Project Summary/Abstract: Pancreatic ductal adenocarcinoma (PDAC) maintains its status as one of most lethal solid cancers with a 5-year survival of 8%. Minor improvements have been attributed to early detection, but the vast majority of patients face a grim prognosis without effective therapeutic intervention. Molecular analysis of patient samples has often been confounded by mixed biological samples, leading to reproducibility challenges. Previously, our lab performed virtual microdissection on bulk RNA-seq patient samples establishing robust prognostic gene signatures describing an aggressive basal-like and drug-responsive classical tumor subtypes highlighting the importance of cancer heterogeneity across patients. Using these signatures as patient classifiers has been an important utility in preliminary clinical trials and therapeutic profiling of patient derived organoids. Building evidence suggests patient unique TME composition impacts PDAC progression and resistance to standard treatments. While patient tissue characterization with bulk measurements has provided key insights into cancer biology, parsing the complex tumor microenvironments requires higher resolution due to the widespread stromal involvement and sparse neoplastic populations. Single-cell sequencing delivers the analytical power to help identify variable TME elements between patients that lead to the distinct prognostic and therapeutic responses. Thus, understanding the extent and role of TME heterogeneity in PDAC across patient tumor subtypes is paramount to widen the door for personalized medicine in oncology. In this proposal, I will establish a comprehensive single-cell atlas of human PDAC TME to significantly lower the barrier between researchers and complex single-cell transcriptomics data to explore novel prognostic and synergistic therapeutic targets. I will use local and public single cell RNA-seq data of PDAC tissue to investigate the extent and role of cellular heterogeneity across patient tumor subtypes. Specifically, I will define molecular signatures and map out the interactome of functional cell types within stromal, lymphocytic, myeloid populations at unprecedented spatial resolution. Ultimately, by integrating high-dimensional data from single-cell RNA-seq and Spatial Transcriptomics, this work will shed light on the intricate tissue pathology while laying down a broad framework for understanding multi-axis cell interactions behind progression and resistance in diverse cancer types.