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dc.date.accessioned2023-12-19T16:49:32Z
dc.date.available2023-12-19T16:49:32Z
dc.date.created2023-07-03T16:29:18Z
dc.date.issued2023
dc.identifier.citationLaLone, Vernon Aizenshtadt, Aleksandra Goertz, John Skottvoll, Frøydis Sved Mota, Marco Barbero You, Junji Zhao, Xiaoyu Berg, Henriette Engen Stokowiec, Justyna Yu, Minzhi Schwendeman, Anna Scholz, Hanne Wilson, Steven Ray Haakon Krauss, Stefan Johannes Karl Stevens, Molly M. . Quantitative chemometric phenotyping of three-dimensional liver organoids by Raman spectral imaging. Cell Reports Methods. 2023, 3(4)
dc.identifier.urihttp://hdl.handle.net/10852/106503
dc.description.abstractConfocal Raman spectral imaging (RSI) enables high-content, label-free visualization of a wide range of molecules in biological specimens without sample preparation. However, reliable quantification of the deconvoluted spectra is needed. Here we develop an integrated bioanalytical methodology, qRamanomics, to qualify RSI as a tissue phantom calibrated tool for quantitative spatial chemotyping of major classes of biomolecules. Next, we apply qRamanomics to fixed 3D liver organoids generated from stem-cell-derived or primary hepatocytes to assess specimen variation and maturity. We then demonstrate the utility of qRamanomics for identifying biomolecular response signatures from a panel of liver-altering drugs, probing drug-induced compositional changes in 3D organoids followed by in situ monitoring of drug metabolism and accumulation. Quantitative chemometric phenotyping constitutes an important step in developing quantitative label-free interrogation of 3D biological specimens.
dc.languageEN
dc.publisherCell Press
dc.rightsAttribution 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.titleQuantitative chemometric phenotyping of three-dimensional liver organoids by Raman spectral imaging
dc.title.alternativeENEngelskEnglishQuantitative chemometric phenotyping of three-dimensional liver organoids by Raman spectral imaging
dc.typeJournal article
dc.creator.authorLaLone, Vernon
dc.creator.authorAizenshtadt, Aleksandra
dc.creator.authorGoertz, John
dc.creator.authorSkottvoll, Frøydis Sved
dc.creator.authorMota, Marco Barbero
dc.creator.authorYou, Junji
dc.creator.authorZhao, Xiaoyu
dc.creator.authorBerg, Henriette Engen
dc.creator.authorStokowiec, Justyna
dc.creator.authorYu, Minzhi
dc.creator.authorSchwendeman, Anna
dc.creator.authorScholz, Hanne
dc.creator.authorWilson, Steven Ray Haakon
dc.creator.authorKrauss, Stefan Johannes Karl
dc.creator.authorStevens, Molly M.
cristin.unitcode185,51,20,10
cristin.unitnameSFF - Hybrid Technology Hub
cristin.ispublishedtrue
cristin.fulltextoriginal
cristin.qualitycode1
dc.identifier.cristin2160471
dc.identifier.bibliographiccitationinfo:ofi/fmt:kev:mtx:ctx&ctx_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.jtitle=Cell Reports Methods&rft.volume=3&rft.spage=&rft.date=2023
dc.identifier.jtitleCell Reports Methods
dc.identifier.volume3
dc.identifier.issue4
dc.identifier.pagecount21
dc.identifier.doihttps://doi.org/10.1016/j.crmeth.2023.100440
dc.type.documentTidsskriftartikkel
dc.type.peerreviewedPeer reviewed
dc.source.issn2667-2375
dc.type.versionPublishedVersion
cristin.articleid100440


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