Literature DB >> 25058407

Comprehensive and Scalable Highly Automated MS-Based Proteomic Workflow for Clinical Biomarker Discovery in Human Plasma.

Loïc Dayon1, Antonio Núñez Galindo1, John Corthésy1, Ornella Cominetti1, Martin Kussmann1.   

Abstract

Over the past decade, mass spectrometric performance has greatly improved in terms of sensitivity, dynamic range, and speed. By contrast, only limited progress has been accomplished with regard to automation, throughput, and robustness of the proteomic sample preparation process upstream of mass spectrometry. The present work delivers an optimized analysis of human plasma samples in both small preclinical and large clinical studies, enabled by the development of a highly automated quantitative proteomic workflow. Several iterative evaluation and validation steps were performed before process "design freeze" and development completion. A robotic liquid handling workflow and platform (including reduction, alkylation, digestion, TMT labeling, pooling, and purification) were shown to provide better quantitative trueness and precision than manual operation at the bench. Depletion of the most abundant human plasma proteins and subsequent buffer exchange were also developed and integrated. Finally, 96 identical pooled human plasma samples were prepared in a 96-well plate format, and each sample was individually subjected to our developed workflow. This test revealed increased throughput and robustness compared with to-date published manual or less automated workflows. Our workflow is ready-to-use for future (pre-) clinical studies. We expect our work to facilitate, accelerate, and improve clinical proteomic discovery in human blood plasma.

Entities:  

Keywords:  automation; blood; clinical proteomics; depletion; isobaric labeling; mass spectrometry; plasma; sample preparation; tandem mass tag

Year:  2014        PMID: 25058407     DOI: 10.1021/pr500635f

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


  13 in total

1.  Large-Scale and Deep Quantitative Proteome Profiling Using Isobaric Labeling Coupled with Two-Dimensional LC-MS/MS.

Authors:  Marina A Gritsenko; Zhe Xu; Tao Liu; Richard D Smith
Journal:  Methods Mol Biol       Date:  2016

2.  Quantitative, multiplexed workflow for deep analysis of human blood plasma and biomarker discovery by mass spectrometry.

Authors:  Hasmik Keshishian; Michael W Burgess; Harrison Specht; Luke Wallace; Karl R Clauser; Michael A Gillette; Steven A Carr
Journal:  Nat Protoc       Date:  2017-07-27       Impact factor: 13.491

3.  Integrated proteomics reveals brain-based cerebrospinal fluid biomarkers in asymptomatic and symptomatic Alzheimer's disease.

Authors:  Lenora Higginbotham; Lingyan Ping; Eric B Dammer; Duc M Duong; Maotian Zhou; Marla Gearing; Cheyenne Hurst; Jonathan D Glass; Stewart A Factor; Erik C B Johnson; Ihab Hajjar; James J Lah; Allan I Levey; Nicholas T Seyfried
Journal:  Sci Adv       Date:  2020-10-21       Impact factor: 14.136

Review 4.  Blood-borne biomarkers and bioindicators for linking exposure to health effects in environmental health science.

Authors:  M Ariel Geer Wallace; Tzipporah M Kormos; Joachim D Pleil
Journal:  J Toxicol Environ Health B Crit Rev       Date:  2016-10-19       Impact factor: 6.393

5.  Discovery of novel plasma biomarkers for future incident venous thromboembolism by untargeted synchronous precursor selection mass spectrometry proteomics.

Authors:  S B Jensen; K Hindberg; T Solomon; E N Smith; J D Lapek; D J Gonzalez; N Latysheva; K A Frazer; S K Braekkan; J-B Hansen
Journal:  J Thromb Haemost       Date:  2018-08-06       Impact factor: 5.824

6.  Quantitative variability of 342 plasma proteins in a human twin population.

Authors:  Yansheng Liu; Alfonso Buil; Ben C Collins; Ludovic C J Gillet; Lorenz C Blum; Lin-Yang Cheng; Olga Vitek; Jeppe Mouritsen; Genevieve Lachance; Tim D Spector; Emmanouil T Dermitzakis; Ruedi Aebersold
Journal:  Mol Syst Biol       Date:  2015-02-04       Impact factor: 11.429

7.  Integrated Analysis Reveals Altered Lipid and Glucose Metabolism and Identifies NOTCH2 as a Biomarker for Parkinson's Disease Related Depression.

Authors:  Mei-Xue Dong; Xia Feng; Xiao-Min Xu; Ling Hu; Yang Liu; Si-Yu Jia; Bo Li; Wei Chen; You-Dong Wei
Journal:  Front Mol Neurosci       Date:  2018-08-31       Impact factor: 5.639

8.  Alzheimer disease pathology and the cerebrospinal fluid proteome.

Authors:  Loïc Dayon; Antonio Núñez Galindo; Jérôme Wojcik; Ornella Cominetti; John Corthésy; Aikaterini Oikonomidi; Hugues Henry; Martin Kussmann; Eugenia Migliavacca; India Severin; Gene L Bowman; Julius Popp
Journal:  Alzheimers Res Ther       Date:  2018-07-18       Impact factor: 6.982

9.  Exploration of human cerebrospinal fluid: A large proteome dataset revealed by trapped ion mobility time-of-flight mass spectrometry.

Authors:  Charlotte Macron; Regis Lavigne; Antonio Núñez Galindo; Michael Affolter; Charles Pineau; Loïc Dayon
Journal:  Data Brief       Date:  2020-05-16

10.  Obesity shows preserved plasma proteome in large independent clinical cohorts.

Authors:  Ornella Cominetti; Antonio Núñez Galindo; John Corthésy; Armand Valsesia; Irina Irincheeva; Martin Kussmann; Wim H M Saris; Arne Astrup; Ruth McPherson; Mary-Ellen Harper; Robert Dent; Jörg Hager; Loïc Dayon
Journal:  Sci Rep       Date:  2018-11-19       Impact factor: 4.379

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