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    Proteomic Biomarker Discovery in 1000 Human Plasma Samples with Mass Spectrometry
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    † Molecular Biomarkers Core, Nestlé Institute of Health Sciences, CH-1015 Lausanne, Switzerland
    ‡ Nutrition and Metabolic Health Group, Nestlé Institute of Health Sciences, CH-1015 Lausanne, Switzerland
    § Department of Nutrition, Exercise and Sports, Faculty of Science, University of Copenhagen, DK-2200 Copenhagen, Denmark
    ∥ NUTRIM, School for Nutrition and Translational Research In Metabolism, Maastricht University Medical Centre, 6200 MD Maastricht, Netherlands
    *Complete address: Molecular Biomarkers Core, Nestlé Institute of Health Sciences SA, EPFL Innovation Park, Bâtiment H, 1015 Lausanne, Switzerland. E-mail: [email protected]. Phone: +41 21 632 6114. Fax: +41 21 632 6499.
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    Journal of Proteome Research

    Cite this: J. Proteome Res. 2016, 15, 2, 389–399
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    https://doi.org/10.1021/acs.jproteome.5b00901
    Published December 1, 2015
    Copyright © 2015 American Chemical Society
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    Abstract

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    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.

    Copyright © 2015 American Chemical Society

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    • Figure S1. Representation of the number of missing values observed in the data set. Figure S2. Correlation of protein quantitative data between both measurement replicates performed on two identical RP-LC MS/MS systems. Figure S3. Boxplots illustrating empirical distributions of different individual proteins. Figures S4–5. Center differences prior to the weight maintenance period: PC score plot. Age and height at screening, weight, BMI, waist-hip ratio at CD1, total of weight loss after the low-caloric diet, triglycerides, total cholesterol, HDL at CD1, Matsuda index, insulogenic index, HOMA-IR and HOMA-IS at CD1 were used as parameters. Figures S6–8. Center differences prior to the weight maintenance period: boxplots of fitted values by center. Figure S9. Center differences based on BMI at CD1. Figure S10. PC score plots for CD3/CD1 measurements across subjects and evaluation of age and gender influence. Figure S11. Boxplots of proteins differentiating genders at CD1. Figure S12. Boxplots of proteins differentiating genders at CD3. Table S1. Ranksum test assessing association between the center’s label and a given continuous value. (PDF)

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    Cite this: J. Proteome Res. 2016, 15, 2, 389–399
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    https://doi.org/10.1021/acs.jproteome.5b00901
    Published December 1, 2015
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