Improving quality of analysis by suppression of unwanted signals through band-selective excitation in NMR spectroscopy for metabolomics studies

Abstract

Nuclear Magnetic Resonance (NMR) spectroscopy stands as a preeminent analytical tool in the eld of metabolomics. Nevertheless, when it comes to identifying metabolites present in scant amounts within various complex mixtures of plants, honey, milk, and biological specimens, NMR-based metabolomics presents a formidable challenge. This predicament arises primarily from the fact that the signals emanating from metabolites existing in low concentrations tend to be overshadowed by the signals of highly concentrated metabolites within NMR spectra. To tackle the issue of intense sugar signals overshadowing the desired metabolite signals, an optimal pulse sequence with band-selective excitation has been proposed for the suppression of sugar's moiety signals (SSMS). This sequence serves the crucial purpose of suppressing unwanted signals, with a particular emphasis on mitigating the interference caused by sugar moieties' signals. We have implemented this comprehensive approach to various NMR techniques, including 1D 1 H presaturation (presat), 2D J-resolved (RES), 2D 1 H-1 H Total Correlation Spectroscopy (TOCSY), and 2D 1 H-13 C Heteronuclear Single Quantum Coherence (HSQC) for the samples of dates-esh, honey, a standard stock solution of glucose, and nine amino acids, and fetal bovine serum.The outcomes of this approach have been signi cant. The suppression of the high-intensity sugar signals has considerably enhanced the visibility and sensitivity of the signals emanating from the desired metabolites. This, in turn, enables the identi cation of a greater number of metabolites. Additionally, it streamlines the experimental process, reducing the time required for the comparative quanti cation of metabolites in statistical studies in the eld of metabolomics.