Literature DB >> 25197792

High-throughput discovery metabolomics.

Tobias Fuhrer1, Nicola Zamboni2.   

Abstract

Non-targeted metabolomics by mass spectrometry has established as the method of choice for investigating metabolic phenotypes in basic and applied research. Compared to other omics, metabolomics provides broad scope and yet direct information on the integrated cellular response with low demand in material and sample preparation. These features render non-targeted metabolomics ideally suited for large scale screens and discovery. Here we review the achievements and potential in high-throughput, non-targeted metabolomics. We found that routine and precise analysis of thousands of small molecular features in thousands of complex samples per day and instrument is already reality, and ongoing developments in microfluidics and integrated interfaces will likely further boost throughput in the next few years.
Copyright © 2014 Elsevier Ltd. All rights reserved.

Mesh:

Year:  2014        PMID: 25197792     DOI: 10.1016/j.copbio.2014.08.006

Source DB:  PubMed          Journal:  Curr Opin Biotechnol        ISSN: 0958-1669            Impact factor:   9.740


  61 in total

1.  Warpgroup: increased precision of metabolomic data processing by consensus integration bound analysis.

Authors:  Nathaniel G Mahieu; Jonathan L Spalding; Gary J Patti
Journal:  Bioinformatics       Date:  2015-09-30       Impact factor: 6.937

2.  Metabolite Structure Assignment Using In Silico NMR Techniques.

Authors:  Susanta Das; Arthur S Edison; Kenneth M Merz
Journal:  Anal Chem       Date:  2020-07-15       Impact factor: 6.986

3.  A three-minute method for high-throughput quantitative metabolomics and quantitative tracing experiments of central carbon and nitrogen pathways.

Authors:  Travis Nemkov; Kirk C Hansen; Angelo D'Alessandro
Journal:  Rapid Commun Mass Spectrom       Date:  2017-04-30       Impact factor: 2.419

4.  Multiplexing and Beyond in Biobehavioral Research.

Authors:  Paul J Mills; Christine T Peterson
Journal:  Psychosom Med       Date:  2016 Jul-Aug       Impact factor: 4.312

5.  The maternal serum metabolome by multisegment injection-capillary electrophoresis-mass spectrometry: a high-throughput platform and standardized data workflow for large-scale epidemiological studies.

Authors:  Meera Shanmuganathan; Zachary Kroezen; Biban Gill; Sandi Azab; Russell J de Souza; Koon K Teo; Stephanie Atkinson; Padmaja Subbarao; Dipika Desai; Sonia S Anand; Philip Britz-McKibbin
Journal:  Nat Protoc       Date:  2021-03-05       Impact factor: 13.491

Review 6.  Experimental design and reporting standards for metabolomics studies of mammalian cell lines.

Authors:  Sarah Hayton; Garth L Maker; Ian Mullaney; Robert D Trengove
Journal:  Cell Mol Life Sci       Date:  2017-07-01       Impact factor: 9.261

7.  Characterizing posttranslational modifications in prokaryotic metabolism using a multiscale workflow.

Authors:  Elizabeth Brunk; Roger L Chang; Jing Xia; Hooman Hefzi; James T Yurkovich; Donghyuk Kim; Evan Buckmiller; Harris H Wang; Byung-Kwan Cho; Chen Yang; Bernhard O Palsson; George M Church; Nathan E Lewis
Journal:  Proc Natl Acad Sci U S A       Date:  2018-10-09       Impact factor: 11.205

Review 8.  The use of mass spectrometry for analysing metabolite biomarkers in epidemiology: methodological and statistical considerations for application to large numbers of biological samples.

Authors:  Mads V Lind; Otto I Savolainen; Alastair B Ross
Journal:  Eur J Epidemiol       Date:  2016-05-26       Impact factor: 8.082

Review 9.  Untargeted Metabolomics Strategies-Challenges and Emerging Directions.

Authors:  Alexandra C Schrimpe-Rutledge; Simona G Codreanu; Stacy D Sherrod; John A McLean
Journal:  J Am Soc Mass Spectrom       Date:  2016-09-13       Impact factor: 3.109

Review 10.  Targeting the untargeted in molecular phenomics with structurally-selective ion mobility-mass spectrometry.

Authors:  Jody Christopher May; Randi Lee Gant-Branum; John Allen McLean
Journal:  Curr Opin Biotechnol       Date:  2016-04-29       Impact factor: 9.740

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