Effective analysis and experimental design for CRISPR screens with scRNA-seq readout

Understanding how cell fate decisions are regulated is a key question in molecular biology.

Building on the CRISPR revolution, exciting technologies (e.g., CROP-seq or direct capture Perturb-seq) induce a genetic perturbation that is characterised, alongside the transcriptome of the cell, by single-cell RNA-sequencing.

This enables a range of experimental designs that can, in principle, shed light on the transcriptional response to gene perturbations, its pathogenic and non-pathogenic variation, and the role of gene regulation during differentiation.

However, current computational methods do not exploit this potential: in particular, the ability to accurately measure the effect of a given perturbation is lacking, and the potential to efficiently explore the space of all possible perturbations and conditions remain untapped.

To address this, I will develop a comprehensive suite of computational tools (i) to infer transcriptomic effects of gene knockouts unconfounded by perturbation efficacy, (ii) for optimal experimental setup to increase insights gained from experiments and improve their scalability, (iii) to identify differences in gene regulation across individuals, and (iv) for the study of knockout effects during differentiation.

This will lead to an improved understanding of gene regulation, its variation across individuals, and to improved differentiation protocols, with important consequences for cell therapy.