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Challenges in deriving high-confidence protein identifications from data gathered by a HUPO plasma proteome collaborative study

Nature Biotechnology volume 24, pages 333–338 (2006)Cite this article

Abstract

The Human Proteome Organization (HUPO) recently completed the first large-scale collaborative study to characterize the human serum and plasma proteomes. The study was carried out in different locations and used diverse methods and instruments to compare and integrate tandem mass spectrometry (MS/MS) data on aliquots of pooled serum and plasma from healthy subjects. Liquid chromatography (LC)-MS/MS data sets from 18 laboratories were matched to the International Protein Index database, and an initial integration exercise resulted in 9,504 proteins identified with one or more peptides, and 3,020 proteins identified with two or more peptides. This article uses a rigorous statistical approach to take into account the length of coding regions in genes, and multiple hypothesis-testing techniques. On this basis, we now present a reduced set of 889 proteins identified with a confidence level of at least 95%. We also discuss the importance of such an integrated analysis in providing an accurate representation of a proteome as well as the value such data sets contain for the high-confidence identification of protein matches to novel exons, some of which may be localized in alternatively spliced forms of known plasma proteins and some in previously nonannotated gene sequences.

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Figure 1: Distribution of protein identifications.
Figure 2: Number of peptides identified as a function of protein concentration.
Figure 3: Distribution of peptides identified for β-2-glycoprotein 1.
Figure 4: Bar plot of the distribution of ORFs types by gene.
Figure 5: Novel ORFs in the APOE gene.

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Acknowledgements

The collaborative HUPO Plasma Protein study and the data analysis presented here have been supported by a trans-National Institutes of Health grant supplement 84982 administered by the National Cancer Institute, by pharmaceutical and technology company sponsors and by voluntary efforts of collaborating laboratories.

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Authors and Affiliations

  1. University of Michigan, 100 Washtenaw Rd., Palmer Commons 2035B, Ann Arbor, 48109, Michigan, USA

    David J States, Gilbert S Omenn, Thomas W Blackwell, Damian Fermin & Samir M Hanash

  2. Fred Hutchinson Cancer Research Center, 1100 Fairview Ave. N., PO Box 19024, Seattle, 98109, Washington, USA

    Jimmy Eng & Samir M Hanash

  3. The Wistar Institute, 3601 Spruce St., Philadelphia, 19104, Pennsylvania, USA

    David W Speicher

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  1. David J States
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  2. Gilbert S Omenn
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Correspondence to Samir M Hanash.

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Supplementary information

Supplementary Fig. 1

Accrual of identifications as a function of sampling. (PDF 20 kb)

Supplementary Fig. 2

Complement component 3 isoforms. (PDF 20 kb)

Supplementary Table 1

Numbers of protein identificaitons by specifmen and by methodologies applied in individual laboratories. (PDF 90 kb)

Supplementary Table 2

List of high-confidence protein identifications. (PDF 116 kb)

Supplementary Table 3

Intragenic peptides not in an annotated exon. (PDF 15 kb)

Supplementary Notes (PDF 25 kb)

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States, D., Omenn, G., Blackwell, T. et al. Challenges in deriving high-confidence protein identifications from data gathered by a HUPO plasma proteome collaborative study. Nat Biotechnol 24, 333–338 (2006). https://doi.org/10.1038/nbt1183

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