PLX180307

GSE111672: Building a tumor atlas: integrating single-cell RNA-Seq with spatial transcriptomics in pancreatic ductal adenocarcinoma

• Organsim human
• Type RNASEQ
• Target gene
• Project ARCHS4

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 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 (ST) method 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 ~20-70 cells. Here, we present an approach for the integration of scRNA-Seq and ST data generated from the same sample of pancreatic cancer tissue and deploy it on primary tumors from two patients. 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. We find that distinct macrophage subpopulations occupy distinct spatial localizations in the tumors. Our results provide a framework for the integration in any tissue of the subpopulation structure defined by scRNA-Seq and tissue architecture revealed by ST. SOURCE: Itai Yanai (itai.yanai@nyumc.org) - Institute for Computational Medicine, NYU Langone Health