High-throughput optimized prime editing-mediated endogenous protein tagging for pooled imaging of protein localization

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.