TraSig

TraSig infers cell-cell interactions from single-cell RNA-sequencing (scRNA-Seq) data by leveraging pseudotime trajectories to identify ligand-receptor pairs with correlated temporal expression.

Key Features:

• Pseudotime ordering utilization: Uses pseudotime to reconstruct continuous cellular trajectories and capture dynamic expression changes across cells.
• Identification of ligand-receptor pairs: Detects and scores ligand-receptor pairs whose expression trajectories are correlated across pseudotime to predict interactions between cell clusters.
• Application across datasets: Applied to multiple scRNA-Seq datasets to infer dynamic cell-cell signaling in diverse biological contexts.
• Experimental validation: Predictions have been validated with functional experiments, including identification of signaling interactions affecting vascular development in liver organoids.

Scientific Applications:

• Cellular communication mapping: Elucidates dynamic cell-cell signaling networks by identifying temporally coordinated ligand-receptor interactions.
• Tissue and organoid development studies: Applied to study developmental processes and organoid formation where temporal signaling dynamics are critical.
• Discovery of novel signaling interactions: Enables generation of experimentally testable hypotheses about ligand-receptor pairs driving biological processes such as vascular development.

Methodology:

Leverages pseudotime ordering of scRNA-Seq data to reconstruct cell trajectories, identifies ligand-receptor pairs with correlated expression across pseudotime, and scores putative interactions between cell clusters.