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DataSet Summary

  • HostingRepository: PanoramaPublic
  • AnnounceDate: 2019-02-02
  • AnnouncementXML: Submission_2019-02-02_08:00:52.xml
  • DigitalObjectIdentifier:
  • ReviewLevel: Peer-reviewed dataset
  • DatasetOrigin: Original data
  • RepositorySupport: Supported dataset by repository
  • PrimarySubmitter: Jarrett Egertson
  • Title: Improving Precursor Selectivity in Data Independent Acquisition Using Overlapping Windows
  • Description: A major goal of proteomics research is the accurate and sensitive identification and quantification of a broad range of proteins within a sample. Data Independent Acquisition (DIA) approaches that acquire MS/MS spectra independently of precursor information have been developed to overcome the reproducibility challenges of data-dependent acquisition and the limited breadth of targeted proteomics strategies. Typical DIA implementations use wide MS/MS isolation windows to acquire comprehensive fragment ion data. However, wide isolation windows produce highly chimeric spectra, limiting the achievable sensitivity and accuracy of quantification and identification. Here we present a DIA strategy in which spectra are collected with overlapping, (rather than adjacent or random) windows and then computationally demultiplexed. This approach improves precursor selectivity by nearly a factor of 2, without incurring any loss in mass range, mass resolution, chromatographic resolution, scan speed, or other key acquisition parameters. We demonstrate a 64% improvement in sensitivity and a 17% improvement in peptides detected in a 6 protein bovine mix spiked into a yeast background. To confirm the method’s applicability to a realistic biological experiment, we also analyze the regulation of the proteasome in yeast grown in rapamycin and show that DIA experiments with overlapping windows can help elucidate its adaptation toward the degradation of oxidatively damaged proteins. Our integrated computational and experimental DIA strategy is compatible with any DIA-capable instrument. The computational demultiplexing algorithm required to analyze the data has been made available as part of the open-source proteomics software tools Skyline and msconvert (Proteowizard), making it easy to apply as part of standard proteomics workflows.
  • SpeciesList: scientific name: Bos taurus; NCBI TaxID: 9913; scientific name: Saccharomyces cerevisiae; NCBI TaxID: 4932;
  • ModificationList: Carbamidomethyl; Carboxymethyl
  • Instrument: Q Exactive

Dataset History

VersionDatetimeStatusChangeLog Entry
02018-11-30 11:08:42ID requested
12018-11-30 12:02:55announced
22019-02-02 08:00:54announcedAdded publication reference

Publication List

  1. Amodei D, Egertson J, MacLean BX, Johnson R, Merrihew GE, Keller A, Marsh D, Vitek O, Mallick P, MacCoss MJ, Improving Precursor Selectivity in Data-Independent Acquisition Using Overlapping Windows. J Am Soc Mass Spectrom, 30(4):669-684(2019) [pubmed]

Keyword List

  1. submitter keyword: yeast, rapamycin, proteasome, multiplexing, data independent acquisition, Q-Exactive, orbitrap, spike-in, quantification, bovine

Contact List

    Michael MacCoss
    • contact affiliation: University of Washington, Dept. of Genome Sciences
    • contact email: maccoss@uw.edu
    • lab head:
    Jarrett Egertson
    • contact affiliation: University of Washington, Dept. of Genome Sciences
    • contact email: jegertso@uw.edu
    • dataset submitter:

Full Dataset Link List

  1. Panorama Public dataset URI

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