Project description:Prime editors (PEs) can mediate versatile genome editing but their efficiency remains low. Here, we developed spegRNA by introducing same-sense mutations at proper positions in the reverse-transcription template of pegRNA to increase PE’s single-base editing efficiency or apegRNA by altering the pegRNA secondary structure to increase PE’s indel-editing efficiency . When used in PE3 and PE5, the efficiencies of sPE3, aPE3, sPE5 and aPE5 were all enhanced significantly.
Project description:Prime editor (PE) has wide application prospects in disease treatment due to its diversity of editing outcomes. However, the editing efficiency of PE still needs to be further improved for therapeutic applications. Here, we increase the probability of hybridization of the pegRNA primer binding site (PBS) to single-stranded DNA flap by adding additional PBS sequence and reverse transcription template (RTT) to the loop region of pegRNA. The selection of loop and the design of loop length are optimized. The resulting modified loop2 epegRNA (ML-epegRNA) showed higher prime editing efficiency than that of the epegRNA in HEK293T cells. In addition, we used ML-epegRNA for PE editing of multiple editing types in multiple cell lines, and the results showed a general improvement in editing efficiency. Overall, the ML-epegRNA expands the capabilities of genome editing tools.
Project description:The subcellular organization of proteins carries important information on cellular state and gene function, yet currently there is a limitation in technologies that enable routine measurement of protein localizations at scale. Here we develop pooled endogenous protein tagging using prime editing to image subcellular localizations for many proteins in parallel within a single heterogeneous cell pool. We constructed three prime editing libraries covering 17,280 pegRNAs to exhaustively tag 60 endogenous proteins spanning diverse localization patterns and explore a broad space of genomic and pegRNA design parameters. We integrate pegRNA features into a computational model with predictive value for tagging efficiency to constrain the search space of effective pegRNAs for large-scale peptide knock-in. Lastly, we show that combining in situ pegRNA sequencing with deep learning image analysis, enables exploration of the subcellular localizations of many proteins following a single pooled lentiviral transduction, setting the stage for scalable studies of proteome dynamics across cell types and perturbations.
2026-09-20 | GSE346707 | GEO
Project description:Enhancing CRISPR prime editing by reducing misfolded pegRNA interactions
