Strategies to enable large-scale proteomics for reproducible research

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, Michael Hecker, Michael Dausmann, Merridee A. Wouters, Keith Ashman, Jean Yang, Peter J. Wild, Anna deFazio, Rosemary L. Balleine, Brett Tully, Ruedi Aebersold, Terence P. Speed, Yansheng Liu, Roger R. Reddel, Phillip J. Robinson and Qing Zhong ()
Additional contact information
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
Anna deFazio: Centre for Cancer Research, Westmead Institute for Medical Research
Rosemary L. Balleine: The University of Sydney
Brett Tully: The University of Sydney
Ruedi Aebersold: Institute of Molecular Systems Biology, ETH Zürich
Terence P. Speed: Walter and Eliza Hall Institute of Medical Research
Yansheng Liu: Yale University School of Medicine
Roger R. Reddel: The University of Sydney
Phillip J. Robinson: The University of Sydney
Qing Zhong: The University of Sydney

Nature Communications, 2020, vol. 11, issue 1, 1-13

Abstract: 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.

Date: 2020
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DOI: 10.1038/s41467-020-17641-3

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