Uncovering the spatial landscape of molecular interactions within the tumor microenvironment through latent spaces.
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ABSTRACT:
SUBMITTER: Deshpande A
PROVIDER: S-EPMC10523348 | biostudies-literature | 2023 Aug
REPOSITORIES: biostudies-literature
Publications
Uncovering the spatial landscape of molecular interactions within the tumor microenvironment through latent spaces.
Deshpande Atul A Loth Melanie M Sidiropoulos Dimitrios N DN Zhang Shuming S Yuan Long L Bell Alexander T F ATF Zhu Qingfeng Q Ho Won Jin WJ Santa-Maria Cesar C Gilkes Daniele M DM Williams Stephen R SR Uytingco Cedric R CR Chew Jennifer J Hartnett Andrej A Bent Zachary W ZW Favorov Alexander V AV Popel Aleksander S AS Yarchoan Mark M Kiemen Ashley A Wu Pei-Hsun PH Fujikura Kohei K Wirtz Denis D Wood Laura D LD Zheng Lei L Jaffee Elizabeth M EM Anders Robert A RA Danilova Ludmila L Stein-O'Brien Genevieve G Kagohara Luciane T LT Fertig Elana J EJ
Cell systems 20230801 8
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Project description:Recent advances in spatial transcriptomics (ST) enable gene expression measurements from a tissue sample while retaining its spatial context. This technology enables unprecedented in situ resolution of the regulatory pathways that underlie the heterogeneity in the tumor and its microenvironment (TME). The direct characterization of cellular co-localization with spatial technologies facilities quantification of the molecular changes resulting from direct cell-cell interaction, as occurs in tumor-immune interactions. We present SpaceMarkers, a novel bioinformatics algorithm to infer molecular changes from cell-cell interaction from latent space analysis of ST data. We apply this approach to infer molecular changes from tumor-immune interactions in Visium spatial transcriptomics data of metastasis, invasive and precursor lesions, and immunotherapy treatment. Further transfer learning in matched scRNA-seq data enabled further quantification of the specific cell types in which SpaceMarkers are enriched. Altogether, SpaceMarkers can identify the location and context-specific molecular interactions within the TME from ST data.
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Project description:Uncovering the spatial landscape of molecular interactions within the tumor microenvironment through latent spaces
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