A CRISPR/Cas13 approach for identifying individual transcript isoform function in cancer

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Abstract One of the major challenges in characterizing transcript isoform cellular function in health and disease is the lack of methods to specifically and efficiently downregulate their expression. RNA-targeting RNA-guided type VI CRISPR/Cas13 systems constitute a recently developed tool to knockdown transcripts in mammalian cells. Their RNase activity is activated by binding of a CRISPR RNA guide (gRNA) complementary to a single-stranded RNA target. The CRISPR/Cas13 systems tested to date have been shown to achieve highly specific knockdown of endogenous transcripts, with minimal off-target effects, outperforming current methodologies such as RNAi. While CRISPR/Cas13 has been shown to work efficiently when targeted to the pre-mRNA, transcriptome-wide strategies that would allow knockdown of transcript isoforms by targeting unique junctions in the mature mRNA molecule have not been explored. Selecting the best gRNA sequences is crucial for successful and specific RNA knockdown mediated by Cas13. Preliminary data from our lab shows that using gRNAs targeting sequences spanning exon-exon junctions in mature mRNA molecules with CRISPR/Cas13d (the smallest Cas13 effector) efficiently reduces transcript expression up to 80%. Moreover, targeting isoform-specific junctions allows for their individual knockdown without affecting non-targeted isoforms. These results suggest that there is no steric constraint in targeting junctions in mature mRNA molecules. Transcriptome analyses of tumors and cancer cell lines have comprehensively described the expression levels and identities of alternative transcript isoforms. In parallel, a number of studies have shown that splicing dysregulation is a hallmark of cancer. The causal link between transcript isoform expression and cancer related phenotypes has not been thoroughly studied. Here, we will expand the applicability of CRISPR/Cas13 systems to the systematic study of transcript isoforms in cancer by 1) combining large-scale experimental data with machine learning approaches to define rules for gRNA design when targeting specific transcript junctions 2) optimizing a pipeline and the computational analysis required for using CRISPR/Cas13 systems in forward transcriptomic pooled screens to interrogate the cellular function of transcript isoforms. By broadening the application of the CRISPR/Cas13 system, our pipeline will provide the molecular tools and computational analysis required for interrogating transcript isoform function in a robust, unbiased and highly expandable manner. We expect that our CRISPR/Cas13 approach will be able to overcome the limitations of current methods in identifying cell-specific isoform expression and/or ratios underlying tumorigenesis and drug resistance. Lastly, the CRISPR/Cas13 approach could be further adapted to perform targeting of transcript isoforms in vivo as an RNA-based therapeutic.