Leveraging multiple Common Fund datasets to rank cell-cell interactions for faster hypothesis generation

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Project Summary Cell-cell interactions (CCIs) are crucial to the maintenance of proper cell functions in tissues, particularly those, like barrier tissues, that orchestrate complex responses to invading pathogens and environmental signals. There are significant opportunities for leveraging existing datasets to generate biological insight by better understanding how CCIs and core transcriptional signatures of cells orchestrate tissue function or disease. Single-cell transcriptomic datasets allow for comprehensive prediction of CCIs in a given disease or tissue of interest. Many computational techniques have been developed to identify ligand-receptor pairs that mediate these CCIs using either bulk and single cell datasets, as well as spatial transcriptomic datasets. However, the analysis of transcriptomics data produces thousands of ligand-receptor interactions that difficult to prioritize for experimental validation. Thus, there is a need for a computational tool that will rank CCIs for experimental validation. Here we propose to create a database of putative CCIs across several epithelial barrier tissues, including skin, intestine, and reproductive tissue. We will then employ a ranking system that uses information from several Common Fund datasets to rank cell-cell interactions for experimental validation. Finally, we will validate our approach using existing spatial transcriptomic datasets. Overall, the results of this work will leverage the wealth of existing data to better contextualize CCIs, allowing the scientific community to prioritize novel CCIs for experimental validation.