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      Data‐independent acquisition‐based SWATH‐MS for quantitative proteomics: a tutorial


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          Many research questions in fields such as personalized medicine, drug screens or systems biology depend on obtaining consistent and quantitatively accurate proteomics data from many samples. SWATHMS is a specific variant of data‐independent acquisition ( DIA) methods and is emerging as a technology that combines deep proteome coverage capabilities with quantitative consistency and accuracy. In a SWATHMS measurement, all ionized peptides of a given sample that fall within a specified mass range are fragmented in a systematic and unbiased fashion using rather large precursor isolation windows. To analyse SWATHMS data, a strategy based on peptide‐centric scoring has been established, which typically requires prior knowledge about the chromatographic and mass spectrometric behaviour of peptides of interest in the form of spectral libraries and peptide query parameters. This tutorial provides guidelines on how to set up and plan a SWATHMS experiment, how to perform the mass spectrometric measurement and how to analyse SWATHMS data using peptide‐centric scoring. Furthermore, concepts on how to improve SWATHMS data acquisition, potential trade‐offs of parameter settings and alternative data analysis strategies are discussed.

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          MultiNotch MS3 Enables Accurate, Sensitive, and Multiplexed Detection of Differential Expression across Cancer Cell Line Proteomes

          Multiplexed quantitation via isobaric chemical tags (e.g., tandem mass tags (TMT) and isobaric tags for relative and absolute quantitation (iTRAQ)) has the potential to revolutionize quantitative proteomics. However, until recently the utility of these tags was questionable due to reporter ion ratio distortion resulting from fragmentation of coisolated interfering species. These interfering signals can be negated through additional gas-phase manipulations (e.g., MS/MS/MS (MS3) and proton-transfer reactions (PTR)). These methods, however, have a significant sensitivity penalty. Using isolation waveforms with multiple frequency notches (i.e., synchronous precursor selection, SPS), we coisolated and cofragmented multiple MS2 fragment ions, thereby increasing the number of reporter ions in the MS3 spectrum 10-fold over the standard MS3 method (i.e., MultiNotch MS3). By increasing the reporter ion signals, this method improves the dynamic range of reporter ion quantitation, reduces reporter ion signal variance, and ultimately produces more high-quality quantitative measurements. To demonstrate utility, we analyzed biological triplicates of eight colon cancer cell lines using the MultiNotch MS3 method. Across all the replicates we quantified 8 378 proteins in union and 6 168 proteins in common. Taking into account that each of these quantified proteins contains eight distinct cell-line measurements, this data set encompasses 174 704 quantitative ratios each measured in triplicate across the biological replicates. Herein, we demonstrate that the MultiNotch MS3 method uniquely combines multiplexing capacity with quantitative sensitivity and accuracy, drastically increasing the informational value obtainable from proteomic experiments.
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            Absolute protein expression profiling estimates the relative contributions of transcriptional and translational regulation.

            We report a method for large-scale absolute protein expression measurements (APEX) and apply it to estimate the relative contributions of transcriptional- and translational-level gene regulation in the yeast and Escherichia coli proteomes. APEX relies upon correcting each protein's mass spectrometry sampling depth (observed peptide count) by learned probabilities for identifying the peptides. APEX abundances agree with measurements from controls, western blotting, flow cytometry and two-dimensional gels, as well as known correlations with mRNA abundances and codon bias, providing absolute protein concentrations across approximately three to four orders of magnitude. Using APEX, we demonstrate that 73% of the variance in yeast protein abundance (47% in E. coli) is explained by mRNA abundance, with the number of proteins per mRNA log-normally distributed about approximately 5,600 ( approximately 540 in E. coli) protein molecules/mRNA. Therefore, levels of both eukaryotic and prokaryotic proteins are set per mRNA molecule and independently of overall protein concentration, with >70% of yeast gene expression regulation occurring through mRNA-directed mechanisms.
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              OpenSWATH enables automated, targeted analysis of data-independent acquisition MS data.


                Author and article information

                Mol Syst Biol
                Mol. Syst. Biol
                Molecular Systems Biology
                John Wiley and Sons Inc. (Hoboken )
                13 August 2018
                August 2018
                : 14
                : 8 ( doiID: 10.1002/msb.v14.8 )
                : e8126
                [ 1 ] Bavarian Center for Biomolecular Mass Spectrometry (BayBioMS) Technical University of Munich (TUM) Freising Germany
                [ 2 ] Department of Biology Institute of Molecular Systems Biology ETH Zurich Zurich Switzerland
                [ 3 ] Department of Systems Biology Columbia University New York NY USA
                [ 4 ] Faculty of Science University of Zurich Zurich Switzerland
                Author notes
                [*] [* ]Corresponding author. Tel: +49 8161 71 6130; E‐mail: tina.ludwig@ 123456tum.de

                These authors contributed equally to this work

                Author information
                © 2018 The Authors. Published under the terms of the CC BY 4.0 license

                This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

                : 28 November 2017
                : 11 May 2018
                : 15 May 2018
                Page count
                Figures: 6, Tables: 1, Pages: 23, Words: 18949
                Funded by: Swiss National Science Foundation Ambizione Grant
                Award ID: PZ00P3_161435
                Funded by: ERC Proteomics v3.0
                Award ID: AdG‐233226
                Funded by: Proteomics 4D
                Award ID: AdG‐670821
                Funded by: PhosphoNetX Project of SystemsX.ch
                Funded by: Swiss National Science Foundation (SNSF)
                Award ID: 31003A_166435
                Funded by: H2020 – PrECISE
                Award ID: 668858
                Custom metadata
                August 2018
                Converter:WILEY_ML3GV2_TO_NLMPMC version:version=5.4.4 mode:remove_FC converted:13.08.2018

                Quantitative & Systems biology
                data‐independent acquisition,mass spectrometry,quantitative proteomics,swath‐ms,systems biology,genome-scale & integrative biology,methods & resources,post-translational modifications, proteolysis & proteomics


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