Literature DB >> 16052625

A proteomic study of the HUPO Plasma Proteome Project's pilot samples using an accurate mass and time tag strategy.

Joshua N Adkins1, Matthew E Monroe, Kenneth J Auberry, Yufeng Shen, Jon M Jacobs, David G Camp, Frank Vitzthum, Karin D Rodland, Richard C Zangar, Richard D Smith, Joel G Pounds.   

Abstract

Characterization of the human blood plasma proteome is critical to the discovery of routinely useful clinical biomarkers. We used an accurate mass and time (AMT) tag strategy with high-resolution mass accuracy cLC-FT-ICR MS to perform a global proteomic analysis of pilot study samples as part of the HUPO Plasma Proteome Project. HUPO reference serum and citrated plasma samples from African Americans, Asian Americans, and Caucasian Americans were analyzed, in addition to a Pacific Northwest National Laboratory reference serum and plasma. The AMT tag strategy allowed us to leverage two previously published "shotgun" proteomics experiments to perform global analyses on these samples in triplicate in less than 4 days total analysis time. A total of 722 (22% with multiple peptide identifications) International Protein Index redundant proteins, or 377 protein families by ProteinProphet, were identified over the six individual HUPO serum and plasma samples. The samples yielded a similar number of identified redundant proteins in the plasma samples (average 446 +/- 23) as found in the serum samples (average 440 +/- 20). These proteins were identified by an average of 956 +/- 35 unique peptides in plasma and 930 +/- 11 unique peptides in serum. In addition to this high-throughput analysis, the AMT tag approach was used with a Z-score normalization to compare relative protein abundances. This analysis highlighted both known differences in serum and citrated plasma such as fibrinogens, and reproducible differences in peptide abundances from proteins such as soluble activin receptor-like kinase 7b and glycoprotein m6b. The AMT tag strategy not only improved our sample throughput but also provided a basis for estimated quantitation.

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Year:  2005        PMID: 16052625      PMCID: PMC2041806          DOI: 10.1002/pmic.200401333

Source DB:  PubMed          Journal:  Proteomics        ISSN: 1615-9853            Impact factor:   3.984


  48 in total

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10.  Application of proteomics in the discovery of candidate protein biomarkers in a diabetes autoantibody standardization program sample subset.

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