A draft network of ligand–receptor-mediated multicellular signalling in human [* Cross-Reference *]

Version 1.0
Jordan A. Ramilowski; Tatyana Goldberg; Jayson Harshbarger; Edda Kloppmann; Marina Lizio; Venkata Satagopam; Masayoshi Itoh; Hideya Kawaji; Piero Carninci; Burkhard Rost; Alistair R. R. Forrest, 2015, "A draft network of ligand–receptor-mediated multicellular signalling in human [* Cross-Reference *]", https://lore.list.lu/citation?persistentId=perma:LIST.LFXLUN, LIST Open Repository, V1
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Description
Cell-to-cell communication across multiple cell types and tissues strictly governs proper functioning of metazoans and extensively relies on interactions between secreted ligands and cell-surface receptors. Herein, we present the first large-scale map of cell-to-cell communication between 144 human primary cell types. We reveal that most cells express tens to hundreds of ligands and receptors to create a highly connected signalling network through multiple ligand-receptor paths. We also observe extensive autocrine signalling with approximately two-thirds of partners possibly interacting on the same cell type. We find that plasma membrane and secreted proteins have the highest cell-type specificity, they are evolutionarily younger than intracellular proteins, and that most receptors had evolved before their ligands. We provide an online tool to interactively query and visualize our networks and demonstrate how this tool can reveal novel cell-to-cell interactions with the prediction that mast cells signal to monoblastic lineages via the CSF1-CSF1R interacting pair. (2015-07-22)
***This entry has been automatically imported via OpenAlex by LIST harvest scripts. Please refer to https://doi.org/10.1038/ncomms8866 for the original and latest version of the publication*** (2026-07-01)
Subject Chemistry; Medicine, Health and Life Sciences
Keyword Receptor, Cell type, Multicellular organism, Cell, Cell biology, Autocrine signalling, Biology, Cell signaling, Signalling, Intracellular, Cell surface receptor, Signal transduction, Cell membrane, Computational biology, Genetics
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