Performance Metrics for Liquid Chromatography-Tandem Mass Spectrometry Systems in Proteomics Analyses

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

doi:10.1074/mcp.m900223-mcp200

Cited by 176 publications

(252 citation statements)

References 25 publications

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“…All data analysis was centralized, and thus, any observed variations were entirely because of the LCMS platform. By applying the performance metrics developed by Rudnick et al (7), several key points emerged: (1) as expected, intralaboratory variation was less than interlaboratory variation; and (2) overall, the interlaboratory variation in peptide identifications and some of the other performance metrics were comparable between instruments, although there were large differences in the average values for some metrics (e.g. MS1 signal intensity, dynamic sampling).…”

mentioning

confidence: 84%

The 2012/2013 ABRF Proteomic Research Group Study: Assessing Longitudinal Intralaboratory Variability in Routine Peptide Liquid Chromatography Tandem Mass Spectrometry Analyses*

Bennett

Xia

Bystrom³

et al. 2015

Molecular & Cellular Proteomics

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Facilities (ABRF-PRG) to design a study to systematically assess the reproducibility of proteomic laboratories over an extended period of time. Developed as an open study, initially 64 participants were recruited from the broader mass spectrometry community to analyze provided aliquots of a six bovine protein tryptic digest mixture every month for a period of nine months. Data were uploaded to a central repository, and the operators answered an accompanying survey. Ultimately, 45 laboratories submitted a minimum of eight LC-MSMS raw data files collected in data-dependent acquisition (DDA) mode. No standard operating procedures were enforced; rather the participants were encouraged to analyze the samples according to usual practices in the laboratory. Unlike previous studies, this investigation was not designed to compare laboratories or instrument configuration, but rather to assess the temporal intralaboratory reproducibility. The outcome of the study was reassuring with 80% of the participating laboratories performing analyses at a medium to high level of reproducibility and quality over the 9-month period. For the groups that had one or more outlying experiments, the major contributing factor that correlated to the survey data was the performance of preventative maintenance prior to the LC-MSMS analyses. Thus, the Protein Research Group of the Association of Biomolecular Resource Facilities recommends that laboratories closely scrutinize the quality control data following such events. Additionally, improved quality control recording is imperative. This longitudinal study provides evidence that mass spectrometry-based proteomics is reproducible. When quality control measures are strictly adhered to, such reproducibility is comparable among many disparate groups. Data from the study are available via ProteomeXchange under the accession code PXD002114. Molecular & Cellular Proteomics 14: 10.1074/mcp.O115.051888, 3299-3309, 2015.The broad-reaching use and application of mass spectrometry-based proteomics in the international research commu-

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mentioning

confidence: 84%

The 2012/2013 ABRF Proteomic Research Group Study: Assessing Longitudinal Intralaboratory Variability in Routine Peptide Liquid Chromatography Tandem Mass Spectrometry Analyses*

Bennett

Xia

Bystrom³

et al. 2015

Molecular & Cellular Proteomics

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“…This consortium set up a study in which different labs analyzed the same sample to test inter-laboratory comparability. For this analysis, we selected the CPTAC sample consisting of yeast digests (60 ng/mL) spiked in with 6.7 fmol/mL of the equimolar mixture of 48 human proteins (Sigma UPS-1), which was processed in triplicate on an LTQ-OrbiTrap mass spectrometer (Rudnick et al 2010). MS2 files were created using the DTA supercharger program (Mortensen et al 2010).…”

Section: Methodsmentioning

confidence: 99%

The Effect of Peptide Identification Search Algorithms on MS2-Based Label-Free Protein Quantification

Degroeve

Staes

Bock

et al. 2012

OMICS: A Journal of Integrative Biology

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Several approaches exist for the quantification of proteins in complex samples processed by liquid chromatography-mass spectrometry followed by fragmentation analysis (MS2). One of these approaches is label-free MS2-based quantification, which takes advantage of the information computed from MS2 spectrum observations to estimate the abundance of a protein in a sample. As a first step in this approach, fragmentation spectra are typically matched to the peptides that generated them by a search algorithm. Because different search algorithms identify overlapping but non-identical sets of peptides, here we investigate whether these differences in peptide identification have an impact on the quantification of the proteins in the sample. We therefore evaluated the effect of using different search algorithms by examining the reproducibility of protein quantification in technical repeat measurements of the same sample. From our results, it is clear that a search engine effect does exist for MS2-based label-free protein quantification methods. As a general conclusion, it is recommended to address the overall possibility of search engine-induced bias in the protein quantification results of label-free MS2-based methods by performing the analysis with two or more distinct search engines.

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“…6,7 In the sixth study of the CPTAC network, identical samples were analyzed by different laboratories on different instruments to benchmark and analyze the interlaboratory variation and to enable proteomic quality assessment. 6,7 Here, only raw data results from one laboratory (LTQ-Orbitrap@86, CPTAC experiment identification number 104, Tranche download hash: NGX3cBUAZXSWvcþ6XF-NIdVhpLPJTO87lzAxUQmwwR2KHUwWDrdFwV1dso3bvxf-7HeXZ4C/juqwEUIz4boC9H3HcLrxEAAAAAAAAmDw==) were used to do the analyses. 6,7 In this study, five yeast lysates were spiked with different concentrations of the Sigma UPS-1 mixture of 48 human proteins.…”

Section: Data Setsmentioning

confidence: 99%

“…6,7 Here, only raw data results from one laboratory (LTQ-Orbitrap@86, CPTAC experiment identification number 104, Tranche download hash: NGX3cBUAZXSWvcþ6XF-NIdVhpLPJTO87lzAxUQmwwR2KHUwWDrdFwV1dso3bvxf-7HeXZ4C/juqwEUIz4boC9H3HcLrxEAAAAAAAAmDw==) were used to do the analyses. 6,7 In this study, five yeast lysates were spiked with different concentrations of the Sigma UPS-1 mixture of 48 human proteins. These samples were analyzed in triplicate on an LTQ-Orbitrap XL generating three data sets for each spiked-in concentration of the Sigma UPS-1 mixture.…”

Section: Data Setsmentioning

confidence: 99%

“…Detailed information concerning the yeast lysate, digestion and mass spectrometric analysis can be found in. 6,7 Mgf files were generated from the raw Thermo results files with DtaSuperCharger. 8 Identification of the mgf spectra was performed according to the original protocol as described in 6,7 using a local installation of Mascot (Version 2.2.04) and the yeast UniprotKB/Swiss-Prot protein database (version 15.14), supplemented with the 48 protein sequences of the UPS-1 mixture.…”

Section: Data Setsmentioning

confidence: 99%

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RIBAR and xRIBAR: Methods for Reproducible Relative MS/MS-based Label-Free Protein Quantification

Gevaert

Martens

2011

J. Proteome Res.

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Mass spectrometry-driven proteomics is increasingly relying on quantitative analyses for biological discoveries. As a result, different methods and algorithms have been developed to perform relative or absolute quantification based on mass spectrometry data. One of the most popular quantification methods are the so-called label-free approaches, which require no special sample processing, and can even be applied retroactively to existing data sets. Of these label-free methods, the MS/MS-based approaches are most often applied, mainly because of their inherent simplicity as compared to MS-based methods. The main application of these approaches is the determination of relative protein amounts between different samples, expressed as protein ratios. However, as we demonstrate here, there are some issues with the reproducibility across replicates of these protein ratio sets obtained from the various MS/MS-based label-free methods, indicating that the existing methods are not optimally robust. We therefore present two new methods (called RIBAR and xRIBAR) that use the available MS/ MS data more effectively, achieving increased robustness. Both the accuracy and the precision of our novel methods are analyzed and compared to the existing methods to illustrate the increased robustness of our new methods over existing ones.

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Performance Metrics for Liquid Chromatography-Tandem Mass Spectrometry Systems in Proteomics Analyses

Cited by 176 publications

References 25 publications

The 2012/2013 ABRF Proteomic Research Group Study: Assessing Longitudinal Intralaboratory Variability in Routine Peptide Liquid Chromatography Tandem Mass Spectrometry Analyses*

The 2012/2013 ABRF Proteomic Research Group Study: Assessing Longitudinal Intralaboratory Variability in Routine Peptide Liquid Chromatography Tandem Mass Spectrometry Analyses*

The Effect of Peptide Identification Search Algorithms on MS2-Based Label-Free Protein Quantification

RIBAR and xRIBAR: Methods for Reproducible Relative MS/MS-based Label-Free Protein Quantification

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