Accurate, fully-automated NMR spectral profiling for metabolomics

PLoS One. 2015 May 27;10(5):e0124219. doi: 10.1371/journal.pone.0124219. eCollection 2015.

Abstract

Many diseases cause significant changes to the concentrations of small molecules (a.k.a. metabolites) that appear in a person's biofluids, which means such diseases can often be readily detected from a person's "metabolic profile"-i.e., the list of concentrations of those metabolites. This information can be extracted from a biofluids Nuclear Magnetic Resonance (NMR) spectrum. However, due to its complexity, NMR spectral profiling has remained manual, resulting in slow, expensive and error-prone procedures that have hindered clinical and industrial adoption of metabolomics via NMR. This paper presents a system, BAYESIL, which can quickly, accurately, and autonomously produce a person's metabolic profile. Given a 1D 1H NMR spectrum of a complex biofluid (specifically serum or cerebrospinal fluid), BAYESIL can automatically determine the metabolic profile. This requires first performing several spectral processing steps, then matching the resulting spectrum against a reference compound library, which contains the "signatures" of each relevant metabolite. BAYESIL views spectral matching as an inference problem within a probabilistic graphical model that rapidly approximates the most probable metabolic profile. Our extensive studies on a diverse set of complex mixtures including real biological samples (serum and CSF), defined mixtures and realistic computer generated spectra; involving > 50 compounds, show that BAYESIL can autonomously find the concentration of NMR-detectable metabolites accurately (~ 90% correct identification and ~ 10% quantification error), in less than 5 minutes on a single CPU. These results demonstrate that BAYESIL is the first fully-automatic publicly-accessible system that provides quantitative NMR spectral profiling effectively-with an accuracy on these biofluids that meets or exceeds the performance of trained experts. We anticipate this tool will usher in high-throughput metabolomics and enable a wealth of new applications of NMR in clinical settings. BAYESIL is accessible at http://www.bayesil.ca.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Algorithms
  • Magnetic Resonance Imaging*
  • Metabolomics / methods*

Grant support

Funding for the whole project was provided by Alberta Innovates–Health Solutions and Alberta/Pfizer Translational Research Fund (http://www.aihealthsolutions.ca), and Metabolomics Innovation Centre (funded by Genome Canada and Genome Alberta, http://www.metabolomicscentre.ca). RG was supported by Natural Sciences and Engineering Research Council of Canada (http://www.nserc-crsng.gc.ca) and Canadian Institutes of Health Research (http://www.cihr-irsc.gc.ca). SR was supported by Alberta Innovates Technology Futures (http://www.albertatechfutures.ca) and Queen Elizabeth II graduate scholarships. RG and SR were supported by Alberta Innovates Centre for Machine Learning (http://www.aicml.ca). DW was supported by CIHR grant reference number 111062. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.