Abstract
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, we 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.
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Acknowledgements
The authors thank I. Papatheodorou (Research Group Leader, EMBL-EBI), B. Kidd (Director, Bristol Myers Squibb (BMS)), R. Loos (Director, BMS) and M. Hall (Senior Scientific Officer, EMBL-EBI) for constructive criticism and proofreading of the original article before this revision.
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N.K. is an employee and shareholder of BMS. M.M. is an employee and shareholder of GSK. B.V.d.S. is an employee and shareholder of UCB Pharma. M.K. is an employee and shareholder of GSK. J.H. is an employee of Boehringer Ingelheim Pharmaceuticals, Inc. B.N. is an employee of Eisai, Inc. J.S.L. is an employee and shareholder of Sanofi. Y.W. was previously a shareholder of BMS. J.P. was previously an employee and shareholder of Sanofi. J.W. is an employee of Pfizer. E.F. is a shareholder of Sanofi and Board Director of Pulmobiotics. A.L. is a GSK shareholder, has consulted for Astex Therapeutics, LifeArc and Syncona and has received research funding from Novo Nordisk and AstraZeneca. X.C. is a former employee and shareholder of AbbVie. E.M.-G., W.B. and J.M. declare no competing interests.
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Glossary
- Barcode
-
A short DNA sequence ‘tag’ to identify reads that originate from the same cell.
- Biomarkers
-
Readouts used to classify biological states, often in the context of patient stratification.
- Cell-type deconvolution
-
Estimation of the proportion of particular cell types in a bulk RNA sequencing sample, based on cell markers or a labelled single-cell expression matrix.
- CRISPR screening
-
A pooled or arrayed screen of cells harbouring CRISPR-mediated gene edits.
- Doublets
-
Sets of two (or more) cells mistakenly considered as single cells, owing to being captured and processed in the same droplet and thus with the same barcode in data.
- Hashing
-
A labelling technique that attaches barcoded antibodies to cell surface proteins, allowing multiplexing of samples for single-cell sequencing, and subsequent disambiguation of sample of origin during analysis.
- Metadata
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A set of data that describe and give information about other data (Oxford dictionary). For example, patient or sample characteristics in an RNA sequencing experiment.
- Seurat
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A popular R package for the quality control, analysis and exploration of single-cell RNA sequencing data.
- Target credentialling
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Also called target qualification. Exploration of target quality more expansively than a straightforward target validation. May include contextually informed enquiries into biological characteristics such as network, pathway or interactome mapping, regulatory landscape or other investigations intended to either help rank target quality or inform on-target biology.
- t-Distributed stochastic neighbour embedding
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(t-SNE). A popular dimensionality reduction technique for the visualization of single-cell experiments.
- Trajectory inference
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Inference from single-cell data of the order of cells along a dynamic biological process (for example, developmental trajectory). Relies on the fact that a heterogeneous sample provides a snapshot view on a mixture of cells in different phases along the developmental or dynamic biological process. Also called ‘pseudo-time analysis’.
- Uniform manifold approximation and projection
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(UMAP). A popular dimensionality reduction technique for the visualization of single-cell experiments, with some advantages in preservation of global data structure and performance compared with t-distributed stochastic neighbour embedding.
- Unique molecular identifier
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(UMI). Reads with the same UMI are from the same mRNA molecule. UMIs help in the assessment of sequencing accuracy and precision.
- Unsupervised clustering
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Analysis grouping of similar samples together that does not require labelling or prior knowledge.
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Cite this article
Van de Sande, B., Lee, J.S., Mutasa-Gottgens, E. et al. Applications of single-cell RNA sequencing in drug discovery and development. Nat Rev Drug Discov 22, 496–520 (2023). https://doi.org/10.1038/s41573-023-00688-4
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DOI: https://doi.org/10.1038/s41573-023-00688-4
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