Literature DB >> 29641209

A Cost-Effective High-Throughput Plasma and Serum Proteomics Workflow Enables Mapping of the Molecular Impact of Total Pancreatectomy with Islet Autotransplantation.

Tue Bjerg Bennike1,2,3,4, Melena D Bellin5,6, Yue Xuan7, Allan Stensballe4, Frederik Trier Møller8, Gregory J Beilman5, Ofer Levy3,9, Zobeida Cruz-Monserrate10, Vibeke Andersen11,12, Judith Steen13, Darwin L Conwell10, Hanno Steen1,2,3.   

Abstract

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.

Entities:  

Keywords:  TPIAT; biomarker; data-independent acquisition; plasma; serum

Mesh:

Substances:

Year:  2018        PMID: 29641209      PMCID: PMC6791589          DOI: 10.1021/acs.jproteome.8b00111

Source DB:  PubMed          Journal:  J Proteome Res        ISSN: 1535-3893            Impact factor:   4.466


  46 in total

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Journal:  J Proteome Res       Date:  2016-09-27       Impact factor: 4.466

4.  MStern Blotting-High Throughput Polyvinylidene Fluoride (PVDF) Membrane-Based Proteomic Sample Preparation for 96-Well Plates.

Authors:  Sebastian T Berger; Saima Ahmed; Jan Muntel; Nerea Cuevas Polo; Richard Bachur; Alex Kentsis; Judith Steen; Hanno Steen
Journal:  Mol Cell Proteomics       Date:  2015-07-29       Impact factor: 5.911

5.  Protein expression profiling reveals distinctive changes in serum proteins associated with chronic pancreatitis.

Authors:  Daniel Hartmann; Klaus Felix; Michael Ehmann; Martina Schnölzer; Sabine Fiedler; Ralf Bogumil; Markus Büchler; Helmut Friess
Journal:  Pancreas       Date:  2007-11       Impact factor: 3.327

6.  High-Throughput Parallel Proteomic Sample Preparation Using 96-Well Polyvinylidene Fluoride (PVDF) Membranes and C18 Purification Plates.

Authors:  Tue Bjerg Bennike; Hanno Steen
Journal:  Methods Mol Biol       Date:  2017

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Authors:  Eric W Deutsch; Attila Csordas; Zhi Sun; Andrew Jarnuczak; Yasset Perez-Riverol; Tobias Ternent; David S Campbell; Manuel Bernal-Llinares; Shujiro Okuda; Shin Kawano; Robert L Moritz; Jeremy J Carver; Mingxun Wang; Yasushi Ishihama; Nuno Bandeira; Henning Hermjakob; Juan Antonio Vizcaíno
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Authors:  Klaus Felix; Oliver Hauck; Stefan Fritz; Ulf Hinz; Martina Schnölzer; Tore Kempf; Uwe Warnken; Angelika Michel; Michael Pawlita; Jens Werner
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Journal:  Nucleic Acids Res       Date:  2015-11-02       Impact factor: 16.971

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3.  Urine Proteomics Reveals Sex-Specific Response to Total Pancreatectomy With Islet Autotransplantation.

Authors:  Tue Bjerg Bennike; Kate Templeton; Kimino Fujimura; Melena D Bellin; Saima Ahmed; Christoph N Schlaffner; Rohit Arora; Zobeida Cruz-Monserrate; Ramy Arnaout; Gregory J Beilman; Amit S Grover; Darwin L Conwell; Hanno Steen
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