Multi-omic genetic regulatory signatures underlying tissue complexity of diabetes in the pancreas at single-cell spatial resolution

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Enter the text here that is the new abstract information for your application. This section must be no longer than 30 lines of text. Diabetes is a complex disease that results from the cumulative temporal effects of genetic and environmental factors. A hallmark of both common and rare forms of diabetes is genetic dysregulation of insulin-producing β cells which reside in islets of Langerhans and are scattered throughout the larger context of the entire pancreas. Although genome wide association studies (GWAS) of type 2 diabetes (T2D), the most common form of diabetes, identified >600 statistically independent signals, they are difficult to translate into biological mechanisms because of their predominant noncoding location. Therefore, we propose to map the context specificity of T2D and related trait GWAS signals in pancreas across multiple dimensions: cell type, age, sex, developmental stage, and genetic background. Our proposal is based on our exciting single-cell multi-omic spatially-resolved pilot data and the well-supported idea that noncoding GWAS signals percolate upwards through complex and hierarchical molecular networks that influence cellular circuits. The initial layer in this hierarchy is chromatin organization, which propagates genetic predisposition onto subsequent molecular layers, likely starting with the transcriptome. Because islet physiology is influenced by the identity and spatial arrangement of surrounding cells within the pancreas, our multimodal molecular analyses will focus on four major pancreatic lineages; endocrine α and β cells, acinar, and ductal cells. In addition, we will incorporate the analysis of tissue resident macrophages because they play an important role in several processes including islet/pancreatic cell differentiation, growth, regeneration, and inflammation. The proposed studies will establish associations between regulatory elements, genes, cell types, tissue organization, and physiological function. Our multidisciplinary research team with complementary expertise in pancreas and islet biology, sequencing technologies, single cell genomics and epigenomics, image data analysis, and machine learning devised a suite of tools and analyses to discover cell state dynamic changes across diverse conditions, and how these changes influence downstream biology from transcriptional regulation, to cellular spatial organization within the pancreas, and finally to tissue-level physiology. This approach, if successful, will enable mechanistic insights across GWAS loci, which can inform new personalized therapeutic strategies.