ASICS: an automatic method for identification and quantification of metabolites in complex 1D 1H NMR spectra
Experiments in metabolomics rely on the identification and quantification of metabolites in complex biological mixtures. This remains one of the major challenges in NMR/mass spectrometry analysis of metabolic profiles. These features are mandatory to make metabolomics asserting a general approach to test a priori formulated hypotheses on the basis of exhaustive metabolome characterization rather than an exploratory tool dealing with unknown metabolic features.
In this article we propose a method, named ASICS, based on a strong statistical theory that handles automatically the metabolites identification and quantification in proton NMR spectra.
A statistical linear model is built to explain a complex spectrum using a library containing pure metabolite spectra. This model can handle local or global chemical shift variations due to experimental conditions using a warping function. A statistical lasso-type estimator identifies and quantifies the metabolites in the complex spectrum. This estimator shows good statistical properties and handles peak overlapping issues.
The performances of the method were investigated on known mixtures (such as synthetic urine) and on plasma datasets from duck and human. Results show noteworthy performances, outperforming current existing methods.
ASICS is a completely automated procedure to identify and quantify metabolites in 1H NMR spectra of biological mixtures. It will enable empowering NMR-based metabolomics by quickly and accurately helping experts to obtain metabolic profiles.
KeywordsMetabolomics Nuclear magnetic resonance Identification of metabolites Quantification of metabolites NIST plasma
This work is part of the project GMO90+ (Grant CHORUS 2101240982) from the French Ministry of Ecology, Sustainable Development and Energy within the national research program RiskOGM. Patrick Tardivel is partially supported by a PhD fellowship from GMO90+. The IDEX of Toulouse “Transversalité 2014” is thanked for its support to this project. The authors also thank the French National Infrastructure of Metabolomics and Fluxomics (MetaboHUB-ANR-11-INBS-0010) for their support. The authors thank Alyssa Bouville and Roselyne Gautier for help in the sample preparation and NMR analyses.
Compliance with ethical standards
Conflict of interest
The authors declare no conflict of interest regarding this work.
This article does not contain any studies with human participants or animals performed by any of the authors.
- CDC (Center for Disease Control and Prevention). (2010). Bisphenol A and other environmental phenols and Parabens in urine. https://www.cdc.gov/nchs/data/nhanes/nhanes_07_08/eph_e_met_phenols_parabens.pdf.
- Guitton, Y., Tremblay-Franco, M., Le Corguillé, G., Martin, J.-F., Pétéra, M., Roger-Mele, P., et al. (2017). Create, run, share, publish, and reference your LC-MS, GC-MS, and NMR data analysis workflows with Workflow4Metabolomics 3.0, the Galaxy online e-infrastructure for metabolomics. International Journal of Biochemistry and Cell Biology. doi: 10.1016/j.biocel.2017.07.002.PubMedGoogle Scholar
- Hastie, T., Tibshirani, R., & Friedman, J. (2009). The elements of statistical learning: Data mining, inference, and prediction (2nd ed.). Springer Series in Statistics.Google Scholar
- Simón-Manso, Y., Lowenthal, M. S., Kilpatrick, L. E., Sampson, M. L., Telu, K. H., Rudnick, P. A., et al. (2013). Metabolite profiling of a NIST standard reference material for human plasma (SRM 1950): GC-MS, LC-MS, NMR, and clinical laboratory analyses, libraries, and web-based resources. Analytical Chemistry, 85(24), 11725–11731.CrossRefPubMedGoogle Scholar
- Tardivel, P.J., Servien, R., & Concordet, D. (2017). A powerful mutiple testing procedure in linear Gaussian model. https://hal.archives-ouvertes.fr/hal-01322077.
- Theron, L., Fernandez, X., Marty-Gasset, N., Pichereaux, C., Rossignol, M., Chambon, C., et al. (2011). Identification by proteomic analysis of early post-mortem markers involved in the variability in fat loss during cooking of mule duck “foie gras”. (2011). Journal of Agricultural and Food Chemistry, 59, 12617–12628.CrossRefPubMedGoogle Scholar
- Tibshirani, R. (1996). Regression shrinkage and selection via the lasso. Journal of the Royal Statistical Society. Series B (Methodological), 58(1), 267–288.Google Scholar