Literature DB >> 33147626

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

Xin Shao1, Jie Liao1, Chengyu Li1, Xiaoyan Lu1, Junyun Cheng1, Xiaohui Fan2.   

Abstract

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.
© The Author(s) 2020. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Entities:  

Keywords:  cell–cell-communication; human; ligand–receptor interaction; mouse; scRNA-seq; single-cell transcriptomics

Year:  2021        PMID: 33147626     DOI: 10.1093/bib/bbaa269

Source DB:  PubMed          Journal:  Brief Bioinform        ISSN: 1467-5463            Impact factor:   11.622


  28 in total

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Authors:  Suoqin Jin; Christian F Guerrero-Juarez; Lihua Zhang; Ivan Chang; Raul Ramos; Chen-Hsiang Kuan; Peggy Myung; Maksim V Plikus; Qing Nie
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8.  Single Cell RNA Sequencing Identifies a Unique Inflammatory Macrophage Subset as a Druggable Target for Alleviating Acute Kidney Injury.

Authors:  Weijian Yao; Ying Chen; Zehua Li; Jing Ji; Abin You; Shanzhao Jin; Yuan Ma; Youlu Zhao; Jinwei Wang; Lei Qu; Hui Wang; Chengang Xiang; Suxia Wang; Gang Liu; Fan Bai; Li Yang
Journal:  Adv Sci (Weinh)       Date:  2022-02-03       Impact factor: 17.521

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Authors:  Lucy MacDonald; Stefano Alivernini; Barbara Tolusso; Aziza Elmesmari; Domenico Somma; Simone Perniola; Annamaria Paglionico; Luca Petricca; Silvia L Bosello; Angelo Carfì; Michela Sali; Egidio Stigliano; Antonella Cingolani; Rita Murri; Vincenzo Arena; Massimo Fantoni; Massimo Antonelli; Francesco Landi; Francesco Franceschi; Maurizio Sanguinetti; Iain B McInnes; Charles McSharry; Antonio Gasbarrini; Thomas D Otto; Mariola Kurowska-Stolarska; Elisa Gremese
Journal:  JCI Insight       Date:  2021-06-18

Review 10.  Applications and analytical tools of cell communication based on ligand-receptor interactions at single cell level.

Authors:  Fen Ma; Siwei Zhang; Lianhao Song; Bozhi Wang; Lanlan Wei; Fengmin Zhang
Journal:  Cell Biosci       Date:  2021-07-03       Impact factor: 7.133

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