ArticleNature communications2020
Predicting cell-to-cell communication networks using NATMI.
Article in Nature communications, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 198 papers, 1 of them a synthesis that pooled it.
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Who cites it
198 citing papers in PubMed, 1 synthesis or guideline pooled it.
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- Identifying potential ligand-receptor interactions by integrating LSTM network and the attention mechanism for cell-cell communication prediction.Journal of translational medicine · 2026Article
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- Immune landscape of the affected brain in Rasmussen encephalitis.Scientific reports · 2026Article
138 more citing papers are in PubMed but not listed here.
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Authors and funding
5 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Development of high throughput single-cell sequencing technologies has made it cost-effective to profile thousands of cells from diverse samples containing multiple cell types. To study how these different cell types work together, here we develop NATMI (Network Analysis Toolkit for Multicellular Interactions). NATMI uses connectomeDB2020 (a database of 2293 manually curated ligand-receptor pairs with literature support) to predict and visualise cell-to-cell communication networks from single-cell (or bulk) expression data. Using multiple published single-cell datasets we demonstrate how NATMI can be used to identify (i) the cell-type pairs that are communicating the most (or most specifically) within a network, (ii) the most active (or specific) ligand-receptor pairs active within a network, (iii) putative highly-communicating cellular communities and (iv) differences in intercellular communication when profiling given cell types under different conditions. Furthermore, analysis of the Tabula Muris (organism-wide) atlas confirms our previous prediction that autocrine signalling is a major feature of cell-to-cell communication networks, while also revealing that hundreds of ligands and their cognate receptors are co-expressed in individual cells suggesting a substantial potential for self-signalling.
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