CellTalkDB: a manually curated database of ligand-receptor interactions in humans and mice

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.

[1]  Piero Carninci,et al.  A draft network of ligand–receptor-mediated multicellular signalling in human , 2015, Nature Communications.

[2]  Feng Qi Han,et al.  Somatic autophagy of axonal mitochondria in ischemic neurons , 2019, The Journal of cell biology.

[3]  K. Gould,et al.  The F-BAR Domain of Rga7 Relies on a Cooperative Mechanism of Membrane Binding with a Partner Protein during Fission Yeast Cytokinesis , 2019, Cell reports.

[4]  Jiandie D. Lin,et al.  Landscape of Intercellular Crosstalk in Healthy and NASH Liver Revealed by Single-Cell Secretome Gene Analysis. , 2019, Molecular cell.

[5]  Yijie Wang,et al.  CHD4 Promotes Breast Cancer Progression as a Coactivator of Hypoxia-Inducible Factors , 2020, Cancer Research.

[6]  Vivien Marx,et al.  A dream of single-cell proteomics , 2019, Nature Methods.

[7]  Xin Shao,et al.  Uncovering an Organ's Molecular Architecture at Single-Cell Resolution by Spatially Resolved Transcriptomics. , 2020, Trends in biotechnology.

[8]  Damian Szklarczyk,et al.  STRING v11: protein–protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets , 2018, Nucleic Acids Res..

[9]  S. Lira,et al.  Mouse CCL8, a CCR8 agonist, promotes atopic dermatitis by recruiting IL-5+ TH2 cells , 2011, Nature Immunology.

[10]  Douglas A. Lauffenburger,et al.  Analysis of Single-Cell RNA-Seq Identifies Cell-Cell Communication Associated with Tumor Characteristics , 2018, Cell reports.

[11]  Evan Z. Macosko,et al.  Highly Parallel Genome-wide Expression Profiling of Individual Cells Using Nanoliter Droplets , 2015, Cell.

[12]  M. Karkkainen,et al.  The Specificity of Receptor Binding by Vascular Endothelial Growth Factor-D Is Different in Mouse and Man* , 2001, The Journal of Biological Chemistry.

[13]  I. Amit,et al.  Lung Single-Cell Signaling Interaction Map Reveals Basophil Role in Macrophage Imprinting , 2018, Cell.

[14]  Allon M. Klein,et al.  Droplet Barcoding for Single-Cell Transcriptomics Applied to Embryonic Stem Cells , 2015, Cell.

[15]  T. Yoshino,et al.  Very late activation antigen 4-vascular cell adhesion molecule 1 interaction is involved in the formation of erythroblastic islands , 1995, The Journal of experimental medicine.

[16]  K. Yao,et al.  Core pluripotency factors promote glycolysis of human embryonic stem cells by activating GLUT1 enhancer , 2019, Protein & Cell.

[17]  Sydney M. Shaffer,et al.  Rare Cell Detection by Single-Cell RNA Sequencing as Guided by Single-Molecule RNA FISH. , 2018, Cell systems.

[18]  David Eisenberg,et al.  Bioinformatic identification of potential autocrine signaling loops in cancers from gene expression profiles , 2001, Nature Genetics.

[19]  Mirjana Efremova,et al.  CellPhoneDB: inferring cell–cell communication from combined expression of multi-subunit ligand–receptor complexes , 2020, Nature Protocols.

[20]  Rui Xue,et al.  scCATCH: Automatic Annotation on Cell Types of Clusters from Single-Cell RNA Sequencing Data , 2020, iScience.

[21]  Sagar,et al.  Systematic Identification of Cell-Cell Communication Networks in the Developing Brain , 2019, iScience.

[22]  Fabian J Theis,et al.  A cellular census of human lungs identifies novel cell states in health and in asthma , 2019, Nature Medicine.

[23]  J. Dayer,et al.  Fibroblast-alveolar cell interactions in sarcoidosis and idiopathic pulmonary fibrosis: evidence for stimulatory and inhibitory cytokine production by alveolar cells. , 1990, The European respiratory journal.

[24]  Jennifer A. Prescher,et al.  Unraveling cell-to-cell signaling networks with chemical biology. , 2017, Nature chemical biology.

[25]  Jacques Colinge,et al.  SingleCellSignalR: inference of intercellular networks from single-cell transcriptomics , 2020, Nucleic acids research.

[26]  Huajun Chen,et al.  New avenues for systematically inferring cell-cell communication: through single-cell transcriptomics data , 2020, Protein & Cell.

[27]  Joanna L. Sharman,et al.  IUPHAR-DB: updated database content and new features , 2012, Nucleic Acids Res..

[28]  Izhar Ben-Shlomo,et al.  Signaling Receptome: A Genomic and Evolutionary Perspective of Plasma Membrane Receptors Involved in Signal Transduction , 2003, Science's STKE.

[29]  Weizhe Hong,et al.  Detecting Activated Cell Populations Using Single-Cell RNA-Seq , 2017, Neuron.

[30]  Imogen Moran,et al.  Single Cell RNA Sequencing of Rare Immune Cell Populations , 2018, Front. Immunol..