Various tools and resources facilitate the selection of gRNAs for the widely used Streptococcus pyogenes-derived Cas9 protein22,29,30,31,32 and alternatives such as AsCas12a and LbCas12a (refs33,34). For both genome-scale and focused CRISPR knockout (CRISPRko) screens, it is advisable to include at least four different gRNAs for each target gene22, although carefully designed and validated gRNA libraries with fewer gRNAs per gene can provide reliable results35. All gRNA libraries should include gRNAs for application-specific positive and negative control genes, which are important to validate the screen. In addition, they should include control gRNAs that target known safe harbour loci or other genomic regions where no specific effects of gene editing are expected, in order to account for the DNA damage response and for non-specific reduction in cell proliferation caused by CRISPRko; this is particularly relevant in aneuploid cancer cells36 or when targeting multiple loci per cell33,37.
In pooled CRISPR screens, the gRNA library is typically cloned into a lentiviral vector, and the cells are transfected at a relatively low multiplicity of infection (MOI), often between 0.3 and 0.5. This is to ensure that few cells receive more than one gRNA simultaneously. As a result, it is not usually necessary to account for potential genetic interactions between different gRNAs in the same cell. Screens with much higher MOIs are being used when cell numbers are limited38, when most gRNAs are not expected to have any effect39 or when studying genetic interactions40. As an alternative to combinatorial screens with high MOIs, multiplexed gRNA expression systems provide finer control of gRNA expression. Such screens can be implemented with paired expression cassettes41,42,43,44,45 or by exploiting the ability of Cas12a to process multiple gRNAs33,37,46,47.
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The number of target genes multiplied by the desired coverage provides a rough estimate for the number of cells that must be infected and maintained during the screen. If this estimate exceeds what is practically feasible, it is typically better to select fewer target genes than to run the screen with low coverage and risk poor reproducibility. Once the scale of the screen has been established, it is advisable to perform a series of pilot experiments for optimization, with the goal to achieve high consistency between biological replicates and to minimize unwanted selective pressures, cell stress or population bottlenecks. Moreover, it is important to ensure that cell culture vessels and media changes support the planned cell numbers and growth rates, particularly for large and logistically challenging screens with cell numbers in the hundreds of millions.
The ongoing development of single-cell multi-omics assays153,154 will likely lead to screens with single-cell read-outs of the genome, epigenome, transcriptome, proteome and/or metabolome. Indeed, scCRISPR-seq screens have already been conducted with a single-cell ATAC-seq read-out, providing insights into the complexities of chromatin regulation155,156,157. Screens with combined measurement of the transcriptome and cell surface protein have also been established147 and applied to study the regulation of immune checkpoints and genes mediating immune evasion in cancer treatment158,159.
A limitation of imaging followed by cell selection and gRNA sequencing is the small number of cellular phenotypes that can be studied in a single experiment. This limitation is addressed by methods that determine the gRNA identity for each cell directly based on the imaging data165,166. One such method uses a microfluidic chip to randomly seed transfected cells into individual trap chambers, where the cells divide and fill each chamber with clones that share the same perturbation and barcode. The induced phenotypes are observed by imaging, and the barcodes are determined by sequential rounds of FISH imaging166. This method was applied to a CRISPRi screen in E. coli, investigating the effect of 235 genes on cell division and the location of the replication fork as measured by live-cell imaging167. A conceptually similar method uses multiplexed error-robust FISH (MERFISH)168 to read the barcodes associated with the perturbations165,169. This method has been applied to screen 54 RNA-binding proteins for their effect on the localization of long non-coding RNAs in mammalian cells169.
To improve gene ranking, many software packages including MAGeCK173, CERES174, CRISPY190 and CRISPRcleanR191 statistically correct for DNA copy number variation, which is a relevant source of bias in CRISPR screens for cell survival and proliferation in cancer cell lines36. These tools use existing profiles of copy number variation (where available) or estimate this effect from gRNAs that target nearby genomic locations in genome-scale libraries. Differences in gRNA efficiency can affect the gene ranking, and statistical models have been developed to model such variability and to account for off-target effects187,192. For robust and interpretable results it is useful to compare gRNA frequencies across related conditions. Several software packages provide built-in support for different study designs, including paired samples, multiple time points or alternative treatment conditions185,193,194. For example, MAGeCKFlute194 and DrugZ195 were designed for the common scenario of comparing gRNA frequencies between two conditions (for example, screens of drug-treated and untreated cells). Finally, dedicated software tools have been developed for the analysis of scCRISPR-seq screens, which account for heterogeneity among perturbed cells140,159,194,196,197.
CRISPR screens are well suited for studying cancer biology given the wide range of available models and the cancer relevance of readily screenable phenotypes such as cell proliferation and drug resistance. Initial studies focused on mapping essential genes in cancer cell lines57,59, following the hypothesis that cancer-specific gene essentiality may indicate worthwhile drug targets. CRISPR screens have been performed in hundreds of cancer cell lines21,51,57,174,220, making it possible to distinguish between cancer-specific effects and core essential genes, and to study the context dependence of gene essentiality. It has been predicted from RNAi data that at least a thousand cancer cell lines need to be screened to observe most cancer-relevant gene dependencies at least once234 and it will take many more to chart reliable genetic fitness landscapes of cancer cells. Encouragingly, CRISPR screening data have been successfully aggregated across different laboratories20,235, suggesting that large-scale efforts that combine data across multiple sites are feasible.
There are multiple challenges faced by in vivo screens. First, delivery of the CRISPR machinery can be challenging and inefficient in living animals; hence, it is often advisable to use transgenic mice that constitutively express the Cas protein. Second, in vivo screens are more limited in scale compared with in vitro screens; it is therefore important to define relevant target genes. Data from the ImmGen consortium277, the Human Cell Atlas278, the BLUEPRINT project279, public databases and the scientific literature can facilitate the design of application-specific gRNA libraries for in vivo screens in haematopoietic cells. Third, the antigenic repertoire of T cells and B cells can affect clonal dynamics independent of the CRISPR-induced perturbations and add noise to the screen, thus requiring multiple replicates.
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