Identifying unknown metabolites using NMR-based metabolic profiling techniques

Isabel Garcia-Perez, Joram M. Posma, Jose Ivan Serrano-Contreras, Claire L. Boulangé, Queenie Chan, Gary Frost, Jeremiah Stamler, Paul Elliott, John C. Lindon, Elaine Holmes*, Jeremy K. Nicholson

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

1 Scopus citations


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.

Original languageEnglish (US)
Pages (from-to)2538-2567
Number of pages30
JournalNature Protocols
Issue number8
StatePublished - Aug 1 2020

ASJC Scopus subject areas

  • Biochemistry, Genetics and Molecular Biology(all)

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