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DrugReSC: targeting disease-critical cell subpopulations with single-cell transcriptomic data for drug repurposing in
Chonghui Liu1,2, Yan Zhang3,4, Yingjian Liang5
1College of Life Science, Northeast Forestry University, 26 Hexing Road, Xiangfang District, Harbin 150040, China.
Abstract:
The field of computational drug repurposing aims to uncover novel therapeutic applications for existing drugs through high-throughput data analysis. However, there is a scarcity of drug repurposing methods leveraging the cellular-level information provided by single-cell RNA sequencing data. To address this need, we propose DrugReSC, an innovative approach to drug repurposing utilizing single-cell RNA sequencing data, intending to target specific cell subpopulations critical to disease pathology. DrugReSC constructs a drug-by-cell matrix representing the transcriptional relationships between individual cells and drugs and utilizes permutation-based methods to assess drug contributions to cellular phenotypic changes. We demonstrate DrugReSC's superior performance compared to existing drug repurposing methods based on bulk or single-cell RNA sequencing data across multiple cancer case studies. In summary, DrugReSC offers a novel perspective on the utilization of single-cell sequencing data in drug repurposing methods, contributing to the advancement of precision medicine for cancer.
Insights
This study introduces DrugReSC, a novel computational method for drug repurposing using single-cell RNA sequencing data. DrugReSC effectively identifies drugs targeting specific cell subpopulations, advancing precision medicine in cancer treatment.
Area of Science:
- Computational biology
- Genomics
- Pharmacology
Background:
- Computational drug repurposing seeks new uses for existing drugs via data analysis.
- Existing methods often lack cellular-level insights from single-cell RNA sequencing (scRNA-seq) data.
- Targeting specific cell subpopulations is crucial for effective disease treatment.
Purpose of the Study:
- To develop an innovative drug repurposing approach using scRNA-seq data.
- To identify drugs that can specifically target disease-critical cell subpopulations.
- To enhance precision medicine applications in oncology.
Main Methods:
- Proposed DrugReSC, a novel computational method for drug repurposing.
- Constructed a drug-by-cell matrix to represent transcriptional cell-drug relationships.
- Employed permutation-based methods to evaluate drug impact on cellular phenotypes.
Main Results:
- DrugReSC demonstrated superior performance compared to existing methods.
- Evaluated performance across multiple cancer case studies using scRNA-seq data.
- Outperformed methods relying on bulk or standard scRNA-seq data analysis.
Conclusions:
- DrugReSC offers a novel strategy for drug repurposing with scRNA-seq data.
- Highlights the potential of leveraging single-cell data for targeted therapeutic discovery.
- Contributes to the advancement of precision medicine for cancer treatment.
