Metabolite Fraction Libraries for Quantitative NMR Metabolomics

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 metabolites, quantified one impurity and one oxidation product, and described between 91 and 96% of the total spectral intensity. The method is demonstrated using the fungus Neurospora crassa, resulting in the identification of 45 metabolites with high confidence and 45 with medium confidence, accounting for 94% of the total spectral intensity.