Identifying unknown metabolites using NMR-based metabolic profiling techniques


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.

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Fig. 1: Overview of our system for metabolite identification based on a combination of analytical and statistical workflows.
Fig. 2: Results from a metabolome-wide association study (the INTERMAP epidemiological study) that identified ascorbic acid linked to BMI in human urine (samples analyzed by sequential 1D NMR, STOCSY, STORM, 2D NMR and spiking of standard).
Fig. 3: Results from a study that identified 2PY and 4PY in urine samples from C57BL/6 mice (samples analyzed by sequential 1D NMR, STOCSY, 2D NMR, SPE, 2D NMR and spiking of standard).
Fig. 4: Results from statistical spectroscopic strategies used for the identification of NAcSPCSO, a biomarker of onion intake, in human urine (samples analyzed by sequential 1D NMR, STORM, RED-STORM, LC-NMR-MS, 2D NMR and spiking of standard).
Fig. 5: Results from an LC-NMR-MS study that identified NAcSPCSO, a biomarker of onion intake, in human urine (samples analyzed by sequential LC-NMR-MS and 2D-NMR analysis).

Code availability

CA-PLS (and PLS, OSC-PLS): The code for executing the PLS, covariate-adjusted (O)PLS and simple orthogonal PLS/PLS-DA is provided in This can be executed in a MATLAB environment.

STORM (and STOCSY): The code for executing both the STOCSY and STORM algorithms is provided in These can be executed in a MATLAB environment.

RED-STORM: The code for executing the RED-STORM algorithm is provided in This can be executed in a MATLAB environment.


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This article presents independent research funded by the UK National Institute for Health Research (NIHR). The views expressed are those of the authors and not necessarily those of the UK National Health Service (NHS), the NIHR or the UK Department of Health. I.G.-P. is supported by a National Institute for Health Research (NIHR) fellowship (NIHR-CDF-2017-10-032). J.M.P. is supported by a Rutherford Fund Fellowship at Health Data Research UK (MR/S004033/1). G.F. is an NIHR Senior Investigator. P.E. is Director of the Medical Research Council (MRC) Centre for Environment and Health (MR/L01341X/1) and acknowledges support from the NIHR Imperial Biomedical Research Centre and the NIHR Health Protection Research Unit in Health Impact of Environmental Hazards (HPRU-2012-10141). P.E. is supported by the UK Dementia Research Institute, supported by UK DRI Ltd., which is funded by the UK MRC, the Alzheimer’s Society and Alzheimer’s Research UK. INTERMAP is supported by the US National Heart, Lung and Blood Institute (grants R01-HL050490 and R01-HL084228), the Chicago Health Research Foundation and national agencies in Japan (grant [A] 090357003) and the UK (R2019EPH). Infrastructure support was provided by the NIHR Imperial Biomedical Research Centre and the UK MEDical BIOinformatics partnership (MR/L01632X/1). I.G.-P. gratefully acknowledges Olaf Beckonert for his guidance. J.K.N. acknowledges the Australian Government Future Food Systems Cooperative Research Centre (CRC). E.H. is supported by the Department of Jobs, Tourism, Science and Innovation, Government of Western Australian, through the Premier’s Science Fellowship Program.

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Writing—review and editing: I.G.-P., J.M.P., J.L., I.S.C., J.S., G.F., P.E., E.H. and J.K.N. Contributed data: P.E., Q.C., C.L.B. and I.G.-P. Figures and tables: I.G.-P. and C.L.B. Sample analysis: I.G.-P. Statistical analysis and software development: J.M.P. Protocol and workflow design: I.G.-P. Funding acquisition: J.M.P., I.G.-P., E.H., P.E. and J.K.N.

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Correspondence to Elaine Holmes or Jeremy K. Nicholson.

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Elliott, P. et al. Sci. Transl. Med. 7, 285ra262 (2015):

Posma, J. M. et al. Anal. Chem. 89, 3300−3309 (2017):

Garcia-Perez, I. et al. Lancet Diabet. Endo. 5, 184−195 (2017):

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Orbán-Németh, Z. et al. Nat. Protoc. 13, 478−494 (2018):

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Garcia-Perez, I., Posma, J.M., Serrano-Contreras, J.I. et al. Identifying unknown metabolites using NMR-based metabolic profiling techniques. Nat Protoc 15, 2538–2567 (2020).

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