Literature DB >> 29074071

Data acquisition workflows in liquid chromatography coupled to high resolution mass spectrometry-based metabolomics: Where do we stand?

François Fenaille1, Pierre Barbier Saint-Hilaire1, Kathleen Rousseau1, Christophe Junot2.   

Abstract

Typical mass spectrometry (MS) based untargeted metabolomics protocols are tedious as well as time- and sample-consuming. In particular, they often rely on "full-scan-only" analyses using liquid chromatography (LC) coupled to high resolution mass spectrometry (HRMS) from which metabolites of interest are first highlighted, and then tentatively identified by using targeted MS/MS experiments. However, this situation is evolving with the emergence of integrated HRMS based-data acquisition protocols able to perform multi-event acquisitions. Most of these protocols, referring to as data dependent and data independent acquisition (DDA and DIA, respectively), have been initially developed for proteomic applications and have recently demonstrated their applicability to biomedical studies. In this context, the aim of this article is to take stock of the progress made in the field of DDA- and DIA-based protocols, and evaluate their ability to change conventional metabolomic and lipidomic data acquisition workflows, through a review of HRMS instrumentation, DDA and DIA workflows, and also associated informatics tools.
Copyright © 2017 Elsevier B.V. All rights reserved.

Entities:  

Keywords:  Data dependent acquisitions; Data independent acquisitions; Liquid chromatography; Mass spectrometry; Metabolome; Metabolomics

Mesh:

Year:  2017        PMID: 29074071     DOI: 10.1016/j.chroma.2017.10.043

Source DB:  PubMed          Journal:  J Chromatogr A        ISSN: 0021-9673            Impact factor:   4.759


  22 in total

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Authors:  Isin T Sakallioglu; Amith S Maroli; Aline De Lima Leite; Robert Powers
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Review 5.  Ecotoxico-lipidomics: An emerging concept to understand chemical-metabolic relationships in comparative fish models.

Authors:  David A Dreier; John A Bowden; Juan J Aristizabal-Henao; Nancy D Denslow; Christopher J Martyniuk
Journal:  Comp Biochem Physiol Part D Genomics Proteomics       Date:  2020-09-11       Impact factor: 2.674

6.  Applications of Chromatography-Ultra High-Resolution MS for Stable Isotope-Resolved Metabolomics (SIRM) Reconstruction of Metabolic Networks.

Authors:  Qiushi Sun; Teresa W-M Fan; Andrew N Lane; Richard M Higashi
Journal:  Trends Analyt Chem       Date:  2019-10-01       Impact factor: 12.296

Review 7.  Canine metabolomics advances.

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Journal:  Metabolomics       Date:  2020-01-18       Impact factor: 4.290

8.  Targeting unique biological signals on the fly to improve MS/MS coverage and identification efficiency in metabolomics.

Authors:  Kevin Cho; Michaela Schwaiger-Haber; Fuad J Naser; Ethan Stancliffe; Miriam Sindelar; Gary J Patti
Journal:  Anal Chim Acta       Date:  2021-01-12       Impact factor: 6.558

9.  Comparative Evaluation of Data Dependent and Data Independent Acquisition Workflows Implemented on an Orbitrap Fusion for Untargeted Metabolomics.

Authors:  Pierre Barbier Saint Hilaire; Kathleen Rousseau; Alexandre Seyer; Sylvain Dechaumet; Annelaure Damont; Christophe Junot; François Fenaille
Journal:  Metabolites       Date:  2020-04-18

10.  Ultra-Performance Liquid Chromatography-Ion Mobility Separation-Quadruple Time-of-Flight MS (UHPLC-IMS-QTOF MS) Metabolomics for Short-Term Biomarker Discovery of Orange Intake: A Randomized, Controlled Crossover Study.

Authors:  Leticia Lacalle-Bergeron; Tania Portolés; Francisco J López; Juan Vicente Sancho; Carolina Ortega-Azorín; Eva M Asensio; Oscar Coltell; Dolores Corella
Journal:  Nutrients       Date:  2020-06-29       Impact factor: 5.717

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