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Identifying unknown metabolites using NMR-based metabolic profiling techniques

Garcia-Perez, I., Posma, J.M., Serrano-Contreras, J.I., Boulangé, C.L., Chan, Q., Frost, G., Stamler, J., Elliott, P., Lindon, J.C., Holmes, E. and Nicholson, J.K. (2020) Identifying unknown metabolites using NMR-based metabolic profiling techniques. Nature Protocols, 15 . pp. 2538-2567.

Link to Published Version: https://doi.org/10.1038/s41596-020-0343-3
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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.

Item Type: Journal Article
Murdoch Affiliation: Australian National Phenome Center
Health Futures Institute
Publisher: Springer Nature
Copyright: © 2020 Springer Nature Limited
URI: http://researchrepository.murdoch.edu.au/id/eprint/57031
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