Quantitative plasma profiling by 1H NMR-based metabolomics: impact of sample treatment

Madrid-Gambin, Francisco; Oller, Sergio; Marco Sola, Santiago; Pozo Mendoza, Óscar J., 1975-; Andrés Lacueva, Cristina; Llorach, Rafael

doi:http://dx.doi.org/10.3389/fmolb.2023.1125582

Quantitative plasma profiling by 1H NMR-based metabolomics: impact of sample treatment

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Madrid-Gambin F, Oller S, Marco S, Pozo ÓJ, Andres-Lacueva C, Llorach R. Quantitative plasma profiling by 1H NMR-based metabolomics: impact of sample treatment. Front Mol Biosci. 2023 Jun 2;10:1125582. DOI: 10.3389/fmolb.2023.1125582

Abstract

Introduction: There is evidence that sample treatment of blood-based biosamples may affect integral signals in nuclear magnetic resonance-based metabolomics. The presence of macromolecules in plasma/serum samples makes investigating low-molecular-weight metabolites challenging. It is particularly relevant in the targeted approach, in which absolute concentrations of selected metabolites are often quantified based on the area of integral signals. Since there are a few treatments of plasma/serum samples for quantitative analysis without a universally accepted method, this topic remains of interest for future research. Methods: In this work, targeted metabolomic profiling of 43 metabolites was performed on pooled plasma to compare four methodologies consisting of Carr-Purcell-Meiboom-Gill (CPMG) editing, ultrafiltration, protein precipitation with methanol, and glycerophospholipid solid-phase extraction (g-SPE) for phospholipid removal; prior to NMR metabolomics analysis. The effect of the sample treatments on the metabolite concentrations was evaluated using a permutation test of multiclass and pairwise Fisher scores. Results: Results showed that methanol precipitation and ultrafiltration had a higher number of metabolites with coefficient of variation (CV) values above 20%. G-SPE and CPMG editing demonstrated better precision for most of the metabolites analyzed. However, differential quantification performance between procedures were metabolite-dependent. For example, pairwise comparisons showed that methanol precipitation and CPMG editing were suitable for quantifying citrate, while g-SPE showed better results for 2-hydroxybutyrate and tryptophan. Discussion: There are alterations in the absolute concentration of various metabolites that are dependent on the procedure. Considering these alterations is essential before proceeding with the quantification of treatment-sensitive metabolites in biological samples for improving biomarker discovery and biological interpretations. The study demonstrated that g-SPE and CPMG editing are effective methods for removing proteins and phospholipids from plasma samples for quantitative NMR analysis of metabolites. However, careful consideration should be given to the specific metabolites of interest and their susceptibility to the sample treatment procedures. These findings contribute to the development of optimized sample preparation protocols for metabolomics studies using NMR spectroscopy.