Precise, predictable genome integrations by deep-learning-assisted design of microhomology-based templates

Abstract

Precise CRISPR-based DNA integration and editing remain challenging, largely because of insufficient control of the repair process. We find that repair at the genome–cargo interface is predictable by deep learning models and adheres to sequence-context-specific rules. On the basis of in silico predictions, we devised a strategy of base-pair tandem repeat repair arms matching microhomologies at double-strand breaks. These repeat homology arms promote frame-retentive cassette integration and reduce deletions both at the target site and within the transgene. We demonstrate precise integrations at 32 loci in HEK293T cells. Germline-transmissible transgene integration and endogenous protein tagging in Xenopus and adult mouse brains demonstrated precise integration during early embryonic cleavage and in nondividing, differentiated cells. Optimized repair arms also facilitated small edits for scarless single-nucleotide or double-nucleotide changes using oligonucleotide templates in vitro and in vivo. We provide the design tool Pythia to facilitate precise genomic integration and editing for experimental and therapeutic purposes for a wide range of target cell types and applications.