Pan Yan, Gao Zijing, Cui Xuejian, Li Zhen, Jiang Rui
Ministry of Education Key Laboratory of Bioinformatics, Bioinformatics Division at the Beijing National Research Center for Information Science and Technology, Center for Synthetic and Systems Biology, Department of Automation, Tsinghua University, FIT 1-107, Beijing 100084, China.
Database (Oxford). 2024 Sep 16;2024. doi: 10.1093/database/baae098.
Cell-cell communication (CCC) through ligand-receptor (L-R) pairs forms the cornerstone for complex functionalities in multicellular organisms. Deciphering such intercellular signaling can contribute to unraveling disease mechanisms and enable targeted therapy. Nonetheless, notable biases and inconsistencies are evident among the inferential outcomes generated by current methods for inferring CCC network. To fill this gap, we developed collectNET (http://health.tsinghua.edu.cn/collectnet) as a comprehensive web platform for analyzing CCC network, with efficient calculation, hierarchical browsing, comprehensive statistics, advanced searching, and intuitive visualization. collectNET provides a reliable online inference service with prior knowledge of three public L-R databases and systematic integration of three mainstream inference methods. Additionally, collectNET has assembled a human CCC atlas, including 126 785 significant communication pairs based on 343 023 cells. We anticipate that collectNET will benefit researchers in gaining a more holistic understanding of cell development and differentiation mechanisms. Database URL: http://health.tsinghua.edu.cn/collectnet.
通过配体-受体(L-R)对进行的细胞间通讯(CCC)构成了多细胞生物复杂功能的基石。解读这种细胞间信号传导有助于揭示疾病机制并实现靶向治疗。尽管如此,当前用于推断CCC网络的方法所产生的推断结果之间存在明显的偏差和不一致。为了填补这一空白,我们开发了collectNET(http://health.tsinghua.edu.cn/collectnet)作为一个用于分析CCC网络的综合网络平台,具有高效计算、分层浏览、综合统计、高级搜索和直观可视化功能。collectNET利用三个公共L-R数据库的先验知识和三种主流推断方法的系统整合,提供可靠的在线推断服务。此外,collectNET还汇编了一个人类CCC图谱,其中包括基于343023个细胞的126785个重要通讯对。我们预计collectNET将有助于研究人员更全面地了解细胞发育和分化机制。数据库网址:http://health.tsinghua.edu.cn/collectnet。