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, TSKs participated in extensive autocrine and paracrine interactions (mostly with CAFs, endothelial cells, macrophages, and MDSCs).
“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
There were 22 trans-CDS associations between known ligand-receptor pairs.
“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]
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.
“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]
The Results section discusses ligand and receptor detection in B cells, and the Discussion mentions a literature review of B-cell proteome findings. Neither addresses predicted ATG dark genes or validation of 7 top-ranked and 2 bottom-ranked genes. Searches for those terms and counts found no relevant passage in our copy; page numbers were not supplied. (LLM, conf=0.75)
Experimental — accuracy not yet measuredAn ATG-specific XGBoost machine learning model was developed to aid in designing relevant experiments.
“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]
The Methods describe FANTOM5 expression profiles and GO/InterPro enrichment analysis, while the Discussion describes a web tool for exploring ligand–receptor signalling. Searches for ATG, XGBoost, and a machine-learning model for experiment design found no passage addressing the claim in our copy. (LLM, conf=0.76)
Experimental — accuracy not yet measuredThe ATG-specific meta-path XGBoost model used 103 ATG-associated (positive) genes and 3468 negatives, collating data from Pharos.
“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]
The Methods section, “Reference set of human protein-coding genes” (page not provided), describes an HGNC/UniProt gene set, not the claimed ATG model. Searches for “ATG-specific meta-path XGBoost,” the 103 positive and 3,468 negative genes, and Pharos found no passage addressing the claim. (LLM, conf=0.77)
Experimental — accuracy not yet measured
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.
“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]
The Methods section ('Reference set of human protein-coding genes'; page unavailable) describes a set of 19,053 genes used for ligand–receptor analyses. Searches for the top 251 genes, ATG associations, and the Autophagy Database found no passage addressing the claimed counts of 43 and 15. (LLM, conf=0.78)
Experimental — accuracy not yet measured
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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) (Figures 6B and 6C; STAR Methods).”
BackgroundSingle-cell immune landscape of human atherosclerotic plaques
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- 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
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