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Predicting cell-to-cell communication networks using NATMI
Rui Hou1, Elena Denisenko1, Huan Ting Ong2
1Harry Perkins Institute of Medical Research, QEII Medical Centre and Centre for Medical Research, The University of Western Australia, Nedlands, WA, 6009, Australia.
Nature Communications
|October 7, 2020
Summary
We developed NATMI, a toolkit for analyzing cell-to-cell communication networks from single-cell data. It identifies key interactions and cellular communities, revealing insights into multicellular systems and autocrine signaling.
Area of Science:
- Computational Biology
- Systems Biology
- Genomics
Background:
- High-throughput single-cell sequencing enables profiling of diverse cell types.
- Understanding multicellular interactions is crucial for biological research.
Purpose of the Study:
- To develop a computational toolkit for predicting and visualizing cell-to-cell communication networks.
- To analyze intercellular communication patterns within complex biological systems.
Main Methods:
- Development of the Network Analysis Toolkit for Multicellular Interactions (NATMI).
- Utilizing connectomeDB2020, a curated database of ligand-receptor pairs.
- Application to multiple published single-cell expression datasets.
Main Results:
- NATMI identifies highly communicating cell-type pairs and specific ligand-receptor interactions.
- The toolkit reveals putative cellular communities and differences in communication under varying conditions.
- Analysis confirms autocrine signaling as a major feature and highlights potential for self-signaling due to co-expressed ligands and receptors.
Conclusions:
- NATMI provides a robust framework for dissecting cell-to-cell communication networks.
- The findings underscore the significance of autocrine signaling and co-expression in cellular communication.
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