Protocol for the characterization of the pancreatic tumor microenvironment using organoid-derived mouse models and single-nuclei RNA sequencing.

Single-nuclei RNA sequencing (snRNA-seq) allows for obtaining gene expression profiles from frozen or hard-to-dissociate tissues at the single-nuclei level. Here, we describe a protocol to obtain snRNA-seq data of pancreatic tumors from orthotopically grafted organoid-derived mouse models. We provide details on the establishment of these mouse models, the isolation of single nuclei from pancreatic tumors, and the analysis of the snRNA-seq datasets. For complete details on the use and execution of this protocol, please refer to Mucciolo et al.1.

EGFR-activated myofibroblasts promote metastasis of pancreatic cancer.

Pancreatic ductal adenocarcinoma (PDAC) has a dismal prognosis. Cancer-associated fibroblasts (CAFs) are recognized potential therapeutic targets, but poor understanding of these heterogeneous cell populations has limited the development of effective treatment strategies. We previously identified transforming growth factor beta (TGF-ß) as a main driver of myofibroblastic CAFs (myCAFs). Here, we show that epidermal growth factor receptor/Erb-B2 receptor (EGFR/ERBB2) signaling is induced by TGF-ß in myCAFs through an autocrine process mediated by amphiregulin. Inhibition of this EGFR/ERBB2-signaling network in PDAC organoid-derived cultures and mouse models differentially impacts distinct CAF subtypes, providing insights into mechanisms underpinning their heterogeneity. Remarkably, EGFR-activated myCAFs promote PDAC metastasis in mice, unmasking functional significance in myCAF heterogeneity. Finally, analyses of other cancer datasets suggest that these processes might operate in other malignancies. These data provide functional relevance to myCAF heterogeneity and identify a candidate target for preventing tumor invasion in PDAC.

Multiomics Integration at Single-Cell Resolution Using Bayesian Networks: A Case Study in Hepatocellular Carcinoma.

Multiomics data integration is one of the leading frontiers of complex disease research and integrative biology. The advances in single-cell sequencing technologies offer yet another crucial dimension in multiomics research. The single-cell studies enable the study and integration of multiomics data simultaneously in the same cell. We report in this study multiomics data integration in single-cell resolution using Bayesian networks (BNs) in a case study of hepatocellular carcinoma (HCC). A BN encodes the conditional dependencies/independencies of variables using a graphical model with an accompanying joint probability. RNA-seq and Reduced Representation Bisulfite Sequencing data were analyzed separately, and copy number variations were estimated by the hidden Markov model method. Several BN models were constructed to reveal omics' causal and associational relationships. These methods were subjected to a validation study using an independent data set. We show the heterogeneity of the multiple cellular layers of HCC at single-cell omics resolution by identifying best-fitted BN models of 295 genes. We also provide novel insights into the multiomics mechanistic relationships in the human lymphocyte antigen class I genes in HCC. To the best of our knowledge, this is the first study to focus on integrating omics data using a machine learning algorithm, BNs, at the single-cell resolution using a case study of HCC.