Transfer learning associates CAFs with EMT and inflammation in tumor cells in human tumors and organoid co-culture in pancreatic ductal adenocarcinoma [MULTIseq]. Transfer learning associates CAFs with EMT and inflammation in tumor cells in human tumors and organoid co-culture in pancreatic ductal adenocarcinoma [MULTIseq]

Pancreatic ductal adenocarcinoma (PDAC) is an aggressive malignancy characterized by an immunosuppressive tumor microenvironment enriched with cancer associated fibroblasts (CAFs). This study provides a convergence approach to identify tumor cell and CAF interactions through the integration of single-cell data from human tumors with human organoid co-culture. Analysis of a comprehensive atlas of PDAC single-cell RNA sequencing (scRNA-seq) data associates CAF density with increased inflammation and epithelial-mesenchymal transition (EMT) in epithelial cells. Transfer learning to transcriptional data from patient-derived organoid and CAF co-cultures provides in silico validation of CAF induction of inflammatory and EMT epithelial cell states. Further experimental validation in co-cultures demonstrates integrin beta 1 (ITGB1) and vascular endothelial factor A (VEGF-A) interactions with Neuropilin-1 (NRP1) mediating CAF and epithelial crosstalk. This study introduces transfer learning from human single-cell data to organoid co-culture for experimental validation of discoveries of cell-cell crosstalk, and identifies fibroblast-mediated regulation of EMT and inflammation. Overall design: Patient matched organoids and cancer associated fibroblasts were generated from PDAC surgical speciments gathered at JHU. Samples were cultured in either monoculture or coculture conditions using a matrigel substrate. For single cell sequencing, samples were underwent the MULTIseq pipeline to multiplex barcodes. Deposited are samples cocultured for 12HR. *************************************************************** Submitter states that missing raw data are being made available for controlled access in dbGaP. ***************************************************************