Mapping shared and unique features in spatial transcriptomics through multivariate curve resolution

[Data availability] The data is publicly available in the open-access repository Zenodo (Ref. 14937256).

Detalles Bibliográficos
Autores: Menéndez-Pedriza, Albert, Blázquez, Mercedes, Navarro-Martín, Laia, Jaumot, Joaquim
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:10261/383541
Acceso en línea:http://hdl.handle.net/10261/383541
https://api.elsevier.com/content/abstract/scopus_id/85219661181
Access Level:acceso abierto
Palabra clave:Spatial transcriptomics
Chemometrics
Image analysis
Multivariate curve resolution
Sea bass gonads
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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oai_identifier_str oai:digital.csic.es:10261/383541
network_acronym_str ES
network_name_str España
repository_id_str
dc.title.none.fl_str_mv Mapping shared and unique features in spatial transcriptomics through multivariate curve resolution
title Mapping shared and unique features in spatial transcriptomics through multivariate curve resolution
spellingShingle Mapping shared and unique features in spatial transcriptomics through multivariate curve resolution
Menéndez-Pedriza, Albert
Spatial transcriptomics
Chemometrics
Image analysis
Multivariate curve resolution
Sea bass gonads
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 Mapping shared and unique features in spatial transcriptomics through multivariate curve resolution
title_full Mapping shared and unique features in spatial transcriptomics through multivariate curve resolution
title_fullStr Mapping shared and unique features in spatial transcriptomics through multivariate curve resolution
title_full_unstemmed Mapping shared and unique features in spatial transcriptomics through multivariate curve resolution
title_sort Mapping shared and unique features in spatial transcriptomics through multivariate curve resolution
dc.creator.none.fl_str_mv Menéndez-Pedriza, Albert
Blázquez, Mercedes
Navarro-Martín, Laia
Jaumot, Joaquim
author Menéndez-Pedriza, Albert
author_facet Menéndez-Pedriza, Albert
Blázquez, Mercedes
Navarro-Martín, Laia
Jaumot, Joaquim
author_role author
author2 Blázquez, Mercedes
Navarro-Martín, Laia
Jaumot, Joaquim
author2_role author
author
author
dc.contributor.none.fl_str_mv Ministerio de Ciencia e Innovación (España)
Agencia Estatal de Investigación (España)
Menéndez-Pedriza, Albert [0000-0002-4960-0379]
Jaumot, Joaquim [0000-0003-1461-3273]
Consejo Superior de Investigaciones Científicas [https://ror.org/02gfc7t72]
dc.subject.none.fl_str_mv Spatial transcriptomics
Chemometrics
Image analysis
Multivariate curve resolution
Sea bass gonads
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 Spatial transcriptomics
Chemometrics
Image analysis
Multivariate curve resolution
Sea bass gonads
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 [Data availability] The data is publicly available in the open-access repository Zenodo (Ref. 14937256).
publishDate 2025
dc.date.none.fl_str_mv 2025
2025
2025
dc.type.none.fl_str_mv info:eu-repo/semantics/article
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Publisher's version
info:eu-repo/semantics/publishedVersion
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dc.identifier.none.fl_str_mv http://hdl.handle.net/10261/383541
https://api.elsevier.com/content/abstract/scopus_id/85219661181
url http://hdl.handle.net/10261/383541
https://api.elsevier.com/content/abstract/scopus_id/85219661181
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
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info:eu-repo/grantAgreement/MCIN/AEI/10.13039
info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2021-2023/PID2021-122929OB-C31
info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2021-2023/PID2021-122929OB-C33
The underlying dataset has been published as supplementary material of the article in the publisher platform at DOI https://doi.org/10.1016/j.microc.2025.113189
https://doi.org/10.1016/j.microc.2025.113189
Sí
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
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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
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spelling Mapping shared and unique features in spatial transcriptomics through multivariate curve resolutionMenéndez-Pedriza, AlbertBlázquez, MercedesNavarro-Martín, LaiaJaumot, JoaquimSpatial transcriptomicsChemometricsImage analysisMultivariate curve resolutionSea bass gonadshttp://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 innovation[Data availability] The data is publicly available in the open-access repository Zenodo (Ref. 14937256).Spatial biology is poised to play a pivotal role in enhancing our understanding of biological systems. Recent advancements have led to the development of a number of analytical pipelines, particularly within the framework of spatial transcriptomics. However, the analysis of spatial transcriptomic data remains computationally challenging. The Multivariate Curve Resolution Alternating Least Squares (MCR-ALS) model has proven to be a powerful chemometric approach, offering a more interpretable representation of complex spatial data compared to other exploratory approaches such as principal component analysis for not imposing an orthogonality constraint. Despite the growing use of similar models, MCR-ALS has yet to be tested for analyzing spatial transcriptomics data. In this study, a critical evaluation of the potential of MCR-ALS-based approaches to complement this key step in spatial transcriptomics analysis is conducted. Specifically, the MCR-ALS evaluation is performed on four samples of European sea bass testis at different early-maturation stages. Our results demonstrate that MCR-ALS is able to provide an accurate interpretation of the data when analyzing tissues both individually and simultaneously. The bilinear resolution effectively identified key spatial regions, which were putatively assigned to specific gonad compartments, in agreement with histological analysis. Furthermore, MCR-ALS models yielded results consistent with those from a standard spatial transcriptomics pipeline, particularly in examining gene expression profiles in specific gonadal regions. Therefore, integrating this chemometric tool into spatial transcriptomics workflow offers significant advantages for unraveling complex biological processes.The research leading to these results has received funding from the Spanish Ministry of Science and Innovation MCIN/AEI/10.13039/501100011033, Grants RYC2019-026426-I, PID2021-122929OB-C31, PID2021-122929OB-C33 and CEX2018-000794-S. AMP also acknowledges a grant PRE2020-094656 funded by MCIN/AEI/10.13039/501100011033 by ESF Investing in your future.With the institutional support of the ‘Severo Ochoa Centre of Excellence’ accreditation (CEX2019-000928-S).Peer reviewedElsevierMinisterio de Ciencia e Innovación (España)Agencia Estatal de Investigación (España)Menéndez-Pedriza, Albert [0000-0002-4960-0379]Jaumot, Joaquim [0000-0003-1461-3273]Consejo 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/publishedVersionapplication/pdfapplication/mswordhttp://hdl.handle.net/10261/383541https://api.elsevier.com/content/abstract/scopus_id/85219661181reponame:DIGITAL.CSIC. Repositorio Institucional del CSICinstname:Consejo Superior de Investigaciones Científicas (CSIC)Inglés#PLACEHOLDER_PARENT_METADATA_VALUE##PLACEHOLDER_PARENT_METADATA_VALUE##PLACEHOLDER_PARENT_METADATA_VALUE#info:eu-repo/grantAgreement/MCIN/AEI/10.13039info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2021-2023/PID2021-122929OB-C31info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2021-2023/PID2021-122929OB-C33The underlying dataset has been published as supplementary material of the article in the publisher platform at DOI https://doi.org/10.1016/j.microc.2025.113189https://doi.org/10.1016/j.microc.2025.113189Síinfo:eu-repo/semantics/openAccessoai:digital.csic.es:10261/3835412026-05-22T06:33:51Z
score 15,811543

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