Dataset Information

Analysis of 1,508 plasma samples of the DiOGenes study - Robust, single shot capillary flow data-independent acquisition to decipher proteomic profiles of weight loss and maintenance

ABSTRACT: Based on our own experience and the evaluation of recent large-scale plasma mass spectrometry (MS) based proteomic studies (1, 2), we identified two outstanding challenges: slow and delicate nano-flow liquid chromatography (LC) and irreproducibility of identification of data-dependent acquisition (DDA). We determined a optimal solution reducing these limitations with robust capillary-flow data-independent acquisition (DIA) MS. This platform is capable of measuring 31 plasma proteomes per day. Using this setup, we acquired a large-scale plasma study of the diet, obesity and genes dietary (DiOGenes) comprising 1,508 samples.

INSTRUMENT(S): TripleTOF 5600, Q Exactive

ORGANISM(S): Homo Sapiens (human)

TISSUE(S): Cell Culture

SUBMITTER: Roland Bruderer

LAB HEAD: Lukas Reiter

PROVIDER: PXD013231 | Pride | 2019-04-05

REPOSITORIES: Pride

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Dataset's files

Source:

			Action	DRS
	Biognosys_iRT_Peptides.fasta	Fasta
	CID1-pool-F10_MSG_R01.raw	Raw
	CID1-pool-F10_MSG_R02.raw	Raw
	CID1-pool-F11_MSG_R01.raw	Raw
	CID1-pool-F11_MSG_R02.raw	Raw

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Publications

Analysis of 1508 Plasma Samples by Capillary-Flow Data-Independent Acquisition Profiles Proteomics of Weight Loss and Maintenance.

Bruderer Roland R Muntel Jan J Müller Sebastian S Bernhardt Oliver M OM Gandhi Tejas T Cominetti Ornella O Macron Charlotte C Carayol Jérôme J Rinner Oliver O Astrup Arne A Saris Wim H M WHM Hager Jörg J Valsesia Armand A Dayon Loïc L Reiter Lukas L

Molecular & cellular proteomics : MCP 20190404 6

Comprehensive, high throughput analysis of the plasma proteome has the potential to enable holistic analysis of the health state of an individual. Based on our own experience and the evaluation of recent large-scale plasma mass spectrometry (MS) based proteomic studies, we identified two outstanding challenges: slow and delicate nano-flow liquid chromatography (LC) and irreproducibility of identification of data-dependent acquisition (DDA). We determined an optimal solution reducing these limita ...[more]

PMID: 30948622

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