4.6 Review

NMR Spectroscopy for Metabolomics Research

期刊

METABOLITES
卷 9, 期 7, 页码 -

出版社

MDPI
DOI: 10.3390/metabo9070123

关键词

metabolomics; NMR; MS; analytical platform; GC-MS; LC-MS; sensitivity; resolution

资金

  1. Genome Alberta (a division of Genome Canada)
  2. Canadian Institutes of Health Research (CIHR)
  3. Western Economic Diversification (WED)
  4. Canada Foundation for Innovation (CFI)
  5. King Abdullah University of Science and Technology Core Labs
  6. Division of Biological and Environmental Sciences and Engineering baseline [BAS/1/1085-01-08, BAS/1/1084-01-08]

向作者/读者索取更多资源

Over the past two decades, nuclear magnetic resonance (NMR) has emerged as one of the three principal analytical techniques used in metabolomics (the other two being gas chromatography coupled to mass spectrometry (GC-MS) and liquid chromatography coupled with single-stage mass spectrometry (LC-MS)). The relative ease of sample preparation, the ability to quantify metabolite levels, the high level of experimental reproducibility, and the inherently nondestructive nature of NMR spectroscopy have made it the preferred platform for long-term or large-scale clinical metabolomic studies. These advantages, however, are often outweighed by the fact that most other analytical techniques, including both LC-MS and GC-MS, are inherently more sensitive than NMR, with lower limits of detection typically being 10 to 100 times better. This review is intended to introduce readers to the field of NMR-based metabolomics and to highlight both the advantages and disadvantages of NMR spectroscopy for metabolomic studies. It will also explore some of the unique strengths of NMR-based metabolomics, particularly with regard to isotope selection /detection, mixture deconvolution via 2D spectroscopy, automation, and the ability to noninvasively analyze native tissue specimens. Finally, this review will highlight a number of emerging NMR techniques and technologies that are being used to strengthen its utility and overcome its inherent limitations in metabolomic applications.

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