The DOI matches its Crossref record; 62 of the 62 references it cites were found in a registry or an index.
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.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The cited source says what the claim attributes to it.
In the citing paper
“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
The cited source says what the claim attributes to it.
In the citing paper
“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]
The check wasn’t confident either way, so it gives no verdict — never disputed.
In the citing paper
“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]
Experimental — accuracy not yet measuredThe check wasn’t confident either way, so it gives no verdict — never disputed.
In the citing paper
“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]
Experimental — accuracy not yet measuredThe check wasn’t confident either way, so it gives no verdict — never disputed.
In the citing paper
“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]
Experimental — accuracy not yet measured
The check wasn’t confident either way, so it gives no verdict — never disputed.
In the citing paper
“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]
Experimental — accuracy not yet measured
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Cytoscape: A software Environment for integrated models of biomolecular interaction networks
Shannon, P., Markiel, A., Ozier, O., Baliga, N. S., Wang, J. T., Ramage, D., Amin, N., Schwikowski, B., & Ideker, T. (2003) · Genome Research · doi:10.1101/gr.1239303 · Cited by 51,888
Gene Ontology: tool for the unification of biology
Ashburner, M., Ball, C. A., Blake, J. A., Botstein, D., Butler, H., Cherry, J. M., Davis, A. P., Dolinski, K., Dwight, S. S., Eppig, J. T., et al. (2000) · Nature Genetics · doi:10.1038/75556 · Cited by 43,206
Gephi: An Open Source Software for Exploring and Manipulating Networks
Bastian, M., Heymann, S., & Jacomy, M. (2009) · Proceedings of the International AAAI Conference on Web and Social Media · doi:10.1609/icwsm.v3i1.13937 · Cited by 11,675
DAVID: Database for Annotation, Visualization, and Integrated Discovery
Dennis, G., Sherman, B. T., Hosack, D. A., Yang, J., Gao, W., Lane, H. C., & Lempicki, R. A. (2003) · Genome Biology · doi:10.1186/gb-2003-4-5-p3 · Cited by 9,327
Inflammation and metabolic disorders
Hotamisligil, G. S. (2006) · Nature · doi:10.1038/nature05485 · Cited by 9,199
Highly parallel genome-wide expression profiling of individual cells using nanoliter droplets.
Macosko, E. Z., Basu, A., Satija, R., Nemesh, J., Shekhar, K., Goldman, M., Tirosh, I., Bialas, A. R., Kamitaki, N., Martersteck, E. M., et al. (2015) · Cell · doi:10.1016/j.cell.2015.05.002 · Cited by 7,110
OrthoMCL: Identification of Ortholog Groups for Eukaryotic Genomes
Li, L., Stoeckert, C. J., & Roos, D. S. (2003) · Genome Research · doi:10.1101/gr.1224503 · Cited by 5,707
STRING v9.1: protein-protein interaction networks, with increased coverage and integration
Franceschini, A., Szklarczyk, D., Frankild, S., Kuhn, M., Simonovic, M., Roth, A., Lin, J., Minguez, P., Bork, P., von Mering, C., et al. (2012) · Nucleic Acids Research · doi:10.1093/nar/gks1094 · Cited by 4,398
Human Protein Reference Database--2009 update
Prasad, T. S. K., Goel, R., Kandasamy, K., Keerthikumar, S., Kumar, S., Mathivanan, S., Telikicherla, D., Raju, R., Shafreen, R. B., Venugopal, A., et al. (2008) · Nucleic Acids Research · doi:10.1093/nar/gkn892 · Cited by 3,252
Bostock, M., Ogievetsky, V., & Heer, J. (2011) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2011.185 · Cited by 3,165
PANTHER: A Library of Protein Families and Subfamilies Indexed by FunctionPreprint
Thomas, P. D., Campbell, M. J., Kejariwal, A., Mi, H., Karlak, B., Daverman, R., Diemer, K., Muruganujan, A., & Narechania, A. (2003) · Genome Research · doi:10.1101/gr.772403 · Cited by 2,978
A draft map of the human proteome
Kim, M.-S., Pinto, S. M., Getnet, D., Nirujogi, R. S., Manda, S. S., Chaerkady, R., Madugundu, A. K., Kelkar, D. S., Isserlin, R., Jain, S., et al. (2014) · Nature · doi:10.1038/nature13302 · Cited by 2,198
A promoter-level mammalian expression atlas
The FANTOM Consortium and the RIKEN PMI and CLST (DGT) (2014) · Nature · doi:10.1038/nature13182 · Cited by 1,982
Mass-spectrometry-based draft of the human proteome
Wilhelm, M., Schlegl, J., Hahne, H., Gholami, A. M., Lieberenz, M., Savitski, M. M., Ziegler, E., Butzmann, L., Gessulat, S., Marx, H., et al. (2014) · Nature · doi:10.1038/nature13319 · Cited by 1,820
Activities at the Universal Protein Resource (UniProt)
The UniProt Consortium (2013) · Nucleic Acids Research · doi:10.1093/nar/gkt1140 · Cited by 1,736
GOstat: find statistically overrepresented Gene Ontologies within a group of genes
Beißbarth, T., & Speed, T. P. (2004) · Bioinformatics · doi:10.1093/bioinformatics/bth088 · Cited by 1,256
Methods for Computing Wagner Trees
Farris, J. S. (1970) · Systematic Biology · doi:10.1093/sysbio/19.1.83 · Cited by 1,243
Apweiler, R. (2001) · Nucleic Acids Research · doi:10.1093/nar/29.1.37 · Cited by 1,079
The Role of Chemokine Receptors in Primary, Effector, and Memory Immune Responses
Sallusto, F., Mackay, C. R., & Lanzavecchia, A. (2000) · Annual Review of Immunology · doi:10.1146/annurev.immunol.18.1.593 · Cited by 1,054
Gorelik, L., & Flavell, R. A. (2000) · Immunity · doi:10.1016/s1074-7613(00)80170-3 · Cited by 959
Leptin receptor signaling in POMC neurons is required for normal body weight homeostasis
Balthasar, N., Coppari, R., McMinn, J., Liu, S. M., Lee, C. E., Tang, V., Kenny, C. D., McGovern, R. A., Chua, S. C., Elmquist, J. K., et al. (2004) · Neuron · doi:10.1016/j.neuron.2004.06.004 · Cited by 935
Gale, N. W., Holland, S. J., Valenzuela, D. M., Flenniken, A. M., Pan, L., Ryan, T. E., Henkemeyer, M. J., Strebhardt, K., Hirai, H., Wilkinson, D. G., et al. (1996) · Neuron · doi:10.1016/s0896-6273(00)80276-7 · Cited by 839
Kroll, K. L., & Amaya, E. (1996) · Development · doi:10.1242/dev.122.10.3173 · Cited by 781
Analysis of human transcriptomes
Velculescu, V. E., Madden, S. L., Zhang, L., Lash, A., Yu, J., Rago, C., Lal, A., Wang, C. J., Beaudry, G. A., Ciriello, K. M., et al. (1999) · Nature Genetics · doi:10.1038/70487 · Cited by 775
The Evolution of Multicellularity: A Minor Major Transition?
Grosberg, R. K., & Strathmann, R. R. (2007) · Annual Review of Ecology, Evolution, and Systematics · doi:10.1146/annurev.ecolsys.36.102403.114735 · Cited by 732
Tushinski, R. J., Oliver, I. T., Guilbert, L. J., Tynan, P. W., Warner, J. R., & Stanley, E. R. (1982) · Cell · doi:10.1016/0092-8674(82)90376-2 · Cited by 663
Pece-Barbara, N., Wrana, J. L., & Letarte, M. (1999) · Journal of Biological Chemistry · doi:10.1074/jbc.274.2.584 · Cited by 564
Lee, J. H., Daugharthy, E. R., Scheiman, J., Kalhor, R., Ferrante, T. C., Terry, R., Turczyk, B. M., Yang, J. L., Lee, H. S., Aach, J., et al. (2015) · Nature Protocols · doi:10.1038/nprot.2014.191 · Cited by 529
The evolution of signalling pathways in animal development
Pires-daSilva, A., & Sommer, R. J. (2003) · Nature Reviews Genetics · doi:10.1038/nrg977 · Cited by 466
Co-evolution of proteins with their interaction partners 1 1Edited by B. Honig
Goh, C.-S., Bogan, A. A., Joachimiak, M. P., Walther, D., & Cohen, F. E. (2000) · Journal of Molecular Biology · doi:10.1006/jmbi.2000.3732 · Cited by 442
Eichmann, A., Corbel, C., Nataf, V., Vaigot, P., Bréant, C., & Douarin, N. M. L. (1997) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.94.10.5141 · Cited by 401
An HMM posterior decoder for sequence feature prediction that includes homology information
Käll, L., Krogh, A., & Sonnhammer, E. L. L. (2005) · Bioinformatics · doi:10.1093/bioinformatics/bti1014 · Cited by 328
LocTree3 prediction of localization
Goldberg, T., Hecht, M., Hamp, T., Karl, T., Yachdav, G., Ahmed, N., Altermann, U., Angerer, P., Ansorge, S., Balasz, K., et al. (2014) · Nucleic Acids Research · doi:10.1093/nar/gku396 · Cited by 320
Cloning and Functional Expression of a Human Eosinophil CC Chemokine Receptor
Combadière, C., Ahuja, S. K., & Murphy, P. M. (1995) · Journal of Biological Chemistry · doi:10.1074/jbc.270.28.16491 · Cited by 250
Eotaxin and eosinophil recruitment: implications for human disease
Rankin, S. M., Conroy, D. M., & Williams, T. J. (2000) · Molecular Medicine Today · doi:10.1016/s1357-4310(99)01635-4 · Cited by 242
A HaemAtlas: characterizing gene expression in differentiated human blood cells
Watkins, N. A., Gusnanto, A., de Bono, B., De, S., Miranda‐Saavedra, D., Hardie, D. L., Angenent, W. G. J., Attwood, A., Ellis, P. D., Erber, W. N., et al. (2009) · Blood · doi:10.1182/blood-2008-06-162958 · Cited by 242
Genenames.org: the HGNC resources in 2013
Gray, K. A., Daugherty, L. C., Gordon, S. M., Seal, R. L., Wright, M. W., & Bruford, E. A. (2012) · Nucleic Acids Research · doi:10.1093/nar/gks1066 · Cited by 240
Phylogenetic patterns of emergence of new genes support a model of frequent de novo evolution
Neme, R., & Tautz, D. (2013) · BMC Genomics · doi:10.1186/1471-2164-14-117 · Cited by 226
Redefinition of the human mast cell transcriptome by deep-CAGE sequencing
Motakis, E., Guhl, S., Ishizu, Y., Itoh, M., Kawaji, H., de Hoon, M., Lassmann, T., Carninci, P., Hayashizaki, Y., Zuberbier, T., et al. (2014) · Blood · doi:10.1182/blood-2013-02-483792 · Cited by 211
Bradfield, P. F., Amft, N., Vernon-Wilson, E. F., Exley, A., Parsonage, G. N., Rainger, G. E., Nash, G. B., Thomas, A. M. C., Simmons, D. L., Salmon, M., et al. (2003) · Arthritis & Rheumatism · doi:10.1002/art.11219 · Cited by 170
The Role of Altered Cell–Cell Communication in Melanoma Progression
Haass, N. K., Smalley, K. S. M., & Herlyn, M. (2004) · Journal of Molecular Histology · doi:10.1023/b:hijo.0000032362.35354.bb · Cited by 166
Ben‐Shlomo, I., Hsu, S. Y., Rauch, R., Kowalski, H. W., & Hsueh, A. J. W. (2003) · Science Signaling · doi:10.1126/stke.2003.187.re9 · Cited by 139
Logical Development of the Cell Ontology
Meehan, T. F., Masci, A. M., Abdulla, A., Cowell, L. G., Blake, J. A., Mungall, C., & Diehl, A. D. (2011) · BMC Bioinformatics · doi:10.1186/1471-2105-12-6 · Cited by 138
Graeber, T. G., & Eisenberg, D. S. (2001) · Nature Genetics · doi:10.1038/ng755 · Cited by 133
Herrera, B. M., van Dinther, M., ten Dijke, P., & Inman, G. J. (2009) · Cancer Research · doi:10.1158/0008-5472.can-09-2912 · Cited by 120
IUPHAR-DB: updated database content and new features
Sharman, J. L., Benson, H. E., Pawson, A. J., Lukito, V., Mpamhanga, C. P., Bombail, V., Davenport, A. P., Peters, J. A., Spedding, M., Harmar, A. J., et al. (2012) · Nucleic Acids Research · doi:10.1093/nar/gks960 · Cited by 112
Carramolino, L., Zaballos, Á., Kremer, L., Villares, R., Martı́n, P., Ardavı́n, C., Martínez‐A, C., & Márquez, G. (2001) · Blood · doi:10.1182/blood.v97.4.850 · Cited by 102
Heinicke, S., Livstone, M. S., Lu, C., Oughtred, R., Kang, F., Angiuoli, S. V., White, O. R., Botstein, D., & Dolinski, K. J. (2007) · PLoS ONE · doi:10.1371/journal.pone.0000766 · Cited by 98
Nomiyama, H., Hieshima, K., Nakayama, T., Sakaguchi, T., Fujisawa, R., Tanase, S., Nishiura, H., Matsuno, K., Takamori, H., Tabira, Y., et al. (2001) · International Immunology · doi:10.1093/intimm/13.8.1021 · Cited by 97
Espeseth, T., Greenwood, P. M., Reinvang, I., Fjell, A. M., Walhovd, K. B., Westlye, L. T., Wehling, E. I., Lundervold, A. J., Rootwelt, H., & Parasuraman, R. (2006) · Cognitive Affective & Behavioral Neuroscience · doi:10.3758/cabn.6.1.31 · Cited by 94
Colony-stimulating factor-1 (CSF-1) delivers a proatherogenic signal to human macrophages
Irvine, K. M., Andrews, M. R., Fernandez-Rojo, M. A., Schroder, K., Burns, C. J., Su, S. V., Wilks, A. F., Parton, R. G., Hume, D. A., & Sweet, M. J. (2008) · Journal of Leukocyte Biology · doi:10.1189/jlb.0808497 · Cited by 81
ProteinHistorian: Tools for the Comparative Analysis of Eukaryote Protein Origin
Capra, J. A., Williams, A. G., & Pollard, K. S. (2012) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1002567 · Cited by 80
A DRD4/BDNF gene–gene interaction associated with maximum BMI in women with bulimia nervosa
Kaplan, A. S., Levitan, R. D., Yilmaz, Z., Davis, C., Tharmalingam, S., & Kennedy, J. L. (2008) · International Journal of Eating Disorders · doi:10.1002/eat.20474 · Cited by 77
Nakayama, T., Kato, Y., Hieshima, K., Nagakubo, D., Kunori, Y., Fujisawa, T., & Yoshie, O. (2004) · The Journal of Immunology · doi:10.4049/jimmunol.173.3.2078 · Cited by 73
Baes, M. I., & Denef, C. (1987) · Endocrinology · doi:10.1210/endo-120-1-280 · Cited by 62
Eastman, C. R. (1905) · Science · doi:10.1126/science.22.555.206-a · Cited by 47
Cappello, P., Caorsi, C., Bosticardo, M., Angelis, S. D., Novelli, F., Forni, G., & Giovarelli, M. (2003) · Journal of Leukocyte Biology · doi:10.1189/jlb.0403146 · Cited by 45
Trans-SILAC: sorting out the non-cell-autonomous proteome
Rechavi, O., Kalman, M., Fang, Y., Vernitsky, H., Jacob‐Hirsch, J., Foster, L. J., Kloog, Y., & Goldstein, I. (2010) · Nature Methods · doi:10.1038/nmeth.1513 · Cited by 32
Zhu, X., Lv, J., Lin, Y., Xiang, M., Gao, B.-H., & Yi-fu, S. (2007) · Gynecologic Oncology · doi:10.1016/j.ygyno.2007.01.046 · Cited by 27
Hasegawa, Y., Takahashi, N., Forrest, A. R. R., Shin, J. W., Kinoshita, Y., Suzuki, H., & Hayashizaki, Y. (2011) · Stem Cells · doi:10.1002/stem.673 · Cited by 21
Reinvang, I., Lundervold, A. J., Wehling, E. I., Rootwelt, H., & Espeseth, T. (2010) · Journal of the International Neuropsychological Society · doi:10.1017/s1355617710000263 · Cited by 14
The role of chemokine receptors in primary, effector and memory immune response
Sallusto, F. (2002) · Experimental Dermatology · doi:10.1034/j.1600-0625.2002.110514.x · Cited by 8
Flagged first, then by citations
Research works printed but not linked: 1
- Dollo, L. The laws of evolution. Bull. Soc. Bel. Geol. Paleontol. 7, 164–166 (1893).
Cited by
Sorted by citations
Inference and analysis of cell-cell communication using CellChat
Jin, S., Guerrero-Juarez, C. F., Zhang, L., Chang, I., Ramos, R., Kuan, C.-H., Myung, P., Plikus, M. V., & Nie, Q. (2021) · Nature Communications · doi:10.1038/s41467-021-21246-9 · Cited by 8,409
The Immune Landscape of Cancer
Thorsson, V., Gibbs, D. L., Brown, S. D., Wolf, D., Bortone, D. S., Ou Yang, T.-H., Porta-Pardo, E., Gao, G. F., Plaisier, C. L., Eddy, J. A., et al. (2018) · Immunity · doi:10.1016/j.immuni.2018.03.023 · Cited by 5,870
Efremova, M., Vento-Tormo, M., Teichmann, S. A., & Vento-Tormo, R. (2020) · Nature Protocols · doi:10.1038/s41596-020-0292-x · Cited by 3,449
Puram, S. V., Tirosh, I., Parikh, A. S., Patel, A. P., Yizhak, K., Gillespie, S., Rodman, C., Luo, C. L., Mroz, E. A., Emerick, K. S., et al. (2017) · Cell · doi:10.1016/j.cell.2017.10.044 · Cited by 2,557
Regev, A., Teichmann, S. A., Lander, E. S., Amit, I., Benoist, C., Birney, E., Bodenmiller, B., Campbell, P., Carninci, P., Clatworthy, M., et al. (2017) · eLife · doi:10.7554/elife.27041 · Cited by 2,232
Self-citation
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.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
