1. Multimodal Analysis of Composition and Spatial Architecture in Human Squamous Cell Carcinoma
- Author
-
Ji, Andrew L., Rubin, Adam J., Thrane, Kim, Jiang, Sizun, Reynolds, David L., Meyers, Robin M., Guo, Margaret G., George, Benson M., Mollbrink, Annelie, Bergenstråhle, Joseph, Larsson, Ludvig, Bai, Yunhao, Zhu, Bokai, Bhaduri, Aparna, Meyers, Jordan M., Rovira-Clave, Xavier, Hollmig, S. Tyler, Aasi, Sumaira Z., Nolan, Garry P., Lundeberg, Joakim, Khavari, Paul A., Ji, Andrew L., Rubin, Adam J., Thrane, Kim, Jiang, Sizun, Reynolds, David L., Meyers, Robin M., Guo, Margaret G., George, Benson M., Mollbrink, Annelie, Bergenstråhle, Joseph, Larsson, Ludvig, Bai, Yunhao, Zhu, Bokai, Bhaduri, Aparna, Meyers, Jordan M., Rovira-Clave, Xavier, Hollmig, S. Tyler, Aasi, Sumaira Z., Nolan, Garry P., Lundeberg, Joakim, and Khavari, Paul A.
- Abstract
To define the cellular composition and architecture of cutaneous squamous cell carcinoma (cSCC), we combined single-cell RNA sequencing with spatial transcriptomics and multiplexed ion beam imaging from a series of human cSCCs and matched normal skin. cSCC exhibited four tumor subpopulations, three recapitulating normal epidermal states, and a tumor-specific keratinocyte (TSK) population unique to cancer, which localized to a fibrovascular niche. Integration of single-cell and spatial data mapped ligand-receptor networks to specific cell types, revealing TSK cells as a hub for intercellular communication. Multiple features of potential immunosuppression were observed, including T regulatory cell (Treg) co-localization with CD8 T cells in compartmentalized tumor stroma. Finally, single-cell characterization of human tumor xenografts and in vivo CRISPR screens identified essential roles for specific tumor subpopulation-enriched gene networks in tumorigenesis. These data define cSCC tumor and stromal cell subpopulations, the spatial niches where they interact, and the communicating gene networks that they engage in cancer., QC 20200818
- Published
- 2020
- Full Text
- View/download PDF