PT  - JOURNAL ARTICLE
AU  - Reuben Moncada
AU  - Florian Wagner
AU  - Marta Chiodin
AU  - Joseph C. Devlin
AU  - Maayan Baron
AU  - Cristina H. Hajdu
AU  - Diane M. Simeone
AU  - Itai Yanai
TI  - Building a tumor atlas: integrating single-cell RNA-Seq data with spatial transcriptomics in pancreatic ductal adenocarcinoma
AID  - 10.1101/254375
DP  - 2018 Jan 01
TA  - bioRxiv
PG  - 254375
4099  - http://biorxiv.org/content/early/2018/03/05/254375.short
4100  - http://biorxiv.org/content/early/2018/03/05/254375.full
AB  - 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 μm 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.