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

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.
• 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.