Metabolite Fraction Libraries for Quantitative NMR Metabolomics

Abstract

Nuclear Magnetic Resonance (NMR) has unique strengths in metabolomics studies, particularly in quantifying mixtures and elucidating the structures of unknown molecules. One-dimensional (1D) proton (1H) NMR is the most common method; however, spectral overlap is significant, making analysis challenging. We present a new approach that utilizes chromatographically separated fractions from a pooled sample, henceforth called a metabolite fraction library (mFL). We developed an algorithm to extract highly correlated peaks from the mFL, collectively forming a metabolite basis set (mBS). The mBS can be fit to NMR profiling data, enabling comprehensive quantification. Applied to 10 mixtures of 53 metabolites, our approach accurately quantified 50, quantified an impurity and an oxidation product, and described between 91-96% of total spectral intensity. The method is demonstrated using the fungus Neurospora crassa, resulting in the identification of 45 metabolites with high confidence, 45 with medium confidence, and accounting for 94% of total spectral intensity.