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

利用多个共同基金数据集对细胞间相互作用进行排名，以更快地生成假设

• 批准号: 10775907
• 负责人: Brittany Anne Goods
• 金额: $ 23.74万
• 依托单位: DARTMOUTH COLLEGE
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-20 至 2024-09-19
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10775907
• 关键词: Atlases; Biological; Biological Process; Cell Communication; Cell Extracts; Cell Surface Receptors; Cell model; Cell physiology; Cells; Communities; Complex; Computational Technique; Data; Data Set; Databases; Disease; Ecosystem; Epithelium; Frequencies; Funding; Gene Expression; Gene Expression Profile; Generations; Human; Intestines; Invaded; Knowledge; Ligands; Maintenance; Mediating; Methods; Multiomic Data; Organism; Running; Signal Transduction; Skin; Standardization; System; Tissues; Validation; Work; cell type; computerized tools; computing resources; cost; data ecosystem; design; graph database; improved; indexing; insight; interest; novel; pathogen; receptor; reproductive; response; single-cell RNA sequencing; transcriptomics

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.

项目摘要 细胞-细胞相互作用（CCI）对于维持组织中的适当细胞功能至关重要，特别是那些， 就像屏障组织一样，协调对入侵病原体和环境信号的复杂反应。 利用现有的数据集，通过更好地了解生物学知识， 了解CCI和细胞的核心转录特征如何协调组织功能或疾病。 单细胞转录组数据集允许在给定的疾病或组织中全面预测CCI， 兴趣已经开发了许多计算技术来鉴定介导的配体-受体对。 这些CCI使用批量和单细胞数据集以及空间转录组数据集。但 对转录组学数据的分析产生了数千种配体-受体相互作用， 实验验证因此，需要一种计算工具，该工具将对实验性CCI进行排名。 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数据库， 包括皮肤、肠和生殖组织。然后，我们将采用一个排名系统， 从几个共同基金数据集中对细胞与细胞的相互作用进行排名以进行实验验证。最后我们将 使用现有的空间转录组数据集验证我们的方法。总的来说，这项工作的结果将 充分利用现有的丰富数据，更好地将CCI置于背景中，使科学界能够优先考虑 用于实验验证的新型CCI。