CellTalkDB: a manually curated database of ligand-receptor interactions in humans and mice
CellTalkDB: a manually curated database of ligand-receptor interactions in humans and mice
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DOI：
10.1093/bib/bbaa269
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发表时间：
2021-07-01
影响因子：
9.5
通讯作者：
Fan, Xiaohui
中科院分区：
文献类型：
--
作者：
Shao, Xin;Liao, Jie;Fan, Xiaohui
多细胞生物中的细胞间通讯通常涉及分泌的配体-受体（LR）相互作用，这对各种生物现象至关重要。单细胞RNA测序（scRNA-seq）的最新进展有效地解决了细胞表型异质性和复杂组织的细胞类型组成，促进了单细胞分辨率下细胞间通讯的系统研究。然而，通过scRNA-seq评估化学信号依赖性细胞间通讯在很大程度上依赖于LR相互作用对的先验知识。我们构建了CellTalkDB（http：//tcm.zju.edu.cn/celltalkdb），这是一个人工管理的人类和小鼠LR相互作用对的综合数据库，包括3398个人类LR对和2033个小鼠LR对，通过使用STRING数据库的已知蛋白质-蛋白质相互作用的文本挖掘和人工验证，每个对都有文献支持的证据。与最大的LR对资源SingleCellSignalR相比，CellTalkDB不仅包括2033个小鼠LR对，还包括377个额外的人类LR对。总之，CellTalkDB中包含的人类和小鼠LR对的数据可以帮助进一步推断和理解基于LR相互作用的细胞-细胞通信，这可能为生物过程的机制提供新的见解。
Cell-cell communications in multicellular organisms generally involve secreted ligand-receptor (LR) interactions, which is vital for various biological phenomena. Recent advancements in single-cell RNA sequencing (scRNA-seq) have effectively resolved cellular phenotypic heterogeneity and the cell-type composition of complex tissues, facilitating the systematic investigation of cell-cell communications at single-cell resolution. However, assessment of chemical-signal-dependent cell-cell communication through scRNA-seq relies heavily on prior knowledge of LR interaction pairs. We constructed CellTalkDB (http://tcm.zju.edu.cn/celltalkdb), a manually curated comprehensive database of LR interaction pairs in humans and mice comprising 3398 human LR pairs and 2033 mouse LR pairs, through text mining and manual verification of known protein-protein interactions using the STRING database, with literature-supported evidence for each pair. Compared with SingleCellSignalR, the largest LR-pair resource, CellTalkDB includes not only 2033 mouse LR pairs but also 377 additional human LR pairs. In conclusion, the data on human and mouse LR pairs contained in CellTalkDB could help to further the inference and understanding of the LR-interaction-based cell-cell communications, which might provide new insights into the mechanism underlying biological processes.