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

Hanna E Röhnisch,Corine Sandström,Ali A Moazzami,Jan Eriksson,Lan V Tran,Elisabeth Müllner

doi:10.1021/acs.analchem.0c04233

Hanna E Röhnisch, Corine Sandström + Show 4 more

Open Access

https://doi.org/10.1021/acs.analchem.0c04233

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Abstract

We have recently presented an Automated Quantification Algorithm (AQuA) and demonstrated its utility for rapid and accurate absolute metabolite quantification in 1H 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 1H 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.

Highlights

Metabolomics analyses of biofluids are widely used to study metabolic changes in relation to different pathophysiological conditions in humans.[1]
Targeted 1H NMR-based metabolomics was applied on human plasma samples, which had been collected using ethylenediaminetetraacetic acid (EDTA) as an anticoagulant
The metabolomics analyses generated a data set of 772 1H NMR spectra, in which 54 human plasma metabolites were targeted for quantification with AQuAbased processing

Summary

■ INTRODUCTION

Metabolomics analyses of biofluids are widely used to study metabolic changes in relation to different pathophysiological conditions in humans.[1]. We have recently introduced an automated quantification algorithm, AQuA, that operates using spectral data extracted from a library consisting of one standard spectrum per metabolite.[14] It was successfully implemented for rapidly quantifying metabolites in plasma samples collected using heparin as an anticoagulant In this implementation, AQuA used one preselected NMR signal per metabolite for determining concentrations and accounted for interferences between metabolite signals, assuming that specific signal positions and line widths displayed small deviations between spectra. That the signals are described by Lorentzian functions, a calibration spectrum with the appropriate characteristics for the experimental spectrum n can be generated for any given compound With this procedure optimized target positions and prediction of the elements in the interference matrix is obtained even if the positions and line widths of some signals vary between spectra. (input and output data) and Figure S3 (proof-of-concept figures)

■ RESULTS AND DISCUSSION

Limitations

■ CONCLUSIONS

■ REFERENCES