Untargeted Comprehensive Two-Dimensional Liquid Chromatography Coupled with High-Resolution Mass Spectrometry Analysis of Rice Metabolome Using Multivariate Curve Resolution

In this work, a new strategy for the chemometric analysis of two-dimensional liquid chromatography-high-resolution mass spectrometry (LC × LC-HRMS) data is proposed. This approach consists of a preliminary compression step along the mass spectrometry (MS) spectral dimension based on the selection of...

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Authors: Navarro-Reig, Meritxell, Jaumot, Joaquim, Baglai, Anna, Vivó-Truyols, Gabriel, Schoenmakers, Peter J., Tauler, Romà
Format: article
Status:Versión aceptada para publicación
Publication Date:2017
Country:España
Institution:Consejo Superior de Investigaciones Científicas (CSIC)
Repository:DIGITAL.CSIC. Repositorio Institucional del CSIC
OAI Identifier:oai:digital.csic.es:10261/158110
Online Access:http://hdl.handle.net/10261/158110
Access Level:Open access
Keyword:Biomolecules
Chromatography
Mass spectrometry
Metabolism
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spelling Untargeted Comprehensive Two-Dimensional Liquid Chromatography Coupled with High-Resolution Mass Spectrometry Analysis of Rice Metabolome Using Multivariate Curve ResolutionNavarro-Reig, MeritxellJaumot, JoaquimBaglai, AnnaVivó-Truyols, GabrielSchoenmakers, Peter J.Tauler, RomàBiomoleculesChromatographyMass spectrometryMetabolismIn this work, a new strategy for the chemometric analysis of two-dimensional liquid chromatography-high-resolution mass spectrometry (LC × LC-HRMS) data is proposed. This approach consists of a preliminary compression step along the mass spectrometry (MS) spectral dimension based on the selection of the regions of interest (ROI), followed by a further data compression along the chromatographic dimension by wavelet transforms. In a secondary step, the multivariate curve resolution alternating least squares (MCR-ALS) method is applied to previously compressed data sets obtained in the simultaneous analysis of multiple LC × LC-HRMS chromatographic runs from multiple samples. The feasibility of the proposed approach is demonstrated by its application to a large experimental data set obtained in the untargeted LC × LC-HRMS study of the effects of different environmental conditions (watering and harvesting time) on the metabolism of multiple rice samples. An untargeted chromatographic setup coupling two different liquid chromatography (LC) columns [hydrophilic interaction liquid chromatography (HILIC) and reversed-phase liquid chromatography (RPLC)] together with an HRMS detector was developed and applied to analyze the metabolites extracted from rice samples at the different experimental conditions. In the case of the metabolomics study taken as example in this work, a total number of 154 metabolites from 15 different families were properly resolved after the application of MCR-ALS. A total of 139 of these metabolites could be identified by their HRMS spectra. Statistical analysis of their concentration changes showed that both watering and harvest time experimental factors had significant effects on rice metabolism. The biochemical insight of the effects of watering and harvesting experimental factors on the changes in concentration of these detected metabolites in the investigated rice samples is attempted. © 2017 American Chemical Society.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 No. 320737. The authors would like to thank CRAG for kindly supplying Japanese rice seeds. CTQ2015- 66254-C2-1-P project from MINCO (Spain) is also acknowledged.Peer reviewedAmerican Chemical SocietyEuropean Research CouncilConsejo Superior de Investigaciones Científicas [https://ror.org/02gfc7t72]201720172017info:eu-repo/semantics/articlehttp://purl.org/coar/resource_type/c_6501Postprintinfo:eu-repo/semantics/acceptedVersionhttp://hdl.handle.net/10261/158110reponame:DIGITAL.CSIC. Repositorio Institucional del CSICinstname:Consejo Superior de Investigaciones Científicas (CSIC)Inglés#PLACEHOLDER_PARENT_METADATA_VALUE#info:eu-repo/grantAgreement/EC/FP7/32073710.1021/acs.analchem.7b01648Síinfo:eu-repo/semantics/openAccessoai:digital.csic.es:10261/1581102026-05-22T06:33:51Z
dc.title.none.fl_str_mv Untargeted Comprehensive Two-Dimensional Liquid Chromatography Coupled with High-Resolution Mass Spectrometry Analysis of Rice Metabolome Using Multivariate Curve Resolution
title Untargeted Comprehensive Two-Dimensional Liquid Chromatography Coupled with High-Resolution Mass Spectrometry Analysis of Rice Metabolome Using Multivariate Curve Resolution
spellingShingle Untargeted Comprehensive Two-Dimensional Liquid Chromatography Coupled with High-Resolution Mass Spectrometry Analysis of Rice Metabolome Using Multivariate Curve Resolution
Navarro-Reig, Meritxell
Biomolecules
Chromatography
Mass spectrometry
Metabolism
title_short Untargeted Comprehensive Two-Dimensional Liquid Chromatography Coupled with High-Resolution Mass Spectrometry Analysis of Rice Metabolome Using Multivariate Curve Resolution
title_full Untargeted Comprehensive Two-Dimensional Liquid Chromatography Coupled with High-Resolution Mass Spectrometry Analysis of Rice Metabolome Using Multivariate Curve Resolution
title_fullStr Untargeted Comprehensive Two-Dimensional Liquid Chromatography Coupled with High-Resolution Mass Spectrometry Analysis of Rice Metabolome Using Multivariate Curve Resolution
title_full_unstemmed Untargeted Comprehensive Two-Dimensional Liquid Chromatography Coupled with High-Resolution Mass Spectrometry Analysis of Rice Metabolome Using Multivariate Curve Resolution
title_sort Untargeted Comprehensive Two-Dimensional Liquid Chromatography Coupled with High-Resolution Mass Spectrometry Analysis of Rice Metabolome Using Multivariate Curve Resolution
dc.creator.none.fl_str_mv Navarro-Reig, Meritxell
Jaumot, Joaquim
Baglai, Anna
Vivó-Truyols, Gabriel
Schoenmakers, Peter J.
Tauler, Romà
author Navarro-Reig, Meritxell
author_facet Navarro-Reig, Meritxell
Jaumot, Joaquim
Baglai, Anna
Vivó-Truyols, Gabriel
Schoenmakers, Peter J.
Tauler, Romà
author_role author
author2 Jaumot, Joaquim
Baglai, Anna
Vivó-Truyols, Gabriel
Schoenmakers, Peter J.
Tauler, Romà
author2_role author
author
author
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 Biomolecules
Chromatography
Mass spectrometry
Metabolism
topic Biomolecules
Chromatography
Mass spectrometry
Metabolism
description In this work, a new strategy for the chemometric analysis of two-dimensional liquid chromatography-high-resolution mass spectrometry (LC × LC-HRMS) data is proposed. This approach consists of a preliminary compression step along the mass spectrometry (MS) spectral dimension based on the selection of the regions of interest (ROI), followed by a further data compression along the chromatographic dimension by wavelet transforms. In a secondary step, the multivariate curve resolution alternating least squares (MCR-ALS) method is applied to previously compressed data sets obtained in the simultaneous analysis of multiple LC × LC-HRMS chromatographic runs from multiple samples. The feasibility of the proposed approach is demonstrated by its application to a large experimental data set obtained in the untargeted LC × LC-HRMS study of the effects of different environmental conditions (watering and harvesting time) on the metabolism of multiple rice samples. An untargeted chromatographic setup coupling two different liquid chromatography (LC) columns [hydrophilic interaction liquid chromatography (HILIC) and reversed-phase liquid chromatography (RPLC)] together with an HRMS detector was developed and applied to analyze the metabolites extracted from rice samples at the different experimental conditions. In the case of the metabolomics study taken as example in this work, a total number of 154 metabolites from 15 different families were properly resolved after the application of MCR-ALS. A total of 139 of these metabolites could be identified by their HRMS spectra. Statistical analysis of their concentration changes showed that both watering and harvest time experimental factors had significant effects on rice metabolism. The biochemical insight of the effects of watering and harvesting experimental factors on the changes in concentration of these detected metabolites in the investigated rice samples is attempted. © 2017 American Chemical Society.
publishDate 2017
dc.date.none.fl_str_mv 2017
2017
2017
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/10261/158110
url http://hdl.handle.net/10261/158110
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
10.1021/acs.analchem.7b01648
Sí
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.publisher.none.fl_str_mv American Chemical Society
publisher.none.fl_str_mv American Chemical Society
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)
reponame_str DIGITAL.CSIC. Repositorio Institucional del CSIC
collection DIGITAL.CSIC. Repositorio Institucional del CSIC
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15,198674

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