CellTalkDB: a manually curated database of ligand–receptor interactions in humans and mice
Tóm tắt
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.
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Tài liệu tham khảo
Gartner, 2017, Unraveling cell-to-cell signaling networks with chemical biology, Nat Chem Biol, 13, 564, 10.1038/nchembio.2391
Shao, 2020, New avenues for systematically inferring cell-cell communication: through single-cell transcriptomics data, Protein Cell, 10.1007/s13238-020-00727-5
Sadahira, 1995, Very late activation antigen 4-vascular cell adhesion molecule 1 interaction is involved in the formation of erythroblastic islands, J Exp Med, 181, 411, 10.1084/jem.181.1.411
Sheikh, 2019, Systematic identification of cell-cell communication networks in the developing brain, iScience, 21, 273, 10.1016/j.isci.2019.10.026
Kumar, 2018, Analysis of single-cell RNA-Seq identifies cell-cell communication associated with tumor characteristics, Cell Rep, 25, 1458, 10.1016/j.celrep.2018.10.047
Xiong, 2019, Landscape of intercellular crosstalk in healthy and NASH liver revealed by single-cell secretome gene analysis, Mol Cell, 75, 644, 10.1016/j.molcel.2019.07.028
Cohen, 2018, Lung single-cell signaling interaction map reveals basophil role in macrophage imprinting, Cell, 175, 1031, 10.1016/j.cell.2018.09.009
Shao, 2020, scCATCH: automatic annotation on cell types of clusters from single-cell RNA sequencing data, iScience, 23, 10.1016/j.isci.2020.100882
Efremova, 2020, CellPhoneDB: inferring cell-cell communication from combined expression of multi-subunit ligand-receptor complexes, Nat Protoc, 10.1038/s41596-020-0292-x
Cabello-Aguilar, 2020, SingleCellSignalR: inference of intercellular networks from single-cell transcriptomics, Nucleic Acids Res, 48, e55, 10.1093/nar/gkaa183
Ramilowski, 2015, A draft network of ligand-receptor-mediated multicellular signalling in human, Nat Commun, 6, 10.1038/ncomms8866
Graeber, 2001, Bioinformatic identification of potential autocrine signaling loops in cancers from gene expression profiles, Nat Genet, 29, 295, 10.1038/ng755
Sharman, 2013, IUPHAR-DB: updated database content and new features, Nucleic Acids Res, 41, D1083, 10.1093/nar/gks960
Ben-Shlomo, 2003, Signaling receptome: a genomic and evolutionary perspective of plasma membrane receptors involved in signal transduction, Sci STKE, 2003, RE9, 10.1126/stke.2003.187.re9
Yu, 2019, Core pluripotency factors promote glycolysis of human embryonic stem cells by activating GLUT1 enhancer, Protein Cell, 10, 668, 10.1007/s13238-019-0637-9
Wang, 2020, CHD4 promotes breast cancer progression as a coactivator of hypoxia-inducible factors, Cancer Res, 80, 3880, 10.1158/0008-5472.CAN-20-1049
Liu, 2019, The F-BAR domain of Rga7 relies on a cooperative mechanism of membrane binding with a partner protein during fission yeast cytokinesis, Cell Rep, 26, 2540, 10.1016/j.celrep.2019.01.112
Zheng, 2019, Somatic autophagy of axonal mitochondria in ischemic neurons, J Cell Biol, 218, 1891, 10.1083/jcb.201804101
Baldwin, 2001, The specificity of receptor binding by vascular endothelial growth factor-d is different in mouse and man, J Biol Chem, 276, 19166, 10.1074/jbc.M100097200
Islam, 2011, Mouse CCL8, a CCR8 agonist, promotes atopic dermatitis by recruiting IL-5+ T(H)2 cells, Nat Immunol, 12, 167, 10.1038/ni.1984
Szklarczyk, 2019, STRING v11: protein-protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets, Nucleic Acids Res, 47, D607, 10.1093/nar/gky1131
Vieira Braga, 2019, A cellular census of human lungs identifies novel cell states in health and in asthma, Nat Med, 25, 1153, 10.1038/s41591-019-0468-5
Rochemonteix-Galve, 1990, Fibroblast-alveolar cell interactions in sarcoidosis and idiopathic pulmonary fibrosis: evidence for stimulatory and inhibitory cytokine production by alveolar cells, Eur Respir J, 3, 653, 10.1183/09031936.93.03060653
Wu, 2017, Detecting activated cell populations using single-cell RNA-Seq, Neuron, 96, 313, 10.1016/j.neuron.2017.09.026
Macosko, 2015, Highly parallel genome-wide expression profiling of individual cells using nanoliter droplets, Cell, 161, 1202, 10.1016/j.cell.2015.05.002
Klein, 2015, Droplet barcoding for single-cell transcriptomics applied to embryonic stem cells, Cell, 161, 1187, 10.1016/j.cell.2015.04.044
Nguyen, 2018, Single cell RNA sequencing of rare immune cell populations, Front Immunol, 9, 1553, 10.3389/fimmu.2018.01553
Torre, 2018, Rare cell detection by single-cell RNA sequencing as guided by single-molecule RNA FISH, Cell Syst, 6, 171, 10.1016/j.cels.2018.01.014
Liao, 2020, Uncovering an Organ's molecular architecture at single-cell resolution by spatially resolved Transcriptomics, Trends Biotechnol, S0167-7799, 30140