BioProject

This study introduces a new imaging, spatial transcriptomics (ST), and single-cell RNA-sequencing (scRNA-seq) integration pipeline to characterize neoplastic cell state transitions during tumorigenesis. More...

This study introduces a new imaging, spatial transcriptomics (ST), and single-cell RNA-sequencing (scRNA-seq) integration pipeline to characterize neoplastic cell state transitions during tumorigenesis. Our semi-supervised analysis pipeline was applied to examine pancreatic intraepithelial neoplasias (PanIN), the most frequent premalignant lesions that can develop into pancreatic adenocarcinoma (PDAC). Their strict diagnosis on FFPE samples has limited previous characterization of human PanINs within their microenvironment through single-cell approaches. We leverage unbiased whole transcriptome FFPE spatial profiling to enable this characterization in a rare cohort of matched low-grade and high-grade PanIN lesions to track progression and map cellular phenotypes relative to scRNA-seq data of advanced PDAC tumors. We demonstrate that cancer associated fibroblasts (CAF), including antigen-presenting CAFs (apCAF), are located close to PanINs. We further observed a transition from CAF-related inflammatory signaling to cellular proliferation during PanIN progression. We use these findings to guide panel design to perform single-cell validation with high-dimensional imaging proteomics and transcriptomics technologies. Altogether, our pipeline for spatial multi-omics characterization provides a resource for future PanIN studies. Moreover, our semi-supervised learning framework to spatial multi-omics has broad applicability across cancer types to decipher the spatiotemporal dynamics of carcinogenesis. Overall design: 5 tissue sections from surgical sections of resected primary pancreatic ductal adenocarcinoma from 3 unique human subjects, which are Xenium data inputs to recreate results of validation experiments conducted as an extension of analyses conducted with GSE254829. Less...