Literature DB >> 29654587

Untargeted GC-MS Metabolomics.

Matthaios-Emmanouil P Papadimitropoulos1,2, Catherine G Vasilopoulou1,3, Christoniki Maga-Nteve1,4, Maria I Klapa5,6,7.   

Abstract

Untargeted metabolomics refers to the high-throughput analysis of the metabolic state of a biological system (e.g., tissue, biological fluid, cell culture) based on the concentration profile of all measurable free low molecular weight metabolites. Gas chromatography-mass spectrometry (GC-MS), being a highly sensitive and high-throughput analytical platform, has been proven a useful tool for untargeted studies of primary metabolism in a variety of applications. As an omic analysis, GC-MS metabolomics is a multistep procedure; thus, standardization of an untargeted GC-MS metabolomics protocol requires the integrated optimization of pre-analytical, analytical, and computational steps. The main difference of GC-MS metabolomics compared to other metabolomics analytical platforms, including liquid chromatography-MS, is the need for the derivatization of the metabolite extracts into volatile and thermally stable derivatives, the latter being quantified in the metabolic profiles. This analytical step requires special care in the optimization of the untargeted GC-MS metabolomics experimental protocol. Moreover, both the derivatization of the original sample and the compound fragmentation that takes place in GC-MS impose specialized GC-MS metabolomic data identification, quantification, normalization and filtering methods. In this chapter, we describe the integrated protocol of untargeted GC-MS metabolomics with both the analytical and computational steps, focusing on the GC-MS specific parts, and provide details on any sample depending differences.

Keywords:  Gas chromatography-mass spectrometry (GC-MS) metabolomics; Metabolic network analysis; Metabolic profiling; Primary metabolism; Untargeted metabolomics

Mesh:

Substances:

Year:  2018        PMID: 29654587     DOI: 10.1007/978-1-4939-7643-0_9

Source DB:  PubMed          Journal:  Methods Mol Biol        ISSN: 1064-3745


  19 in total

1.  Cognitive analysis of metabolomics data for systems biology.

Authors:  Erica L-W Majumder; Elizabeth M Billings; H Paul Benton; Richard L Martin; Amelia Palermo; Carlos Guijas; Markus M Rinschen; Xavier Domingo-Almenara; J Rafael Montenegro-Burke; Bradley A Tagtow; Robert S Plumb; Gary Siuzdak
Journal:  Nat Protoc       Date:  2021-01-22       Impact factor: 13.491

Review 2.  Mass Spectrometry-based Metabolomics in Translational Research.

Authors:  Su Jung Kim; Ha Eun Song; Hyo Yeong Lee; Hyun Ju Yoo
Journal:  Adv Exp Med Biol       Date:  2021       Impact factor: 2.622

Review 3.  Mass spectrometry-based metabolomics in microbiome investigations.

Authors:  Anelize Bauermeister; Helena Mannochio-Russo; Letícia V Costa-Lotufo; Alan K Jarmusch; Pieter C Dorrestein
Journal:  Nat Rev Microbiol       Date:  2021-09-22       Impact factor: 78.297

Review 4.  Mass spectrometry-based metabolomics: a guide for annotation, quantification and best reporting practices.

Authors:  Saleh Alseekh; Asaph Aharoni; Yariv Brotman; Kévin Contrepois; John D'Auria; Jan Ewald; Jennifer C Ewald; Paul D Fraser; Patrick Giavalisco; Robert D Hall; Matthias Heinemann; Hannes Link; Jie Luo; Steffen Neumann; Jens Nielsen; Leonardo Perez de Souza; Kazuki Saito; Uwe Sauer; Frank C Schroeder; Stefan Schuster; Gary Siuzdak; Aleksandra Skirycz; Lloyd W Sumner; Michael P Snyder; Huiru Tang; Takayuki Tohge; Yulan Wang; Weiwei Wen; Si Wu; Guowang Xu; Nicola Zamboni; Alisdair R Fernie
Journal:  Nat Methods       Date:  2021-07-08       Impact factor: 47.990

Review 5.  Existing and Emerging Metabolomic Tools for ALS Research.

Authors:  Christine Germeys; Tijs Vandoorne; Valérie Bercier; Ludo Van Den Bosch
Journal:  Genes (Basel)       Date:  2019-12-05       Impact factor: 4.096

6.  Comparative genomics and metabolomics analysis of Riemerella anatipestifer strain CH-1 and CH-2.

Authors:  Jibin Liu; Anchun Cheng; Mingshu Wang; Mafeng Liu; Dekang Zhu; Qiao Yang; Ying Wu; Renyong Jia; Shun Chen; Xinxin Zhao; Shaqiu Zhang; Juan Huang; Xumin Ou; Sai Mao; Qun Gao; Xingjian Wen; Ling Zhang; Yunya Liu; Yanling Yu; Bin Tian; Leichang Pan; Mujeeb Ur Rehman; Xiaoyue Chen
Journal:  Sci Rep       Date:  2021-01-12       Impact factor: 4.379

7.  Metabolic Profiling Indicates Diversity in the Metabolic Physiologies Associated With Maternal Postpartum Depressive Symptoms.

Authors:  Emma Bränn; Christina Malavaki; Emma Fransson; Maria-Konstantina Ioannidi; Hanna E Henriksson; Fotios C Papadopoulos; George P Chrousos; Maria I Klapa; Alkistis Skalkidou
Journal:  Front Psychiatry       Date:  2021-06-25       Impact factor: 4.157

8.  Untargeted Plasma Metabolomics Unravels a Metabolic Signature for Tissue Sensitivity to Glucocorticoids in Healthy Subjects: Its Implications in Dietary Planning for a Healthy Lifestyle.

Authors:  Nicolas C Nicolaides; Maria-Konstantina Ioannidi; Eleni Koniari; Ifigeneia Papageorgiou; Anastasia Bartzeliotou; Amalia Sertedaki; Maria I Klapa; Evangelia Charmandari
Journal:  Nutrients       Date:  2021-06-21       Impact factor: 5.717

9.  GC-MS Based Metabolite Profiling to Monitor Ripening-Specific Metabolites in Pineapple (Ananas comosus).

Authors:  Muhammad Maulana Malikul Ikram; Sobir Ridwani; Sastia Prama Putri; Eiichiro Fukusaki
Journal:  Metabolites       Date:  2020-03-31

10.  Exploring the Ability of Electronic Nose Technology to Recognize Interstitial Lung Diseases (ILD) by Non-Invasive Breath Screening of Exhaled Volatile Compounds (VOC): A Pilot Study from the European IPF Registry (eurIPFreg) and Biobank.

Authors:  Ekaterina Krauss; Jana Haberer; Olga Maurer; Guillermo Barreto; Fotios Drakopanagiotakis; Maria Degen; Werner Seeger; Andreas Guenther
Journal:  J Clin Med       Date:  2019-10-16       Impact factor: 4.241

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