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A comparison of high-throughput plasma NMR protocols for comparative untargeted metabolomics

INTRODUCTION: When analyzing the human plasma metabolome with Nuclear Magnetic Resonance (NMR) spectroscopy, the Carr–Purcell–Meiboom–Gill (CPMG) experiment is commonly employed for large studies. However, this process can lead to compromised statistical analyses due to residual macromolecule signal...

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Autores principales: Bliziotis, Nikolaos G., Engelke, Udo F. H., Aspers, Ruud L. E. G., Engel, Jasper, Deinum, Jaap, Timmers, Henri J. L. M., Wevers, Ron A., Kluijtmans, Leo A. J.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer US 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7196944/
https://www.ncbi.nlm.nih.gov/pubmed/32358672
http://dx.doi.org/10.1007/s11306-020-01686-y
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author Bliziotis, Nikolaos G.
Engelke, Udo F. H.
Aspers, Ruud L. E. G.
Engel, Jasper
Deinum, Jaap
Timmers, Henri J. L. M.
Wevers, Ron A.
Kluijtmans, Leo A. J.
author_facet Bliziotis, Nikolaos G.
Engelke, Udo F. H.
Aspers, Ruud L. E. G.
Engel, Jasper
Deinum, Jaap
Timmers, Henri J. L. M.
Wevers, Ron A.
Kluijtmans, Leo A. J.
author_sort Bliziotis, Nikolaos G.
collection PubMed
description INTRODUCTION: When analyzing the human plasma metabolome with Nuclear Magnetic Resonance (NMR) spectroscopy, the Carr–Purcell–Meiboom–Gill (CPMG) experiment is commonly employed for large studies. However, this process can lead to compromised statistical analyses due to residual macromolecule signals. In addition, the utilization of Trimethylsilylpropanoic acid (TSP) as an internal standard often leads to quantification issues, and binning, as a spectral summarization step, can result in features not clearly assignable to metabolites. OBJECTIVES: Our aim was to establish a new complete protocol for large plasma cohorts collected with the purpose of describing the comparative metabolic profile of groups of samples. METHODS: We compared the conventional CPMG approach to a novel procedure that involves diffusion NMR, using the Longitudinal Eddy-Current Delay (LED) experiment, maleic acid (MA) as the quantification reference and peak picking for spectral reduction. This comparison was carried out using the ultrafiltration method as a gold standard in a simple sample classification experiment, with Partial Least Squares–Discriminant Analysis (PLS-DA) and the resulting metabolic signatures for multivariate data analysis. In addition, the quantification capabilities of the method were evaluated. RESULTS: We found that the LED method applied was able to detect more metabolites than CPMG and suppress macromolecule signals more efficiently. The complete protocol was able to yield PLS-DA models with enhanced classification accuracy as well as a more reliable set of important features than the conventional CPMG approach. Assessment of the quantitative capabilities of the method resulted in good linearity, recovery and agreement with an established amino acid assay for the majority of the metabolites tested. Regarding repeatability, ~ 85% of all peaks had an adequately low coefficient of variation (< 30%) in replicate samples. CONCLUSION: Overall, our comparison yielded a high-throughput untargeted plasma NMR protocol for optimized data acquisition and processing that is expected to be a valuable contribution in the field of metabolic biomarker discovery. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1007/s11306-020-01686-y) contains supplementary material, which is available to authorized users.
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spelling pubmed-71969442020-05-05 A comparison of high-throughput plasma NMR protocols for comparative untargeted metabolomics Bliziotis, Nikolaos G. Engelke, Udo F. H. Aspers, Ruud L. E. G. Engel, Jasper Deinum, Jaap Timmers, Henri J. L. M. Wevers, Ron A. Kluijtmans, Leo A. J. Metabolomics Original Article INTRODUCTION: When analyzing the human plasma metabolome with Nuclear Magnetic Resonance (NMR) spectroscopy, the Carr–Purcell–Meiboom–Gill (CPMG) experiment is commonly employed for large studies. However, this process can lead to compromised statistical analyses due to residual macromolecule signals. In addition, the utilization of Trimethylsilylpropanoic acid (TSP) as an internal standard often leads to quantification issues, and binning, as a spectral summarization step, can result in features not clearly assignable to metabolites. OBJECTIVES: Our aim was to establish a new complete protocol for large plasma cohorts collected with the purpose of describing the comparative metabolic profile of groups of samples. METHODS: We compared the conventional CPMG approach to a novel procedure that involves diffusion NMR, using the Longitudinal Eddy-Current Delay (LED) experiment, maleic acid (MA) as the quantification reference and peak picking for spectral reduction. This comparison was carried out using the ultrafiltration method as a gold standard in a simple sample classification experiment, with Partial Least Squares–Discriminant Analysis (PLS-DA) and the resulting metabolic signatures for multivariate data analysis. In addition, the quantification capabilities of the method were evaluated. RESULTS: We found that the LED method applied was able to detect more metabolites than CPMG and suppress macromolecule signals more efficiently. The complete protocol was able to yield PLS-DA models with enhanced classification accuracy as well as a more reliable set of important features than the conventional CPMG approach. Assessment of the quantitative capabilities of the method resulted in good linearity, recovery and agreement with an established amino acid assay for the majority of the metabolites tested. Regarding repeatability, ~ 85% of all peaks had an adequately low coefficient of variation (< 30%) in replicate samples. CONCLUSION: Overall, our comparison yielded a high-throughput untargeted plasma NMR protocol for optimized data acquisition and processing that is expected to be a valuable contribution in the field of metabolic biomarker discovery. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1007/s11306-020-01686-y) contains supplementary material, which is available to authorized users. Springer US 2020-05-01 2020 /pmc/articles/PMC7196944/ /pubmed/32358672 http://dx.doi.org/10.1007/s11306-020-01686-y Text en © The Author(s) 2020 Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
spellingShingle Original Article
Bliziotis, Nikolaos G.
Engelke, Udo F. H.
Aspers, Ruud L. E. G.
Engel, Jasper
Deinum, Jaap
Timmers, Henri J. L. M.
Wevers, Ron A.
Kluijtmans, Leo A. J.
A comparison of high-throughput plasma NMR protocols for comparative untargeted metabolomics
title A comparison of high-throughput plasma NMR protocols for comparative untargeted metabolomics
title_full A comparison of high-throughput plasma NMR protocols for comparative untargeted metabolomics
title_fullStr A comparison of high-throughput plasma NMR protocols for comparative untargeted metabolomics
title_full_unstemmed A comparison of high-throughput plasma NMR protocols for comparative untargeted metabolomics
title_short A comparison of high-throughput plasma NMR protocols for comparative untargeted metabolomics
title_sort comparison of high-throughput plasma nmr protocols for comparative untargeted metabolomics
topic Original Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7196944/
https://www.ncbi.nlm.nih.gov/pubmed/32358672
http://dx.doi.org/10.1007/s11306-020-01686-y
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