10.2.8 The 2-DE strategy for the analysis of serum proteins. 10.2.8.1 2-DE. 10.2.8.2 In-gel digestion via automated workstation. 10.2.8.3 Protein spot identification by MALDI-TOF-MS/MS. 10.2.9 Shotgun strategy for the analysis of serum proteins. 10.2.10 Protein fractionation strategy for the analysis of serum proteins. 10.2.11 Offline shotgun strategy for the analysis of serum proteins. 10.2.12 Optimised nanoRP-HPLC-nanoESI IT-MS/MS for the reanalysis of offline SCX-separated peptides (offline-nanospray strategy). 10.3 Integrated analysis of the whole data sets. 10.3.1 Protein grouping analysis. 10.3.2 Sequence clustering. 10.4 Results and discussion. 10.4.1 Depletion of the highly abundant serum proteins. 10.4.2 The 2-DE strategy for the analysis of serum proteins. 10.4.3 2-D HPLC fractionation for the analysis of serum proteins. 10.4.4 Shotgun strategy for the analysis of serumproteins with online SCX. 10.4.5 Shotgun strategy for the analysis of serumproteins with offline SCX. 10.4.6 Offline SCX shotgun-nanospray strategy for the analysis of serum proteins. 10.4.7 Comparison of the five strategies for the analysis of the human serum proteome. 10.5 Concluding remarks. 10.6 References. 11 A proteomic study of the HUPO Plasma Proteome Project's pilot samples using an accurate mass and time tag strategy (Joshua N. Adkins, Matthew E. Monroe, Kenneth J. Auberry, Yufeng Shen, Jon M. Jacobs, David G. Camp II, Frank Vitzthum, Karin D. Rodland, Richard, C. Zangar, Richard D. Smith and Joel G. Pounds). 11.1 Introduction. 11.2 Materials and methods. 11.2.1 Human blood serum and plasma. 11.2.2 Depletion of Igs and trypsin digestion. 11.2.3 Peptide cleanup. 11.2.4 Capillary RP-LC. 11.2.5 IT-MS. 11.2.6 SEQUEST identification of peptides. 11.2.7 Putative mass and time tag database from SEQUESTresults. 11.2.8 FT-ICR-MS. 11.2.9 cLC-FT-ICR MS data analysis. 11.2.10 OmniViz cluster and visual analysis. 11.3 Results. 11.3.1 PuMT tag database. 11.3.2 Summary of peptide/protein identifications by AMT tags. 11.3.3 Protein concentration estimates from ion current. 11.3.4 Global protein analysis. 11.4 Discussion. 11.4.1 Application of FT-ICR MS as a proteomic technology bridge. 11.4.2 Confidence in any MS-based proteomic approach. 11.4.3 Peptide/protein redundancy. 11.4.4 Identification sensitivity versus specificity. 11.4.5 Throughput and differential analysis. 11.5 References. 12 Analysis of Human Proteome Organization Plasma Proteome Project (HUPOPPP) reference specimens using surface enhanced laser desorption/ionization-time of flight (SELDI-TOF) mass spectrometry:Multi-institution correlation of spectra and identification of biomarkers (Alex J. Rai, Paul M. Stemmer, Zhen Zhang, Bao-ling Adam,William T.Morgan, Rebecca E. Caffrey, Vladimir N. Podust, Manisha Patel, Lih-yin Lim, Natalia V. Shipulina, Daniel W. Chan, O. John Semmes and Hon-chiu Eastwood Leung). 12.1 Introduction. 12.2 Materials and methods. 12.2.1 Sample preparation. 12.2.2 Sample preprocessing. 12.2.3 Target (CM10) chip preparation and sample incubation. 12.2.4 Scanning protocol. 12.2.5 Data processing. 12.2.6 Bioinformatics analysis of data and correlation coefficient matrix. 12.2.7 Protein purification, SDS-PAGE analysis, and extraction of proteins. 12.2.8 Peptide mass fingerprinting (PMF). 12.2.9 MS/MS analysis. 12.2.10 Western blot analysis. 12.3 Results. 12.4 Discussion. 12.5 References. 13 An evaluation, comparison, and accurate benchmarking of several publicly availableMS/MS search algorithms: Sensitivity and specificity analysis (Eugene A. Kapp, Frederic Schutz, Lisa M. Connolly, John A. Chakel, Jose E. Meza, Christine A.Miller, David Fenyo, Jimmy K. Eng, Joshua N. Adkins, Gilbert S.Omenn and Richard J. Simpson). 13.1 Introduction. 13.1.1 Heuristic algorithms. 13.1.2 Probabilistic algorithms. 13.2 Materials and methods. 13.2.1 HUPO-PPP reference specimens. 13.2.2 Sample preparation and MS analysis. 13.2.3 Protein sequence databases. 13.2.4 MS/MS database search strategy. 13.2.5 Web interface for data validation, integration, and cross annotation. 13.2.6 ROC curve generation. 13.3 Results and discussion. 13.3.1 Comparison of MS/MS search algorithms. 13.4 Concluding remarks. 13.5 References. 14 Human Plasma PeptideAtlas (Eric W.Deutsch, Jimmy K.Eng,Hui Zhang, Nichole L.King, Alexey I.Nesvizhskii, Biaoyang Lin, Hookeun Lee, Eugene C. Yi, RetoOssola and Ruedi Aebersold). 14.1 References. 15 Do we want our data raw? Including binary mass spectrometry data in public proteomics data repositories (Lennart Martens, Alexey I. Nesvizhskii, Henning Hermjakob, Marcin Adamski, Gilbert S. Omenn, Joel Vandekerckhove and Kris Gevaert). 15.1 References. 16 A functional annotation of subproteomes in human plasma (Peipei Ping, Thomas M. Vondriska, Chad J. Creighton, TKB Gandhi, Ziping Yang, Rajasree Menon, Min-Seok Kwon, Sang Yun Cho, Garry Drwal, Markus Kellmann, Suraj Peri, Shubha Suresh, Mads Gronborg, Henrik Molina, Raghothama Chaerkady, B. Rekha, Arun S. Shet, Robert E. Gerszten, Haifeng Wu" Mark Raftery, Valerie Wasinger, Peter Schulz-Knappe, Samir M. Hanash, Young-ki Paik, William S. Hancock, David J. States, Gilbert S. Omenn and Akhilesh Pandey). 16.1 Introduction. 16.2 Materials and methods. 16.2.1 Coagulation pathway and protein interaction network analysis. 16.2.2 Gene ontology annotations. 16.2.3 Analysis of MS-derived data for identification of proteolytic events and post-translational modifications. 16.3 Results and discussion. 16.3.1 Bioinformatic analyses of the functional subproteomes. 16.3.2 Proteins involved in the blood coagulation pathway. 16.3.3 Proteins potentially derived from mononuclear phagocytes. 16.3.4 Proteins involved in inflammation. 16.3.5 Analyzing the peptide subproteome of human plasma. 16.3.6 Liver related plasma proteins. 16.3.7 Cardiovascular system related plasma proteins. 16.3.8 Glycoproteins. 16.3.9 DNA-binding proteins. 16.3.10 Annotation through reanalysis of mass spectrometry data. 16.4 Concluding remarks. 16.5 References. 17 Cardiovascular-related proteins identified in human plasma by the HUPO Plasma Proteome Project Pilot Phase (BeniamT. Berhane, Chenggong Zong, David A. Liem, AaronHuang, Steven Le, Ricky D. Edmondson, Richard C. Jones, Xin Qiao, Julian P. Whitelegge, Peipei Ping and Thomas M. Vondriska). 17.1 Introduction. 17.1.1 HUPO Plasma Proteome Project pilot phase. 17.1.2 Need for novel insights into cardiovascular disease. 17.2 Materials and methods. 17.3 Groups of cardiovascular-related proteins. 17.3.1 Markers of inflammation and CVD. 17.3.2 Vascular and coagulation proteins. 17.3.3 Signaling proteins. 17.3.4 Growth- and differentiation-associated proteins. 17.3.5 Cytoskeletal proteins. 17.3.6 Transcription factors. 17.3.7 Channel and receptor proteins. 17.3.8 Heart failure- and remodeling-related proteins. 17.4 Functional analyses and implications. 17.4.1 Organ specific cardiovascular-related proteins in plasma. 17.4.2 Novel cardiovascular-related proteins identified in plasma. 17.5 Methodology considerations. 17.6 Conclusions and future directions. 17.7 References.
