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

Frontiers in Molecular Biosciences

Molecular Biosciences

Óscar J. Pozo¹  Francisco Madrid-Gambin²  Rafael Llorach³  Cristina Andres-Lacueva³  Sergio Oller⁴  Santiago Marco⁴ 

[1] Applied Metabolomics Research Group, IMIM—Institut Hospital del Mar d’Investigacions Mèdiques, Barcelona, Spain;Applied Metabolomics Research Group, IMIM—Institut Hospital del Mar d’Investigacions Mèdiques, Barcelona, Spain;Signal and Information Processing for Sensing Systems, Institute for Bioengineering of Catalonia (IBEC), The Barcelona Institute of Science and Technology, Barcelona, Spain;Biomarkers and Nutrimetabolomics Laboratory, Department of Nutrition, Food Science and Gastronomy, Faculty of Pharmacy and Food Sciences, Campus Torribera, University of Barcelona, Sant Coloma de Gramanet, Spain;Food Innovation Network (XIA), Santa Coloma de Gramanet, Spain;Institut de Recerca en Nutrició i Seguretat Alimentària (INSA-UB), Santa Coloma de Gramanet, Spain;Centro de Investigación Biomédica en Red de Fragilidad y Envejecimiento Saludable (CIBERFES), Instituto de Salud Carlos III, Madrid, Spain;Signal and Information Processing for Sensing Systems, Institute for Bioengineering of Catalonia (IBEC), The Barcelona Institute of Science and Technology, Barcelona, Spain;Department of Electronics and Biomedical Engineering, Faculty of Physics, University of Barcelona, Barcelona, Spain;

关键词: metabolomics;    nuclear magnetic resonance;    plasma;    quantitative analysis;    quantification;    sample treatment;   

DOI  :  10.3389/fmolb.2023.1125582

 received in 2022-12-16, accepted in 2023-05-22,  发布年份 2023

来源: Frontiers

【 摘 要 】

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.

【 授权许可】

Unknown   
Copyright © 2023 Madrid-Gambin, Oller, Marco, Pozo, Andres-Lacueva and Llorach.