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Cited by 285Single-cell and spatial transcriptomicsBioinformatics and Genomic NetworksGene Regulatory Network Analysis

Predicting cell-to-cell communication networks using NATMI

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Hou, Rui, Denisenko, Elena, Ong, Huan Ting, Ramilowski, Jordan A., Forrest, Alistair R. R.·2020·Nature Communications
DOI 10.1038/s41467-020-18873-zPMC PMC7538930
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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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Cited by

Sorted by citations

Authors
Hou, R., Denisenko, E., Ong, H. T., Ramilowski, J. A., & Forrest, A. R. R.
Affiliations
  • The University of Western Australia (AU)
  • RIKEN Center for Integrative Medical Sciences (JP)
Corresponding author
Forrest, Alistair R. R.
ORCID
Venue
Nature Communications
Publisher
Springer Science and Business Media LLC
Published
2020-10-06
Volume
11
Issue
1
Pages
5011-5011
ISSN
2041-1723 (ISSN-L)
DOI
10.1038/s41467-020-18873-z
PMC
PMC7538930
OpenAlex
W3092318937
Cited by
285
References
10
Citations by year
2026: 52 · 2025: 59 · 2024: 64 · 2023: 47 · 2022: 46 · 2021: 16 · 2020: 1
FWCI
8.92
Type
journal article
Language
en
Open access
Licence
Locations
  • Nature Communications (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • UWA Profiles and Research Repository (UWA) (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • RePEc: Research Papers in Economics (submittedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders
  • Conquer Cancer Foundation
  • Australian Government
  • Ministry of Education, Culture, Sports, Science and Technology
  • Government of Western Australia
  • Medical Research Council
  • National Health and Medical Research Council (1146323, 1154524)
  • RIKEN
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
  • 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
Keywords
cell-to-cell communication, intercellular communication networks, ligand-receptor pairs, single-cell transcriptomics, multicellular interactions, autocrine signaling, Tabula Muris
MeSH terms
Data Visualization, Age Factors, Animals, Cell Communication, Female, Ligands, Mammary Glands, Animal, Proteins, Software, User-Computer Interface, Databases, Factual, Computational Biology, Autocrine Communication, Mice, Single-Cell Analysis

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