Strategies to enable large-scale proteomics for reproducible research

被引:81
|
作者
Poulos, Rebecca C. [1 ]
Hains, Peter G. [1 ]
Shah, Rohan [1 ]
Lucas, Natasha [1 ]
Xavier, Dylan [1 ]
Manda, Srikanth S. [1 ]
Anees, Asim [1 ]
Koh, Jennifer M. S. [1 ]
Mahboob, Sadia [1 ]
Wittman, Max [1 ]
Williams, Steven G. [1 ]
Sykes, Erin K. [1 ]
Hecker, Michael [1 ]
Dausmann, Michael [1 ]
Wouters, Merridee A. [1 ]
Ashman, Keith [2 ]
Yang, Jean [3 ]
Wild, Peter J. [4 ,5 ]
deFazio, Anna [6 ,7 ,8 ]
Balleine, Rosemary L. [1 ]
Tully, Brett [1 ]
Aebersold, Ruedi [9 ,10 ]
Speed, Terence P. [11 ,12 ]
Liu, Yansheng [13 ,14 ]
Reddel, Roger R. [1 ]
Robinson, Phillip J. [1 ]
Zhong, Qing [1 ]
机构
[1] Univ Sydney, Fac Med & Hlth, Childrens Med Res Inst, ProCan, Westmead, NSW, Australia
[2] SCIEX Ltd, 2 Gilda Court, Mulgrave, Vic, Australia
[3] Univ Sydney, Sch Math & Stat, Sydney, NSW, Australia
[4] Univ Hosp Frankfurt, Dr Senckenberg Inst Pathol, Frankfurt, Germany
[5] Univ Hosp Zurich, Dept Pathol & Mol Pathol, Zurich, Switzerland
[6] Westmead Inst Med Res, Ctr Canc Res, Westmead, NSW, Australia
[7] Univ Sydney, Fac Med & Hlth, Westmead, NSW, Australia
[8] Westmead Hosp, Dept Gynaecol Oncol, Westmead, NSW, Australia
[9] Swiss Fed Inst Technol, Inst Mol Syst Biol, Dept Biol, Zurich, Switzerland
[10] Univ Zurich, Fac Sci, Zurich, Switzerland
[11] Walter & Eliza Hall Inst Med Res, Bioinformat Div, Parkville, Vic, Australia
[12] Univ Melbourne, Dept Math & Stat, Melbourne, Vic, Australia
[13] Yale Univ, Sch Med, Dept Pharmacol, New Haven, CT 06510 USA
[14] Yale Univ, Yale Canc Biol Inst, West Haven, CT USA
基金
澳大利亚国家健康与医学研究理事会; 英国医学研究理事会;
关键词
PROTEOGENOMIC CHARACTERIZATION; UNWANTED VARIATION; MASS-SPECTROMETRY; PROTEINS; PEPTIDE; TANDEM;
D O I
10.1038/s41467-020-17641-3
中图分类号
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号
07 ; 0710 ; 09 ;
摘要
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 similar to 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.
引用
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页数:13
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