PC3T

PC3T predicts small-molecule perturbations that induce cellular transitions using time-course gene expression signatures.

Key Features:

• Signature-driven prediction: PC3T operates on gene expression signatures to infer chemical-induced cellular transitions.
• Enrichment of Small Molecules: Identifies and enriches small molecules that can induce specific cellular transitions, validated with experimental data from both bulk and single-cell datasets.
• Predictive Modeling: Predicts reprogramming capabilities exemplified by conversion of fibroblasts into hepatic progenitor-like cells (HPLCs) that display epithelial cell-like morphology, HPLC-specific gene expression patterns, glycogen storage, and lipid accumulation.
• Comprehensive Data Resource: Includes a manually curated resource of 224 time-course gene expression datasets covering 153 different cell types.

Scientific Applications:

• Regenerative medicine: Supports development of chemical strategies for reprogramming cells relevant to regenerative therapies.
• Disease modeling: Enables generation of chemically induced cell states for modeling disease-relevant phenotypes.
• Drug discovery: Facilitates identification of small molecules that modulate cell states for therapeutic development.

Methodology:

PC3T employs a signature-driven computational approach that integrates computational predictions with experimental validation using a manually curated collection of 224 time-course gene expression datasets across 153 cell types.