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Updated: Jun 30, 2025

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ICELLNET v2: a versatile method for cell-cell communication analysis from human transcriptomic data.

Lucile Massenet-Regad1,2, Vassili Soumelis1,3,4

  • 1Université Paris Cité, INSERM U976 HIPI, Paris, F-75010, France.

Bioinformatics (Oxford, England)
|March 15, 2024
PubMed
Summary
This summary is machine-generated.

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ICELLNET, a tool for cell-cell communication analysis, has been significantly updated. It now features an expanded database and optimized single-cell RNA sequencing analysis for broader biological applications.

Area of Science:

  • Computational Biology
  • Systems Biology
  • Bioinformatics

Background:

  • Inferring cell-cell communication networks from transcriptomic data is crucial for understanding biological systems.
  • Existing methods often simplify ligand-receptor interactions, overlooking complex multi-subunit interactions.
  • ICELLNET was previously developed to address this by considering multi-subunit complexes in communication inference.

Purpose of the Study:

  • To present a major update and enhancement of the ICELLNET computational framework.
  • To improve the accuracy and scope of cell-cell communication inference from transcriptomic data.
  • To facilitate biological interpretation and prioritization of communication pathways.

Main Methods:

  • Expanded the ICELLNET ligand-receptor database from 380 to 1669 curated interactions.

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  • Integrated key molecular families involved in immune crosstalk, cell adhesion, and Wnt signaling.
  • Optimized the ICELLNET framework specifically for single-cell RNA sequencing (scRNA-seq) data analysis.
  • Developed new visualization tools for enhanced interpretation of cell-cell communication results.

    • Main Results:

    • A substantially larger and more comprehensive ligand-receptor interaction database.
    • Enhanced capability to analyze complex communication pathways, including immune and Wnt signaling.
    • Improved performance and applicability for single-cell transcriptomic datasets.
    • New visualizations aiding in the prioritization and biological understanding of inferred communications.

    Conclusions:

    • The updated ICELLNET provides a more powerful and versatile tool for cell-cell communication network inference.
    • The expanded database and scRNA-seq optimization broaden its utility across diverse biological research fields.
    • Enhanced visualization facilitates deeper biological insights from transcriptomic communication data.