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dc.contributor.authorZhao, Tongtong
dc.contributor.authorChiang, Zachary D
dc.contributor.authorMorriss, Julia W
dc.contributor.authorLaFave, Lindsay M
dc.contributor.authorMurray, Evan M
dc.contributor.authorDel Priore, Isabella
dc.contributor.authorMeli, Kevin
dc.contributor.authorLareau, Caleb A
dc.contributor.authorNadaf, Naeem M
dc.contributor.authorLi, Jilong
dc.contributor.authorEarl, Andrew S
dc.contributor.authorMacosko, Evan Z
dc.contributor.authorJacks, Tyler
dc.contributor.authorBuenrostro, Jason D
dc.contributor.authorChen, Fei
dc.date.accessioned2022-12-09T18:51:58Z
dc.date.available2022-12-09T18:51:58Z
dc.date.issued2022
dc.identifier.urihttps://hdl.handle.net/1721.1/146822
dc.description.abstractThe state and behaviour of a cell can be influenced by both genetic and environmental factors. In particular, tumour progression is determined by underlying genetic aberrations1-4 as well as the makeup of the tumour microenvironment5,6. Quantifying the contributions of these factors requires new technologies that can accurately measure the spatial location of genomic sequence together with phenotypic readouts. Here we developed slide-DNA-seq, a method for capturing spatially resolved DNA sequences from intact tissue sections. We demonstrate that this method accurately preserves local tumour architecture and enables the de novo discovery of distinct tumour clones and their copy number alterations. We then apply slide-DNA-seq to a mouse model of metastasis and a primary human cancer, revealing that clonal populations are confined to distinct spatial regions. Moreover, through integration with spatial transcriptomics, we uncover distinct sets of genes that are associated with clone-specific genetic aberrations, the local tumour microenvironment, or both. Together, this multi-modal spatial genomics approach provides a versatile platform for quantifying how cell-intrinsic and cell-extrinsic factors contribute to gene expression, protein abundance and other cellular phenotypes.en_US
dc.language.isoen
dc.publisherSpringer Science and Business Media LLCen_US
dc.relation.isversionof10.1038/S41586-021-04217-4en_US
dc.rightsCreative Commons Attribution-Noncommercial-Share Alikeen_US
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/4.0/en_US
dc.sourcePMCen_US
dc.titleSpatial genomics enables multi-modal study of clonal heterogeneity in tissuesen_US
dc.typeArticleen_US
dc.identifier.citationZhao, Tongtong, Chiang, Zachary D, Morriss, Julia W, LaFave, Lindsay M, Murray, Evan M et al. 2022. "Spatial genomics enables multi-modal study of clonal heterogeneity in tissues." Nature, 601 (7891).
dc.contributor.departmentMassachusetts Institute of Technology. Department of Biologyen_US
dc.relation.journalNatureen_US
dc.eprint.versionAuthor's final manuscripten_US
dc.type.urihttp://purl.org/eprint/type/JournalArticleen_US
eprint.statushttp://purl.org/eprint/status/PeerRevieweden_US
dc.date.updated2022-12-09T18:46:39Z
dspace.orderedauthorsZhao, T; Chiang, ZD; Morriss, JW; LaFave, LM; Murray, EM; Del Priore, I; Meli, K; Lareau, CA; Nadaf, NM; Li, J; Earl, AS; Macosko, EZ; Jacks, T; Buenrostro, JD; Chen, Fen_US
dspace.date.submission2022-12-09T18:46:44Z
mit.journal.volume601en_US
mit.journal.issue7891en_US
mit.licenseOPEN_ACCESS_POLICY
mit.metadata.statusAuthority Work and Publication Information Neededen_US


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