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Strategies to enable large-scale proteomics for reproducible research

Author

Listed:
  • Rebecca C. Poulos

    (The University of Sydney)

  • Peter G. Hains

    (The University of Sydney)

  • Rohan Shah

    (The University of Sydney)

  • Natasha Lucas

    (The University of Sydney)

  • Dylan Xavier

    (The University of Sydney)

  • Srikanth S. Manda

    (The University of Sydney)

  • Asim Anees

    (The University of Sydney)

  • Jennifer M. S. Koh

    (The University of Sydney)

  • Sadia Mahboob

    (The University of Sydney)

  • Max Wittman

    (The University of Sydney)

  • Steven G. Williams

    (The University of Sydney)

  • Erin K. Sykes

    (The University of Sydney)

  • Michael Hecker

    (The University of Sydney)

  • Michael Dausmann

    (The University of Sydney)

  • Merridee A. Wouters

    (The University of Sydney)

  • Keith Ashman

    (Sciex, 2 Gilda Court)

  • Jean Yang

    (The University of Sydney)

  • Peter J. Wild

    (University Hospital Frankfurt
    University Hospital Zurich)

  • Anna deFazio

    (Centre for Cancer Research, Westmead Institute for Medical Research
    The University of Sydney
    Westmead Hospital)

  • Rosemary L. Balleine

    (The University of Sydney)

  • Brett Tully

    (The University of Sydney)

  • Ruedi Aebersold

    (Institute of Molecular Systems Biology, ETH Zürich
    University of Zürich)

  • Terence P. Speed

    (Walter and Eliza Hall Institute of Medical Research
    University of Melbourne)

  • Yansheng Liu

    (Yale University School of Medicine
    Yale University)

  • Roger R. Reddel

    (The University of Sydney)

  • Phillip J. Robinson

    (The University of Sydney)

  • Qing Zhong

    (The University of Sydney)

Abstract

Reproducible research is the bedrock of experimental science. To enable the deployment of large-scale proteomics, we assess the reproducibility of mass spectrometry (MS) over time and across instruments and develop computational methods for improving quantitative accuracy. We perform 1560 data independent acquisition (DIA)-MS runs of eight samples containing known proportions of ovarian and prostate cancer tissue and yeast, or control HEK293T cells. Replicates are run on six mass spectrometers operating continuously with varying maintenance schedules over four months, interspersed with ~5000 other runs. We utilise negative controls and replicates to remove unwanted variation and enhance biological signal, outperforming existing methods. We also design a method for reducing missing values. Integrating these computational modules into a pipeline (ProNorM), we mitigate variation among instruments over time and accurately predict tissue proportions. We demonstrate how to improve the quantitative analysis of large-scale DIA-MS data, providing a pathway toward clinical proteomics.

Suggested Citation

  • Rebecca C. Poulos & Peter G. Hains & Rohan Shah & Natasha Lucas & Dylan Xavier & Srikanth S. Manda & Asim Anees & Jennifer M. S. Koh & Sadia Mahboob & Max Wittman & Steven G. Williams & Erin K. Sykes , 2020. "Strategies to enable large-scale proteomics for reproducible research," Nature Communications, Nature, vol. 11(1), pages 1-13, December.
    • Handle: RePEc:nat:natcom:v:11:y:2020:i:1:d:10.1038_s41467-020-17641-3
    DOI: 10.1038/s41467-020-17641-3
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    Cited by:

    1. Hannah Voß & Simon Schlumbohm & Philip Barwikowski & Marcus Wurlitzer & Matthias Dottermusch & Philipp Neumann & Hartmut Schlüter & Julia E. Neumann & Christoph Krisp, 2022. "HarmonizR enables data harmonization across independent proteomic datasets with appropriate handling of missing values," Nature Communications, Nature, vol. 13(1), pages 1-15, December.
    2. Henry Webel & Lili Niu & Annelaura Bach Nielsen & Marie Locard-Paulet & Matthias Mann & Lars Juhl Jensen & Simon Rasmussen, 2024. "Imputation of label-free quantitative mass spectrometry-based proteomics data using self-supervised deep learning," Nature Communications, Nature, vol. 15(1), pages 1-15, December.
    3. Simon J. Pelletier & Mickaël Leclercq & Florence Roux-Dalvai & Matthijs B. Geus & Shannon Leslie & Weiwei Wang & TuKiet T. Lam & Angus C. Nairn & Steven E. Arnold & Becky C. Carlyle & Frédéric Precios, 2024. "BERNN: Enhancing classification of Liquid Chromatography Mass Spectrometry data with batch effect removal neural networks," Nature Communications, Nature, vol. 15(1), pages 1-15, December.

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