Integrating single-cell RNA-Seq with spatial transcriptomics in pancreatic ductal adenocarcinoma using multimodal intersection analysis

Abstract

To understand tissue architecture it is necessary to understand both which cell types are present and their physical relationships to one another. Single-cell RNA-Seq (scRNA-Seq) has made significant progress towards the unbiased and systematic characterization of cell populations within a tissue by studying hundreds and thousands of cells in a single experiment. However, the characterization of the spatial organization of individual cells within a tissue 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 m spots across the tissue, each capturing the transcriptomes of [~]10-20 cells. Here, we present an approach for the integration of scRNA-Seq and ST data generated from the same sample of pancreatic cancer tissue. Using markers for cell types identified by scRNA-Seq, we robustly deconvolved the cell type composition of each ST spot to generate a spatial atlas of cell proportions across the tissue. Studying this atlas, we found that distinct spatial localizations accompany each of the cell populations that we identified. Our results provide a framework for creating a tumor atlas by mapping single-cell populations to their spatial region, as well as the inference of cell architecture in any tissue.