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

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          Abstract

          We established a robust capillary-flow data-independent acquisition MS platform capable of measuring 31 plasma proteomes per day without the need of repeated acquisition of the same sample. We acquired 1508 samples of the DiOGenes study (multicentered, Europa-wide caloric restriction weight loss and maintenance study of overweight and obese, non-diabetic participants). This was achieved using a single analytical column. Comprehensive biological reactions to weight loss and maintenance were observed.

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          Highlights

          • Robust capillary-flow DIA was established at 31 clinical samples per day.

          • 1508 samples of dietary intervention study DiOGenes were measured on a single column.

          • Comprehensive biological reactions to weight loss and maintenance were observed.

          • Comparison to independent studies shows high reproducibility of potential biomarkers.

          Abstract

          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 limitations with robust capillary-flow data-independent acquisition (DIA) MS. This platform can measure 31 plasma proteomes per day. Using this setup, we acquired a large-scale plasma study of the diet, obesity and genes dietary (DiOGenes) comprising 1508 samples. Proving the robustness, the complete acquisition was achieved on a single analytical column. Totally, 565 proteins (459 identified with two or more peptide sequences) were profiled with 74% data set completeness. On average 408 proteins (5246 peptides) were identified per acquisition (319 proteins in 90% of all acquisitions). The workflow reproducibility was assessed using 34 quality control pools acquired at regular intervals, resulting in 92% data set completeness with CVs for protein measurements of 10.9%.

          The profiles of 20 apolipoproteins could be profiled revealing distinct changes. The weight loss and weight maintenance resulted in sustained effects on low-grade inflammation, as well as steroid hormone and lipid metabolism, indicating beneficial effects. Comparison to other large-scale plasma weight loss studies demonstrated high robustness and quality of biomarker candidates identified. Tracking of nonenzymatic glycation indicated a delayed, slight reduction of glycation in the weight maintenance phase. Using stable-isotope-references, we could directly and absolutely quantify 60 proteins in the DIA.

          In conclusion, we present herein the first large-scale plasma DIA study and one of the largest clinical research proteomic studies to date. Application of this fast and robust workflow has great potential to advance biomarker discovery in plasma.

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          Most cited references42

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          Hepatokines: linking nonalcoholic fatty liver disease and insulin resistance

          In this Review, the authors describe the factors that influence the development of hepatic steatosis and discuss the evidence base that links steatosis to insulin resistance. They explore how steatosis alters the secretion of hepatokines from the liver, and how these secretome alterations regulate glucose metabolism and insulin action in non-hepatic tissues.
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            mProphet: automated data processing and statistical validation for large-scale SRM experiments.

            Selected reaction monitoring (SRM) is a targeted mass spectrometric method that is increasingly used in proteomics for the detection and quantification of sets of preselected proteins at high sensitivity, reproducibility and accuracy. Currently, data from SRM measurements are mostly evaluated subjectively by manual inspection on the basis of ad hoc criteria, precluding the consistent analysis of different data sets and an objective assessment of their error rates. Here we present mProphet, a fully automated system that computes accurate error rates for the identification of targeted peptides in SRM data sets and maximizes specificity and sensitivity by combining relevant features in the data into a statistical model.
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              Optimization of Experimental Parameters in Data-Independent Mass Spectrometry Significantly Increases Depth and Reproducibility of Results *

              Comprehensive, reproducible and precise analysis of large sample cohorts is one of the key objectives of quantitative proteomics. Here, we present an implementation of data-independent acquisition using its parallel acquisition nature that surpasses the limitation of serial MS2 acquisition of data-dependent acquisition on a quadrupole ultra-high field Orbitrap mass spectrometer. In deep single shot data-independent acquisition, we identified and quantified 6,383 proteins in human cell lines using 2-or-more peptides/protein and over 7100 proteins when including the 717 proteins that were identified on the basis of a single peptide sequence. 7739 proteins were identified in mouse tissues using 2-or-more peptides/protein and 8121 when including the 382 proteins that were identified based on a single peptide sequence. Missing values for proteins were within 0.3 to 2.1% and median coefficients of variation of 4.7 to 6.2% among technical triplicates. In very complex mixtures, we could quantify 10,780 proteins and 12,192 proteins when including the 1412 proteins that were identified based on a single peptide sequence. Using this optimized DIA, we investigated large-protein networks before and after the critical period for whisker experience-induced synaptic strength in the murine somatosensory cortex 1-barrel field. This work shows that parallel mass spectrometry enables proteome profiling for discovery with high coverage, reproducibility, precision and scalability.
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                Author and article information

                Journal
                Mol Cell Proteomics
                Mol. Cell Proteomics
                mcprot
                mcprot
                MCP
                Molecular & Cellular Proteomics : MCP
                The American Society for Biochemistry and Molecular Biology
                1535-9476
                1535-9484
                June 2019
                4 April 2019
                4 April 2019
                : 18
                : 6
                : 1242-1254
                Affiliations
                [1]From the ‡Biognosys, 8952 Zurich-Schlieren, Switzerland;
                [2]§Nestlé Institute of Health Sciences, 1015 Lausanne, Switzerland;
                [3]¶Department of Nutrition, Exercise and Sports, Faculty of Science, University of Copenhagen, 2200 Copenhagen, Denmark;
                [4]‖NUTRIM, School for Nutrition, Toxicology and Metabolism, Department of Human Biology, Maastricht University Medical Centre, 6200 MD Maastricht, The Netherlands
                Author notes
                ** To whom correspondence should be addressed: Wagistrasse 21, 8952 Schlieren, Switzerland, Tel.: +41 (0)44 738 20 40; Fax: +41 (0)44 738 20 49; E-mail: lukas.reiter@ 123456biognosys.com .

                Author contributions: R.B., O.M.B., O.C., C.M., J.C., O.R., A.A., W.H.S., J.H., A.V., L.D., and L.R. designed research; R.B., J.M., and S.M. performed research; R.B., T.G., and L.D. contributed new reagents/analytic tools; R.B., J.C., and A.V. analyzed data; R.B., O.R., A.V., and L.D. wrote the paper.

                Author information
                https://orcid.org/0000-0002-1049-5524
                https://orcid.org/0000-0002-8499-270X
                https://orcid.org/0000-0001-5751-3139
                Article
                RA118.001288
                10.1074/mcp.RA118.001288
                6553938
                30948622
                756ce0bb-ccbb-494d-8662-5a4d33d1ccb1
                © 2019 Bruderer et al.

                Published by The American Society for Biochemistry and Molecular Biology, Inc.

                Author's Choice—Final version open access under the terms of the Creative Commons CC-BY license.

                History
                : 23 December 2018
                : 26 March 2019
                Funding
                Funded by: EC | Horizon 2020 Framework Programme (H2020), https://dx.doi.org/10.13039/100010661;
                Award ID: 686282
                Award Recipient :
                Funded by: EC | Sixth Framework Programme (FP6), https://dx.doi.org/10.13039/100011103;
                Award ID: FP6–2005- 513946
                Award Recipient :
                Categories
                Research

                Molecular biology
                plasma or serum analysis,swath-ms,label-free quantification,clinical proteomics,absolute quantification,data-independent acquisition,high throughput,single shot,stable isotope standards

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