Bayesian Deconvolution and Quantification of Metabolites from J-Resolved NMR Spectroscopy

Two-dimensional (2D) nuclear magnetic resonance (NMR) methods have become increasingly popular in metabolomics, since they have considerable potential to accurately identify and quantify metabolites within complex biological samples. 2D H-1 J-resolved (JRES) NMR spectroscopy is a widely used method that expands overlapping resonances into a second dimension. However, existing analytical processing methods do not fully exploit the information in the JRES spectrum and, more importantly, do not provide measures of uncertainty associated with the estimates of quantities of interest, such as metabolite concentration. Combining the data-generating mechanisms and the extensive prior knowledge available in online databases, we develop a Bayesian method to analyse 2D JRES data, which allows for automatic deconvolution, identification and quantification of metabolites. The model extends and improves previous work on one-dimensional NMR spectral data. Our approach is based on a combination of B-spline tight wavelet frames and theoretical templates, and thus enables the automatic incorporation of expert knowledge within the inferential framework. Posterior inference is performed through specially devised Markov chain Monte Carlo methods. We demonstrate the performance of our approach via analyses of datasets from serum and urine, showing the advantages of our proposed approach in terms of identification and quantification of metabolites.

Automated summary

Two-dimensional NMR methods are popular in metabolomics for accurately identifying and quantifying metabolites, but existing processing methods lack information utilization and uncertainty measures. Researchers propose a Bayesian method for analyzing 2D JRES data, allowing for automatic deconvolution, identification, and quantification of metabolites.