Blood is an ideal body fluid for the discovery or monitoring of diagnostic and prognostic protein biomarkers. However, discovering robust biomarkers requires the analysis of large numbers of samples to appropriately represent interindividual variability. To address this analytical challenge, we established a high-throughput and cost-effective proteomics workflow for accurate and comprehensive proteomics at an analytical depth applicable for clinical studies. For validation, we processed 1 μL each from 62 plasma samples in 96-well plates and analyzed the product by quantitative data-independent acquisition liquid chromatography/mass spectrometry; the data were queried using feature quantification with Spectronaut. To show the applicability of our workflow to serum, we analyzed a unique set of samples from 48 chronic pancreatitis patients, pre and post total pancreatectomy with islet autotransplantation (TPIAT) surgery. We identified 16 serum proteins with statistically significant abundance alterations, which represent a molecular signature distinct from that of chronic pancreatitis. In summary, we established a cost-efficient high-throughput workflow for comprehensive proteomics using PVDF-membrane-based digestion that is robust, automatable, and applicable to small plasma and serum volumes, e.g., finger stick. Application of this plasma/serum proteomics workflow resulted in the first mapping of the molecular implications of TPIAT on the serum proteome.
Bibliographical noteFunding Information:
O.L. and H.S.’s laboratories are supported by the National Institute of Allergy and Infectious Diseases of the National Institutes of Health under Human Immunology Project Consortium (HIPC) Award Number U19AI118608. The research reported in this publication was also supported by the National Cancer Institute and National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK) under award number U01DK108327 (H.S., D.C.). M.D.B.’s laboratory was supported by grants from the American Diabetes Association (1-11-CT-06) and the National Institutes of Health (K23 DK084315). The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. Furthermore, we acknowledge the following grants that enabled the work described in this manuscript: T.B.B., funding from the Lundbeck Foundation (R181-2014-3372 and R275-2017-2219), The Carlsberg Foundation (CF14-0561), and A. P. Mølle; A.S., funding from the Obelske Family Foundation, the Svend Andersen Foundation, the Spar Nord Foundation, and the Danish National Mass Spectrometry Platform for Functional Proteomics (PRO-MS); and V.A., funding from the Lundbeck Foundation (R126-2012-12194), Hospital of Southern Jutland, University of Southern Denmark, and “Knud og Edith Eriksens Mindefond”.
© Copyright 2018 American Chemical Society.
- data-independent acquisition