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Related Concept Videos

Cell Signaling in Plants01:25

Cell Signaling in Plants

Plant cells communicate to coordinate their cycle of growth, flowering and fruiting, and activities in roots, shoots, and leaves in response to the changing environmental conditions. Plant signaling is distinct from animal signaling. Plants primarily utilize enzyme-linked receptors, whereas the largest class of cell-surface receptors in animals are G-protein coupled receptors (GPCRs). Unlike animals, receptor tyrosine kinases are rare in plants. Instead, plants have a diverse class of...
Cell Adhesion in Plants01:14

Cell Adhesion in Plants

Plants have rigid cell walls that are made up of cell wall polysaccharides that mediate cell-cell adhesion. The primary cell walls of plants consist of two independent and interacting polysaccharide networks: a pectin matrix that embeds the second network comprising cellulose and hemicelluloses.
Pectins are complex heteropolymers mainly composed of negatively-charged α-D-glucopyranosyl uronic acid and some neutral glycosyl residues such as α-L-rhamnopyranose, α-L-arabinofuranose, and...
Animal and Plant Cell Structure01:30

Animal and Plant Cell Structure

Animal and plant cells not only differ in their structure, function, and mode of nutrition but also in how they reproduce, specialize, and organize into complex structures.
Cell Division
Though both plant and animal cells divide by mitosis (for non-gametic cells) and meiosis (for gametic cells), they differ in the specifics of this process. Unlike animal cells, plant cells lack centrosomes — an organelle responsible for organizing the spindle fibers and segregating the chromosomes during cell...
Cell-surface Signaling01:21

Cell-surface Signaling

Hormones—or any molecule that binds to a receptor, known as a ligand—that are lipid-insoluble (water-soluble) are not able to diffuse across the cell membrane. In order to be able to affect a cell without entering it, these hormones bind to receptors on the cell membrane. When a first messenger, a hormone, binds to a receptor, a signal cascade is set off, causing second messengers, proteins inside the cell, to become activated, resulting in downstream effects.

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Related Experiment Video

Updated: May 14, 2026

Using R, Seurat, and CellChat to Analyze a Single-Cell Transcriptomics Dataset of Mouse Skin Wound Healing
08:58

Using R, Seurat, and CellChat to Analyze a Single-Cell Transcriptomics Dataset of Mouse Skin Wound Healing

Published on: August 1, 2025

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
PubMed
Summary
This summary is machine-generated.

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.

Keywords:
cell–cell communicationgraph convolutional networksligand–receptor pairsplant hormonessingle‐cell transcriptomics

More Related Videos

Isolation and Transcriptome Analysis of Plant Cell Types
08:53

Isolation and Transcriptome Analysis of Plant Cell Types

Published on: April 7, 2023

Related Experiment Videos

Last Updated: May 14, 2026

Using R, Seurat, and CellChat to Analyze a Single-Cell Transcriptomics Dataset of Mouse Skin Wound Healing
08:58

Using R, Seurat, and CellChat to Analyze a Single-Cell Transcriptomics Dataset of Mouse Skin Wound Healing

Published on: August 1, 2025

Isolation and Transcriptome Analysis of Plant Cell Types
08:53

Isolation and Transcriptome Analysis of Plant Cell Types

Published on: April 7, 2023

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.