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Metabolomics for personalized medicine: the input of analytical chemistry from biomarker discovery to point-of-care tests
Metabolomics refers to the large-scale detection, quantification, and analysis of small molecules (metabolites) in biological media. Although metabolomics, alone or combined with other omics data, has already demonstrated its relevance for patient stratification in the frame of research projects and...
Autores principales: | , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer Berlin Heidelberg
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8386160/ https://www.ncbi.nlm.nih.gov/pubmed/34432105 http://dx.doi.org/10.1007/s00216-021-03586-z |
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author | Castelli, Florence Anne Rosati, Giulio Moguet, Christian Fuentes, Celia Marrugo-Ramírez, Jose Lefebvre, Thibaud Volland, Hervé Merkoçi, Arben Simon, Stéphanie Fenaille, François Junot, Christophe |
author_facet | Castelli, Florence Anne Rosati, Giulio Moguet, Christian Fuentes, Celia Marrugo-Ramírez, Jose Lefebvre, Thibaud Volland, Hervé Merkoçi, Arben Simon, Stéphanie Fenaille, François Junot, Christophe |
author_sort | Castelli, Florence Anne |
collection | PubMed |
description | Metabolomics refers to the large-scale detection, quantification, and analysis of small molecules (metabolites) in biological media. Although metabolomics, alone or combined with other omics data, has already demonstrated its relevance for patient stratification in the frame of research projects and clinical studies, much remains to be done to move this approach to the clinical practice. This is especially true in the perspective of being applied to personalized/precision medicine, which aims at stratifying patients according to their risk of developing diseases, and tailoring medical treatments of patients according to individual characteristics in order to improve their efficacy and limit their toxicity. In this review article, we discuss the main challenges linked to analytical chemistry that need to be addressed to foster the implementation of metabolomics in the clinics and the use of the data produced by this approach in personalized medicine. First of all, there are already well-known issues related to untargeted metabolomics workflows at the levels of data production (lack of standardization), metabolite identification (small proportion of annotated features and identified metabolites), and data processing (from automatic detection of features to multi-omic data integration) that hamper the inter-operability and reusability of metabolomics data. Furthermore, the outputs of metabolomics workflows are complex molecular signatures of few tens of metabolites, often with small abundance variations, and obtained with expensive laboratory equipment. It is thus necessary to simplify these molecular signatures so that they can be produced and used in the field. This last point, which is still poorly addressed by the metabolomics community, may be crucial in a near future with the increased availability of molecular signatures of medical relevance and the increased societal demand for participatory medicine. GRAPHICAL ABSTRACT: [Image: see text] |
format | Online Article Text |
id | pubmed-8386160 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Springer Berlin Heidelberg |
record_format | MEDLINE/PubMed |
spelling | pubmed-83861602021-08-25 Metabolomics for personalized medicine: the input of analytical chemistry from biomarker discovery to point-of-care tests Castelli, Florence Anne Rosati, Giulio Moguet, Christian Fuentes, Celia Marrugo-Ramírez, Jose Lefebvre, Thibaud Volland, Hervé Merkoçi, Arben Simon, Stéphanie Fenaille, François Junot, Christophe Anal Bioanal Chem Review Metabolomics refers to the large-scale detection, quantification, and analysis of small molecules (metabolites) in biological media. Although metabolomics, alone or combined with other omics data, has already demonstrated its relevance for patient stratification in the frame of research projects and clinical studies, much remains to be done to move this approach to the clinical practice. This is especially true in the perspective of being applied to personalized/precision medicine, which aims at stratifying patients according to their risk of developing diseases, and tailoring medical treatments of patients according to individual characteristics in order to improve their efficacy and limit their toxicity. In this review article, we discuss the main challenges linked to analytical chemistry that need to be addressed to foster the implementation of metabolomics in the clinics and the use of the data produced by this approach in personalized medicine. First of all, there are already well-known issues related to untargeted metabolomics workflows at the levels of data production (lack of standardization), metabolite identification (small proportion of annotated features and identified metabolites), and data processing (from automatic detection of features to multi-omic data integration) that hamper the inter-operability and reusability of metabolomics data. Furthermore, the outputs of metabolomics workflows are complex molecular signatures of few tens of metabolites, often with small abundance variations, and obtained with expensive laboratory equipment. It is thus necessary to simplify these molecular signatures so that they can be produced and used in the field. This last point, which is still poorly addressed by the metabolomics community, may be crucial in a near future with the increased availability of molecular signatures of medical relevance and the increased societal demand for participatory medicine. GRAPHICAL ABSTRACT: [Image: see text] Springer Berlin Heidelberg 2021-08-25 2022 /pmc/articles/PMC8386160/ /pubmed/34432105 http://dx.doi.org/10.1007/s00216-021-03586-z Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open Access This 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/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Review Castelli, Florence Anne Rosati, Giulio Moguet, Christian Fuentes, Celia Marrugo-Ramírez, Jose Lefebvre, Thibaud Volland, Hervé Merkoçi, Arben Simon, Stéphanie Fenaille, François Junot, Christophe Metabolomics for personalized medicine: the input of analytical chemistry from biomarker discovery to point-of-care tests |
title | Metabolomics for personalized medicine: the input of analytical chemistry from biomarker discovery to point-of-care tests |
title_full | Metabolomics for personalized medicine: the input of analytical chemistry from biomarker discovery to point-of-care tests |
title_fullStr | Metabolomics for personalized medicine: the input of analytical chemistry from biomarker discovery to point-of-care tests |
title_full_unstemmed | Metabolomics for personalized medicine: the input of analytical chemistry from biomarker discovery to point-of-care tests |
title_short | Metabolomics for personalized medicine: the input of analytical chemistry from biomarker discovery to point-of-care tests |
title_sort | metabolomics for personalized medicine: the input of analytical chemistry from biomarker discovery to point-of-care tests |
topic | Review |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8386160/ https://www.ncbi.nlm.nih.gov/pubmed/34432105 http://dx.doi.org/10.1007/s00216-021-03586-z |
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