Comprehensive analysis of end-modified long dsDNA donors in CRISPR-mediated endogenous tagging
Takagi, R.; Hata, S.; Tei, C.; Mabuchi, A.; Anzai, R.; Fukuyama, M.; Yamamoto, S.; Chinen, T.; Toyoda, A.; Kitagawa, D.
Show abstract
CRISPR-mediated endogenous tagging is a powerful gene editing technique for studying protein dynamics and function in their native cellular environment. While the use of 5 modified DNA donors has emerged as a promising strategy to improve the typically low efficiency of knock-in gene editing, the underlying mechanisms remain poorly understood. In this study, we conducted a comprehensive analysis of end-modified long linear dsDNA donors in CRISPR-mediated endogenous tagging in human non-transformed cells. In-depth analysis of repair patterns reveals that 5 biotinylation of dsDNA donors significantly reduces imprecise insertions, thereby enhancing homology-directed repair (HDR)-mediated precise insertion efficiency. Notably, the impact of biotinylation on repair patterns resembles that of non-homologous end joining (NHEJ) pathway inhibition, suggesting its role in preventing NHEJ-mediated mis-integration. Moreover, combining biotin modification with NHEJ inhibitor treatment further improves bi-allelic knock-in efficiency. Overall, this study provides novel insights into the mechanisms by which 5 modifications enhance precise knock-ins and demonstrates their potential for achieving high-efficient, prercise endogenous tagging in human cells.
Matching journals
The top 5 journals account for 50% of the predicted probability mass.
Similar papers in this journal
Similar papers in this journal
Similar papers in this journal
- A large-scale sORF screen identifies putative microproteins and provides insights into their interaction partners, localisation and function 94%
- Distinct functions of PAXX and MRI during chromosomal end joining 93%
- Dynamics of endogenous PARP1 and PARP2 during DNA damage revealed by live-cell single-molecule imaging 92%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.