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PlantCellChat: an R-based toolkit for predicting plant cell-cell communication from single-cell and spatial
Wei Liu1, Xiangrong Zhu1, Mingji Wu2
1College of Life Sciences, Fujian Agriculture and Forestry University, Fuzhou, 350002, China.
The Plant Journal : for Cell and Molecular Biology
|May 12, 2026
Summary
PlantCellChat is a new R package for studying plant cell-cell communication (CCC) using single-cell data. It predicts CCC networks and identifies hormone receptors, aiding plant stress response research.
Area of Science:
- Plant biology
- Computational biology
- Genomics
Background:
- Cell-cell communication (CCC) is crucial for plant development and responses.
- Studying plant CCC is challenging due to limited databases and tools compared to animal systems.
Purpose of the Study:
- To introduce PlantCellChat, a software package for analyzing plant CCC at single-cell resolution.
- To provide tools for predicting CCC networks and identifying hormone receptors in plants.
Main Methods:
- Integrated ligand-receptor interaction data from five model plant species.
- Developed computational tools for CCC network prediction using scRNA-seq and spatial transcriptomics.
- Introduced a deep learning model (PCC-GCN) for plant hormone receptor classification.
Main Results:
- Demonstrated PlantCellChat's utility on rice and Arabidopsis datasets under stress conditions.
- Predicted stress-associated alterations in intercellular communication.
- Identified candidate ligand-receptor pairs involved in plant stress responses.
Conclusions:
- PlantCellChat offers a powerful resource for plant CCC research.
- The software facilitates the study of intercellular communication in response to environmental stimuli.
- PCC-GCN aids in understanding plant hormone signaling pathways.
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