Dataset Information

Proteomics at large: Discovery and replication of plasma proteomic biomarkers in independent cohorts - Overweight and obese patients of the Weight Management Clinical program of The Ottawa Hospital

ABSTRACT: Holistic human proteome maps are expected to complement comprehensive profile assessment of health and disease phenotypes. However, methodologies to analyze proteomes in human tissue or body fluid samples at relevant scale and performance are still limited in clinical research. Their deployment and demonstration in large enough human populations are even sparser. In the present study, we have characterized and compared the plasma proteomes of two large independent cohorts of obese and overweight individuals using shotgun MS-based proteomics. One cohort comes from a Canadian single-center clinical weight management program while the other one belongs to a multicenter pan-European weight loss study (i.e., the DiOGenes study for which MS proteomic data have been already deposited to the ProteomeXchange Consortium with the dataset identifier PXD005216). The MS proteomic data deposited herein corresponds to the cohort coming from the Weight Management Clinical program of The Ottawa Hospital.

INSTRUMENT(S): LTQ Orbitrap Elite

ORGANISM(S): Homo Sapiens (human)

TISSUE(S): Blood Plasma

SUBMITTER: Loïc Dayon

LAB HEAD: Loïc Dayon

PROVIDER: PXD009350 | Pride | 2018-11-23

REPOSITORIES: Pride

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Dataset's files

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Publications

Obesity shows preserved plasma proteome in large independent clinical cohorts.

Cominetti Ornella O Núñez Galindo Antonio A Corthésy John J Valsesia Armand A Irincheeva Irina I Kussmann Martin M Saris Wim H M WHM Astrup Arne A McPherson Ruth R Harper Mary-Ellen ME Dent Robert R Hager Jörg J Dayon Loïc L

Scientific reports 20181119 1

Holistic human proteome maps are expected to complement comprehensive profile assessment of health and disease phenotypes. However, methodologies to analyze proteomes in human tissue or body fluid samples at relevant scale and performance are still limited in clinical research. Their deployment and demonstration in large enough human populations are even sparser. In the present study, we have characterized and compared the plasma proteomes of two large independent cohorts of obese and overweight ...[more]

PMID: 30451909

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Project description:Being inspired by metabolomic data processing, we have developed a bioinformatic pipeline that optimizes the processing of mass spectral data obtained from isobaric Tandem Mass Tag (TMT) experiments. Our method focuses on the tandem mass spectral level by first quantifying and then identifying (QtI), while preserving unidentified spectra for further investigations. The raw datasets were previously generated [1, 2]. Two-proteome model experiments were considered where identical pools of human CSF or plasma samples were mixed with E. coli samples at different concentrations. E. coli protein extract was spiked in 400 µL of CSF at amounts of 0, 2, 3, 5, 6.25, and 7.5 µg. Such sets of 6 spiked CSF samples were prepared in triplicate for comparison using sixplex isobaric tagging and analyzed in triplicates on two independent but identical LC MS/MS, for a total of 18 raw files [1]; this experiment is called “CSF-E.coli”. E. coli protein extract was spiked at 0, 2.5, 5, 6.25, 12.5, and 25 µg in 30 µL human plasma. Such sets of 6 spiked plasma samples were prepared in quadruplicate for comparison using sixplex isobaric tagging and analyzed in triplicates on one LC MS/MS, for a total of 12 raw files [2]; this experiment is referred as “Plasma-E.coli”.The so-called “96samples-CSF” experiment consists of 16 replicate TMT sixplex experiments measuring identical CSF samples from the pool described above [2], analyzed in triplicates on one LCMS/MS for a total of 48 raw files. The so-called “96samples-plasma” experiment consists of 16 replicate TMT sixplex experiments measuring identical plasma samples from the pool described before, analyzed in triplicates on one LC MS/MS for a total of 48 raw files [1]. References: [1] Dayon, L., Núñez Galindo, A., Corthésy, J., Cominetti, O. & Kussmann, M. Comprehensive and scalable highly automated MS-based proteomic workflow for clinical biomarker discovery in human plasma. J. Proteome Res. 13, 3837-3845 (2014). [2] Núñez Galindo, A., Kussmann, M. & Dayon, L. Proteomics of Cerebrospinal Fluid: Throughput and Robustness Using a Scalable Automated Analysis Pipeline for Biomarker Discovery. Anal. Chem. 87, 10755-10761 (2015).