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

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• dc.contributor.author Madrid-Gambin, Francisco
• dc.contributor.author Oller, Sergio
• dc.contributor.author Marco Sola, Santiago
• dc.contributor.author Pozo Mendoza, Óscar J., 1975-
• dc.contributor.author Andrés Lacueva, Cristina
• dc.contributor.author Llorach, Rafael
• dc.date.accessioned 2024-03-06T06:58:24Z
• dc.date.available 2024-03-06T06:58:24Z
• dc.date.issued 2023
• dc.description.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.
• dc.description.sponsorship This work was accomplished as part of the Food4Brain project and the coordinated project TargetML granted by the Spanish MINECO (PID2020-114921RB-C21 and PID2021-126543OB-C21, respectively) and with the support of the Fundació La Marató de TV3 (project 202123). The work also received funding from CIBERFES, funded by the Instituto de Salud Carlos III and co-funded by the European Regional Development Fund’s “A way to make Europe” and the Generalitat de Catalunya’s Agency AGAUR (2017SGR1546 and 2017SGR1721). F.M-G was supported by Grant FJC2018-035791-I funded by MCIN/AEI/10.13039/501100011033. C.A-L. is grateful for the ICREA Academia Award 2018. Additional financial support was provided by the Institut de Bioenginyeria de Catalunya (IBEC). IBEC is a member of the CERCA Programme/Generalitat de Catalunya.
• dc.format.mimetype application/pdf
• dc.identifier.citation 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
• dc.identifier.doi http://dx.doi.org/10.3389/fmolb.2023.1125582
• dc.identifier.issn 2296-889X
• dc.identifier.uri http://hdl.handle.net/10230/59329
• dc.language.iso eng
• dc.publisher Frontiers
• dc.relation.ispartof Front Mol Biosci. 2023 Jun 2;10:1125582
• dc.relation.projectID info:eu-repo/grantAgreement/ES/2PE/PID2020-114921RB-C21
• dc.relation.projectID info:eu-repo/grantAgreement/ES/3PE/PID2021-126543OB-C21
• dc.relation.projectID info:eu-repo/grantAgreement/ES/2PE/FJC2018-035791-I
• dc.rights © 2023 Madrid-Gambin, Oller, Marco, Pozo, Andres-Lacueva and Llorach. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) (http://creativecommons.org/licenses/by/4.0/). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
• dc.rights.accessRights info:eu-repo/semantics/openAccess
• dc.rights.uri http://creativecommons.org/licenses/by/4.0/
• dc.subject.keyword Metabolomics
• dc.subject.keyword Nuclear magnetic resonance
• dc.subject.keyword Plasma
• dc.subject.keyword Quantification
• dc.subject.keyword Quantitative analysis
• dc.subject.keyword Sample treatment
• dc.title Quantitative plasma profiling by 1H NMR-based metabolomics: impact of sample treatment
• dc.type info:eu-repo/semantics/article
• dc.type.version info:eu-repo/semantics/publishedVersion