%0 Journal Article
%T PanIN and CAF transitions in pancreatic carcinogenesis revealed with spatial data integration.
%A Bell ATF
%A Mitchell JT
%A Kiemen AL
%A Lyman M
%A Fujikura K
%A Lee JW
%A Coyne E
%A Shin SM
%A Nagaraj S
%A Deshpande A
%A Wu PH
%A Sidiropoulos DN
%A Erbe R
%A Stern J
%A Chan R
%A Williams S
%A Chell JM
%A Ciotti L
%A Zimmerman JW
%A Wirtz D
%A Ho WJ
%A Zaidi N
%A Thompson E
%A Jaffee EM
%A Wood LD
%A Fertig EJ
%A Kagohara LT
%J Cell Syst
%V 15
%N 8
%D 2024 Aug 21
%M 39116880
%F 11.091
%R 10.1016/j.cels.2024.07.001
%X This study introduces a new imaging, spatial transcriptomics (ST), and single-cell RNA-sequencing integration pipeline to characterize neoplastic cell state transitions during tumorigenesis. We applied a semi-supervised analysis pipeline to examine premalignant pancreatic intraepithelial neoplasias (PanINs) that can develop into pancreatic ductal adenocarcinoma (PDAC). Their strict diagnosis on formalin-fixed and paraffin-embedded (FFPE) samples limited the single-cell characterization of human PanINs within their microenvironment. We leverage whole transcriptome FFPE ST to enable the study of a rare cohort of matched low-grade (LG) and high-grade (HG) PanIN lesions to track progression and map cellular phenotypes relative to single-cell PDAC datasets. We demonstrate that cancer-associated fibroblasts (CAFs), including antigen-presenting CAFs, are located close to PanINs. We further observed a transition from CAF-related inflammatory signaling to cellular proliferation during PanIN progression. We validate these findings with single-cell high-dimensional imaging proteomics and transcriptomics technologies. Altogether, our semi-supervised learning framework for spatial multi-omics has broad applicability across cancer types to decipher the spatiotemporal dynamics of carcinogenesis.