Mapping rich genotype-phenotype landscapes with single-cell CRISPR screens

A central goal of genetics is to define the phenotypic consequences of genetic perturbations. Single-cell CRISPR screens such as Perturb-seq (pooled single-cell RNA-sequencing CRISPR screens) represent an emerging tool to systematically construct genotype-phenotype maps by pairing high-dimensional genetic perturbations with rich phenotypic readouts in single cells. However, to date, these screens have been deployed at a limited scale.The first chapter of this thesis addresses a major technological hinderance to the scalable application of Perturb-seq: reliance on indirect indexing of single-guide RNAs (sgRNAs). I present direct-capture Perturb-seq, a versatile screening approach in which expressed sgRNAs are sequenced alongside single-cell transcriptomes. Direct-capture Perturb-seq enables detection of multiple distinct sgRNA sequences from individual cells and thus allows pooled single-cell CRISPR screens to be easily paired with combinatorial perturbation libraries that contain dual-guide expression vectors. I then demonstrate the utility of this approach for high-throughput investigations of genetic interactions and, leveraging this ability, dissect epistatic interactions between cholesterol biogenesis and DNA repair. Using direct capture Perturb-seq, I also show that targeting individual genes with multiple sgRNAs per cell improves the efficacy of CRISPR interference and activation, facilitating the use of compact, highly active CRISPR libraries for large-scale single-cell screens. Last, I show that hybridization-based target enrichment permits sensitive, specific sequencing of informative transcripts from single-cell RNA-seq experiments.The second chapter builds on the first chapter by applying direct capture Perturb-seq and multiplexed CRISPR interference (CRISPRi) to perform the first genome-scale Perturb-seq screens. From these data, I yield a blueprint for the construction and analysis of rich genotype-phenotype maps. I show that genes can be clustered by transcriptional phenotypes across many essential cellular processes and reveal new roles for poorly characterized genes in ribosome biogenesis, transcription, and respiration. Beyond clustering genes, these data enable in-depth dissection of the functional consequences of genetic perturbations on a remarkable array of complex, composite phenotypes—including RNA processing, differentiation, and chromosomal instability. Leveraging this ability, I comprehensively identify genetic drivers and consequences of aneuploidy, and I uncover unanticipated perturbation-specific regulation of the mitochondrial genome. This thesis establishes Perturb-seq as a scalable tool for the principled exploration of multidimensional cellular behaviors, gene function, and regulatory networks