研究动态
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CellCommuNet:通过对正常和疾病状态下的人类和小鼠组织进行单细胞 RNA 测序得出的细胞间通信网络图谱。

CellCommuNet: an atlas of cell-cell communication networks from single-cell RNA sequencing of human and mouse tissues in normal and disease states.

发表日期:2023 Oct 18
作者: Qinfeng Ma, Qiang Li, Xiao Zheng, Jianbo Pan
来源: NUCLEIC ACIDS RESEARCH

摘要:

细胞间通讯作为多细胞生物的基本特征,对于维持细胞、器官和整个生物体的生物学功能和微环境稳态至关重要。细胞间通讯的改变会导致许多疾病,包括癌症。单细胞 RNA 测序 (scRNA-seq) 通过分析配体-受体相互作用,为研究细胞间通讯提供了一种强大的方法。在这里,我们介绍 CellCommuNet (http://www.inbirg.com/cellcommunet/),这是一个综合数据资源,用于探索正常和疾病状态下人类和小鼠组织的 scRNA-seq 数据中的细胞间通信网络。 CellCommuNet 目前包括来自多个来源的 376 个单一数据集,以及源自同一研究的疾病和正常样本之间的 118 个比较数据集。 CellCommuNet 提供有关细胞之间通信强度和相关信号通路的信息,并有助于探索健康状态和疾病状态之间细胞间通信的差异。用户还可以搜索特定的信号传导途径、配体-受体对和感兴趣的细胞类型。 CellCommuNet 提供交互式图形,说明不同状态下的细胞间通信,从而能够对疾病样本和对照样本之间的通信强度进行差异分析。这个综合数据库旨在成为生物学家研究细胞间通信网络的宝贵资源。© 作者 2023。由牛津大学出版社代表核酸研究出版。
Cell-cell communication, as a basic feature of multicellular organisms, is crucial for maintaining the biological functions and microenvironmental homeostasis of cells, organs, and whole organisms. Alterations in cell-cell communication contribute to many diseases, including cancers. Single-cell RNA sequencing (scRNA-seq) provides a powerful method for studying cell-cell communication by enabling the analysis of ligand-receptor interactions. Here, we introduce CellCommuNet (http://www.inbirg.com/cellcommunet/), a comprehensive data resource for exploring cell-cell communication networks in scRNA-seq data from human and mouse tissues in normal and disease states. CellCommuNet currently includes 376 single datasets from multiple sources, and 118 comparison datasets between disease and normal samples originating from the same study. CellCommuNet provides information on the strength of communication between cells and related signalling pathways and facilitates the exploration of differences in cell-cell communication between healthy and disease states. Users can also search for specific signalling pathways, ligand-receptor pairs, and cell types of interest. CellCommuNet provides interactive graphics illustrating cell-cell communication in different states, enabling differential analysis of communication strength between disease and control samples. This comprehensive database aims to be a valuable resource for biologists studying cell-cell communication networks.© The Author(s) 2023. Published by Oxford University Press on behalf of Nucleic Acids Research.