Literature DB >> 27700092

An Automated Pipeline to Monitor System Performance in Liquid Chromatography-Tandem Mass Spectrometry Proteomic Experiments.

Michael S Bereman, Joshua Beri, Vagisha Sharma1, Cory Nathe2, Josh Eckels2, Brendan MacLean1, Michael J MacCoss1.   

Abstract

We report the development of a completely automated pipeline to monitor system suitability in bottom-up proteomic experiments. LC-MS/MS runs are automatically imported into Skyline and multiple identification-free metrics are extracted from targeted peptides. These data are then uploaded to the Panorama Skyline document repository where metrics can be viewed in a web-based interface using powerful process control techniques, including Levey-Jennings and Pareto plots. The interface is versatile and takes user input, which allows the user significant control over the visualization of the data. The pipeline is vendor and instrument-type neutral, supports multiple acquisition techniques (e.g., MS 1 filtering, data-independent acquisition, parallel reaction monitoring, and selected reaction monitoring), can track performance of multiple instruments, and requires no manual intervention aside from initial setup. Data can be viewed from any computer with Internet access and a web browser, facilitating sharing of QC data between researchers. Herein, we describe the use of this pipeline, termed Panorama AutoQC, to evaluate LC-MS/MS performance in a range of scenarios including identification of suboptimal instrument performance, evaluation of ultrahigh pressure chromatography, and identification of the major sources of variation throughout years of peptide data collection.

Entities:  

Keywords:  control charts; process control; proteomics; system suitability; tandem mass spectrometry

Mesh:

Year:  2016        PMID: 27700092      PMCID: PMC5406750          DOI: 10.1021/acs.jproteome.6b00744

Source DB:  PubMed          Journal:  J Proteome Res        ISSN: 1535-3893            Impact factor:   4.466


  17 in total

1.  Reagent for Evaluating Liquid Chromatography-Tandem Mass Spectrometry (LC-MS/MS) Performance in Bottom-Up Proteomic Experiments.

Authors:  Joshua Beri; Michael M Rosenblatt; Ethan Strauss; Marjeta Urh; Michael S Bereman
Journal:  Anal Chem       Date:  2015-11-10       Impact factor: 6.986

2.  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

3.  Skyline: an open source document editor for creating and analyzing targeted proteomics experiments.

Authors:  Brendan MacLean; Daniela M Tomazela; Nicholas Shulman; Matthew Chambers; Gregory L Finney; Barbara Frewen; Randall Kern; David L Tabb; Daniel C Liebler; Michael J MacCoss
Journal:  Bioinformatics       Date:  2010-02-09       Impact factor: 6.937

4.  Quality control in LC-MS/MS.

Authors:  Thomas Köcher; Peter Pichler; Remco Swart; Karl Mechtler
Journal:  Proteomics       Date:  2011-02-07       Impact factor: 3.984

Review 5.  Tools for monitoring system suitability in LC MS/MS centric proteomic experiments.

Authors:  Michael S Bereman
Journal:  Proteomics       Date:  2014-12-23       Impact factor: 3.984

6.  Statistical quality control procedures.

Authors:  James O Westgard
Journal:  Clin Lab Med       Date:  2012-12-20       Impact factor: 1.935

7.  A framework for installable external tools in Skyline.

Authors:  Daniel Broudy; Trevor Killeen; Meena Choi; Nicholas Shulman; Deepak R Mani; Susan E Abbatiello; Deepak Mani; Rushdy Ahmad; Alexandria K Sahu; Birgit Schilling; Kaipo Tamura; Yuval Boss; Vagisha Sharma; Bradford W Gibson; Steven A Carr; Olga Vitek; Michael J MacCoss; Brendan MacLean
Journal:  Bioinformatics       Date:  2014-05-09       Impact factor: 6.937

8.  Implementation of a multirule, multistage quality control program in a clinical laboratory computer system.

Authors:  A A Eggert; J O Westgard; P L Barry; K A Emmerich
Journal:  J Med Syst       Date:  1987-12       Impact factor: 4.460

9.  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

10.  Improved quality control processing of peptide-centric LC-MS proteomics data.

Authors:  Melissa M Matzke; Katrina M Waters; Thomas O Metz; Jon M Jacobs; Amy C Sims; Ralph S Baric; Joel G Pounds; Bobbie-Jo M Webb-Robertson
Journal:  Bioinformatics       Date:  2011-08-18       Impact factor: 6.937

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  22 in total

1.  Skyline for Small Molecules: A Unifying Software Package for Quantitative Metabolomics.

Authors:  Kendra J Adams; Brian Pratt; Neelanjan Bose; Laura G Dubois; Lisa St John-Williams; Kevin M Perrott; Karina Ky; Pankaj Kapahi; Vagisha Sharma; Michael J MacCoss; M Arthur Moseley; Carol A Colton; Brendan X MacLean; Birgit Schilling; J Will Thompson
Journal:  J Proteome Res       Date:  2020-03-26       Impact factor: 4.466

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

Authors:  Eralp Dogu; Sara Mohammad-Taheri; Susan E Abbatiello; Michael S Bereman; Brendan MacLean; Birgit Schilling; Olga Vitek
Journal:  Mol Cell Proteomics       Date:  2017-05-08       Impact factor: 5.911

3.  Precision Medicine.

Authors:  Jennifer E Van Eyk; Kimia Sobhani
Journal:  Circulation       Date:  2018-11-13       Impact factor: 29.690

4.  Methods for Proteomic Analyses of Mycobacteria.

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

5.  Exposure to BMAA mirrors molecular processes linked to neurodegenerative disease.

Authors:  Joshua Beri; Tara Nash; Rubia M Martin; Michael S Bereman
Journal:  Proteomics       Date:  2017-08-24       Impact factor: 3.984

Review 6.  The Skyline ecosystem: Informatics for quantitative mass spectrometry proteomics.

Authors:  Lindsay K Pino; Brian C Searle; James G Bollinger; Brook Nunn; Brendan MacLean; Michael J MacCoss
Journal:  Mass Spectrom Rev       Date:  2017-07-09       Impact factor: 10.946

7.  Discovery proteomics of human placental tissue.

Authors:  Allyson L Mellinger; Krista McCoy; Duy An T Minior; Taufika Islam Williams
Journal:  Rapid Commun Mass Spectrom       Date:  2021-09-06       Impact factor: 2.586

Review 8.  Clinical applications of quantitative proteomics using targeted and untargeted data-independent acquisition techniques.

Authors:  Jesse G Meyer; Birgit Schilling
Journal:  Expert Rev Proteomics       Date:  2017-05       Impact factor: 3.940

9.  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

10.  Diversity of Amyloid-beta Proteoforms in the Alzheimer's Disease Brain.

Authors:  Norelle C Wildburger; Thomas J Esparza; Richard D LeDuc; Ryan T Fellers; Paul M Thomas; Nigel J Cairns; Neil L Kelleher; Randall J Bateman; David L Brody
Journal:  Sci Rep       Date:  2017-08-25       Impact factor: 4.379

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