bioRxivBioengineering
Self-cutting and integrating CRISPR plasmids (SCIPs) enable targeted genomic integration of large genetic payloads for rapid cell engineering
D. Bloemberg, D. Sosa-Miranda, T. Nguyen, R. D. Weeratna, S. McComb
Abstract
Since observations that CRISPR nucleases function in mammalian cells, many strategies have been devised to adapt them for genetic engineering. Here, we investigated self-cutting and integrating CRISPR-Cas9 plasmids (SCIPs) as easy-to-use gene editing tools that insert themselves at CRISPR-guided locations. SCIPs demonstrated similar expression kinetics and gene disruption efficiency in mouse (EL4) and human (Jurkat) cells, with stable integration in 3-6% of transfected cells. Clonal sequencing analysis indicated that integrants showed bi- or mono-allelic integration of entire CRISPR plasmids in predictable orientations and with limited indel formation. Interestingly, including longer homology arms (HAs) (500 bp) in varying orientations only modestly increased knock-in efficiency ([~]2-fold). Using a SCIP-payload design (SCIPpay) which liberates a promoter-less sequence flanked by HAs thereby requiring perfect homology-directed repair (HDR) for transgene expression, longer HAs resulted in higher integration efficiency and precision of the payload but did not affect integration of the remaining plasmid sequence. As proofs-of-concept, we used SCIPpay to 1) insert a gene fragment encoding tdTomato into the CD69 locus of Jurkat cells, thereby creating a cell line that reports T cell activation, and 2) insert a chimeric antigen receptor (CAR) gene into the TRAC locus. Here, we demonstrate that SCIPs function as simple, efficient, and programmable tools useful for generating gene knock-out/knock-in cell lines and suggest future utility in knock-in site screening/optimization, unbiased off-target site identification, and multiplexed, iterative, and/or library-scale automated genome engineering.
- organism: mouse
- organism: human
- assay: CRISPR screen
- research focus: gene regulatory network inference
- research focus: cell type annotation
- approach: genetic perturbation / CRISPR
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