Peak fitting in 2D 1H–13C HSQC NMR spectra for metabolomic studies

Abstract

A modified Lorentzian distribution function is used to model peaks in two-dimensional (2D) 1H–13C heteronuclear single quantum coherence (HSQC) nuclear magnetic resonance (NMR) spectra. The model fit is used to determine accurate chemical shifts from genuine signals in complex metabolite mixtures such as blood. The algorithm can be used to extract features from a set of spectra from different samples for exploratory metabolomics. First a reference spectrum is created in which the peak intensities are given by the median value over all samples at each point in the 2D spectra so that 1H–13C correlations in any spectra are accounted for. The mathematical model provides a footprint for each peak in the reference spectrum, which can be used to bin the 1H–13C correlations in each HSQC spectrum. The binned intensities are then used as variables in multivariate analyses and those found to be discriminatory are rapidly identified by cross referencing the chemical shifts of the bins with a database of 13C and 1H chemical shift correlations from known metabolites.