Single-cell technologies, particularly single-cell RNA sequencing (scRNA-seq) methods, together with associated computational tools and the growing availability of public data resources, are transforming drug discovery and development. New opportunities are emerging in target identification owing to improved disease understanding through cell subtyping, and highly multiplexed functional genomics screens incorporating scRNA-seq are enhancing target credentialling and prioritization. ScRNA-seq is also aiding the selection of relevant preclinical disease models and providing new insights into drug mechanisms of action. In clinical development, scRNA-seq can inform decision-making via improved biomarker identification for patient stratification and more precise monitoring of drug response and disease progression. Here, researchers illustrate how scRNA-seq methods are being applied in key steps in drug discovery and development, and discuss ongoing challenges for their implementation in the pharmaceutical industry.
Computational methods used in single-cell data analysis for drug discovery and development
Representation of the computational tools and/or methods (see Supplementary Table 1 for further details and URLs for the various tools), currently used by pharmaceutical companies for data handling and to probe biological insights through cell-type annotation to reveal genotype and/or phenotype and functional assignment. B cell receptor; CNV, copy number variation; eQTL, expression quantitative trait loci; scATAC-seq, single-cell sequencing assay for transposase-accessible chromatin; scDNA-seq, single-cell DNA sequencing; scRNA-seq, single-cell RNA sequencing; SNV, single-nucleotide variant; ST, spatial transcriptomics; TCR, T cell receptor.