Literature DB >> 28483925

MSstatsQC: Longitudinal System Suitability Monitoring and Quality Control for Targeted Proteomic Experiments.

Eralp Dogu1,2, Sara Mohammad-Taheri1, Susan E Abbatiello3, Michael S Bereman4, Brendan MacLean5, Birgit Schilling6, Olga Vitek7,8.   

Abstract

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.
© 2017 by The American Society for Biochemistry and Molecular Biology, Inc.

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Year:  2017        PMID: 28483925      PMCID: PMC5500765          DOI: 10.1074/mcp.M116.064774

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


  20 in total

Review 1.  Selected reaction monitoring-based proteomics: workflows, potential, pitfalls and future directions.

Authors:  Paola Picotti; Ruedi Aebersold
Journal:  Nat Methods       Date:  2012-05-30       Impact factor: 28.547

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

Authors:  Keiryn L Bennett; Xia Wang; Cory E Bystrom; Matthew C Chambers; Tracy M Andacht; Larry J Dangott; Félix Elortza; John Leszyk; Henrik Molina; Robert L Moritz; Brett S Phinney; J Will Thompson; Maureen K Bunger; David L Tabb
Journal:  Mol Cell Proteomics       Date:  2015-10-04       Impact factor: 5.911

Review 3.  System suitability in bioanalytical LC/MS/MS.

Authors:  Chad J Briscoe; Mark R Stiles; David S Hage
Journal:  J Pharm Biomed Anal       Date:  2007-03-13       Impact factor: 3.935

4.  Metriculator: quality assessment for mass spectrometry-based proteomics.

Authors:  Ryan M Taylor; Jamison Dance; Russ J Taylor; John T Prince
Journal:  Bioinformatics       Date:  2013-09-02       Impact factor: 6.937

Review 5.  Selected reaction monitoring applied to proteomics.

Authors:  Sebastien Gallien; Elodie Duriez; Bruno Domon
Journal:  J Mass Spectrom       Date:  2011-03       Impact factor: 1.982

6.  Large-Scale Interlaboratory Study to Develop, Analytically Validate and Apply Highly Multiplexed, Quantitative Peptide Assays to Measure Cancer-Relevant Proteins in Plasma.

Authors:  Susan E Abbatiello; Birgit Schilling; D R Mani; Lisa J Zimmerman; Steven C Hall; Brendan MacLean; Matthew Albertolle; Simon Allen; Michael Burgess; Michael P Cusack; Mousumi Gosh; Victoria Hedrick; Jason M Held; H Dorota Inerowicz; Angela Jackson; Hasmik Keshishian; Christopher R Kinsinger; John Lyssand; Lee Makowski; Mehdi Mesri; Henry Rodriguez; Paul Rudnick; Pawel Sadowski; Nell Sedransk; Kent Shaddox; Stephen J Skates; Eric Kuhn; Derek Smith; Jeffery R Whiteaker; Corbin Whitwell; Shucha Zhang; Christoph H Borchers; Susan J Fisher; Bradford W Gibson; Daniel C Liebler; Michael J MacCoss; Thomas A Neubert; Amanda G Paulovich; Fred E Regnier; Paul Tempst; Steven A Carr
Journal:  Mol Cell Proteomics       Date:  2015-02-18       Impact factor: 5.911

7.  Bioconductor: open software development for computational biology and bioinformatics.

Authors:  Robert C Gentleman; Vincent J Carey; Douglas M Bates; Ben Bolstad; Marcel Dettling; Sandrine Dudoit; Byron Ellis; Laurent Gautier; Yongchao Ge; Jeff Gentry; Kurt Hornik; Torsten Hothorn; Wolfgang Huber; Stefano Iacus; Rafael Irizarry; Friedrich Leisch; Cheng Li; Martin Maechler; Anthony J Rossini; Gunther Sawitzki; Colin Smith; Gordon Smyth; Luke Tierney; Jean Y H Yang; Jianhua Zhang
Journal:  Genome Biol       Date:  2004-09-15       Impact factor: 13.583

8.  SIMPATIQCO: a server-based software suite which facilitates monitoring the time course of LC-MS performance metrics on Orbitrap instruments.

Authors:  Peter Pichler; Michael Mazanek; Frederico Dusberger; Lisa Weilnböck; Christian G Huber; Christoph Stingl; Theo M Luider; Werner L Straube; Thomas Köcher; Karl Mechtler
Journal:  J Proteome Res       Date:  2012-10-22       Impact factor: 4.466

9.  Targeted peptide measurements in biology and medicine: best practices for mass spectrometry-based assay development using a fit-for-purpose approach.

Authors:  Steven A Carr; Susan E Abbatiello; Bradley L Ackermann; Christoph Borchers; Bruno Domon; Eric W Deutsch; Russell P Grant; Andrew N Hoofnagle; Ruth Hüttenhain; John M Koomen; Daniel C Liebler; Tao Liu; Brendan MacLean; D R Mani; Elizabeth Mansfield; Hendrik Neubert; Amanda G Paulovich; Lukas Reiter; Olga Vitek; Ruedi Aebersold; Leigh Anderson; Robert Bethem; Josip Blonder; Emily Boja; Julianne Botelho; Michael Boyne; Ralph A Bradshaw; Alma L Burlingame; Daniel Chan; Hasmik Keshishian; Eric Kuhn; Christopher Kinsinger; Jerry S H Lee; Sang-Won Lee; Robert Moritz; Juan Oses-Prieto; Nader Rifai; James Ritchie; Henry Rodriguez; Pothur R Srinivas; R Reid Townsend; Jennifer Van Eyk; Gordon Whiteley; Arun Wiita; Susan Weintraub
Journal:  Mol Cell Proteomics       Date:  2014-01-17       Impact factor: 5.911

10.  A HUPO test sample study reveals common problems in mass spectrometry-based proteomics.

Authors:  Alexander W Bell; Eric W Deutsch; Catherine E Au; Robert E Kearney; Ron Beavis; Salvatore Sechi; Tommy Nilsson; John J M Bergeron
Journal:  Nat Methods       Date:  2009-06       Impact factor: 28.547

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  6 in total

1.  Methods for Proteomic Analyses of Mycobacteria.

Authors:  Carolina Mehaffy; Megan Lucas; Nicole A Kruh-Garcia; Karen M Dobos
Journal:  Methods Mol Biol       Date:  2021

2.  A Sensitive and Controlled Data-Independent Acquisition Method for Proteomic Analysis of Cell Therapies.

Authors:  Camille Lombard-Banek; Kerstin I Pohl; Edward J Kwee; John T Elliott; John E Schiel
Journal:  J Proteome Res       Date:  2022-04-11       Impact factor: 5.370

3.  QCloud: A cloud-based quality control system for mass spectrometry-based proteomics laboratories.

Authors:  Cristina Chiva; Roger Olivella; Eva Borràs; Guadalupe Espadas; Olga Pastor; Amanda Solé; Eduard Sabidó
Journal:  PLoS One       Date:  2018-01-11       Impact factor: 3.240

4.  Quality assessment and interference detection in targeted mass spectrometry data using machine learning.

Authors:  Shadi Toghi Eshghi; Paul Auger; W Rodney Mathews
Journal:  Clin Proteomics       Date:  2018-10-06       Impact factor: 3.988

5.  Machine Learning Reveals Protein Signatures in CSF and Plasma Fluids of Clinical Value for ALS.

Authors:  Michael S Bereman; Joshua Beri; Jeffrey R Enders; Tara Nash
Journal:  Sci Rep       Date:  2018-11-05       Impact factor: 4.379

Review 6.  Proteomics and Lipidomics in Inflammatory Bowel Disease Research: From Mechanistic Insights to Biomarker Identification.

Authors:  Bjoern Titz; Raffaella M Gadaleta; Giuseppe Lo Sasso; Ashraf Elamin; Kim Ekroos; Nikolai V Ivanov; Manuel C Peitsch; Julia Hoeng
Journal:  Int J Mol Sci       Date:  2018-09-15       Impact factor: 5.923

  6 in total

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