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Research article - Peer-reviewed, 2023

Extended automated quantification algorithm (AQuA) for targeted 1H NMR metabolomics of highly complex samples: application to plant root exudates

Alexandersson, Elin; Sandstrom, Corine; Meijer, Johan; Nestor, Gustav; Broberg, Anders; Rohnisch, Hanna E.

Abstract

Introduction The Automated Quantification Algorithm (AQuA) is a rapid and efficient method for targeted NMR-based metabolomics, currently optimised for blood plasma. AQuA quantifies metabolites from 1D-H-1 NMR spectra based on the height of only one signal per metabolite, which minimises the computational time and workload of the method without compromising the quantification accuracy.Objectives To develop a fast and computationally efficient extension of AQuA for quantification of selected metabolites in highly complex samples, with minimal prior sample preparation. In particular, the method should be capable of handling interferences caused by broad background signals.Methods An automatic baseline correction function was combined with AQuA into an automated workflow, the extended AQuA, for quantification of metabolites in plant root exudate NMR spectra that contained broad background signals and baseline distortions. The approach was evaluated using simulations as well as a spike-in experiment in which known metabolite amounts were added to a complex sample matrix.Results The extended AQuA enables accurate quantification of metabolites in 1D-H-1 NMR spectra with varying complexity. The method is very fast (< 1 s per spectrum) and can be fully automated.Conclusions The extended AQuA is an automated quantification method intended for 1D-H-1 NMR spectra containing broad background signals and baseline distortions. Although the method was developed for plant root exudates, it should be readily applicable to any NMR spectra displaying similar issues as it is purely computational and applied to NMR spectra post-acquisition.

Keywords

Targeted metabolomics; Automated quantification; Baseline correction; AQuA; Root exudate; NMR

Published in

Metabolomics
2023, Volume: 20, number: 1, article number: 11
Publisher: SPRINGER