Journal article
Identifying unknown metabolites using NMR-based metabolic profiling techniques
Nature Protocols, Vol.15, pp.2538-2567
2020
Abstract
Metabolic profiling of biological samples provides important insights into multiple physiological and pathological processes but is hindered by a lack of automated annotation and standardized methods for structure elucidation of candidate disease biomarkers. Here we describe a system for identifying molecular species derived from nuclear magnetic resonance (NMR) spectroscopy-based metabolic phenotyping studies, with detailed information on sample preparation, data acquisition and data modeling. We provide eight different modular workflows to be followed in a recommended sequential order according to their level of difficulty. This multi-platform system involves the use of statistical spectroscopic tools such as Statistical Total Correlation Spectroscopy (STOCSY), Subset Optimization by Reference Matching (STORM) and Resolution-Enhanced (RED)-STORM to identify other signals in the NMR spectra relating to the same molecule. It also uses two-dimensional NMR spectroscopic analysis, separation and pre-concentration techniques, multiple hyphenated analytical platforms and data extraction from existing databases. The complete system, using all eight workflows, would take up to a month, as it includes multi-dimensional NMR experiments that require prolonged experiment times. However, easier identification cases using fewer steps would take 2 or 3 days. This approach to biomarker discovery is efficient and cost-effective and offers increased chemical space coverage of the metabolome, resulting in faster and more accurate assignment of NMR-generated biomarkers arising from metabolic phenotyping studies. It requires a basic understanding of MATLAB to use the statistical spectroscopic tools and analytical skills to perform solid phase extraction (SPE), liquid chromatography (LC) fraction collection, LC-NMR-mass spectroscopy and one-dimensional and two-dimensional NMR experiments.
Details
- Title
- Identifying unknown metabolites using NMR-based metabolic profiling techniques
- Authors/Creators
- I. Garcia-Perez (Author/Creator) - Imperial College LondonJ.M. Posma (Author/Creator) - Imperial College LondonJ.I. Serrano-Contreras (Author/Creator) - Imperial College LondonC.L. Boulangé (Author/Creator) - Imperial College LondonQ. Chan (Author/Creator) - Imperial College LondonG. Frost (Author/Creator) - Imperial College LondonJ. Stamler (Author/Creator) - Northwestern UniversityP. Elliott (Author/Creator) - Health Data Research UKJ.C. Lindon (Author/Creator) - Imperial College LondonE. Holmes (Author/Creator) - Imperial College LondonJ.K. Nicholson (Author/Creator) - Australian National University
- Publication Details
- Nature Protocols, Vol.15, pp.2538-2567
- Publisher
- Springer Nature
- Identifiers
- 991005542276907891
- Copyright
- © 2020 Springer Nature Limited
- Murdoch Affiliation
- Australian National Phenome Centre; Health Futures Institute
- Language
- English
- Resource Type
- Journal article
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- Collaboration types
- Domestic collaboration
- International collaboration
- Citation topics
- 2 Chemistry
- 2.211 Mass Spectrometry
- 2.211.990 Metabolomics
- Web Of Science research areas
- Biochemical Research Methods
- ESI research areas
- Biology & Biochemistry