bioRxivCancer BiologyPreprint
Inflammatory Signaling in Pancreatic Cancer Transfers Between a Single-cell RNA Sequencing Atlas and Co-Culture
B. Kinny-Koster, S. Guinn, J. A. Tandurella, J. T. Mitchell, D. N. Sidiropoulos, M. Loth, M. R. Lyman, A. B. Pucsek, T. T. Seppala, C. Cherry, R. Suri, H. Zlomke, J. He, C. L. Wolfgang, J. Yu, L. Zheng, D. P. Ryan, D. T. Ting, A. Kimmelman, A. Gupta, L. Danilova, J. Elisseef, L. D. Wood, G. Stein-O'Brien, L. T. Kagohara, , J. W. Zimmerman
Abstract
Pancreatic ductal adenocarcinoma (PDAC) is an aggressive malignancy characterized by a heterogeneous tumor microenvironment (TME) that is enriched with cancer associated fibroblasts (CAFs)1. Cell-cell interactions involving these CAFs promote an immunosuppressive phenotype with altered inflammatory gene expression. While single-cell transcriptomics provides a tool to dissect the complex intercellular pathways that regulate cancer-associated inflammation in human tumors, complementary experimental systems for mechanistic validation remain limited. This study integrated single-cell data from human tumors and novel organoid co-cultures to study the PDAC TME. We derived a comprehensive atlas of PDAC gene expression from six published human single-cell RNA sequencing (scRNA-seq) datasets2-7 to characterize intercellular signaling pathways between epithelial tumor cells and CAFs that regulate the inflammatory TME. Analysis of the epithelial cell compartment identified global gene expression pathways that modulate inflammatory signaling and are correlated with CAF composition. We then generated patient-derived organoid-CAF co-cultures to serve as a biological model of the cellular interactions learned from human tissue in the atlas. Transfer learning analysis to additional scRNA-seq data of this co-culture system and mechanistic experiments confirmed the epithelial response to fibroblast signaling. This bidirectional approach of complementary computational and in vitro applications provides a framework for future studies identifying important mechanisms of intercellular interactions in PDAC.
- organism: human
- assay: scRNA-seq
- research focus: cell-cell communication
- research focus: clustering
- approach: deep learning
- approach: experimental assay
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