Cost-effective generation of precise label-free quantitative proteomes in high-throughput by microLC and data-independent acquisition View Full Text


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Article Info

DATE

2018-12

AUTHORS

Jakob Vowinckel, Aleksej Zelezniak, Roland Bruderer, Michael Mülleder, Lukas Reiter, Markus Ralser

ABSTRACT

Quantitative proteomics is key for basic research, but needs improvements to satisfy an increasing demand for large sample series in diagnostics, academia and industry. A switch from nanoflowrate to microflowrate chromatography can improve throughput and reduce costs. However, concerns about undersampling and coverage have so far hampered its broad application. We used a QTOF mass spectrometer of the penultimate generation (TripleTOF5600), converted a nanoLC system into a microflow platform, and adapted a SWATH regime for large sample series by implementing retention time- and batch correction strategies. From 3 µg to 5 µg of unfractionated tryptic digests that are obtained from proteomics-typical amounts of starting material, microLC-SWATH-MS quantifies up to 4000 human or 1750 yeast proteins in an hour or less. In the acquisition of 750 yeast proteomes, retention times varied between 2% and 5%, and quantified the typical peptide with 5-8% signal variation in replicates, and below 20% in samples acquired over a five-months period. Providing precise quantities without being dependent on the latest hardware, our study demonstrates that the combination of microflow chromatography and data-independent acquisition strategies has the potential to overcome current bottlenecks in academia and industry, enabling the cost-effective generation of precise quantitative proteomes in large scale. More... »

PAGES

4346

References to SciGraph publications

  • 2014-09-16. A repository of assays to quantify 10,000 human proteins by SWATH-MS in SCIENTIFIC DATA
  • 2012-07. Analyzing the phenotypic and functional complexity of lymphocytes using CyTOF (cytometry by time-of-flight) in CELLULAR & MOLECULAR IMMUNOLOGY
  • 2014-03. Minimal, encapsulated proteomic-sample processing applied to copy-number estimation in eukaryotic cells in NATURE METHODS
  • 2010-12. Integrated multilaboratory systems biology reveals differences in protein metabolism between two reference yeast strains in NATURE COMMUNICATIONS
  • 2015-03. Building high-quality assay libraries for targeted analysis of SWATH MS data in NATURE PROTOCOLS
  • 2014-10. A sentinel protein assay for simultaneously quantifying cellular processes in NATURE METHODS
  • 2015-03. DIA-Umpire: comprehensive computational framework for data-independent acquisition proteomics in NATURE METHODS
  • 2016-09. TRIC: an automated alignment strategy for reproducible protein quantification in targeted proteomics in NATURE METHODS
  • 2010-09. Mass spectrometry in high-throughput proteomics: ready for the big time in NATURE METHODS
  • 2003-10. Global analysis of protein expression in yeast in NATURE
  • 2016-09. Mass-spectrometric exploration of proteome structure and function in NATURE
  • 2014-05. Mass-spectrometry-based draft of the human proteome in NATURE
  • 2016-02-01. The metabolic background is a global player in Saccharomyces gene expression epistasis in NATURE MICROBIOLOGY
  • 2008-10. Comprehensive mass-spectrometry-based proteome quantification of haploid versus diploid yeast in NATURE
  • 2012-10. A cross-platform toolkit for mass spectrometry and proteomics in NATURE BIOTECHNOLOGY
  • 2012-12. A prototrophic deletion mutant collection for yeast metabolomics and systems biology in NATURE BIOTECHNOLOGY
  • Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1038/s41598-018-22610-4

    DOI

    http://dx.doi.org/10.1038/s41598-018-22610-4

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1101436526

    PUBMED

    https://www.ncbi.nlm.nih.gov/pubmed/29531254


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    JSON-LD is a popular format for linked data which is fully compatible with JSON.

    curl -H 'Accept: application/ld+json' 'https://scigraph.springernature.com/pub.10.1038/s41598-018-22610-4'

    N-Triples is a line-based linked data format ideal for batch operations.

    curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1038/s41598-018-22610-4'

    Turtle is a human-readable linked data format.

    curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1038/s41598-018-22610-4'

    RDF/XML is a standard XML format for linked data.

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    283 The Molecular Biology of Metabolism Laboratory, The Francis Crick Institute, 1 Midland Rd, NW1 1AT, London, UK
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    285 https://www.grid.ac/institutes/grid.452834.c schema:alternateName Science for Life Laboratory
    286 schema:name Department of Biochemistry and Cambridge Systems Biology Centre, University of Cambridge, 80 Tennis Court Rd, CB2 1GA, Cambridge, UK
    287 Department of Biology and Biological Engineering, Chalmers University of Technology, Kemigården 10, SE-412 96, Göteborg, Sweden
    288 Science for Life Laboratory, Tomtebodavägen 23 A, 17165, Solna, Sweden
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    290 https://www.grid.ac/institutes/grid.5335.0 schema:alternateName University of Cambridge
    291 schema:name Biognosys AG, Wagistrasse 21, CH-8952, Schlieren, Switzerland
    292 Department of Biochemistry and Cambridge Systems Biology Centre, University of Cambridge, 80 Tennis Court Rd, CB2 1GA, Cambridge, UK
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