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Cell-surface Signaling

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 5, 2026

Transcriptome Analysis of Single Cells

Transcriptome Analysis of Single Cells

Published on: April 25, 2011

CellChat for systematic analysis of cell-cell communication from single-cell transcriptomics.

Suoqin Jin^1,2, Maksim V Plikus^3,4, Qing Nie^5,6,7

¹School of Mathematics and Statistics, Wuhan University, Wuhan, China. sqjin@whu.edu.cn.

Nature Protocols

|September 17, 2024

Summary

This summary is machine-generated.

CellChat v2 is a computational tool that infers and analyzes cell-cell communication networks from single-cell transcriptomic data. This updated version enhances communication analysis across different biological conditions.

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Last Updated: May 5, 2026

Transcriptome Analysis of Single Cells

Transcriptome Analysis of Single Cells

Published on: April 25, 2011

Single-cell Microinjection for Cell Communication Analysis

Single-cell Microinjection for Cell Communication Analysis

Published on: February 26, 2017

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues

Published on: January 10, 2019

Area of Science:

Computational Biology
Genomics
Systems Biology

Background:

Single-cell sequencing advances enable systematic exploration of cell-cell communication.
Integrating molecular interactions into network analysis remains a challenge.
Previous CellChat tool facilitated cell-cell communication network inference.

Purpose of the Study:

Provide a protocol for CellChat v2, an updated tool for analyzing cell-cell communication.
Enable inference and comparative analysis of intercellular communication from single-cell data.
Facilitate identification of altered communication, signals, and cell populations across conditions.

Main Methods:

Utilizes a simplified mass-action model for signaling probability quantification.
Incorporates ligand-receptor interactions, multisubunit structures, and cofactor modulation.
Employs quantitative metrics and machine learning for systematic network analysis.

Main Results:

CellChat v2 offers enhanced comparison functionalities and an expanded ligand-receptor database.
Includes rich functional annotations and an Interactive CellChat Explorer.
Provides a protocol for analyzing single-cell transcriptomic data for cell communication.

Conclusions:

CellChat v2 provides a user-friendly protocol for in-depth cell-cell communication analysis.
The tool facilitates the identification of biological condition-specific communication changes.
Accessible R implementation and tutorials support broad adoption in single-cell data analysis.