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(University of Michigan), David J. States(University of Michigan), Marcin Adamski(University of Michigan), Thomas W. Blackwell(University of Michigan), Rajasree Menon(University of Michigan), Henning Hermjakob, Rolf Apweiler, Brian B. Haab(Van Andel Institute), Richard J. Simpson(The Royal Melbourne Hospital), James S. Eddes(Olivia Newton-John Cancer Wellness & Research Centre), Eugene A. Kapp(Olivia Newton-John Cancer Wellness & Research Centre), Robert L. Moritz(Olivia Newton-John Cancer Wellness & Research Centre), Daniel W. Chan(Johns Hopkins University), Alex J. Rai(Johns Hopkins University), Arie Admon(Technion – Israel Institute of Technology), Ruedi Aebersold(Institute for Systems Biology), Jimmy K. Eng(Institute for Systems Biology), William S. Hancock(Northeastern University), Stanley A. Hefta(Bristol-Myers Squibb (Germany)), Helmut E. Meyer(Ruhr University Bochum), Young‐Ki Paik(Yonsei University), Jong‐Shin Yoo(Korea Basic Science Institute), Peipei Ping(University of California, Los Angeles), Joel G. Pounds(Pacific Northwest National Laboratory), Joshua Adkins(Pacific Northwest National Laboratory), Xiaohong Qian(Academy of Military Medical Sciences), Rong Wang(Icahn School of Medicine at Mount Sinai), Valerie C. Wasinger(UNSW Sydney), Chi Yue Wu(Institute of Biological Chemistry, Academia Sinica), Xiaohang Zhao(Academy of Medical Sciences), Rong Zeng(Institute for Biological Sciences), Alexander I. Archakov(Institute of Biomedical Chemistry), Akira Tsugita(NEC (Japan)), Ilan Beer(IBM Research - Haifa), Akhilesh Pandey(Johns Hopkins University), Michael Pisano(Abterra Biosciences (United States)), Philip Andrews(University of Michigan), Harald Tammen, David W. Speicher(The Wistar Institute), Samir Hanash(University of Michigan)
PROTEOMICS
August 1, 2005
Cited by 791Open Access
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Abstract

HUPO initiated the Plasma Proteome Project (PPP) in 2002. Its pilot phase has (1) evaluated advantages and limitations of many depletion, fractionation, and MS technology platforms; (2) compared PPP reference specimens of human serum and EDTA, heparin, and citrate-anti-coagulated plasma; and (3) created a publicly-available knowledge base (www.bioinformatics.med.umich.edu/hupo/ppp; www.ebi.ac.uk/pride). Thirty-five participating laboratories in 13 countries submitted datasets. Working groups addressed (a) specimen stability and protein concentrations; (b) protein identifications from 18 MS/MS datasets; (c) independent analyses from raw MS-MS spectra; (d) search engine performance, subproteome analyses, and biological insights; (e) antibody arrays; and (f) direct MS/SELDI analyses. MS-MS datasets had 15 710 different International Protein Index (IPI) protein IDs; our integration algorithm applied to multiple matches of peptide sequences yielded 9504 IPI proteins identified with one or more peptides and 3020 proteins identified with two or more peptides (the Core Dataset). These proteins have been characterized with Gene Ontology, InterPro, Novartis Atlas, OMIM, and immunoassay-based concentration determinations. The database permits examination of many other subsets, such as 1274 proteins identified with three or more peptides. Reverse protein to DNA matching identified proteins for 118 previously unidentified ORFs. We recommend use of plasma instead of serum, with EDTA (or citrate) for anticoagulation. To improve resolution, sensitivity and reproducibility of peptide identifications and protein matches, we recommend combinations of depletion, fractionation, and MS/MS technologies, with explicit criteria for evaluation of spectra, use of search algorithms, and integration of homologous protein matches. This Special Issue of PROTEOMICS presents papers integral to the collaborative analysis plus many reports of supplementary work on various aspects of the PPP workplan. These PPP results on complexity, dynamic range, incomplete sampling, false-positive matches, and integration of diverse datasets for plasma and serum proteins lay a foundation for development and validation of circulating protein biomarkers in health and disease.


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