Integrating single-cell and bulk transcriptomic perturbation resources reveals complementary therapeutic spaces for drug repurposing

Abstract

Background Transcriptome-based drug repurposing can accelerate therapeutic discovery, but is limited by fragmented resources, inconsistent quality control, and reliance on single perturbation databases. Methods We developed CDRPipe (Computational Drug Repurposing Pipeline), a unified framework that interrogates disease signatures against drug perturbation signatures generated by distinct experimental technologies. Specifically, CDRPipe harmonizes microarray perturbation profiles from the Connectivity Map (CMap; 1,968 quality-filtered experiments) with pseudo-bulk profiles derived from large-scale single-cell RNA sequencing experiments in the Tahoe-100M database (56,827 experiments). CDRPipe standardizes preprocessing, computes rank-based connectivity scores and evaluates significance using empirical null models. We applied CDRPipe to 233 curated disease signatures from GEO and CREEDS and evaluated performance using known drug-disease associations from Open Targets. Results Single-cell-derived pseudo-bulk profiles recovered more annotated therapeutics than microarray profiles (mean recall 47.3% vs 18.5%; Wilcoxon signed-rank test p < 10–11), though these differences partly reflect differences in drug library composition and clinical annotation coverage. Importantly, the two resources were highly complementary, with only 3.5% overlap in recovered drugs, indicating that integrating predictions across independent perturbation resources expands therapeutic coverage and enables identification of high-confidence consensus candidates. Case studies in autoimmune disease and endometriosis further demonstrate that CDRPipe recovers clinically relevant therapies while revealing technology-dependent patterns of discovery. Conclusions Integrating heterogeneous transcriptomic perturbation resources improves the robustness and interpretability of transcriptional drug repurposing. Consensus predictions supported by independent resources represent higher-confidence candidates for downstream experimental and clinical validation, and CDRPipe provides an openly available framework to support this integrated approach.