CRISPRi and CRISPRa Screens in Mammalian Cells for Precision Biology and Medicine

Abstract

Next-generation DNA sequencing technologies have led to a massive accumulation of genomic and transcriptomic data from patients and healthy individuals. The major challenge ahead is to understand the functional significance of the elements of the human genome and transcriptome, and implications for diagnosis and treatment. Genetic screens in mammalian cells are a powerful approach to systematically elucidating gene function in health and disease states. In particular, recently developed CRISPR/Cas9-based screening approaches have enormous potential to uncover mechanisms and therapeutic strategies for human diseases. The focus of this review is the use of CRISPR interference (CRISPRi) and CRISPR activation (CRISPRa) for genetic screens in mammalian cells. We introduce the underlying technology and present different types of CRISPRi/a screens, including those based on cell survival/proliferation, sensitivity to drugs or toxins, fluorescent reporters, and single-cell transcriptomes. Combinatorial screens, in which large numbers of gene pairs are targeted to construct genetic interaction maps, reveal pathway relationships and protein complexes. We compare and contrast CRISPRi and CRISPRa with alternative technologies, including RNA interference (RNAi) and CRISPR nuclease-based screens. Finally, we highlight challenges and opportunities ahead.

Figure 1. Technologies to perturb gene function in mammalian cells for pooled genetic screens

Loss-of-function technologies include: RNA interference (RNAi); CRISPR nuclease (CRISPRn), in which Cas9-mediated DNA cleavage directed to the coding region of a gene by a single guide RNA (sgRNA) results in error-prone repair by cellular non-homologous end joining pathways, thereby disrupting gene function (in particular when frame shifts are introduced); and CRISPR interference (CRISPRi), in which catalytically dead Cas9 (dCas9) fused to a transcriptional repressor domain (e.g., KRAB) is recruited to the transcription start site (TSS) of an endogenous gene, as specified by an sgRNA, to repress transcription. Gain-of-function technologies include: Overexpression of Open Reading Frames (ORFs) as transgenes; and CRISPR activation (CRISPRa), in which transcriptional activators are recruited via sgRNAs and dCas9 to TSSs of endogenous genes to induce their overexpression. To achieve high levels of overexpression with a single sgRNA, CRISPRa methods recruit more than one transcriptional activator to a given TSS. Multiple activator domains are either directly fused to dCas9 (e.g., VP64, p65 and RTA in the VPR system), recruited to a protein scaffold fused to dCas9 (e.g. VP64 fused to superfolder GFP (sfGFP) and an antibody single-chain variable fragment (scFv) targeting a GCN4 epitope, which are recruited to a tandem array of 10 copies of the GCN4 epitope in the SunCas system^, ), or recruited to an RNA scaffold fused to the sgRNA (e.g. p65 and HSF1 transcriptional activation domains fused to MS2 coat protein (MCP), dimers of which are recruited to MS2 RNA hairpins in the Synergistic Activation Mediator (SAM) system.

Figure 2. Strategies for pooled genetic screens using CRISPRi or CRISPRa

Mammalian cells expressing CRISPRi or CRISPRa machinery are transduced by pooled lentiviral libraries for expression of single guide RNAs (sgRNAs) with low multiplicity of infection, resulting in stable integration of mostly a single sgRNA expression construct per cell. (A) The effect of sgRNAs on cell viability and growth or sensitivity to a selective pressure can be quantified by determining the frequencies of cells expressing each sgRNAs in populations at the t₀ timepoint and after culture in the absence or presence of selective pressure. (B) The effect of sgRNAs on a phenotype monitored by a fluorescent reporter or antibody can be quantified by next-generation sequencing of populations separated based on phenotype by fluorescence-activated cell sorting (FACS). (C) CRISPR-based genetic screens can be coupled to droplet-based single-cell RNA sequencing to obtain high-dimensional transcriptomic phenotypes for each genetic perturbation.

Figure 3. Genetic interactions reveal functional relationships between genes

(A, B) Genetic interactions are defined as the deviation of the observed phenotype of a combinatorial gene perturbation from the expected phenotype based on the observed phenotypes of the individual perturbations. They indicate a functional relationship between the two genes, as visualized by the pathway motifs. X, Y, Z: genes. P: observed phenotype. (C) Systematic genetic interaction maps reveal functional clusters of genes based on the similarity of their genetic interaction patterns, as exemplified by our previously published map for ricin resistance. Examples for different types of interactions are highlighted: members of protein complexes (the TRAPP complex and others), paralogues (RAB1A and RAB1B), and regulatory pathways (the small GTPase ARF1 and its nucleotide exchange factor GBF1).