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Despite the protective membrane that separates a cell from the environment, cells need the ability to detect and respond to environmental changes. Additionally, cells often need to communicate with one another. Unicellular and multicellular organisms use a variety of cell signaling mechanisms to communicate to respond to the environment.

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Electric Cell-substrate Impedance Sensing for the Quantification of Endothelial Proliferation, Barrier Function, and Motility

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ESICCC as a systematic computational framework for evaluation, selection, and integration of cell-cell communication

Jiaxin Luo^1,2, Minghua Deng³, Xuegong Zhang⁴

¹Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou 510080, China.

Genome Research

|October 12, 2023

Summary

This study benchmarks cell-cell communication (CCC) inference tools using a new framework, ESICCC. RNAMagnet, CellChat, and scSeqComm excel for ligand-receptor inference, while stMLnet and HoloNet lead for ligand/receptor-target regulation with spatial transcriptomics data.

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Area of Science:

Computational Biology
Genomics
Bioinformatics

Background:

Cell-cell communication (CCC) is fundamental for multicellular organism development and function.
Advancements in single-cell RNA sequencing (scRNA-seq) and spatial transcriptomics (ST) have spurred the development of numerous CCC inference methods.
A comprehensive comparison of these CCC inference methods is lacking.

Purpose of the Study:

To systematically benchmark and compare the performance of existing CCC inference methods.
To identify the most accurate and robust tools for ligand-receptor and ligand/receptor-target inference.
To provide practical guidelines and resources for researchers applying CCC inference.

Main Methods:

Development of the ESICCC benchmark framework.
Evaluation of 18 ligand-receptor (LR) and 5 ligand/receptor-target inference methods.
Utilized 116 diverse datasets including scRNA-seq, ST, perturbation, and cell type-specific data.

Main Results:

RNAMagnet, CellChat, and scSeqComm demonstrated superior performance for intercellular LR inference from scRNA-seq data.
stMLnet and HoloNet were identified as top methods for predicting ligand/receptor-target regulation using ST data.
Identified key factors influencing CCC inference, including prior interaction data, scoring algorithms, signaling complexity, and spatial context.

Conclusions:

The ESICCC framework provides a robust evaluation of CCC inference tools.
Specific methods are recommended based on data type and inference goal (LR vs. L/R-target).
An ensemble pipeline, CCCbank, and a decision-tree guideline are provided to aid practical application and future method development.