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

项目摘要 细胞-细胞相互作用(CCI)对于维持组织中正常的细胞功能至关重要，尤其是那些， 就像屏障组织一样，它们对入侵的病原体和环境信号做出复杂的反应。 利用现有数据集通过更好的方式生成生物学洞察力具有重要的机会 了解CCIS和细胞的核心转录信号如何协调组织功能或疾病。 单细胞转录数据集允许在给定的疾病或组织中全面预测CCIS 利息。已经发展了许多计算技术来识别配体-受体对 这些CCIS使用批量和单细胞数据集，以及空间转录数据集。然而， 对转录组数据的分析产生了数以千计的配体-受体相互作用，这些作用很难确定优先顺序 实验验证。因此，需要一种计算工具来对CCIS进行实验评级 验证。在这里，我们建议创建一个跨几个上皮屏障组织的假定CCI的数据库， 包括皮肤、肠道和生殖组织。然后，我们将使用一个使用信息的排名系统 从几个共同基金的数据集中对细胞间的相互作用进行排名，以进行实验验证。最后，我们会 使用现有的空间转录数据集验证我们的方法。总体而言，这项工作的结果将 利用丰富的现有数据更好地了解CCIS的背景，使科学界能够确定优先顺序 用于实验验证的新型CCIS。

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.