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Updated: Nov 16, 2025

Genome Editing in Mammalian Cell Lines using CRISPR-Cas
Published on: April 11, 2019
Self-Cutting and Integrating CRISPR Plasmids Enable Targeted Genomic Integration of Genetic Payloads for Rapid Cell
Darin Bloemberg1, Carmen Daniela Sosa-Miranda1, Tina Nguyen1
1Human Health Therapeutics Research Center, National Research Council Canada, Ottawa, Canada.
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 insertion or deletion formation. Interestingly, including longer homology arms (HAs; 500 bp) in varying orientations only modestly increased knock-in efficiency (by around twofold). Using a SCIP-payload design (SCIPpay) that liberates a promoter-less sequence flanked by HAs thereby requiring perfect homology-directed repair 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 insert (1) a gene fragment encoding tdTomato into the CD69 locus of Jurkat cells, thereby creating a cell line that reports T-cell activation, and (2) a chimeric antigen receptor 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 we suggest future utility in knock-in site screening/optimization, unbiased off-target site identification, and multiplexed, iterative, and/or library-scale automated genome engineering.
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