Optimization of gene knockout approaches and practical solutions to sgRNA selection challenges in hPSCs with inducible Cas9 system
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Rationale
CRISPR/Cas9 has been extensively used to knock out genes, allowing the study of genetic loss-of-function in human pluripotent stem cells (hPSCs). However, the current use of the Cas9-sgRNA plasmid or iCas9 system for gene editing in hPSCs has resulted in limited and inconsistent editing efficiency, as well as labor-intensive work. Additionally, identifying single-guide RNAs (sgRNAs) with high cleavage efficiency and distinguishing them from ineffective ones, which efficiently induce frameshift INDELs (Indels and Deletions) but fail to eliminate target proteins expression, are major challenges in gene knockout experiments.
Methods
This study addresses above issues using an optimized doxycycline-induced spCas9-expressing hPSCs (hPSCs-iCas9) system. We initially developed this system by optimizing a number of parameters to maximize INDELs introducing efficiency in hPSCs-iCas9 cells. The INDELs determined by this system were then compared to predicted scores from three cleavage efficiency scoring algorithms to validate the algorithms’ accuracy and consistency. Furthermore, we conducted gene knockout using a set of sgRNAs targeting different exons of the ACE2 gene to achieve approximately 80% INDELs for each targeting locus. Western blotting was then performed to detect ACE2 protein expression levels, enabling the identification of potentially ineffective sgRNAs.
Results
Several critical factors, including cell tolerance to nucleofection stress, sgRNA stability, nucleofection frequency, and the cell-to-sgRNA ratio, were found to have significant impact on editing efficiency in hPSCs-iCas9. Fine-tuning these parameters markedly improved this efficiency, resulting in up to 93% INDELs for single gene knockout. The three scoring algorithms exhibited significant differences or even conflicts in scoring cleavage efficiency. Through comparing experimental observations to predicted scores, we discovered that the Benchling algorithm outperformed the other two in terms of accuracy and consistency. Furthermore, a sgRNA targeting exon 2 of ACE2 gene was quickly identified as ineffective, as evidenced by the edited cells pool containing 80% INDELs while ACE2 protein expression retained unchanged detected by Western blot.
Conclusion
The findings of this study offer valuable insights into the optimal design of gene knockout experiments in hPSCs and provide practical solutions to sgRNA selection challenges for gene editing.
