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A draft network of ligand–receptor-mediated multicellular signalling in human
Open accessAbstract
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.
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“Based on a database of ligand-receptor pairs (Ramilowski et al., 2015), TSKs participated in extensive autocrine and paracrine interactions (mostly with CAFs, endothelial cells, macrophages, and MDSCs)”
From Multimodal Analysis of Composition and Spatial Architecture in Human Squamous Cell Carcinoma (2020) · cites this paper as Ramilowski et al., 2015
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“There were also 22 trans-CDS associations between known ligand–receptor pairs37 (Supplementary Table 11).”
From Rare variant associations with plasma protein levels in the UK Biobank (2023) · cites this paper as [37]
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“A literature review of newly published experimental reports was used to validate 7 of the top 20 and 2 of the bottom predicted ATG dark genes (73).”
From Artificial Intelligence for Drug Discovery: Are We There Yet? (2024) · cites this paper as [73]
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“Given the complexity of ATG-associated genomic datasets, an ATG-specific XGBoost machine learning model was developed to aid in designing relevant experiments (73).”
From Artificial Intelligence for Drug Discovery: Are We There Yet? (2024) · cites this paper as [73]
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“The ATG-specific meta-path XGBoost model used 103 ATG-associated (positive) genes and 3468 negatives (not ATG-associated genes), collating data from Pharos.”
From Artificial Intelligence for Drug Discovery: Are We There Yet? (2024) · cites this paper as [73]
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“Among the top 251 genes predicted to have ATG associations, 43 genes were already in the Autophagy Database, and another 15 had potential ATG associations.”
From Artificial Intelligence for Drug Discovery: Are We There Yet? (2024) · cites this paper as [73]
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Metadata
- Authors
- Ramilowski, J. A., Goldberg, T., Harshbarger, J., Kloppmann, E., Lizio, M., Satagopam, V. P., Itoh, M., Kawaji, H., Carninci, P., Rost, B., & Forrest, A. R. R.
- Affiliations
- RIKEN (JP)
- Technical University of Munich (DE)
- Luxembourg Centre for Systems Biomedicine (LU)
- The University of Western Australia (AU)
- Corresponding author
- Ramilowski, Jordan A.
- Forrest, Alistair R. R.
- ORCID
- Venue
- Nature Communications
- Published
- 2015-07-22
- Volume
- 6
- Issue
- 1
- Pages
- 7866-7866
- ISSN
- 2041-1723 (ISSN-L)
- PMC
- PMC4525178
- OpenAlex
- W1016059894
- Cited by
- 887
- References
- 62
- Citations by year
- 2026: 58 · 2025: 92 · 2024: 78 · 2023: 120 · 2022: 132 · 2021: 154 · 2020: 122 · 2019: 67 · 2018: 37 · 2017: 20 · 2016: 3 · 2015: 1
- FWCI
- 8.83
- Type
- journal article
- Language
- en
- Update notices
- correction (2016-02-10)
- Open access
- gold
- doi.org
- Locations
- Nature Communications (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- UWA Profiles and Research Repository (UWA) (publishedVersion, other-oa)
- PubMed (publishedVersion)
- Open Repository and Bibliography (University of Luxembourg) (submittedVersion, cc-by-nc-sa)
- RePEc: Research Papers in Economics (submittedVersion)
- Funders
- Conquer Cancer Foundation
- Alexander von Humboldt-Stiftung
- Ministry of Education, Culture, Sports, Science and Technology
- National Institutes of Health (GM095315)
- RIKEN
- National Institute of General Medical Sciences (U54GM095315)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene Regulatory Network Analysis
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Receptor Mechanisms and Signaling
- Keywords
- cell-to-cell communication, ligand-receptor interactions, autocrine signaling, secreted proteins, plasma membrane proteins, protein evolution, mast cells, network visualization tools
- MeSH terms
- Animals, Cell Communication, Humans, Ligands, Receptors, Cell Surface, Software, Evolution, Molecular
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
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