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J Proteome Res. 2016 Feb 5;15(2):389-99. doi: 10.1021/acs.jproteome.5b00901. Epub 2015 Dec 22.

Proteomic Biomarker Discovery in 1000 Human Plasma Samples with Mass Spectrometry.

Author information

  • 1Molecular Biomarkers Core, Nestlé Institute of Health Sciences , CH-1015 Lausanne, Switzerland.
  • 2Nutrition and Metabolic Health Group, Nestlé Institute of Health Sciences , CH-1015 Lausanne, Switzerland.
  • 3Department of Nutrition, Exercise and Sports, Faculty of Science, University of Copenhagen , DK-2200 Copenhagen, Denmark.
  • 4NUTRIM, School for Nutrition and Translational Research In Metabolism, Maastricht University Medical Centre , 6200 MD Maastricht, Netherlands.

Abstract

The overall impact of proteomics on clinical research and its translation has lagged behind expectations. One recognized caveat is the limited size (subject numbers) of (pre)clinical studies performed at the discovery stage, the findings of which fail to be replicated in larger verification/validation trials. Compromised study designs and insufficient statistical power are consequences of the to-date still limited capacity of mass spectrometry (MS)-based workflows to handle large numbers of samples in a realistic time frame, while delivering comprehensive proteome coverages. We developed a highly automated proteomic biomarker discovery workflow. Herein, we have applied this approach to analyze 1000 plasma samples from the multicentered human dietary intervention study "DiOGenes". Study design, sample randomization, tracking, and logistics were the foundations of our large-scale study. We checked the quality of the MS data and provided descriptive statistics. The data set was interrogated for proteins with most stable expression levels in that set of plasma samples. We evaluated standard clinical variables that typically impact forthcoming results and assessed body mass index-associated and gender-specific proteins at two time points. We demonstrate that analyzing a large number of human plasma samples for biomarker discovery with MS using isobaric tagging is feasible, providing robust and consistent biological results.

KEYWORDS:

TMT; biomarker; blood; body fluid; clinical proteomics; mass spectrometry; obesity; tandem mass tags

PMID:
26620284
DOI:
10.1021/acs.jproteome.5b00901
[PubMed - indexed for MEDLINE]
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