TraSig: inferring cell-cell interactions from pseudotime ordering of scRNA-Seq data

Area of Science:

• Computational Biology
• Genomics
• Systems Biology

Background:

• Single-cell RNA sequencing (scRNA-Seq) enables the study of cellular heterogeneity and dynamics.
• Inferring cell-cell interactions from scRNA-Seq data is crucial for understanding complex biological systems.
• Existing methods often lack the ability to fully leverage dynamic information present in scRNA-Seq trajectories.

Purpose of the Study:

• To introduce TraSig, a novel computational method for improving cell-cell interaction inference in scRNA-Seq studies.
• To utilize dynamic information from cell trajectories to identify significant ligand-receptor pairs.
• To score interacting cell clusters based on identified signaling interactions.

Main Methods:

• Development of TraSig, a computational tool leveraging cell trajectory data.
• Identification of ligand-receptor pairs exhibiting similar dynamic trajectories.
• Scoring of cell clusters based on inferred cell-cell communication networks.
• Application and validation of TraSig on multiple scRNA-Seq datasets.

Main Results:

• TraSig successfully identified unique and significant cell-cell interactions.
• The method demonstrated improved prediction accuracy compared to prior computational approaches.
• Functional experiments validated TraSig's predictions in the context of liver organoid vascular development.
• Novel signaling interactions impacting vascular development were discovered.

Conclusions:

• TraSig offers a powerful approach to infer cell-cell interactions by integrating dynamic trajectory information.
• The method enhances the discovery of biologically relevant signaling pathways from scRNA-Seq data.
• TraSig has significant implications for understanding developmental processes and disease mechanisms.