Details

Autor(en) / Beteiligte

• Poulos, Rebecca C.
• Hains, Peter G.
• Shah, Rohan
• Lucas, Natasha
• Xavier, Dylan
• Manda, Srikanth S.
• Anees, Asim
• Koh, Jennifer M. S.
• Mahboob, Sadia
• Wittman, Max
• Williams, Steven G.
• Sykes, Erin K.
• Hecker, Michael
• Dausmann, Michael
• Wouters, Merridee A.
• Ashman, Keith
• Yang, Jean
• Wild, Peter J.
• deFazio, Anna
• Balleine, Rosemary L.
• Tully, Brett
• Aebersold, Ruedi
• Speed, Terence P.
• Liu, Yansheng
• Reddel, Roger R.
• Robinson, Phillip J.
• Zhong, Qing

Titel

Strategies to enable large-scale proteomics for reproducible research

Ist Teil von

• Nature communications, 2020-07, Vol.11 (1), p.3793-3793, Article 3793

Ort / Verlag

London: Nature Publishing Group

Erscheinungsjahr

2020

Link zum Volltext

Quelle

Alma/SFX Local Collection

Beschreibungen/Notizen

• 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.