Untargeted metabolomics HRMS data processing using regions of interest and multivariate curve resolution approaches to unveil health-to-disease transition

Early diagnosis has the potential to prevent or minimize the progression of diseases. The discovery of biomarkers that permit to pinpoint the health-to-disease transition is thus of crucial importance to improve diagnostic efficiency and treatment. Over the last years, untargeted metabolomic analysi...

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Detalles Bibliográficos
Autores: Barreiros, Luisa, Sampaio-Maia, Benedita, Alencastre, Inês Soares, Tauler, Romà, Segundo, Marcela A.
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2025
País:España
Institución:Consejo Superior de Investigaciones Científicas (CSIC)
Repositorio:DIGITAL.CSIC. Repositorio Institucional del CSIC
OAI Identifier:oai:digital.csic.es:10261/388914
Acceso en línea:http://hdl.handle.net/10261/388914
https://api.elsevier.com/content/abstract/scopus_id/105003923571
Access Level:acceso abierto
Palabra clave:Untargeted analysis
Biomarkers
Chemometrics
Chronic kidney disease
Data processing
High-resolution mass spectrometry
http://metadata.un.org/sdg/3
http://metadata.un.org/sdg/9
Ensure healthy lives and promote well-being for all at all ages
Build resilient infrastructure, promote inclusive and sustainable industrialization and foster innovation
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network_name_str España
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dc.title.none.fl_str_mv Untargeted metabolomics HRMS data processing using regions of interest and multivariate curve resolution approaches to unveil health-to-disease transition
title Untargeted metabolomics HRMS data processing using regions of interest and multivariate curve resolution approaches to unveil health-to-disease transition
spellingShingle Untargeted metabolomics HRMS data processing using regions of interest and multivariate curve resolution approaches to unveil health-to-disease transition
Barreiros, Luisa
Untargeted analysis
Biomarkers
Chemometrics
Chronic kidney disease
Data processing
High-resolution mass spectrometry
http://metadata.un.org/sdg/3
http://metadata.un.org/sdg/9
Ensure healthy lives and promote well-being for all at all ages
Build resilient infrastructure, promote inclusive and sustainable industrialization and foster innovation
title_short Untargeted metabolomics HRMS data processing using regions of interest and multivariate curve resolution approaches to unveil health-to-disease transition
title_full Untargeted metabolomics HRMS data processing using regions of interest and multivariate curve resolution approaches to unveil health-to-disease transition
title_fullStr Untargeted metabolomics HRMS data processing using regions of interest and multivariate curve resolution approaches to unveil health-to-disease transition
title_full_unstemmed Untargeted metabolomics HRMS data processing using regions of interest and multivariate curve resolution approaches to unveil health-to-disease transition
title_sort Untargeted metabolomics HRMS data processing using regions of interest and multivariate curve resolution approaches to unveil health-to-disease transition
dc.creator.none.fl_str_mv Barreiros, Luisa
Sampaio-Maia, Benedita
Alencastre, Inês Soares
Tauler, Romà
Segundo, Marcela A.
author Barreiros, Luisa
author_facet Barreiros, Luisa
Sampaio-Maia, Benedita
Alencastre, Inês Soares
Tauler, Romà
Segundo, Marcela A.
author_role author
author2 Sampaio-Maia, Benedita
Alencastre, Inês Soares
Tauler, Romà
Segundo, Marcela A.
author2_role author
author
author
author
dc.contributor.none.fl_str_mv 0000-0003-3481-5809
0000-0001-9368-8075
0000-0003-2938-0214
Consejo Superior de Investigaciones Científicas [https://ror.org/02gfc7t72]
dc.subject.none.fl_str_mv Untargeted analysis
Biomarkers
Chemometrics
Chronic kidney disease
Data processing
High-resolution mass spectrometry
http://metadata.un.org/sdg/3
http://metadata.un.org/sdg/9
Ensure healthy lives and promote well-being for all at all ages
Build resilient infrastructure, promote inclusive and sustainable industrialization and foster innovation
topic Untargeted analysis
Biomarkers
Chemometrics
Chronic kidney disease
Data processing
High-resolution mass spectrometry
http://metadata.un.org/sdg/3
http://metadata.un.org/sdg/9
Ensure healthy lives and promote well-being for all at all ages
Build resilient infrastructure, promote inclusive and sustainable industrialization and foster innovation
description Early diagnosis has the potential to prevent or minimize the progression of diseases. The discovery of biomarkers that permit to pinpoint the health-to-disease transition is thus of crucial importance to improve diagnostic efficiency and treatment. Over the last years, untargeted metabolomic analysis of biomatrices has become a valuable tool to identify biomarkers of health status. However, it is still a recent research field with many challenges to address, namely the implementation of data treatment strategies that allow to deal efficiently with the highly complex data sets generated, without losing accuracy or relevant information. This work pursued the application of the chemometrics method Regions of Interest-Multivariate Curve Resolution (ROIMCR) to the MS-based metabolomic profiling of plasma samples aiming at the identification of chronic kidney disease (CKD) biomarkers. ROIMCR successfully resolved the plasma profiles of three groups of individuals: healthy controls, intermediate stage (pre-dialysis) and end-stage (in dialysis) CKD patients. Positive (MS1+) and negative (MS1−) data were processed simultaneously without requiring previous time alignment, resulting in time-effective analysis with increased metabolite coverage and identification. Multivariate analysis (PCA, PLS-DA, ASCA) revealed that samples were clustered according to health status and permitted to determine the most influential metabolites in differentiating groups. Metabolites identification evidenced recognized biomarkers of CKD, validating the proposed approach, and potential new indicators of disease onset and progression. A new analytical workflow involving instrumental analysis by UHPLC-HRMS and data treatment by chemometrics method ROIMCR, followed by multivariate analysis, is available for plasma metabolomic profiling. This platform can be easily adapted to other biomatrices and diseases, and applied to profile the same individual along time, fostering the development of personalized medicine.
publishDate 2025
dc.date.none.fl_str_mv 2025
2025
2025
dc.type.none.fl_str_mv info:eu-repo/semantics/article
http://purl.org/coar/resource_type/c_6501
Publisher's version
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/10261/388914
https://api.elsevier.com/content/abstract/scopus_id/105003923571
url http://hdl.handle.net/10261/388914
https://api.elsevier.com/content/abstract/scopus_id/105003923571
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv Microchemical Journal
https://doi.org/10.1016/j.microc.2025.113737
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)
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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spelling Untargeted metabolomics HRMS data processing using regions of interest and multivariate curve resolution approaches to unveil health-to-disease transitionBarreiros, LuisaSampaio-Maia, BeneditaAlencastre, Inês SoaresTauler, RomàSegundo, Marcela A.Untargeted analysisBiomarkersChemometricsChronic kidney diseaseData processingHigh-resolution mass spectrometryhttp://metadata.un.org/sdg/3http://metadata.un.org/sdg/9Ensure healthy lives and promote well-being for all at all agesBuild resilient infrastructure, promote inclusive and sustainable industrialization and foster innovationEarly diagnosis has the potential to prevent or minimize the progression of diseases. The discovery of biomarkers that permit to pinpoint the health-to-disease transition is thus of crucial importance to improve diagnostic efficiency and treatment. Over the last years, untargeted metabolomic analysis of biomatrices has become a valuable tool to identify biomarkers of health status. However, it is still a recent research field with many challenges to address, namely the implementation of data treatment strategies that allow to deal efficiently with the highly complex data sets generated, without losing accuracy or relevant information. This work pursued the application of the chemometrics method Regions of Interest-Multivariate Curve Resolution (ROIMCR) to the MS-based metabolomic profiling of plasma samples aiming at the identification of chronic kidney disease (CKD) biomarkers. ROIMCR successfully resolved the plasma profiles of three groups of individuals: healthy controls, intermediate stage (pre-dialysis) and end-stage (in dialysis) CKD patients. Positive (MS1+) and negative (MS1−) data were processed simultaneously without requiring previous time alignment, resulting in time-effective analysis with increased metabolite coverage and identification. Multivariate analysis (PCA, PLS-DA, ASCA) revealed that samples were clustered according to health status and permitted to determine the most influential metabolites in differentiating groups. Metabolites identification evidenced recognized biomarkers of CKD, validating the proposed approach, and potential new indicators of disease onset and progression. A new analytical workflow involving instrumental analysis by UHPLC-HRMS and data treatment by chemometrics method ROIMCR, followed by multivariate analysis, is available for plasma metabolomic profiling. This platform can be easily adapted to other biomatrices and diseases, and applied to profile the same individual along time, fostering the development of personalized medicine.This work received financial support from PT national funds (FCT/MECI, Fundação para a Ciência e a Tecnologia and Ministério da Educação, Ciência e Inovação) through project 2022.06012.PTDC DOI 10.54499/2022.06012.PTDC and project UID/50006 - Laboratório Associado para a Química Verde - Tecnologias e Processos Limpos. L. Barreiros acknowledges funding from FCT through program DL 57/2016 – Norma transitória. The HRMS analyses were performed using equipment available at the Mass Spectrometry Laboratory of CEMUP, U.Porto, and the authors are grateful for the technical support provided. The authors thank the researchers of the Department of Environmental Chemistry at IDAEA-CSIC, particularly Flávia Yamamoto and Carlos Pérez-López, for their valuable help in successfully implementing the data processing methods. The authors also acknowledge Manuel Pestana (i3S, FMUP) for aiding in the biological interpretation of assigned metabolites.Peer reviewedElsevier0000-0003-3481-58090000-0001-9368-80750000-0003-2938-0214Consejo Superior de Investigaciones Científicas [https://ror.org/02gfc7t72]202520252025info:eu-repo/semantics/articlehttp://purl.org/coar/resource_type/c_6501Publisher's versioninfo:eu-repo/semantics/publishedVersionhttp://hdl.handle.net/10261/388914https://api.elsevier.com/content/abstract/scopus_id/105003923571reponame:DIGITAL.CSIC. Repositorio Institucional del CSICinstname:Consejo Superior de Investigaciones Científicas (CSIC)InglésMicrochemical Journalhttps://doi.org/10.1016/j.microc.2025.113737Síinfo:eu-repo/semantics/openAccessoai:digital.csic.es:10261/3889142026-05-22T06:33:51Z
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