Genetic screens in human cells using the CRISPR-Cas9 system

Abstract

The bacterial clustered regularly interspaced short palindromic repeats (CRISPR)-Cas9 system for genome editing has greatly expanded the toolbox for mammalian genetics, enabling the rapid generation of isogenic cell lines and mice with modified alleles. Here, we describe a pooled, loss-of-function genetic screening approach suitable for both positive and negative selection that uses a genome-scale lentiviral single-guide RNA (sgRNA) library. sgRNA expression cassettes were stably integrated into the genome, which enabled a complex mutant pool to be tracked by massively parallel sequencing. We used a library containing 73,000 sgRNAs to generate knockout collections and performed screens in two human cell lines. A screen for resistance to the nucleotide analog 6-thioguanine identified all expected members of the DNA mismatch repair pathway, whereas another for the DNA topoisomerase II (TOP2A) poison etoposide identified TOP2A, as expected, and also cyclin-dependent kinase 6, CDK6. A negative selection screen for essential genes identified numerous gene sets corresponding to fundamental processes. Last, we show that sgRNA efficiency is associated with specific sequence motifs, enabling the prediction of more effective sgRNAs. Collectively, these results establish Cas9/sgRNA screens as a powerful tool for systematic genetic analysis in mammalian cells.

Fig. 1. A pooled approach for genetic screening in mammalian cells using a lentiviral CRISPR/Cas9 system

(A) Outline of sgRNA library construction and genetic screening strategy (B) Immunoblot analysis of wild-type KBM7 cells and KBM7 cells transduced with a doxycycline inducible FLAG-Cas9 construct upon doxycycline induction. S6K1 was used as a loading control. (C) Sufficiency of single copy sgRNAs to induce genomic cleavage. Cas9-expressing KBM7 cells were transduced with AAVS1-targeting sgRNA lentivirus at low MOI. The SURVEYOR mutation detection assay was performed on cells at the indicated days post-infection (dpi). Briefly, mutations resulting from cleavage of the AAVS1 locus were detected through PCR amplification of a 500-bp amplicon flanking the target sequence, re-annealing of the PCR product and selective digestion of mismatched heteroduplex fragments. (D) Characterization of mutations induced by CRISPR/Cas9 as analyzed by high-throughput sequencing. (E) sgRNA library design pipeline. (F) Example of sgRNAs designed for PSMA4. sgRNAs targeting constitutive exonic coding sequences nearest to the start codon were chosen for construction. (G) Composition of genome-scale sgRNA library.

Fig. 2. Resistance screens using CRISPR/Cas9

(A) Raw abundance (%) of sgRNA barcodes after 12 days of selection with 6-thioguanine (6-TG). (B) Mismatch repair (MMR) deficiency confers resistance to 6-TG. Diagram depicts cellular DNA repair processes. Only sgRNAs targeting components of the DNA MMR pathway were enriched. Diagram modified and adapted from (32). (C) Primary etoposide screening data. The count for a sgRNA is defined as the number of reads that perfectly match the sgRNA target sequence. (D) sgRNAs from both screens were ranked by their differential abundance between the treated versus untreated populations. For clarity, sgRNAs with no change in abundance are omitted. (E) Gene hit identification by comparing differential abundances of all sgRNAs targeting a gene to differential abundances of non-targeting sgRNAs in a one-sided Kolmogorov-Smirnov test. p-values are corrected for multiple hypothesis testing. (F) Immunoblot analysis of WT and sgRNA-modified HL60 cells 1 week after infection. S6K1 was used as a loading control. (G) Viability, as measured by cellular ATP concentration, of WT and sgRNA-modified HL60 cells at indicated etoposide concentrations. Error bars denote standard deviation (n=3).

Fig. 3. Negative selection screens using CRISPR/Cas9 reveal rules governing sgRNA efficacy

(A) Selective depletion of sgRNAs targeting exons of BCR and ABL1 present in the fusion protein. Individual sgRNAs are plotted according to their target sequence position along each gene and the height of each bar indicates the level of depletion observed. Boxes indicate individual exons. (B) Cas9-dependent depletion of sgRNAs targeting ribosomal proteins. Cumulative distribution function plots of log₂ fold changes in sgRNA abundance before and after twelve cell doublings in Cas9-KBM7, Cas9-HL60 and WT-KBM7 cells. (C) Requirement of similar sets of ribosomal protein genes for proliferation in the HL60 and KBM7 cells. Gene scores are defined as the median log₂ fold change of all sgRNAs targeting a gene. (D) Depleted sgRNAs target genes involved in fundamental biological processes. Gene Set Enrichment Analysis was performed on genes ranked by their combined depletion scores from screens in HL60 and KBM7 cells. Vertical lines underneath the x-axis denote members of the gene set analyzed. (E) Features influencing sgRNA efficacy. Depletion (log₂ fold change) of sgRNAs targeting ribosomal protein genes was used as an indicator of sgRNA efficacy. Correlation between log₂ fold changes and spacer %GC content (left), exon position targeted (middle) and strand targeted (right) are depicted. (*p<0.05) (F) sgRNA target sequence preferences for Cas9 loading and cleavage efficiency. Position-specific nucleotide preferences for Cas9 loading are determined by counting sgRNAs bound to Cas9 normalized to the number of corresponding genomic integrations. Heatmaps depict sequence-dependent variation in Cas9 loading (top) and ribosomal protein gene-targeting sgRNA depletion (bottom). The color scale represents the median value (of Cas9 affinity or log₂ fold-change) for all sgRNAs with the specified nucleotide at the specified position. (G) sgRNA efficacy prediction. Ribosomal protein gene-targeting sgRNAs were designated as ‘weak’ or ‘strong’ based their log₂ fold change and used to train a support-vector-machine (SVM) classifier. As an independent test, the SVM was used to predict the efficacy of sgRNAs targeting 400 essential non-ribosomal genes. (*p<0.05)