Analysis of intracellular and intercellular crosstalk from omics data

Area of Science:

• Computational Biology
• Systems Biology
• Genomics

Background:

• Disease phenotypes arise from altered molecular interactions exceeding resilience thresholds.
• Assessing intra- and inter-cellular molecular interaction alterations is a significant challenge in omics research.

Purpose of the Study:

• To introduce Ulisse, a new computational approach for analyzing molecular interactions and cell-cell communications.
• To complement existing enrichment, pathway crosstalk, and cell-cell communication analyses.
• To identify genes associated with specific phenotypes by analyzing molecular interaction alterations.

Main Methods:

• Ulisse analyzes gene lists with quantitative alteration information from omics studies.
• It quantifies pathway crosstalk using two distinct null models for statistical robustness.
• The approach highlights alterations in components regulating inter-process and inter-cellular interactions.

Main Results:

• Ulisse was applied to single-cell RNA sequencing data from triple-negative breast cancer samples.
• The study demonstrated the alteration of pathway crosstalks and cell-cell communications in these samples.
• The approach successfully identified genes associated with the disease phenotype.

Conclusions:

• Crosstalk analysis, as implemented by Ulisse, is a valuable tool for biomedical research.
• Ulisse aids in translating complex multi-omics data into actionable biological insights.
• The method enhances the toolkit for understanding disease mechanisms at a molecular level.