sc2DAT: workflow for targeting tumor subpopulations of single cells
Giacomo B Marino1, Anna I Byrd1, Nasheath Ahmed1
1Department of Pharmacological Sciences, Mount Sinai Center for Bioinformatics, Icahn School of Medicine at Mount Sinai, New York, NY 10029, United States.
Summary:
The rapid increase in volume, diversity, and quality of single-cell omics profiling opens new opportunities for drug and target discovery. While there are already many workflows developed for analysis and visualization of data collected with single-cell RNA-seq, few workflows output ranked drugs and targets specific for subpopulation of single cells. Here, we present the single cells to drugs and targets (sc2DAT) workflow, a web-based software application for predicting cell surface targets and therapeutic compounds tailored to target-specific cell types automatically identified from scRNA-seq and bulk RNA-seq datasets. sc2DAT can be used to develop hypotheses about selectively eliminating malignant subpopulation of cells in cancer, or reprogram disease tissues toward a healthier phenotype using compounds from the LINCS L1000 dataset. Such compounds are hypothesized to either reverse or mimic the direction of changes in gene expression signatures, restoring the subpopulation of cells towards a healthier phenotype.
Availability And Implementation:
sc2DAT is available from: https://sc2dat.maayanlab.cloud; the source code is available from: https://github.com/MaayanLab/sc2DAT.
Insights
The single cells to drugs and targets (sc2DAT) workflow identifies cell surface targets and drugs for specific cell types from single-cell RNA sequencing data. This tool aids in developing strategies for cancer therapy and disease tissue reprogramming.
Area of Science:
- Computational biology
- Genomics
- Drug discovery
Background:
- Single-cell omics profiling generates vast datasets, offering new avenues for drug and target discovery.
- Existing single-cell RNA sequencing (scRNA-seq) workflows often lack the ability to rank drugs and targets for specific cell subpopulations.
- Identifying cell-type-specific targets is crucial for developing precision therapies.
Purpose of the Study:
- To present the single cells to drugs and targets (sc2DAT) workflow, a web-based application.
- To enable prediction of cell surface targets and therapeutic compounds for specific cell types identified from scRNA-seq and bulk RNA-seq data.
- To facilitate hypothesis generation for targeted cancer therapy and disease tissue reprogramming.
Main Methods:
- Development of a web-based software application, sc2DAT.
- Utilizing scRNA-seq and bulk RNA-seq datasets for cell type identification.
- Leveraging the LINCS L1000 dataset for compound identification and gene expression signature analysis.
Main Results:
- sc2DAT predicts cell surface targets and therapeutic compounds tailored to specific cell types.
- The workflow enables the identification of compounds that can selectively eliminate malignant cells.
- sc2DAT can identify compounds to reprogram diseased tissues toward a healthier phenotype by reversing or mimicking gene expression changes.
Conclusions:
- The sc2DAT workflow provides a novel approach for drug and target discovery using single-cell omics data.
- It offers a platform for developing targeted therapeutic strategies for cancer and other diseases.
- sc2DAT supports hypothesis-driven research by linking gene expression signatures to potential therapeutic interventions.
