Evaluation and rational design of guide RNAs for efficient CRISPR/Cas9-mediated mutagenesis in Ciona
Abstract
The CRISPR/Cas9 system has emerged as an important tool for various genome engineering applications. A current obstacle to high throughput applications of CRISPR/Cas9 is the imprecise prediction of highly active single guide RNAs (sgRNAs). We previously implemented the CRISPR/Cas9 system to induce tissue-specific mutations in the tunicate Ciona. In the present study, we designed and tested 83 single guide RNA (sgRNA) vectors targeting 23 genes expressed in the cardiopharyngeal progenitors and surrounding tissues of Ciona embryo. Using high-throughput sequencing of mutagenized alleles, we identified guide sequences that correlate with sgRNA mutagenesis activity and used this information for the rational design of all possible sgRNAs targeting the Ciona transcriptome. We also describe a one-step cloning-free protocol for the assembly of sgRNA expression cassettes. These cassettes can be directly electroporated as unpurified PCR products into Ciona embryos for sgRNA expression in vivo, resulting in high frequency of CRISPR/Cas9-mediated mutagenesis in somatic cells of electroporated embryos. We found a strong correlation between the frequency of an Ebf loss-of-function phenotype and the mutagenesis efficacies of individual Ebf-targeting sgRNAs tested using this method. We anticipate that our approach can be scaled up to systematically design and deliver highly efficient sgRNAs for the tissue-specific investigation of gene functions in Ciona.
Additional Information
© 2017 Elsevier Inc. Received 6 October 2016, Revised 6 February 2017, Accepted 5 March 2017, Available online 22 March 2017. We are grateful to Farhana Salek, Kristyn Millan, and Aakarsha Pandey for technical assistance; Tara Rock for advice on next-generation sequencing; Rahul Satija for sequencing the libraries and for his invaluable insights into the sgRNA sequence analysis; Justin S. Bois, Shyam Saladi, Elena K. Perry, and the High Performance Computing team at NYU for their help troubleshooting the bioinformatic analysis. This work was funded by an NIHK99 HD084814 award to A.S., NIH R01 GM096032 award to L.C., award 15CVD01 from the Leducq Foundation to L.C., and an NYU Biology Masters Research Grant to S.G.Attached Files
Accepted Version - nihms867131.pdf
Submitted - 041632.full.pdf
Supplemental Material - mmc1.xlsx
Supplemental Material - mmc2.xlsx
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Supplemental Material - mmc4.xlsx
Supplemental Material - mmc5.xlsx
Supplemental Material - mmc6.xlsx
Supplemental Material - mmc7.pdf
Supplemental Material - mmc8.pdf
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Additional details
- PMCID
- PMC5502750
- Eprint ID
- 77749
- Resolver ID
- CaltechAUTHORS:20170525-092037907
- NIH
- HD084814
- NIH
- R01 GM096032
- Leducq Foundation
- 15CVD01
- New York University (NYU)
- Created
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2017-05-25Created from EPrint's datestamp field
- Updated
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2023-06-01Created from EPrint's last_modified field