Identification of metabolites from complex mixtures by 3D correlation of 1H NMR, MS and LC data using the SCORE-metabolite-ID approach

Not only in metabolomics studies, but also in natural product chemistry, reliable identification of metabolites usually requires laborious steps of isolation and purification and remains a bottleneck in many studies. Direct metabolite identification from a complex mixture without individual isolation is therefore a preferred approach, but due to the large number of metabolites present in natural products, this approach is often hampered by signal overlap in the respective H-1 NMR spectra. This paper presents a method for the three-dimensional mathematical correlation of NMR with MS data over the third dimension of the time course of a chromatographic fractionation. The MATLAB application SCORE-metabolite-ID (Semi-automatic COrrelation analysis for REliable metabolite IDentification) provides semi-automatic detection of correlated NMR and MS data, allowing NMR signals to be related to associated mass-to-charge ratios from ESI mass spectra. This approach enables fast and reliable dereplication of known metabolites and facilitates the dynamic analysis for the identification of unknown compounds in any complex mixture. The strategy was validated using an artificial mixture and further tested on a polar extract of a pine nut sample. Straightforward identification of 40 metabolites could be shown, including the identification of beta-d-glucopyranosyl-1-N-indole-3-acetyl-N-l-aspartic acid (1) and N-alpha-(2-hydroxy-2-carboxymethylsuccinyl)-l-arginine (2), the latter being identified in a food sample for the first time.

Automated summary

This paper presents a three-dimensional mathematical correlation method for the direct identification of metabolites from complex mixtures using NMR and MS data. The approach allows for semi-automatic detection of correlated NMR and MS data, facilitating the identification of known metabolites and unknown compounds in any complex mixture.