Phenotype-driven identification of epithelial signalling clusters

dc.contributorAalto-yliopistofi
dc.contributorAalto Universityen
dc.contributor.authorMarques, Elsaen_US
dc.contributor.authorPeltola, Tomien_US
dc.contributor.authorKaski, Samuelen_US
dc.contributor.authorKlefström, Juhaen_US
dc.contributor.departmentDepartment of Computer Scienceen
dc.contributor.groupauthorCentre of Excellence in Computational Inference, COINen
dc.contributor.groupauthorProfessorship Kaski Samuelen
dc.contributor.groupauthorHelsinki Institute for Information Technology (HIIT)en
dc.contributor.groupauthorProbabilistic Machine Learningen
dc.contributor.organizationUniversity of Helsinkien_US
dc.date.accessioned2018-03-16T10:33:23Z
dc.date.available2018-03-16T10:33:23Z
dc.date.issued2018-03-05en_US
dc.description.abstractIn metazoans, epithelial architecture provides a context that dynamically modulates most if not all epithelial cell responses to intrinsic and extrinsic signals, including growth or survival signalling and transforming oncogene action. Three-dimensional (3D) epithelial culture systems provide tractable models to interrogate the function of human genetic determinants in establishment of context-dependency. We performed an arrayed genetic shRNA screen in mammary epithelial 3D cultures to identify new determinants of epithelial architecture, finding that the key phenotype impacting shRNAs altered not only the data population average but even more noticeably the population distribution. The broad distributions were attributable to sporadic gene silencing actions by shRNA in unselected populations. We employed Maximum Mean Discrepancy concept to capture similar population distribution patterns and demonstrate here the feasibility of the test in identifying an impact of shRNA in populations of 3D structures. Integration of the clustered morphometric data with protein-protein interactions data enabled hypothesis generation of novel biological pathways underlying similar 3D phenotype alterations. The results present a new strategy for 3D phenotype-driven pathway analysis, which is expected to accelerate discovery of context-dependent gene functions in epithelial biology and tumorigenesis.en
dc.description.versionPeer revieweden
dc.format.mimetypeapplication/pdfen_US
dc.identifier.citationMarques, E, Peltola, T, Kaski, S & Klefström, J 2018, 'Phenotype-driven identification of epithelial signalling clusters', Scientific Reports, vol. 8, no. 1, 4034, pp. 1-13. https://doi.org/10.1038/s41598-018-22293-xen
dc.identifier.doi10.1038/s41598-018-22293-xen_US
dc.identifier.issn2045-2322
dc.identifier.otherPURE UUID: 8e852eaa-1c9d-4d83-bc3c-7ee5b76916a4en_US
dc.identifier.otherPURE ITEMURL: https://research.aalto.fi/en/publications/8e852eaa-1c9d-4d83-bc3c-7ee5b76916a4en_US
dc.identifier.otherPURE FILEURL: https://research.aalto.fi/files/18108906/s41598_018_22293_x_1.pdfen_US
dc.identifier.urihttps://aaltodoc.aalto.fi/handle/123456789/30292
dc.identifier.urnURN:NBN:fi:aalto-201803161762
dc.language.isoenen
dc.publisherSpringer
dc.relation.ispartofseriesScientific Reportsen
dc.relation.ispartofseriesVolume 8, issue 1, pp. 1-13en
dc.rightsopenAccessen
dc.titlePhenotype-driven identification of epithelial signalling clustersen
dc.typeA1 Alkuperäisartikkeli tieteellisessä aikakauslehdessäfi
dc.type.versionpublishedVersion

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