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dc.contributor.authorOlmos, Víctores_ES
dc.contributor.authorMarro, Mónicaes_ES
dc.contributor.authorLoza-Alvarez, Pabloes_ES
dc.contributor.authorRaldúa, Demetrioes_ES
dc.contributor.authorPrats, Evaes_ES
dc.contributor.authorPiña, Benjamines_ES
dc.contributor.authorTauler, Romàes_ES
dc.contributor.authorDe Juan, Annaes_ES
dc.date.accessioned2019-03-01T12:56:01Z-
dc.date.available2019-03-01T12:56:01Z-
dc.date.issued2019-03-01-
dc.identifier.citationTalanta - the International Journal of Pure and Applied Analyt Chemistry 194: 390-398 (2019)es_ES
dc.identifier.urihttp://hdl.handle.net/10261/177098-
dc.description.abstractThe use of hyperspectral imaging techniques in biological studies has increased in the recent years. Hyperspectral images (HSI) provide chemical information and preserve the morphology and original structure of heterogeneous biological samples, which can be potentially useful in environmental –omics studies when effects due to several factors, e.g., contaminant exposure, phenotype,… at a specific tissue level need to be investigated. Yet, no available strategies exist to exploit adequately this kind of information. This work offers a novel chemometric strategy to pass from the raw image information to useful knowledge in terms of statistical assessment of the multifactor effects of interest in –omic studies. To do so, unmixing of the hyperspectral image measurement is carried out to provide tissue-specific information. Afterwards, several specific ANOVA-Simultaneous Component Analysis (ASCA) models are generated to properly assess and interpret the diverse effect of the factors of interest on the spectral fingerprints of the different tissues characterized. The unmixing step is performed by Multivariate Curve Resolution-Alternating Least Squares (MCR-ALS) on multisets of biological images related to each studied condition and provides reliable HSI spectral signatures and related image maps for each specific tissue in the regions imaged. The variability associated with these signatures within a population is obtained through an MCR-based resampling step on representative pixel subsets of the images analyzed. All spectral fingerprints obtained for a particular tissue in the different conditions studied are used to obtain the related ASCA model that will help to assess the significance of the factors studied on the tissue and, if relevant, to describe the associated fingerprint modifications. The potential of the approach is assessed in a real case of study linked to the investigation of the effect of exposure time to chlorpyrifos-oxon (CPO) on ocular tissues of different phenotypes of zebrafish larvae from Raman HSI of eye cryosections. The study allowed the characterization of melanin, crystalline and internal eye tissue and the phenotype, exposure time and the interaction of the two factors were found to be significant in the changes found in all kind of tissues. Factor-related changes in the spectral fingerprint were described and interpreted per each kind of tissue characterized. © 2018 Elsevier B.V.es_ES
dc.description.sponsorshipThe 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. 32073 (CHEMAGEB project). The authors of this work belong to the network of recognized research groups by the Catalan government (2017 SGR 753 / 2017 SGR 902) and acknowledge the support of the Spanish government through project CTQ2015-66254-C2-2-P , and from NATO (SfP project MD.SFPP 984777 ). ICFO acknowledges financial support from the Spanish Ministry of Economy and Competitiveness through the “Severo Ochoa” program for Centres of Excellence in R&D ( SEV-2015-0522 ), from Fundación Cellex , Fundación Mig-Puig and from Generalitat de Catalunya through the CERCA program", Laserlab-Europe ( EU-H2020 654148 ), Fundació la Marató de TV3 (Molecular imaging of the retina in patients with Multiple Sclerosis by Raman Spectroscopy, 20142030 ), and the National Institutes of Health (NIH, grant 5R21CA187890-02 ). Appendix Aes_ES
dc.language.isoenges_ES
dc.publisherElsevieres_ES
dc.relationinfo:eu-repo/grantAgreement/EC/FP7/320737es_ES
dc.relationMINECO/ICTI2013-2016/CTQ2015-66254-C2-2-Pes_ES
dc.relationMINECO/ICTI2013-2016/SEV-2015-0522es_ES
dc.relation.isversionofPostprintes_ES
dc.rightsembargoedAccesses_ES
dc.subjectANOVA-Simultaneous Component Analysis (ASCA)es_ES
dc.subjectEnvironmental –omicses_ES
dc.subjectHyperspectral imaginges_ES
dc.subjectMultivariate Curve Resolution-Alternating Least Squares (MCR-ALS)es_ES
dc.subjectRaman spectroscopyes_ES
dc.titleAssessment of tissue-specific multifactor effects in environmental –omics studies of heterogeneous biological samples: Combining hyperspectral image information and chemometricses_ES
dc.typeartículoes_ES
dc.identifier.doi10.1016/j.talanta.2018.10.029-
dc.description.peerreviewedPeer reviewedes_ES
dc.relation.publisherversionhttps://doi.org/10.1016/j.talanta.2018.10.029es_ES
dc.embargo.terms2021-03-01es_ES
dc.contributor.funderEuropean Research Counciles_ES
dc.contributor.funderMinisterio de Economía y Competitividad (España)es_ES
dc.relation.csices_ES
oprm.item.hasRevisionno ko 0 false*
dc.identifier.funderhttp://dx.doi.org/10.13039/501100000781es_ES
dc.identifier.funderhttp://dx.doi.org/10.13039/501100003329es_ES
dc.contributor.orcidRaldúa, Demetrio [0000-0001-5256-1641]es_ES
dc.contributor.orcidPrats, Eva [0000-0001-7838-2027]es_ES
dc.contributor.orcidPiña, Benjamin [0000-0001-9216-2768]es_ES
dc.contributor.orcidTauler, Romà [0000-0001-8559-9670]es_ES
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