MSstatsQC: Longitudinal System Suitability Monitoring and Quality Control for Targeted Proteomic Experiments
2017; Elsevier BV; Volume: 16; Issue: 7 Linguagem: Inglês
10.1074/mcp.m116.064774
ISSN1535-9484
AutoresEralp Doğu, Sara Mohammad-Taheri, Susan E. Abbatiello, Michael S. Bereman, Brendan MacLean, Birgit Schilling, Olga Vitek,
Tópico(s)Metabolomics and Mass Spectrometry Studies
ResumoSelected Reaction Monitoring (SRM) is a powerful tool for targeted detection and quantification of peptides in complex matrices. An important objective of SRM is to obtain peptide quantifications that are (1) suitable for the investigation, and (2) reproducible across laboratories and runs. The first objective is achieved by system suitability tests (SST), which verify that mass spectrometric instrumentation performs as specified. The second objective is achieved by quality control (QC), which provides in-process quality assurance of the sample profile. A common aspect of SST and QC is the longitudinal nature of the data. Although SST and QC have received a lot of attention in the proteomic community, the currently used statistical methods are limited. This manuscript improves upon the statistical methodology for SST and QC that is currently used in proteomics. It adapts the modern methods of longitudinal statistical process control, such as simultaneous and time weighted control charts and change point analysis, to SST and QC of SRM experiments, discusses their advantages, and provides practical guidelines. Evaluations on simulated data sets, and on data sets from the Clinical Proteomics Technology Assessment for Cancer (CPTAC) consortium, demonstrated that these methods substantially improve our ability of real time monitoring, early detection and prevention of chromatographic and instrumental problems. We implemented the methods in an open-source R-based software package MSstatsQC and its web-based graphical user interface. They are available for use stand-alone, or for integration with automated pipelines. Although the examples focus on targeted proteomics, the statistical methods in this manuscript apply more generally to quantitative proteomics. Selected Reaction Monitoring (SRM) is a powerful tool for targeted detection and quantification of peptides in complex matrices. An important objective of SRM is to obtain peptide quantifications that are (1) suitable for the investigation, and (2) reproducible across laboratories and runs. The first objective is achieved by system suitability tests (SST), which verify that mass spectrometric instrumentation performs as specified. The second objective is achieved by quality control (QC), which provides in-process quality assurance of the sample profile. A common aspect of SST and QC is the longitudinal nature of the data. Although SST and QC have received a lot of attention in the proteomic community, the currently used statistical methods are limited. This manuscript improves upon the statistical methodology for SST and QC that is currently used in proteomics. It adapts the modern methods of longitudinal statistical process control, such as simultaneous and time weighted control charts and change point analysis, to SST and QC of SRM experiments, discusses their advantages, and provides practical guidelines. Evaluations on simulated data sets, and on data sets from the Clinical Proteomics Technology Assessment for Cancer (CPTAC) consortium, demonstrated that these methods substantially improve our ability of real time monitoring, early detection and prevention of chromatographic and instrumental problems. We implemented the methods in an open-source R-based software package MSstatsQC and its web-based graphical user interface. They are available for use stand-alone, or for integration with automated pipelines. Although the examples focus on targeted proteomics, the statistical methods in this manuscript apply more generally to quantitative proteomics. Mass spectrometry-based Selected Reaction Monitoring (SRM) 1The abbreviations used are: SRM, selected reaction monitoring; CPTAC, Clinical Proteomics Technology Assessment for Cancer of the National Cancer Institute; CUSUM, cumulative sum; FWHM, full width at half maximum; QC, quality control; SPC, statistical process control; SST, system suitability testing; XmR, Individual and Moving Range Control Charts.1The abbreviations used are: SRM, selected reaction monitoring; CPTAC, Clinical Proteomics Technology Assessment for Cancer of the National Cancer Institute; CUSUM, cumulative sum; FWHM, full width at half maximum; QC, quality control; SPC, statistical process control; SST, system suitability testing; XmR, Individual and Moving Range Control Charts. is a powerful tool for targeted detection and quantification of peptides in complex biological mixtures and matrices (1.Picotti P. Aebersold R. Selected reaction monitoring-based proteomics: workflows, potential, pitfalls and future directions.Nat. Meth. 2012; 9: 555-566Crossref PubMed Scopus (996) Google Scholar, 2.Gallien S. Duriez E. Domon B. Selected reaction monitoring applied to proteomics.J. Mass Spectrom. 2011; 46: 298-312Crossref PubMed Scopus (236) Google Scholar, 3.Bantscheff M. Lemeer S. Savitski M.M. Kuster B. Quantitative mass spectrometry in proteomics: critical review update from 2007 to the present.Anal. Bioanal. Chem. 2012; 404: 939-965Crossref PubMed Scopus (581) Google Scholar). SRM assays can identify and quantify targeted analytes with great specificity and accuracy. They are increasingly adopted by the biomedical community, for applications ranging from clinical diagnostic, to research and exploratory studies. An important objective of SRM assays is to produce peptide quantifications that are (1) suitable for the investigation, and (2) reproducible across laboratories and runs (4.Carr S.A. Abbatiello S.E. Ackermann B.L. Borchers C. Domon B. Deutsch E.W. Grant R.P. Hoofnagle A.N. Hüttenhain R. Koomen J.M. Liebler D.C. Liu T. MacLean B. Mani D.R. Mansfield E. Neubert H. Paulovich A.G. Reiter L. Vitek O. Aebersold R. Anderson L. Bethem R. Blonder J. Boja E. Botelho J. Boyne M. Bradshaw R.A. Burlingame A.L. Chan D. Keshishian H. Kuhn E. Kinsinger C. Lee J.S. Lee S.W. Moritz R. Oses-Prieto J. Rifai N. Ritchie J. Rodriguez H. Srinivas P.R. Townsend R.R. Van Eyk J. Whiteley G. Wiita A. Weintraub S. Targeted peptide measurements in biology and medicine: best practices for mass spectrometry-based assay development using a fit-for-purpose approach.Mol. Cell. Proteomics. 2014; 13: 907-917Abstract Full Text Full Text PDF PubMed Scopus (404) Google Scholar). As the complexity of the experiments and of the instrumentation grows, so does the need to characterize the accuracy and the consistency of the results. To help achieve this, the United States Pharmacopeia (USP) chapter introduced four components of data quality: analytical instrument qualification, analytical method validation, system suitability testing, and quality control checks (5.United States Pharmacopeia (U. S. P.) (2012) General Chapter ➜ Analytical instrument qualification.Google Scholar). Analytical instrument qualification presents evidence that the instrument, its setup and calibration are suitable for the intended use. Analytical method validation demonstrates that the assay performed on the instrument can produce reliable results, and reports characteristics such as accuracy, precision, specificity, detection limit, and quantification limit (6.United States Pharmacopeia (U. S. P.) (2007) General Chapter ❙ Validation of compendial methods.Google Scholar, 7.Food and Drug Administration (FDA). (1994) Reviewer Guidance: Validation of Chromatographic methods (CDER-FDA),Google Scholar). At the same time, the practical utility of SRM assays depends not only on the general properties of the instrument and of the assay, but also on whether their implementation worked as intended in an experimental setting. A same SRM assay of a same biological material can produce variable results, depending on conditions such as laboratories, instruments, operators, or time of data acquisition (8.Abbatiello S.E. Schilling B. Mani D.R. Zimmerman L.J. Hall S.C. MacLean B. Albertolle M. Allen S. Burgess M. Cusack M.P. Ghosh M. Hedrick V. Held J.M. Inerowicz H.D. Jackson A. Keshishian H. Kinsinger C.R. Lyssand J. Makowski L. Mesri M. Rodriguez H. Rudnick P. Sadowski P. Sedransk N. Shaddox K. Skates S.J. Kuhn E. Smith D. Whiteaker J.R. Whitwell C. Zhang S. Borchers C.H. Fisher S.J. Gibson B.W. Liebler D.C. MacCoss M.J. Neubert T.A. Paulovich A.G. Regnier F.E. Tempst P. Carr S.A. Large-scale inter-laboratory study to develop, analytically validate and apply highly multiplexed, quantitative peptide assays to measure cancer-relevant proteins in plasma.Mol. Cell. Proteomics. 2015; 1Google Scholar). Substantial reproducibility gains can be achieved by minimizing this undue variation (4.Carr S.A. Abbatiello S.E. Ackermann B.L. Borchers C. Domon B. Deutsch E.W. Grant R.P. Hoofnagle A.N. Hüttenhain R. Koomen J.M. Liebler D.C. Liu T. MacLean B. Mani D.R. Mansfield E. Neubert H. Paulovich A.G. Reiter L. Vitek O. Aebersold R. Anderson L. Bethem R. Blonder J. Boja E. Botelho J. Boyne M. Bradshaw R.A. Burlingame A.L. Chan D. Keshishian H. Kuhn E. Kinsinger C. Lee J.S. Lee S.W. Moritz R. Oses-Prieto J. Rifai N. Ritchie J. Rodriguez H. Srinivas P.R. Townsend R.R. Van Eyk J. Whiteley G. Wiita A. Weintraub S. Targeted peptide measurements in biology and medicine: best practices for mass spectrometry-based assay development using a fit-for-purpose approach.Mol. Cell. Proteomics. 2014; 13: 907-917Abstract Full Text Full Text PDF PubMed Scopus (404) Google Scholar, 8.Abbatiello S.E. Schilling B. Mani D.R. Zimmerman L.J. Hall S.C. MacLean B. Albertolle M. Allen S. Burgess M. Cusack M.P. Ghosh M. Hedrick V. Held J.M. Inerowicz H.D. Jackson A. Keshishian H. Kinsinger C.R. Lyssand J. Makowski L. Mesri M. Rodriguez H. Rudnick P. Sadowski P. Sedransk N. Shaddox K. Skates S.J. Kuhn E. Smith D. Whiteaker J.R. Whitwell C. Zhang S. Borchers C.H. Fisher S.J. Gibson B.W. Liebler D.C. MacCoss M.J. Neubert T.A. Paulovich A.G. Regnier F.E. Tempst P. Carr S.A. Large-scale inter-laboratory study to develop, analytically validate and apply highly multiplexed, quantitative peptide assays to measure cancer-relevant proteins in plasma.Mol. Cell. Proteomics. 2015; 1Google Scholar). To this end, system suitability tests (SST) verify that a laboratory system satisfies the prespecified criteria immediately before sample analysis. SST relies on a series of reference materials and decision rules, designed to separately test aspects of the system such as consistency of the response, carryover, retention time stability, mass accuracy, or signal-to-noise (9.United States Pharmacopeia (U. S. P.) (2012) General Chapter ⇷ Chromatography. 1,Google Scholar, 10.Swartz M.E. Krull I.S. Handbook of analytical validation. CRC Press, Boca Raton2012: 23-24Google Scholar). If a component of the SST test fails, the instrument is stopped to remedy the problem. In contrast, quality control (QC) uses reference materials and/or calibration standards, and decision rules, to provide an in-process assurance during sample analysis. When reference materials cannot be part of the biological sample, a separate QC sample, e.g. a spiked protein mixture of known composition, or a cell lysate or other mixture that mimics the biological material of interest, is interleaved between the biological samples (10.Swartz M.E. Krull I.S. Handbook of analytical validation. CRC Press, Boca Raton2012: 23-24Google Scholar, 11.Dong M. Paul R. Gershanov L. Getting the peaks perfect: System suitability for HPLC.Todays Chem. Work. 2001; 10: 38-42Google Scholar). Because SST and QC can use different reference materials, metrics, and decision rules, their results do not necessarily agree. For example, when the system passes SST but the experiment fails QC, the measurement system does not need to be re-evaluated, and the problem may lay elsewhere (e.g. in sample storage or processing). Alternatively, an instrument may have unsuitable performance, but produce measurements with acceptable QC (12.Briscoe C.J. Stiles M.R. Hage D.S. System suitability in bioanalytical LC/MS/MS.J. Pharm. Biomed. Anal. 2007; 44: 484-491Crossref PubMed Scopus (78) Google Scholar). A common aspect of SST and QC is the longitudinal nature of the input data, and of data-driven decisions. An accurate, reproducible and objective monitoring and decision-making requires the use of statistical methodology. The same general statistical methodology for summarizing longitudinal profiles can be applied to SST and QC. A state-of-the art approach for longitudinal profiling is Statistical Process Control (SPC). SPC is a collection of statistical methods and graphical summaries that generate warning flags when undesirable deviations occur, and help identify and eliminate the root causes of these deviations (13.Shewhart W.A. Application of statistical methods to manufacturing problems.J. Franklin Inst. 1938; 226: 163-186Crossref Scopus (12) Google Scholar, 14.Shewhart W.A. Some applications of statistical methods to the analysis of physical and engineering data.Bell Syst. Tech. J. 1924; 3: 43-87Crossref Scopus (88) Google Scholar, 15.Levey S. Jennings E.R. The use of control charts in the clinical laboratory.Am. J. Clin. Pathol. 1950; 20: 1059-1066Crossref PubMed Google Scholar). The use of SPC in manufacturing, automotive industry, food, service, and healthcare has demonstrated excellent performance in reducing internal and external failure costs (16.Montgomery D.C. Introduction to statistical quality control. John Wiley & Sons, 2007: 385-414Google Scholar). Previous reports cite striking results, such as saving millions of dollars in expenses, reducing cycle times by half or more, and reduction of processing errors (16.Montgomery D.C. Introduction to statistical quality control. John Wiley & Sons, 2007: 385-414Google Scholar). SPC has been widely adopted in clinical laboratories (17.Westgard J.O. Groth T. Power functions for statistical control rules.Clin. Chem. 1979; 25: 863-869Crossref PubMed Scopus (87) Google Scholar), and its applications in mass spectrometry-based proteomics increasingly appear. For example, Bramwell (18.Bramwell D. An introduction to statistical process control in research proteomics.J. Proteomics. 2013; 95: 3-21Crossref PubMed Scopus (16) Google Scholar) demonstrated the potential of SPC in a 2D DIGE experiment. Other applications are presented in Bourmaud et al. (19.Bourmaud A. Gallien S. Domon B. A quality control of proteomic experiments based on multiple isotopologous internal standards.EuPA Open Proteomics. 2015; 8: 16-21Crossref Scopus (9) Google Scholar), Bereman et al. (20.Bereman M.S. Johnson R. Bollinger J. Boss Y. Shulman N. MacLean B. Hoofnagle A.N. MacCoss M.J. Implementation of statistical process control for proteomic experiments via LC MS/MS.J. Am. Soc. Mass Spectrom. 2014; 25: 581-587Crossref PubMed Scopus (34) Google Scholar), and Bereman et al. (21.Bereman M.S. Beri J. Sharma V. Nathe C. Eckels J. MacLean B. MacCoss M.J. An automated pipeline to monitor system performance in liquid chromatography tandem mass spectrometry proteomic experiments.J. Proteome Res. 2016; 15: 4763-4769Crossref PubMed Scopus (38) Google Scholar). However, the statistical methodology used in these applications is limited to control charts that monitor large changes in mean of a metric, and do not match the sensitivity and the accuracy of more modern, state-of-the-art methods. The contribution of this manuscript is in adapting a broader class of modern SPC methods, beyond what is currently used in mass spectrometry-based proteomics, to the context of SRM experiments. The methods include simultaneous monitoring mean of a metric in addition to its variability, time weighted control charts, and change point analysis. We provide practical guidelines for using these methods, and for making decisions from multivariate measurements, in the context of both SST and QC. We demonstrated the advantages of these methods using simulated longitudinal QC data sets for single peptides, and using experimental longitudinal SST data sets from the Clinical Proteomics Technology Assessment for Cancer (CPTAC) consortium evaluating multimetrics and multipeptide criteria of performance. We implemented the methods in an open-source R-based (22.Team R.C. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria2015Google Scholar) software package MSstatsQC and its graphical user interface, which can be used stand-alone, or integrated into larger and more automated data analysis pipelines. We argue that this statistical methodology should become part of the daily practice of SRM-based investigations. Although all the examples in this manuscript focus on targeted proteomics, the statistical methods are general, and can be in principle applied to other quantitative mass spectrometry-based workflows. Several noteworthy studies focused on QC monitoring of large-scale label-free proteomic experiments with data dependent acquisition (DDA). They considered two aspects: which metrics to monitor, and how to monitor them. An interlaboratory study by the Human Proteome Organization (HUPO) revealed common reproducibility problems in proteomic experiments, and raised awareness of quality control (23.Bell A.W. Deutsch E.W. Au C.E. Kearney R.E. Beavis R. Sechi S. Nilsson T. Bergeron J.J.M. A HUPO test sample study reveals common problems in mass spectrometry-based proteomics.Nat. Meth. 2009; 6: 423-430Crossref PubMed Scopus (274) Google Scholar). The National Institute of Standards and Technology (NIST) in collaboration with CPTAC identified 46 metrics, obtained with the software pipeline NISTMSQC, for evaluating the performance of LC-MS/MS DDA (24.Rudnick P.A. Clauser K.R. Kilpatrick L.E. Tchekhovskoi D.V. Neta P. Billheimer D.D. Blackman R.K. Bunk D.M. Cardasis H.L. Ham J.L. Jaffe J.D. Kinsinger C.R. Mesri M. Neubert T.A. Schilling B. Tabb D.L. Tegeler T.J. Vega L. Variyath A.M. Wang M. Wang P. Whiteaker J.R. Zimmerman L.J. Carr S.A. Fisher S.J. Gibson B.W. Paulovich A.G. Regnier F.E. Rodriguez H. Spiegelman C. Tempst P. Liebler D.C. Stein S.E. Network C. Az T. York N. Research A. Performance metrics for evaluating liquid chromatography-tandem mass spectrometry systems in shotgun proteomics.Mol. Cell. Biol. 2009; 9: 225-241Google Scholar). This approach was not widely adopted because of several drawbacks, including applicability to only one vendor and search algorithm, complexity of data extraction, and lack of visual representation for less experienced users (20.Bereman M.S. Johnson R. Bollinger J. Boss Y. Shulman N. MacLean B. Hoofnagle A.N. MacCoss M.J. Implementation of statistical process control for proteomic experiments via LC MS/MS.J. Am. Soc. Mass Spectrom. 2014; 25: 581-587Crossref PubMed Scopus (34) Google Scholar). Another tool for LC-MS/MS DDA, QuaMeter (25.Ma Z.Q. Polzin K.O. Dasari S. Chambers M.C. Schilling B. Gibson B.W. Tran B.Q. Vega-Montoto L. Liebler D.C. Tabb D.L. QuaMeter: Multivendor performance metrics for LC-MS/MS proteomics instrumentation.Anal. Chem. 2012; 84: 5845-5850Crossref PubMed Scopus (44) Google Scholar), offered a more flexible way to generate metrics, but required manual evaluation. An outcome of these studies was an extensive list of metrics, used to monitor chromatographic and electrospray stability, and mass spectrometer performance. LC performance can be monitored by evaluating peak retention times and intensities, peak widths, and total peak areas of peptides (20.Bereman M.S. Johnson R. Bollinger J. Boss Y. Shulman N. MacLean B. Hoofnagle A.N. MacCoss M.J. Implementation of statistical process control for proteomic experiments via LC MS/MS.J. Am. Soc. Mass Spectrom. 2014; 25: 581-587Crossref PubMed Scopus (34) Google Scholar, 24.Rudnick P.A. Clauser K.R. Kilpatrick L.E. Tchekhovskoi D.V. Neta P. Billheimer D.D. Blackman R.K. Bunk D.M. Cardasis H.L. Ham J.L. Jaffe J.D. Kinsinger C.R. Mesri M. Neubert T.A. Schilling B. Tabb D.L. Tegeler T.J. Vega L. Variyath A.M. Wang M. Wang P. Whiteaker J.R. Zimmerman L.J. Carr S.A. Fisher S.J. Gibson B.W. Paulovich A.G. Regnier F.E. Rodriguez H. Spiegelman C. Tempst P. Liebler D.C. Stein S.E. Network C. Az T. York N. Research A. Performance metrics for evaluating liquid chromatography-tandem mass spectrometry systems in shotgun proteomics.Mol. Cell. Biol. 2009; 9: 225-241Google Scholar, 26.Pichler P. Mazanek M. Dusberger F. Weilnböck L. Huber C.G. Stingl C. Luider T.M. Straube W.L. Köcher T. Mechtler K. SIMPATIQCO: A server-based software suite which facilitates monitoring the time course of LC-MS performance metrics on orbitrap instruments.J. Proteome Res. 2012; 11: 5540-5547Crossref PubMed Scopus (43) Google Scholar). Electrospray stability can be monitored by its impact on MS1 and MS/MS ion injection times, and on number of acquired MS1 and MS/MS scans (26.Pichler P. Mazanek M. Dusberger F. Weilnböck L. Huber C.G. Stingl C. Luider T.M. Straube W.L. Köcher T. Mechtler K. SIMPATIQCO: A server-based software suite which facilitates monitoring the time course of LC-MS performance metrics on orbitrap instruments.J. Proteome Res. 2012; 11: 5540-5547Crossref PubMed Scopus (43) Google Scholar). Mass accuracy is commonly used to monitor mass spectrometer performance (20.Bereman M.S. Johnson R. Bollinger J. Boss Y. Shulman N. MacLean B. Hoofnagle A.N. MacCoss M.J. Implementation of statistical process control for proteomic experiments via LC MS/MS.J. Am. Soc. Mass Spectrom. 2014; 25: 581-587Crossref PubMed Scopus (34) Google Scholar, 21.Bereman M.S. Beri J. Sharma V. Nathe C. Eckels J. MacLean B. MacCoss M.J. An automated pipeline to monitor system performance in liquid chromatography tandem mass spectrometry proteomic experiments.J. Proteome Res. 2016; 15: 4763-4769Crossref PubMed Scopus (38) Google Scholar). In addition to choosing informative metrics, an equally important concern is the choice of statistical methodology to monitor their values in time. SIMPATIQCO (26.Pichler P. Mazanek M. Dusberger F. Weilnböck L. Huber C.G. Stingl C. Luider T.M. Straube W.L. Köcher T. Mechtler K. SIMPATIQCO: A server-based software suite which facilitates monitoring the time course of LC-MS performance metrics on orbitrap instruments.J. Proteome Res. 2012; 11: 5540-5547Crossref PubMed Scopus (43) Google Scholar) proposed run charts with green (safe), yellow (warning), and red (out-of-control) regions, which used median absolute deviation of metrics to detect abrupt changes in QC. Likewise, bean plots and run charts were used to monitor QC metrics with Metriculator (27.Taylor R.M. Dance J. Taylor R.J. Prince J.T. Metriculator: Quality assessment for mass spectrometry-based proteomics.Bioinformatics. 2013; 29: 2948-2949Crossref PubMed Scopus (17) Google Scholar), however, the tool did not implement thresholds for distinguishing undesirable system variation from noise. Bennett et al. (28.Bennett K.L. Wang X. Bystrom C.E. Chambers M.C. Andacht T.M. Dangott L.J. Elortza F. Leszyk J. Molina H. Moritz R.L. Phinney B.S. Thompson J.W. Bunger M.K. Tabb D.L. The 2012/2013 ABRF Proteomic Research Group Study: Assessing longitudinal intralaboratory variability in routine peptide liquid chromatography tandem mass spectrometry analyses.Mol. Cell. Proteomics. 2015; 14: 3299-3309Abstract Full Text Full Text PDF PubMed Scopus (8) Google Scholar) analyzed multisite DDA experiments with QuaMeter and NISTMSQC. The authors used principle component analysis and control charts to highlight patterns of between-laboratory variation, however the longitudinal aspect of the study was limited to only nine time points. More recently, research interest shifted to QC of targeted SRM experiments. Statistical Process Control in Proteomics (SProCoP) (20.Bereman M.S. Johnson R. Bollinger J. Boss Y. Shulman N. MacLean B. Hoofnagle A.N. MacCoss M.J. Implementation of statistical process control for proteomic experiments via LC MS/MS.J. Am. Soc. Mass Spectrom. 2014; 25: 581-587Crossref PubMed Scopus (34) Google Scholar) and Panorama AutoQC (21.Bereman M.S. Beri J. Sharma V. Nathe C. Eckels J. MacLean B. MacCoss M.J. An automated pipeline to monitor system performance in liquid chromatography tandem mass spectrometry proteomic experiments.J. Proteome Res. 2016; 15: 4763-4769Crossref PubMed Scopus (38) Google Scholar) implemented statistical methods from the SPC toolbox. SProCop is an open-source R-based plug-in to the Skyline software (29.MacLean B. Tomazela D.M. Shulman N. Chambers M. Finney G.L. Frewen B. Kern R. Tabb D.L. Liebler D.C. MacCoss M.J. Skyline: an open source document editor for creating and analyzing targeted proteomics experiments.Bioinformatics. 2010; 26: 966-968Crossref PubMed Scopus (2983) Google Scholar) and a Skyline external tool (30.Beeley C. Web application development with R using Shiny. Packt Publishing Ltd, Birmingham, U.K.2016Google Scholar). It takes as input metrics such as retention time, total peak area, full peak width at half maximum (FWHM) or peak asymmetry. It implements control charts (called Z charts) to monitor standardized outputs for each peptide, and applies constant decision thresholds. Likewise, Panorama AutoQC is an open-source interface between Skyline and Panorama server (31.Sharma V. Eckels J. Taylor G.K. Shulman N.J. Stergachis A.B. Joyner S.A. Yan P. Whiteaker J.R. Halusa G.N. Schilling B. Gibson B.W. Colangelo C.M. Paulovich A.G. Carr S.A. Jaffe J.D. MacCoss M.J. MacLean B. Panorama: a targeted proteomics knowledge base.J. Proteome Res. 2014; 13: 4205-4210Crossref PubMed Scopus (149) Google Scholar), which uses similar input metrics, and implements control charts for nonstandardized outputs, where decision thresholds can depend on a metric and on a peptide. Variation of each metric is assumed to be stable over time. Although a lot of method development in proteomics has already been devoted to QC, fewer publications discuss standardizing and monitoring system suitability testing of SRM assays. Although SST guidelines for clinical laboratories are available (9.United States Pharmacopeia (U. S. P.) (2012) General Chapter ⇷ Chromatography. 1,Google Scholar), metrics and acceptance criteria for multiplexed discovery and validation SRM assays (called tier 2 and 3 in (4.Carr S.A. Abbatiello S.E. Ackermann B.L. Borchers C. Domon B. Deutsch E.W. Grant R.P. Hoofnagle A.N. Hüttenhain R. Koomen J.M. Liebler D.C. Liu T. MacLean B. Mani D.R. Mansfield E. Neubert H. Paulovich A.G. Reiter L. Vitek O. Aebersold R. Anderson L. Bethem R. Blonder J. Boja E. Botelho J. Boyne M. Bradshaw R.A. Burlingame A.L. Chan D. Keshishian H. Kuhn E. Kinsinger C. Lee J.S. Lee S.W. Moritz R. Oses-Prieto J. Rifai N. Ritchie J. Rodriguez H. Srinivas P.R. Townsend R.R. Van Eyk J. Whiteley G. Wiita A. Weintraub S. Targeted peptide measurements in biology and medicine: best practices for mass spectrometry-based assay development using a fit-for-purpose approach.Mol. Cell. Proteomics. 2014; 13: 907-917Abstract Full Text Full Text PDF PubMed Scopus (404) Google Scholar)) are mostly empirically established (12.Briscoe C.J. Stiles M.R. Hage D.S. System suitability in bioanalytical LC/MS/MS.J. Pharm. Biomed. Anal. 2007; 44: 484-491Crossref PubMed Scopus (78) Google Scholar). A notable example is a comprehensive study conducted by Abbatiello et al. (8.Abbatiello S.E. Schilling B. Mani D.R. Zimmerman L.J. Hall S.C. MacLean B. Albertolle M. Allen S. Burgess M. Cusack M.P. Ghosh M. Hedrick V. Held J.M. Inerowicz H.D. Jackson A. Keshishian H. Kinsinger C.R. Lyssand J. Makowski L. Mesri M. Rodriguez H. Rudnick P. Sadowski P. Sedransk N. Shaddox K. Skates S.J. Kuhn E. Smith D. Whiteaker J.R. Whitwell C. Zhang S. Borchers C.H. Fisher S.J. Gibson B.W. Liebler D.C. MacCoss M.J. Neubert T.A. Paulovich A.G. Regnier F.E. Tempst P. Carr S.A. Large-scale inter-laboratory study to develop, analytically validate and apply highly multiplexed, quantitative peptide assays to measure cancer-relevant proteins in plasma.Mol. Cell. Proteomics. 2015; 1Google Scholar, 32.Abbatiello S.E. Mani D.R. Schilling B. Maclean B. Zimmerman L.J. Feng X. Cusack M.P. Sedransk N. Hall S.C. Addona T. Allen S. Dodder N.G. Ghosh M. Held J.M. Hedrick V. Inerowicz H.D. Jackson A. Keshishian H. Kim J.W. Lyssand J.S. Riley C.P. Rudnick P. Sadowski P. Shaddox K. Smith D. Tomazela D. Wahlander A. Waldemarson S. Whitwell C.A. You J. Zhang S. Kinsinger C.R. Mesri M. Rodriguez H. Borchers C.H. Buck C. Fisher S.J. Gibson B.W. Liebler D. Maccoss M. Neubert T.A. Paulovich A. Regnier F. Skates S.J. Tempst P. Wang M. Carr S.A. Design, implementation and multisite evaluation of a system suitability protocol for the quantitative assessment of instrument performance in liquid chromatography-multiple reaction monitoring-MS (LC-MRM-MS).Mol. Cell. Proteomics. 2013; 12: 2623-2639Abstract Full Text Full Text PDF PubMed Scopus (84) Google Scholar) as part of CPTAC, to design SST for nanoHPLC-MRM-MS peptide-based assays. The study contributed an adaptable plan and acceptance criteria for metrics such peak area and retention time, derived from benchmarks on a wide range of instruments, vendors, and laboratories. This protocol, setup, and metrics to monitor are available for use by other laboratories. This manuscript contributes to the proteomic community additional statistical SPC methods for longitudinal monitoring of both SST and QC. These methods are frequently used in other areas of industry and research, but have not yet made their way to targeted proteomics. The methods include simultaneous monitoring of mean and variation of a metric (XmR and ZmR charts), time weighted control charts to detect small changes (CUSUMmand CUSUMvcharts), change point analysis for identifying time of a change, and maps for high-dimensional decision making. For SRM experiments, the methods take as input quantitative metrics such as retention time, total peak area, and peak asymmetry, or any other quantitative metric of the experimentalist's choice. They help make decisions according to user defined criteria, or according to deviations fro
Referência(s)