Literature DB >> 19858499

Interlaboratory study characterizing a yeast performance standard for benchmarking LC-MS platform performance.

Amanda G Paulovich1, Dean Billheimer, Amy-Joan L Ham, Lorenzo Vega-Montoto, Paul A Rudnick, David L Tabb, Pei Wang, Ronald K Blackman, David M Bunk, Helene L Cardasis, Karl R Clauser, Christopher R Kinsinger, Birgit Schilling, Tony J Tegeler, Asokan Mulayath Variyath, Mu Wang, Jeffrey R Whiteaker, Lisa J Zimmerman, David Fenyo, Steven A Carr, Susan J Fisher, Bradford W Gibson, Mehdi Mesri, Thomas A Neubert, Fred E Regnier, Henry Rodriguez, Cliff Spiegelman, Stephen E Stein, Paul Tempst, Daniel C Liebler.   

Abstract

Optimal performance of LC-MS/MS platforms is critical to generating high quality proteomics data. Although individual laboratories have developed quality control samples, there is no widely available performance standard of biological complexity (and associated reference data sets) for benchmarking of platform performance for analysis of complex biological proteomes across different laboratories in the community. Individual preparations of the yeast Saccharomyces cerevisiae proteome have been used extensively by laboratories in the proteomics community to characterize LC-MS platform performance. The yeast proteome is uniquely attractive as a performance standard because it is the most extensively characterized complex biological proteome and the only one associated with several large scale studies estimating the abundance of all detectable proteins. In this study, we describe a standard operating protocol for large scale production of the yeast performance standard and offer aliquots to the community through the National Institute of Standards and Technology where the yeast proteome is under development as a certified reference material to meet the long term needs of the community. Using a series of metrics that characterize LC-MS performance, we provide a reference data set demonstrating typical performance of commonly used ion trap instrument platforms in expert laboratories; the results provide a basis for laboratories to benchmark their own performance, to improve upon current methods, and to evaluate new technologies. Additionally, we demonstrate how the yeast reference, spiked with human proteins, can be used to benchmark the power of proteomics platforms for detection of differentially expressed proteins at different levels of concentration in a complex matrix, thereby providing a metric to evaluate and minimize pre-analytical and analytical variation in comparative proteomics experiments.

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Year:  2009        PMID: 19858499      PMCID: PMC2830837          DOI: 10.1074/mcp.M900222-MCP200

Source DB:  PubMed          Journal:  Mol Cell Proteomics        ISSN: 1535-9476            Impact factor:   5.911


  32 in total

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  66 in total

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2.  Recommendations for mass spectrometry data quality metrics for open access data (corollary to the Amsterdam Principles).

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8.  Peptide-level Robust Ridge Regression Improves Estimation, Sensitivity, and Specificity in Data-dependent Quantitative Label-free Shotgun Proteomics.

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Review 10.  Status and prospects for discovery and verification of new biomarkers of cardiovascular disease by proteomics.

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