Literature DB >> 32239573

Fast quantitative 2D NMR for metabolomics and lipidomics: A tutorial.

Estelle Martineau1,2, Jean-Nicolas Dumez1, Patrick Giraudeau1,3.   

Abstract

Nuclear magnetic resonance (NMR) is a well-known analytical technique for the analysis of complex mixtures. Its quantitative capability makes it ideally suited to metabolomics or lipidomics studies involving large sample collections of complex biological samples. To overcome the ubiquitous limitation of spectral overcrowding when recording 1D NMR spectra on such samples, the acquisition of 2D NMR spectra allows a better separation between overlapped resonances while yielding accurate quantitative data when appropriate analytical protocols are implemented. Moreover, the experiment duration can be considerably reduced by applying fast acquisition methods. Here, we describe the general workflow to acquire fast quantitative 2D NMR spectra in the "omics" context. It is illustrated on three representative and complementary experiments: UF COSY, ZF-TOCSY with nonuniform sampling, and HSQC with nonuniform sampling. After giving some details and recommendations on how to apply this protocol, its implementation in the case of targeted and untargeted metabolomics/lipidomics studies is described.
© 2019 John Wiley & Sons, Ltd.

Entities:  

Keywords:  2D NMR spectroscopy; fast methods; lipidomics; metabolomics; quantitative analysis; targeted; untargeted

Year:  2019        PMID: 32239573     DOI: 10.1002/mrc.4899

Source DB:  PubMed          Journal:  Magn Reson Chem        ISSN: 0749-1581            Impact factor:   2.447


  4 in total

Review 1.  Coupling Machine Learning and Lipidomics as a Tool to Investigate Metabolic Dysfunction-Associated Fatty Liver Disease. A General Overview.

Authors:  Helena Castañé; Gerard Baiges-Gaya; Anna Hernández-Aguilera; Elisabet Rodríguez-Tomàs; Salvador Fernández-Arroyo; Pol Herrero; Antoni Delpino-Rius; Nuria Canela; Javier A Menendez; Jordi Camps; Jorge Joven
Journal:  Biomolecules       Date:  2021-03-22

2.  Variable-temperature NMR spectroscopy for metabolite identification in biological materials.

Authors:  Ewa K Nawrocka; Mateusz Urbańczyk; Kamil Koziński; Krzysztof Kazimierczuk
Journal:  RSC Adv       Date:  2021-11-03       Impact factor: 4.036

Review 3.  The exposome paradigm to predict environmental health in terms of systemic homeostasis and resource balance based on NMR data science.

Authors:  Jun Kikuchi; Shunji Yamada
Journal:  RSC Adv       Date:  2021-09-13       Impact factor: 4.036

Review 4.  Automatic 1D 1H NMR Metabolite Quantification for Bioreactor Monitoring.

Authors:  Roy Chih Chung Wang; David A Campbell; James R Green; Miroslava Čuperlović-Culf
Journal:  Metabolites       Date:  2021-03-09
  4 in total

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