Abstract
To understand tissue architecture, it is necessary to understand both which cell types are present and the physical relationships among them. Single-cell RNA-Seq (scRNA-Seq) has made significant progress towards the unbiased and systematic identification of cell populations within a tissue, however, the characterization of their spatial organization within it has been more elusive. The recently introduced ‘spatial transcriptomics’ method (ST) reveals the spatial pattern of gene expression within a tissue section at a resolution of a thousand 100 µm spots across the tissue, each capturing the transcriptomes of multiple cells. Here, we present an approach for the integration of scRNA-Seq and ST data generated from the same sample, and deploy it on primary pancreatic tumors from two patients. Applying our multimodal intersection analysis (MIA), we annotated the distinct micro-environment of each cell type identified by scRNA-Seq. We further found that subpopulations of ductal cells, macrophages, dendritic cells, and cancer cells have spatially restricted localizations across the tissue, as well as distinct co-enrichments with other cell types. Our mapping approach provides an efficient framework for the integration of the scRNA-Seq-defined subpopulation structure and the ST-defined tissue architecture in any tissue.