MCA: A Computational Framework to Study microRNAs in Cell-Cell Interactions

MCA：研究细胞间相互作用中 microRNA 的计算框架

• 批准号: 2120907
• 负责人: Haiyan Hu
• 金额: $ 39.88万
• 依托单位: The University of Central Florida Board of Trustees
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-09-01 至 2025-08-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2120907&HistoricalAwards=false
• 关键词: MCA; Computational; Framework; Study; microRNAs

Cell to cell Interactions are essential for the overall function of multicellular organisms. Recent studies have discovered that non-coding microRNAs (miRNAs) can be transferred from one cell to another cell to perform certain physiological functions. Understanding which miRNAs participate in this intercellular crosstalk and how they function under specific environmental conditions is essential to gaining a new perspective of intercellular communications. Biotechnology advancement has generated a large amount of transcriptomics data that motivate efficient computational strategies to understand miRNAs in cell-cell interactions, which is fundamental to modeling biological systems. This project investigation miRNAs in cell-cell interactions will generate methods and tools that will be freely available to the scientific community. The research will be incorporated into graduate, undergraduate and K-12 education, and will be disseminated to the research community and the public to enhance scientific understanding. The project seeks to integrate large-scale transcriptomics data and create efficient computational algorithms to model miRNA activities relevant to cell-cell interactions under different conditions. The advanced statistical algorithms and probabilistic models of miRNA activities related to cell-cell interactions promise to unveil various roles of miRNAs in cell communications and phenotype formulation. The research is expected to not only advance scientific understanding of cell communications but also stimulate interest in developing and advancing efficient computational modeling and data integration methods in the informatics research field. Outputs of the project will be made available through the project website (http://hulab.ucf.edu/research/projects/cell2cell/).This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

细胞与细胞之间的相互作用对于多细胞生物的整体功能是必不可少的。最近的研究发现，非编码microRNAs（miRNAs）可以从一个细胞转移到另一个细胞，以执行某些生理功能。了解哪些miRNAs参与这种细胞间串扰以及它们在特定环境条件下如何发挥作用对于获得细胞间通信的新视角至关重要。生物技术的进步产生了大量的转录组学数据，这些数据激发了有效的计算策略来理解细胞-细胞相互作用中的miRNA，这是建模生物系统的基础。 该项目研究细胞间相互作用中的miRNAs将产生可供科学界免费使用的方法和工具。该研究将纳入研究生，本科和K-12教育，并将传播给研究界和公众，以提高科学的理解。该项目旨在整合大规模转录组学数据，并创建有效的计算算法来模拟不同条件下与细胞-细胞相互作用相关的miRNA活性。与细胞-细胞相互作用相关的miRNA活性的先进统计算法和概率模型有望揭示miRNA在细胞通信和表型形成中的各种作用。该研究不仅有望促进对细胞通信的科学理解，还将激发人们对开发和推进信息学研究领域的高效计算建模和数据集成方法的兴趣。 该项目的成果将通过项目网站（http：//hulab.ucf.edu/research/projects/cell2cell/）提供。该奖项反映了NSF的法定使命，并被认为值得通过使用基金会的知识价值和更广泛的影响审查标准进行评估来支持。

项目成果

INSISTC: Incorporating network structure information for single-cell type classification

INSISTC：结合网络结构信息进行单细胞类型分类

• DOI: 10.1016/j.ygeno.2022.110480
• 发表时间: 2022
• 期刊: Genomics
• 影响因子: 4.4
• 作者: Zheng, Hansi;Wang, Saidi;Li, Xiaoman;Hu, Haiyan
• 通讯作者: Hu, Haiyan