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Automated Workflow for Large-Scale Selected Reaction Monitoring Experiments

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Institute of Molecular Systems Biology, ETH Zurich, Zurich, Switzerland
Department of Immunotechnology, Lund University, S-22100 Lund, Sweden
§ Faculty of Science, University of Zurich, Zurich, Switzerland
*E-mail: [email protected]. Phone: +41 44 633 2195. Fax: +41 44 633 10 51.
Cite this: J. Proteome Res. 2012, 11, 3, 1644–1653
Publication Date (Web):January 30, 2012
https://doi.org/10.1021/pr200844d
Copyright © 2012 American Chemical Society

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    Abstract

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    Targeted proteomics allows researchers to study proteins of interest without being drowned in data from other, less interesting proteins or from redundant or uninformative peptides. While the technique is mostly used for smaller, focused studies, there are several reasons to conduct larger targeted experiments. Automated, highly robust software becomes more important in such experiments. In addition, larger experiments are carried out over longer periods of time, requiring strategies to handle the sometimes large shift in retention time often observed. We present a complete proof-of-principle software stack that automates most aspects of selected reaction monitoring workflows, a targeted proteomics technology. The software allows experiments to be easily designed and carried out. The steps automated are the generation of assays, generation of mass spectrometry driver files and methods files, and the import and analysis of the data. All data are normalized to a common retention time scale, the data are then scored using a novel score model, and the error is subsequently estimated. We also show that selected reaction monitoring can be used for label-free quantification. All data generated are stored in a relational database, and the growing resource further facilitates the design of new experiments. We apply the technology to a large-scale experiment studying how Streptococcus pyogenes remodels its proteome under stimulation of human plasma.

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    Table S1: The 240 models created (model.id) are summarized. rmds.mode: rmsd of the relative intensities (1) and rmsd of the log relative intensities (2). rt.mode: Δt (1), Δt2(2), Δt4 (3). neg.sel.mode: random (1), close by RT (4), close by rmsd (2), and close by RT and rmsd (3,5). n.transitions: n transitions per assay. The parameters fitted using glm (eq 3) are presented in columns a, b, and c. Evaluation was done using area under the curve (AUC) and the average sensitivity for 3–10 transitions at 0.1, 1, 2, 3, 4, and 5% in column avg.fdr0001–fdr005. As no model was best in all categories, we selected model 33 as the final model. See text for details. This material is available free of charge via the Internet at http://pubs.acs.org.

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    Cited By

    This article is cited by 19 publications.

    1. Stephen W. Holman, Lynn McLean, and Claire E. Eyers . RePLiCal: A QconCAT Protein for Retention Time Standardization in Proteomics Studies. Journal of Proteome Research 2016, 15 (3) , 1090-1102. https://doi.org/10.1021/acs.jproteome.5b00988
    2. Johan Teleman, Christofer Karlsson, Sofia Waldemarson, Karin Hansson, Peter James, Johan Malmström, and Fredrik Levander . Automated Selected Reaction Monitoring Software for Accurate Label-Free Protein Quantification. Journal of Proteome Research 2012, 11 (7) , 3766-3773. https://doi.org/10.1021/pr300256x
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    6. Ola Kilsgård, Christofer Karlsson, Erik Malmström, Johan Malmström. Differential compartmentalization of Streptococcus pyogenes virulence factors and host protein binding properties as a mechanism for host adaptation. International Journal of Medical Microbiology 2016, 306 (7) , 504-516. https://doi.org/10.1016/j.ijmm.2016.06.007
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    8. Peter Bults, Nico C van de Merbel, Rainer Bischoff. Quantification of biopharmaceuticals and biomarkers in complex biological matrices: a comparison of liquid chromatography coupled to tandem mass spectrometry and ligand binding assays. Expert Review of Proteomics 2015, 12 (4) , 355-374. https://doi.org/10.1586/14789450.2015.1050384
    9. Esmaeil Atashpaz-Gargari, Ulisses M Braga-Neto, Edward R Dougherty. Modeling and systematic analysis of biomarker validation using selected reaction monitoring. EURASIP Journal on Bioinformatics and Systems Biology 2014, 2014 (1) https://doi.org/10.1186/s13637-014-0017-y
    10. Tanveer S. Batth, Pragya Singh, Vikram R. Ramakrishnan, Mirta M.L. Sousa, Leanne Jade G. Chan, Huu M. Tran, Eric. G. Luning, Eva H.Y. Pan, Khanh M. Vuu, Jay D. Keasling, Paul D. Adams, Christopher J. Petzold. A targeted proteomics toolkit for high-throughput absolute quantification of Escherichia coli proteins. Metabolic Engineering 2014, 26 , 48-56. https://doi.org/10.1016/j.ymben.2014.08.004
    11. Natarajan Perumal, Sebastian Funke, Norbert Pfeiffer, Franz H. Grus. Characterization of lacrimal proline‐rich protein 4 ( PRR 4) in human tear proteome. PROTEOMICS 2014, 14 (13-14) , 1698-1709. https://doi.org/10.1002/pmic.201300039
    12. Hannes L Röst, George Rosenberger, Pedro Navarro, Ludovic Gillet, Saša M Miladinović, Olga T Schubert, Witold Wolski, Ben C Collins, Johan Malmström, Lars Malmström, Ruedi Aebersold. OpenSWATH enables automated, targeted analysis of data-independent acquisition MS data. Nature Biotechnology 2014, 32 (3) , 219-223. https://doi.org/10.1038/nbt.2841
    13. Kristofer Wollein Waldetoft, Christofer Karlsson, Magnus Gram, Johan Malmström, Matthias Mörgelin, Inga-Maria Frick, Lars Björck. Surface proteins of group G Streptococcus in different phases of growth: patterns of production and implications for the host–bacteria relationship. Microbiology 2014, 160 (2) , 279-286. https://doi.org/10.1099/mic.0.071332-0
    14. Christian Schiffmann, Rasmus Hansen, Sven Baumann, Anja Kublik, Per Halkjær Nielsen, Lorenz Adrian, Martin von Bergen, Nico Jehmlich, Jana Seifert. Comparison of targeted peptide quantification assays for reductive dehalogenases by selective reaction monitoring (SRM) and precursor reaction monitoring (PRM). Analytical and Bioanalytical Chemistry 2014, 406 (1) , 283-291. https://doi.org/10.1007/s00216-013-7451-7
    15. Marianne Sandin, Johan Teleman, Johan Malmström, Fredrik Levander. Data processing methods and quality control strategies for label-free LC–MS protein quantification. Biochimica et Biophysica Acta (BBA) - Proteins and Proteomics 2014, 1844 (1) , 29-41. https://doi.org/10.1016/j.bbapap.2013.03.026
    16. Erik Malmström, Alzbeta Davidova, Matthias Mörgelin, Adam Linder, Michael Larsen, Klaus Qvortrup, Pontus Nordenfelt, Oonagh Shannon, Olga Dzupova, Michal Holub, Johan Malmström, Heiko Herwald. Targeted mass spectrometry analysis of neutrophil-derived proteins released during sepsis progression. Thrombosis and Haemostasis 2014, 112 (12) , 1230-1243. https://doi.org/10.1160/th14-04-0312
    17. María del Carmen Mena, Juan Pablo Albar. Next Generation Instruments and Methods for Proteomics. 2013, 15-67. https://doi.org/10.1002/9781118537282.ch2
    18. Miroslava Stastna, Jennifer E. Van Eyk. Analysis of protein isoforms: Can we do it better?. PROTEOMICS 2012, 12 (19-20) , 2937-2948. https://doi.org/10.1002/pmic.201200161
    19. Christofer Karlsson, Lars Malmström, Ruedi Aebersold, Johan Malmström. Proteome-wide selected reaction monitoring assays for the human pathogen Streptococcus pyogenes. Nature Communications 2012, 3 (1) https://doi.org/10.1038/ncomms2297

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