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Interlaboratory Study Characterizing a Yeast Performance Standard for Benchmarking LC-MS Platform Performance

2009; Elsevier BV; Volume: 9; Issue: 2 Linguagem: Inglês

10.1074/mcp.m900222-mcp200

ISSN

1535-9484

Autores

Amanda G. Paulovich, 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 Tegeler, Asokan Mulayath Variyath, Mu Wang, Jeffrey R. Whiteaker, Lisa J. Zimmerman, David Fenyö, 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, D.C. Liebler,

Tópico(s)

Metabolomics and Mass Spectrometry Studies

Resumo

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 preanalytical and analytical variation in comparative proteomics experiments. 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 preanalytical and analytical variation in comparative proteomics experiments. Access to proteomics performance standards is essential for several reasons. First, to generate the highest quality data possible, proteomics laboratories routinely benchmark and perform quality control (QC) 1The abbreviations used are:QCquality controlCPTACClinical Proteomic Technology Assessment for CancerFDRfalse discovery rateNISTNational Institute of Standards and TechnologySOPstandard operating procedureUPS1Universal Proteomics StandardLTQlinear trap quadrupoleCVcoefficient of variationSASPECTsignificant analysis of peptide countsTAPtandem affinity purificationRPLCreverse phase LC. 1The abbreviations used are:QCquality controlCPTACClinical Proteomic Technology Assessment for CancerFDRfalse discovery rateNISTNational Institute of Standards and TechnologySOPstandard operating procedureUPS1Universal Proteomics StandardLTQlinear trap quadrupoleCVcoefficient of variationSASPECTsignificant analysis of peptide countsTAPtandem affinity purificationRPLCreverse phase LC. monitoring of the performance of their instrumentation using standards. Second, appropriate standards greatly facilitate the development of improvements in technologies by providing a timeless standard with which to evaluate new protocols or instruments that claim to improve performance. For example, it is common practice for an individual laboratory considering purchase of a new instrument to require the vendor to run "demo" samples so that data from the new instrument can be compared head to head with existing instruments in the laboratory. Third, large scale proteomics studies designed to aggregate data across laboratories can be facilitated by the use of a performance standard to measure reproducibility across sites or to compare the performance of different LC-MS configurations or sample processing protocols used between laboratories to facilitate development of optimized standard operating procedures (SOPs). quality control Clinical Proteomic Technology Assessment for Cancer false discovery rate National Institute of Standards and Technology standard operating procedure Universal Proteomics Standard linear trap quadrupole coefficient of variation significant analysis of peptide counts tandem affinity purification reverse phase LC. quality control Clinical Proteomic Technology Assessment for Cancer false discovery rate National Institute of Standards and Technology standard operating procedure Universal Proteomics Standard linear trap quadrupole coefficient of variation significant analysis of peptide counts tandem affinity purification reverse phase LC. Most individual laboratories have adopted their own QC standards, which range from mixtures of known synthetic peptides to digests of bovine serum albumin or more complex mixtures of several recombinant proteins (1Klimek J. Eddes J.S. Hohmann L. Jackson J. Peterson A. Letarte S. Gafken P.R. Katz J.E. Mallick P. Lee H. Schmidt A. Ossola R. Eng J.K. Aebersold R. Martin D.B. The standard protein mix database: a diverse data set to assist in the production of improved Peptide and protein identification software tools.J. Proteome Res. 2008; 7: 96-103Crossref PubMed Scopus (143) Google Scholar). However, because each laboratory performs QC monitoring in isolation, it is difficult to compare the performance of LC-MS platforms throughout the community. Several standards for proteomics are available for request or purchase (2Barker P.E. Wagner P.D. Stein S.E. Bunk D.M. Srivastava S. Omenn G.S. Standards for plasma and serum proteomics in early cancer detection: a needs assessment report from the national institute of standards and technology—National Cancer Institute Standards, Methods, Assays, Reagents and Technologies Workshop, August 18–19, 2005.Clin. Chem. 2006; 52: 1669-1674Crossref PubMed Scopus (31) Google Scholar, 3Vitzthum F. Siest G. Bunk D.M. Preckel T. Wenz C. Hoerth P. Schulz-Knappe P. Tammen H. Adamkiewicz J. Merlini G. Anderson N.L. Metrological sharp shooting for plasma proteins and peptides: the need for reference materials for accurate measurements in clinical proteomics and in vitro diagnostics to generate reliable results.Proteomics Clin. Appl. 2007; 1: 1016-1035Crossref PubMed Scopus (10) Google Scholar). RM8327 is a mixture of three peptides developed as a reference material in collaboration between the National Institute of Standards and Technology (NIST) and the Association of Biomolecular Resource Facilities. Mixtures of 15–48 purified human proteins are also available, such as the HUPO (Human Proteome Organisation) Gold MS Protein Standard (Invitrogen), the Universal Proteomics Standard (UPS1; Sigma), and CRM470 from the European Union Institute for Reference Materials and Measurements. Although defined mixtures of peptides or proteins can address some benchmarking and QC needs, there is an additional need for more complex reference materials to fully represent the challenges of LC-MS data acquisition in complex matrices encountered in biological samples (2Barker P.E. Wagner P.D. Stein S.E. Bunk D.M. Srivastava S. Omenn G.S. Standards for plasma and serum proteomics in early cancer detection: a needs assessment report from the national institute of standards and technology—National Cancer Institute Standards, Methods, Assays, Reagents and Technologies Workshop, August 18–19, 2005.Clin. Chem. 2006; 52: 1669-1674Crossref PubMed Scopus (31) Google Scholar, 3Vitzthum F. Siest G. Bunk D.M. Preckel T. Wenz C. Hoerth P. Schulz-Knappe P. Tammen H. Adamkiewicz J. Merlini G. Anderson N.L. Metrological sharp shooting for plasma proteins and peptides: the need for reference materials for accurate measurements in clinical proteomics and in vitro diagnostics to generate reliable results.Proteomics Clin. Appl. 2007; 1: 1016-1035Crossref PubMed Scopus (10) Google Scholar). Although it has not been widely distributed as a reference material, the yeast Saccharomyces cerevisiae proteome has been extensively used by the proteomics community to characterize the capabilities of a variety of LC-MS-based approaches (4de Godoy L.M. Olsen J.V. de Souza G.A. Li G. Mortensen P. Mann M. Status of complete proteome analysis by mass spectrometry: SILAC labeled yeast as a model system.Genome Biol. 2006; 7: R50Crossref PubMed Scopus (231) Google Scholar, 5Shevchenko A. Jensen O.N. Podtelejnikov A.V. Sagliocco F. Wilm M. Vorm O. Mortensen P. Shevchenko A. Boucherie H. Mann M. Linking genome and proteome by mass spectrometry: large-scale identification of yeast proteins from two dimensional gels.Proc. Natl. Acad. Sci. U.S.A. 1996; 93: 14440-14445Crossref PubMed Scopus (1300) Google Scholar, 6Washburn M.P. Wolters D. Yates 3rd, J.R. Large-scale analysis of the yeast proteome by multidimensional protein identification technology.Nat. Biotechnol. 2001; 19: 242-247Crossref PubMed Scopus (4082) Google Scholar, 7Peng J. Elias J.E. Thoreen C.C. Licklider L.J. Gygi S.P. Evaluation of multidimensional chromatography coupled with tandem mass spectrometry (LC/LC-MS/MS) for large-scale protein analysis: the yeast proteome.J. Proteome Res. 2003; 2: 43-50Crossref PubMed Scopus (1382) Google Scholar, 8Wei J. Sun J. Yu W. Jones A. Oeller P. Keller M. Woodnutt G. Short J.M. Global proteome discovery using an online three-dimensional LC-MS/MS.J. Proteome Res. 2005; 4: 801-808Crossref PubMed Scopus (86) Google Scholar, 9Garrels J.I. McLaughlin C.S. Warner J.R. Futcher B. Latter G.I. Kobayashi R. Schwender B. Volpe T. Anderson D.S. Mesquita-Fuentes R. Payne W.E. Proteome studies of Saccharomyces cerevisiae: identification and characterization of abundant proteins.Electrophoresis. 1997; 18: 1347-1360Crossref PubMed Scopus (116) Google Scholar, 10Perrot M. Sagliocco F. Mini T. Monribot C. Schneider U. Shevchenko A. Mann M. Jenö P. Boucherie H. Two-dimensional gel protein database of Saccharomyces cerevisiae (update 1999).Electrophoresis. 1999; 20: 2280-2298Crossref PubMed Scopus (118) Google Scholar, 11de Godoy L.M. Olsen J.V. Cox J. Nielsen M.L. Hubner N.C. Fröhlich F. Walther T.C. Mann M. Comprehensive mass-spectrometry-based proteome quantification of haploid versus diploid yeast.Nature. 2008; 455: 1251-1254Crossref PubMed Scopus (739) Google Scholar, 12Piening B.D. Wang P. Bangur C.S. Whiteaker J. Zhang H. Feng L.C. Keane J.F. Eng J.K. Tang H. Prakash A. McIntosh M.W. Paulovich A. Quality control metrics for LC-MS feature detection tools demonstrated on Saccharomyces cerevisiae proteomic profiles.J. Proteome Res. 2006; 5: 1527-1534Crossref PubMed Scopus (29) Google Scholar, 13Nägele E. Vollmer M. Hörth P. Improved 2D nano-LC/MS for proteomics applications: a comparative analysis using yeast proteome.J. Biomol. Tech. 2004; 15: 134-143PubMed Google Scholar, 14Usaite R. Wohlschlegel J. Venable J.D. Park S.K. Nielsen J. Olsson L. Yates Iii J.R. Characterization of global yeast quantitative proteome data generated from the wild-type and glucose repression Saccharomyces cerevisiae strains: the comparison of two quantitative methods.J. Proteome Res. 2008; 7: 266-275Crossref PubMed Scopus (94) Google Scholar, 15Picotti P. Bodenmiller B. Mueller L.N. Domon B. Aebersold R. Full dynamic range proteome analysis of S. cerevisiae by targeted proteomics.Cell. 2009; 138: 795-806Abstract Full Text Full Text PDF PubMed Scopus (647) Google Scholar). Yeast provides a uniquely attractive complex performance standard for several reasons. Yeast encodes a complex proteome consisting of ∼4,500 proteins expressed during normal growth conditions (7Peng J. Elias J.E. Thoreen C.C. Licklider L.J. Gygi S.P. Evaluation of multidimensional chromatography coupled with tandem mass spectrometry (LC/LC-MS/MS) for large-scale protein analysis: the yeast proteome.J. Proteome Res. 2003; 2: 43-50Crossref PubMed Scopus (1382) Google Scholar, 16Ghaemmaghami S. Huh W.K. Bower K. Howson R.W. Belle A. Dephoure N. O'Shea E.K. Weissman J.S. Global analysis of protein expression in yeast.Nature. 2003; 425: 737-741Crossref PubMed Scopus (2995) Google Scholar, 17Huh W.K. Falvo J.V. Gerke L.C. Carroll A.S. Howson R.W. Weissman J.S. O'Shea E.K. Global analysis of protein localization in budding yeast.Nature. 2003; 425: 686-691Crossref PubMed Scopus (3299) Google Scholar, 18Washburn M.P. Koller A. Oshiro G. Ulaszek R.R. Plouffe D. Deciu C. Winzeler E. Yates 3rd, J.R. Protein pathway and complex clustering of correlated mRNA and protein expression analyses in Saccharomyces cerevisiae.Proc. Natl. Acad. Sci. U.S.A. 2003; 100: 3107-3112Crossref PubMed Scopus (308) Google Scholar). The concentration range of yeast proteins is sufficient to challenge the dynamic range of conventional mass spectrometers; the abundance of proteins ranges from fewer than 50 to more than 106 molecules per cell (4de Godoy L.M. Olsen J.V. de Souza G.A. Li G. Mortensen P. Mann M. Status of complete proteome analysis by mass spectrometry: SILAC labeled yeast as a model system.Genome Biol. 2006; 7: R50Crossref PubMed Scopus (231) Google Scholar, 15Picotti P. Bodenmiller B. Mueller L.N. Domon B. Aebersold R. Full dynamic range proteome analysis of S. cerevisiae by targeted proteomics.Cell. 2009; 138: 795-806Abstract Full Text Full Text PDF PubMed Scopus (647) Google Scholar, 16Ghaemmaghami S. Huh W.K. Bower K. Howson R.W. Belle A. Dephoure N. O'Shea E.K. Weissman J.S. Global analysis of protein expression in yeast.Nature. 2003; 425: 737-741Crossref PubMed Scopus (2995) Google Scholar). Additionally, 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 (5Shevchenko A. Jensen O.N. Podtelejnikov A.V. Sagliocco F. Wilm M. Vorm O. Mortensen P. Shevchenko A. Boucherie H. Mann M. Linking genome and proteome by mass spectrometry: large-scale identification of yeast proteins from two dimensional gels.Proc. Natl. Acad. Sci. U.S.A. 1996; 93: 14440-14445Crossref PubMed Scopus (1300) Google Scholar, 9Garrels J.I. McLaughlin C.S. Warner J.R. Futcher B. Latter G.I. Kobayashi R. Schwender B. Volpe T. Anderson D.S. Mesquita-Fuentes R. Payne W.E. Proteome studies of Saccharomyces cerevisiae: identification and characterization of abundant proteins.Electrophoresis. 1997; 18: 1347-1360Crossref PubMed Scopus (116) Google Scholar, 16Ghaemmaghami S. Huh W.K. Bower K. Howson R.W. Belle A. Dephoure N. O'Shea E.K. Weissman J.S. Global analysis of protein expression in yeast.Nature. 2003; 425: 737-741Crossref PubMed Scopus (2995) Google Scholar, 17Huh W.K. Falvo J.V. Gerke L.C. Carroll A.S. Howson R.W. Weissman J.S. O'Shea E.K. Global analysis of protein localization in budding yeast.Nature. 2003; 425: 686-691Crossref PubMed Scopus (3299) Google Scholar, 19Futcher B. Latter G.I. Monardo P. McLaughlin C.S. Garrels J.I. A sampling of the yeast proteome.Mol. Cell. Biol. 1999; 19: 7357-7368Crossref PubMed Scopus (512) Google Scholar, 20Gygi S.P. Rochon Y. Franza B.R. Aebersold R. Correlation between protein and mRNA abundance in yeast.Mol. Cell. Biol. 1999; 19: 1720-1730Crossref PubMed Scopus (3187) Google Scholar) as well as LC-MS/MS data sets showing good correlation between LC-MS/MS detection efficiency and the protein abundance estimates (4de Godoy L.M. Olsen J.V. de Souza G.A. Li G. Mortensen P. Mann M. Status of complete proteome analysis by mass spectrometry: SILAC labeled yeast as a model system.Genome Biol. 2006; 7: R50Crossref PubMed Scopus (231) Google Scholar, 11de Godoy L.M. Olsen J.V. Cox J. Nielsen M.L. Hubner N.C. Fröhlich F. Walther T.C. Mann M. Comprehensive mass-spectrometry-based proteome quantification of haploid versus diploid yeast.Nature. 2008; 455: 1251-1254Crossref PubMed Scopus (739) Google Scholar, 12Piening B.D. Wang P. Bangur C.S. Whiteaker J. Zhang H. Feng L.C. Keane J.F. Eng J.K. Tang H. Prakash A. McIntosh M.W. Paulovich A. Quality control metrics for LC-MS feature detection tools demonstrated on Saccharomyces cerevisiae proteomic profiles.J. Proteome Res. 2006; 5: 1527-1534Crossref PubMed Scopus (29) Google Scholar, 15Picotti P. Bodenmiller B. Mueller L.N. Domon B. Aebersold R. Full dynamic range proteome analysis of S. cerevisiae by targeted proteomics.Cell. 2009; 138: 795-806Abstract Full Text Full Text PDF PubMed Scopus (647) Google Scholar). Finally, it is inexpensive and easy to produce large quantities of yeast protein extract for distribution. In this study, we describe large scale production of a yeast S. cerevisiae performance standard, which we offer to the community through NIST. Through a series of interlaboratory studies, we created a reference data set characterizing the yeast performance standard and defining reasonable performance of ion trap-based LC-MS platforms in expert laboratories using a series of performance metrics. This publicly available data set provides a basis for additional laboratories using the yeast standard to benchmark their own performance as well as to improve upon the current status by evolving protocols, improving instrumentation, or developing new technologies. Finally, we demonstrate how the yeast performance standard, 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. An SOP for preparation of the yeast performance standard was developed based on the approach of Piening et al. (12Piening B.D. Wang P. Bangur C.S. Whiteaker J. Zhang H. Feng L.C. Keane J.F. Eng J.K. Tang H. Prakash A. McIntosh M.W. Paulovich A. Quality control metrics for LC-MS feature detection tools demonstrated on Saccharomyces cerevisiae proteomic profiles.J. Proteome Res. 2006; 5: 1527-1534Crossref PubMed Scopus (29) Google Scholar) with modifications to allow for scale-up. Production was outsourced to Boston Biochem (Cambridge, MA). The full protocol is given in supplemental Section A; initial characterization of the preparation is presented in supplemental Section B. In brief, S. cerevisiae strain BY4741 (MATa, leu2Δ0, met15Δ0, ura3Δ0, his3Δ1) was grown in a 10-liter batch of rich (yeast extract peptone dextrose) medium at 30 °C in a fermentor to an A600 of 0.93. The yeast were harvested by continuous flow centrifugation (yield, 5.4 g wet weight), and the cell pellet was washed three times with ice-cold water. The cells were lysed by incubation with ice-cold trichloroacetic acid (10% final concentration in 160-ml total volume) for 1 h at 4 °C. The protein precipitate was collected by centrifugation, washed twice with 160 ml of cold 90% acetone, and pelleted again. The resulting material was lyophilized and stored at −80 °C. The total yield of lyophilized yeast lysate was ∼0.75 g. Lyophilized yeast lysate (∼11 mg) was reconstituted in 50 mm ammonium bicarbonate containing 2 mg/ml RapiGest SF (Waters), heated at 60 °C for 45 min, and sonicated for 5 min on ice. Next, 50 mm DTT in 50 mm ammonium bicarbonate was added to yield a final DTT concentration of 5 mm, and the sample was incubated at 60 °C for 30 min. After cooling to room temperature, 200 mm iodoacetamide in water was added to yield a final concentration of 10 mm, and the alkylation reaction was left to proceed at room temperature in the dark for 30 min. To quench alkylation, 100 mm DTT in 50 mm ammonium bicarbonate was added to the sample to yield a final concentration of 10 mm. Prior to the addition of trypsin, an additional volume of 50 mm ammonium bicarbonate was added to the sample to reduce the RapiGest concentration to 0.1%. Trypsin (0.5 µg/µl in 20 mm aqueous HCl) was then added to the yeast lysate sample in a 1:50 ratio to the total protein amount. The sample was digested overnight (18 h) at 37 °C with gentle swirling. After digestion, to inactivate trypsin and cleave the RapiGest, concentrated trifluoroacetic acid was added to the sample to yield a concentration of 0.5%. The sample was then incubated again at 37 °C for 60 min followed by centrifugation at 10,000 rpm for 10 min. The supernatant was transferred to a new sample tube and lyophilized to dryness; after lyophilization, the dried digest was resuspended in 0.1% aqueous formic acid to yield a concentration that would correspond to ∼60 ng/µl total yeast protein prior to digestion. Where indicated, 48 human proteins (Sigma UPS1) were spiked into the reconstituted yeast performance standard (supplemental Section C). Each laboratory was asked to follow an SOP for collection of all data in Study 6. A detailed description of the SOP is provided in supplemental Section C. Parameters and settings specified in the SOPs were derived by a combination of consensus among the participants and limited method optimization studies. The SOPs do not represent fully optimized methods and are not intended to be prescriptive for the field. The SOPs were used instead to minimize variation due to factors that could be anticipated and controlled. Each laboratory was allowed to use its own favorite protocol for Study 8, and the individual protocols are summarized in supplemental Section D. Four models of mass spectrometer were used: LTQ, LTQ-XL, LTQ-XL-Orbitrap, and LTQ-Orbitrap (see supplemental section J). In each case, MS/MS spectra were collected in the LTQ. For the LTQ-Orbitrap instruments, MS1 spectra used to determine the precursors selected for MS/MS were collected at 60,000 resolution in the Orbitrap. These high resolution scans enabled precursor selection to be limited to precursors that exhibited both a charge of 2+ or higher and an isotope cluster from which the monoisotopic peak could be discerned. The low resolution MS1 scans on LTQ instruments did not enable these precursor selection criteria. A complete description of the acquisition parameters and other instrument configuration parameters can be found in supplemental Sections C, D, and J. For Studies 6 and 8, centroided tandem mass spectra were converted to peak lists in mzXML format by the msConvert tool of ProteoWizard 1.6.0 (21Kessner D. Chambers M. Burke R. Agus D. Mallick P. ProteoWizard: open source software for rapid proteomics tools development.Bioinformatics. 2008; 24: 2534-2536Crossref PubMed Scopus (1225) Google Scholar). The software was configured to centroid MS scans. Peptides were identified against the S. cerevisiae Genome Database orf_trans_all, downloaded April 6, 2007. These 6,718 sequences were augmented by 48 UPS1 sequences (Sigma) (supplemental Section E), 23 NCI20 sequences (supplemental Section F), and 74 contaminant protein sequences; the full database was then doubled in size by adding the reversed version of each sequence. The FASTA file is available at http://cptac.tranche.proteomecommons.org/. The MyriMatch database search algorithm version 1.6.0 (22Tabb D.L. Fernando C.G. Chambers M.C. MyriMatch: highly accurate tandem mass spectral peptide identification by multivariate hypergeometric analysis.J. Proteome Res. 2007; 6: 654-661Crossref PubMed Scopus (446) Google Scholar) identified tandem mass spectra to peptide sequences. Semitryptic peptide candidates were included as possible matches. The configuration defined proteolytic cleavage sites after any Lys or Arg (whether or not Pro was the next residue) or after a Met at the N terminus of a protein, allowing for up to two missed cleavages. Potential modifications included oxidation of methionines, formation of N-terminal pyroglutamine, deamidation of Asn-Gly motifs, and carbamidomethylation of cysteines, all as variable modifications. For the LTQ, precursors were allowed to be up to 1.25 m/z from the average mass of the peptide. For the Orbitrap, precursor ions were required to fall within 10 ppm of the database peptide with ppm computed from m/z values. To retain identifications in which the peptide monoisotope had been miscalled by the instrument control software, MyriMatch also sought matches in which a neutron had been added to or subtracted from each database peptide. Fragment ions were uniformly required to fall within 0.5 m/z of the monoisotope. IDPicker version 2.5 (23Ma Z.Q. Dasari S. Chambers M.C. Litton M.D. Sobecki S.M. Zimmerman L.J. Halvey P.J. Schilling B. Drake P.M. Gibson B.W. Tabb D.L. IDPicker 2.0: improved protein assembly with high discrimination peptide identification filtering.J. Proteome Res. 2009; 8: 3872-3881Crossref PubMed Scopus (268) Google Scholar, 24Zhang B. Chambers M.C. Tabb D.L. Proteomic parsimony through bipartite graph analysis improves accuracy and transparency.J. Proteome Res. 2007; 6: 3549-3557Crossref PubMed Scopus (264) Google Scholar) applied a 2% false identification rate per raw file at the peptide-spectrum match level and applied parsimony to the protein lists, requiring all proteins to match at least two distinct peptide sequences and to match at least 13 spectra (one per instrument per study). The two-peptide rule was applied globally, not by instrument. Hence, proteins on the list might have a single peptide for a given instrument. In contrast, the two-peptide rule was applied per raw file in the statistics code that generated the outputs displayed in Table I, Table III as well as Fig. 2, Fig. 3. IDPicker reports can be downloaded from http://cptac.tranche.proteomecommons.org/.Table ISummary of LC-MS results, average and CV (in percentages), for unspiked yeast reference proteomeTotal no. of yeast spectraTotal no. of yeast pept. sequencesYeast proteins identified usingCN50aCN50 values denote the copy number corresponding to 50% probability of detection for a randomly selected yeast protein. CN50 values are derived from logistic regression coefficients obtained by regressing yeast protein detection (yes/no) against log10 TAP copy number (12). CN50 is then converted to the copy number scale by taking 10 to the CN50 power. All mean and CV calculations are performed on the copy number scale. (CN50 values for all runs of all instruments are provided in supplemental Section H.)Performance metricsbFour performance metrics (described in supplemental Section G and in Table II), designed to diagnose LC-MS issues, are provided for individual instruments. C-3A is median peak widths for unique peptides; C-2A is retention period over which 50% of the identified peptides eluted; DS-2B is the number of MS2 spectra produced over C-2A; IS-3B is the ratio of the number of 3+/2+ charge states for all peptide identifications.1 pept.2 pept.>2 pept.C-3AC-2ADS-2BIS-3BAvgCVAvgCVAvgCVAvgCVAvgCVAvgCVAvgCVAvgCVAvgCVAvgCVA. Study 8 (600 ng loaded on column)[email protected][email protected]7,6002.05,4011.33431.11815.35741.918,0392.710.11.5321.05,3741.20.40.4[email protected]4,5680.53,2570.63214.51659.94041.127,4162.630.20.9372.05,2722.00.50.6[email protected]4,5485.53,9015.33637.01690.34775.822,3935.616.30.8322.54,9303.70.321.4[email protected]6,9141.35,4881.83442.01664.86021.417,7381.914.00.2410.96,8532.10.35.4[email protected]5,9980.95,0231.83634.01908.75723.617,9591.619.41.0321.94,5692.20.34.7[email protected]8,3361.07,2111.03133.21825.47240.813,6563.012.70.7351.45,6951.00.40.8Interlaboratory variationAll LTQs5,57231.54,18626.33436.11715.048517.622,61620.718.947.3347.45,1924.40.417.0All Orbitraps7,08316.65,90719.53407.41797.063312.716,45114.715.420.13610.55,70617.40.35.6B. Study 8 (120 ng loaded on column)[email protected][email protected]4,5836.53,5634.92785.511812.54293.226,2824.910.11.5271.34,0381.30.35.5[email protected]2,12412.11,66513.92294.6955.323011.958,65614.232.12.5299.04,0818.60.46.0[email protected]2,3672.32,1281.92755.91177.32914.544,1576.916.41.7263.43,8763.40.31.6[email protected]5,6542.15,0811.93311.317412.75730.818,1591.913.90.5342.05,0751.40.217.1[email protected]4,3831.13,8681.13472.51781.14743.222,6151.218.21.4252.53,3650.50.21.7[email protected]6,4911.75,9022.03442.41875.96490.815,3050.112.50.8322.84,7622.70.30.2Interlaboratory variationAll LTQs3,02544.82,45240.426110.511011.831732.143,03237.619.550.5277.33,9985.30.323.8All Orbitraps5,50919.34,95120.73412.51803.556515.618,69319.714.917.33013.44,40118.00.219.6C. Study 6 SOP (unspiked yeast; 120 ng loaded on column)[email protected][email protected]2,9413.82,4343.43262.31469.83200.835,4743.420.80.3294.14,2384.50.316.0[em

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