Earlier this year, 3 separate studies (Adamson, et al., Datlinger, et al., and Dixit, et al.) demonstrated an approach to combine pooled CRISPR genetic screening with RNA expression profiling at the single-cell level. Integrating these two approaches enables knockout-specific expression data to be generated for each of the targeted genes in a pooled CRISPR sgRNA screen. For the first time, it is possible to identify specific gene expression changes induced by tens of thousands of gene knockouts in a single assay—providing a powerful tool to dissect the genes and pathways driving phenotypic changes, disease development, and drug response mechanisms.
In addition to simply correlating changes in gene expression with specific gene knockouts, the recent studies show that it also possible to combine such analysis with perturbations induced by drugs. For example, in the publications noted above, the authors report on several instances where expression levels of several key genes change in response to a gene knockout that increases cell sensitivity to a specific compound. These sorts of results provide a basis to sort out specific pathways that regulate drug sensitivity or resistance and work out the mechanism of action of therapeutically active compounds.
The ability to link particular gene knockouts from a pooled screen with changes in gene expression requires that the sgRNA sequences transduced into the cells during a pooled screen can be identified in both the genomic DNA and mRNA cell fractions. In this way, sgRNA depletion or enrichment in the overall cell population from a pooled screen can be assessed by standard PCR amplification/NGS analysis, and single-cell gene expression results can be collated based the same guide sequence extracted from the RNA-seq transcriptome analysis.
To enable this dual extraction of the sgRNA sequence from both the DNA and RNA cell fractions, the lentiviral vectors for the CRISPR pooled library needs to be configured so that the promoter-sgRNA expression cassette is located on the transcribed sequence of the reporter or drug resistant marker. As a result, mRNA transcripts contain a copy of the full sgRNA expression cassette.
The publication by Datlinger, et al. provides the most efficient and simple vector design to get both effective sgRNA expression while also having the whole sgRNA sequence expressed in a larger mRNA transcript. The CROP-seq vector described in the Datlinger publication is a modified version of lentiGuide-Puro, where the U6-promoter-sgRNA expression cassette has been moved into the 3′-LTR region, just downstream of the puromycin gene. The sgRNA is effectively expressed from the U6 promoter as was previously demonstrated, for example, in some of the original lentiviral vectors for expression of shRNA effectors, including our first-generation vectors (now sold by SBI). In fact, this same sort of approach can be used for RNA-analysis of pooled RNA interference (RNAi) screens with shRNA libraries. Barcodes can also easily be incorporated into the design to identify the shRNA and/or tracking clonal variations.
With our standard Cellecta vectors, we typically clone the sgRNA (or shRNA) upstream of the selection genes. However, we also now offer the Scribe™ Vector variations with the sgRNA/shRNA expression cassette just downstream of the puromycin gene in the 3’-LTR region approximately 70 nucleotides upstream of the poly-A site (see Figure). Like the CROP-seq vector in Datlinger’s publication, the sgRNA Scribe Vector cassette is situated in the 3 UTR of the puromycin transcript where it is expressed with the puromycin gene transcript so it can be read in both the genomic DNA and the RNA fractions.
Using this configuration, then, we can produce and CRISPR or RNAi libraries where each sgRNA or shRNA insert is both detectable by typical targeted genomic sequencing to assess depletion or enrichment, as well as by single-cell RNA-seq analysis. Custom sgRNA or shRNA libraries made with Cellecta Express vectors provide a way for researchers to link expression data with specific knockout or knockdown phenotypes.
Adamson, B., et al., A Multiplexed Single-Cell CRISPR Screening Platform Enables Systematic Dissection of the Unfolded Protein Response. Cell 167: 1867–1882.e21 (2016)
Datlinger, P., et al., Pooled CRISPR Screening with Single-Cell Transcriptome Read-Out. Nat Methods 14: 297–301 (2017).
Dixit, A., et al., Pertub-Seq: Dissecting Molecular Circuits with Scalable Single-Cell RNA Profiling of Pooled Genetic Screens. Cell 167: 1853–1866 (2016)