Improved Automated Quantification Algorithm (AQuA) and Its Application to NMR-Based Metabolomics of EDTA-Containing Plasma

Abstract

We have recently presented an Automated Quantification Algorithm (AQuA) and demonstrated its utility for rapid and accurate absolute metabolite quantification in 1 H NMR spectra in which positions and line widths of signals were predicted from a constant metabolite spectral library. The AQuA quantifies based on one preselected signal per metabolite and employs library spectra to model interferences from other metabolite signals. However, for some types of spectra, the interspectral deviations of signal positions and line widths can be pronounced; hence, interferences cannot be modeled using a constant spectral library. We here address this issue and present an improved AQuA that handles interspectral deviations. The improved AQuA monitors and characterizes the appearance of specific signals in each spectrum and automatically adjusts the spectral library to model interferences accordingly. The performance of the improved AQuA was tested on a large data set from plasma samples collected using ethylenediaminetetraacetic acid (EDTA) as an anticoagulant ( n = 772). These spectra provided a suitable test system for the improved AQuA since EDTA signals (i) vary in intensity, position, and line width between spectra and (ii) interfere with many signals from plasma metabolites targeted for quantification ( n = 54). Without the improvement, ca. 20 out of the 54 metabolites would have been overestimated. This included acetylcarnitine and ornithine, which are considered particularly difficult to quantify with 1 H NMR in EDTA-containing plasma. Furthermore, the improved AQuA performed rapidly (<10 s for all spectra). We believe that the improved AQuA provides a basis for automated quantification in other data sets where specific signals show interspectral deviations.

NMR Metabolomics Methods for Investigating Disease

“…However, several advanced approaches have been introduced aimed at alleviating the problem. For example, the Automated Quantification Algorithm (AQuA) was developed and its utility was demonstrated for rapid and accurate absolute metabolite quantification in 1 H NMR spectra . This improved version of a method introduced earlier monitors specific signals in spectra and adjusts the spectral library to model interferences automatically.…”

Metabolites in fish and humans as a response to different food ingredients : a metabolomics approach

“…The AQuA algorithm was selected in this thesis for an automated quantification of plasma metabolites in human. AQuA uses spectral data obtained from a database including one metabolite per spectrum library to quantify metabolites in experimental spectra while accounting for interferences Röhnisch et al, 2021). This method includes manual spectral pre-processing, identification of metabolites using previous literature (Nagana and databases ChenomX and HMDB.…”

Compound-Specific 1D 1H NMR Pulse Sequence Selection for Metabolomics Analyses

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