Spatial transcriptomics (ST) is a powerful approach for cancers molecular and cellular characterization. Pancreatic intraepithelial neoplasia (PanIN) is a pancreatic ductal adenocarcinoma (PDAC) premalignancy diagnosed from formalin-fixed and paraffin-embedded (FFPE) specimens limiting single-cell based investigations. We developed a new FFPE ST analysis protocol for PanINs complemented with novel transfer learning approaches. The first transfer learning approach, to assign cell types to ST spots and integrate the transcriptional signatures, shows that PanINs are surrounded by PDAC cancer associated fibroblasts (CAFs) subtypes, including the rare antigen-presenting CAFs. Furthermore, most PanINs are of the classical PDAC subtype while one sample expresses cancer stem cell markers. A second transfer learning approach, to integrate ST PanIN data with PDAC scRNA-seq data, identifies a shift between inflammatory and proliferative signaling as PanINs progress to PDAC. Our data support a model of inflammatory signaling and PanIN-CAF interactions promoting premalignancy progression and PDAC immunosuppressive characteristics.
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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