Untargeted assignment and automatic integration of 1H NMR metabolomic datasets using a multivariate curve resolution approach

In this article, we propose the use of the Multivariate Curve Resolution - Alternating Least Squares (MCR-ALS) chemometrics method to resolve the 1H NMR spectra and concentration of the individual metabolites in their mixtures in untargeted metabolomics studies. A decision tree-based strategy is pre...

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Detalles Bibliográficos
Autores: Puig-Castellví, Francesc, Alfonso, Ignacio, Tauler, Romà
Tipo de recurso: artículo
Estado:Versión aceptada para publicación
Fecha de publicación:2017
País:España
Institución:Consejo Superior de Investigaciones Científicas (CSIC)
Repositorio:DIGITAL.CSIC. Repositorio Institucional del CSIC
OAI Identifier:oai:digital.csic.es:10261/270270
Acceso en línea:http://hdl.handle.net/10261/270270
https://api.elsevier.com/content/abstract/scopus_id/85013392592
Access Level:acceso abierto
Palabra clave:Nuclear magnetic resonance
Metabolomics
Multivariate curve resolution
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spelling Untargeted assignment and automatic integration of 1H NMR metabolomic datasets using a multivariate curve resolution approachPuig-Castellví, FrancescAlfonso, IgnacioTauler, RomàNuclear magnetic resonanceMetabolomicsMultivariate curve resolutionIn this article, we propose the use of the Multivariate Curve Resolution - Alternating Least Squares (MCR-ALS) chemometrics method to resolve the 1H NMR spectra and concentration of the individual metabolites in their mixtures in untargeted metabolomics studies. A decision tree-based strategy is presented to optimally select and implement spectra estimates and equality constraints during MCR-ALS optimization. The proposed method has been satisfactorily evaluated using different 1H NMR metabolomics datasets. In a first study, 1H NMR spectra of the metabolites in a simulated mixture were successfully recovered and assigned. In a second study, more than 30 metabolites were characterized and quantified from an experimental unknown mixture analyzed by 1H NMR. In this work, MCR-ALS is shown to be a convenient tool for metabolite investigation and sample screening using 1H NMR, and it opens a new path for performing metabolomics studies with this chemometric technique.The research leading to these results has received funding from the European Research Council under the European Union's Seventh Framework Programme (FP/2007–2013)/ERC Grant Agreement n. 320737 and the Spanish Ministry of Economy and Competitiveness (CTQ2015-66254-C2-1-P).Peer reviewedElsevierEuropean Research CouncilConsejo Superior de Investigaciones Científicas [https://ror.org/02gfc7t72]202220222017info:eu-repo/semantics/articlehttp://purl.org/coar/resource_type/c_6501Postprintinfo:eu-repo/semantics/acceptedVersionhttp://hdl.handle.net/10261/270270https://api.elsevier.com/content/abstract/scopus_id/85013392592reponame:DIGITAL.CSIC. Repositorio Institucional del CSICinstname:Consejo Superior de Investigaciones Científicas (CSIC)Inglés#PLACEHOLDER_PARENT_METADATA_VALUE#info:eu-repo/grantAgreement/EC/FP7/320737Analytica chimica acta10.1016/j.aca.2017.02.010Síinfo:eu-repo/semantics/openAccessoai:digital.csic.es:10261/2702702026-05-22T06:33:51Z
dc.title.none.fl_str_mv Untargeted assignment and automatic integration of 1H NMR metabolomic datasets using a multivariate curve resolution approach
title Untargeted assignment and automatic integration of 1H NMR metabolomic datasets using a multivariate curve resolution approach
spellingShingle Untargeted assignment and automatic integration of 1H NMR metabolomic datasets using a multivariate curve resolution approach
Puig-Castellví, Francesc
Nuclear magnetic resonance
Metabolomics
Multivariate curve resolution
title_short Untargeted assignment and automatic integration of 1H NMR metabolomic datasets using a multivariate curve resolution approach
title_full Untargeted assignment and automatic integration of 1H NMR metabolomic datasets using a multivariate curve resolution approach
title_fullStr Untargeted assignment and automatic integration of 1H NMR metabolomic datasets using a multivariate curve resolution approach
title_full_unstemmed Untargeted assignment and automatic integration of 1H NMR metabolomic datasets using a multivariate curve resolution approach
title_sort Untargeted assignment and automatic integration of 1H NMR metabolomic datasets using a multivariate curve resolution approach
dc.creator.none.fl_str_mv Puig-Castellví, Francesc
Alfonso, Ignacio
Tauler, Romà
author Puig-Castellví, Francesc
author_facet Puig-Castellví, Francesc
Alfonso, Ignacio
Tauler, Romà
author_role author
author2 Alfonso, Ignacio
Tauler, Romà
author2_role author
author
dc.contributor.none.fl_str_mv European Research Council
Consejo Superior de Investigaciones Científicas [https://ror.org/02gfc7t72]
dc.subject.none.fl_str_mv Nuclear magnetic resonance
Metabolomics
Multivariate curve resolution
topic Nuclear magnetic resonance
Metabolomics
Multivariate curve resolution
description In this article, we propose the use of the Multivariate Curve Resolution - Alternating Least Squares (MCR-ALS) chemometrics method to resolve the 1H NMR spectra and concentration of the individual metabolites in their mixtures in untargeted metabolomics studies. A decision tree-based strategy is presented to optimally select and implement spectra estimates and equality constraints during MCR-ALS optimization. The proposed method has been satisfactorily evaluated using different 1H NMR metabolomics datasets. In a first study, 1H NMR spectra of the metabolites in a simulated mixture were successfully recovered and assigned. In a second study, more than 30 metabolites were characterized and quantified from an experimental unknown mixture analyzed by 1H NMR. In this work, MCR-ALS is shown to be a convenient tool for metabolite investigation and sample screening using 1H NMR, and it opens a new path for performing metabolomics studies with this chemometric technique.
publishDate 2017
dc.date.none.fl_str_mv 2017
2022
2022
dc.type.none.fl_str_mv info:eu-repo/semantics/article
http://purl.org/coar/resource_type/c_6501
Postprint
info:eu-repo/semantics/acceptedVersion
format article
status_str acceptedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/10261/270270
https://api.elsevier.com/content/abstract/scopus_id/85013392592
url http://hdl.handle.net/10261/270270
https://api.elsevier.com/content/abstract/scopus_id/85013392592
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv #PLACEHOLDER_PARENT_METADATA_VALUE#
info:eu-repo/grantAgreement/EC/FP7/320737
Analytica chimica acta
10.1016/j.aca.2017.02.010
Sí
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.publisher.none.fl_str_mv Elsevier
publisher.none.fl_str_mv Elsevier
dc.source.none.fl_str_mv reponame:DIGITAL.CSIC. Repositorio Institucional del CSIC
instname:Consejo Superior de Investigaciones Científicas (CSIC)
instname_str Consejo Superior de Investigaciones Científicas (CSIC)
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collection DIGITAL.CSIC. Repositorio Institucional del CSIC
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