Fig. 1
From: Predicting adenine base editing efficiencies in different cellular contexts by deep learning
High-throughput ABE screening in HEK293T cells using target-matched sgRNA libraries. a Strategy of correcting pathogenic mutations without bystander editing by sgRNA tiling. The sgRNA not including the coding bystander within the editing window is shadowed darker. b Schematics of the ABE screen in HEK293T cells using plasmid transfection for ABE delivery. c Total editing efficiencies for each PAM in HEK293T cells after 10 days ABE selection with SpRY-ABE8e and SpRY-ABEmax (top row), SpG-ABE8e and SpG-ABEmax (middle row), SpCas9-ABE8e and SpCas9-ABEmax (bottom row). Y-axis indicates the 1st nucleotide of the PAM motif, the x-axis the 2nd and 3.rd nucleotide of the PAM. d Editing window for SpRY-ABE8e and SpRY-ABEmax (top row), SpG-ABE8e and SpG-ABEmax (middle row), SpCas9-ABE8e and SpCas9-ABEmax (bottom row). Datasets were filtered for best PAMs (NRN for SpRY, NGN for SpG, and NGG for SpCas9). e Correction of pathogenic mutations in the library with- or without inducing non-silent bystander mutations for different base editors. Cut-offs were ≥ 10% for on-target editing and ≤ 0.5% for bystander editing. Target sites with on-target editing below 10% were defined as not corrected. Number of target sites (n) for SpRY-ABE8e: 11838, SpRY-ABEmax: 11497, SpG-ABE8e: 10287, SpG-ABEmax: 9400, SpCas9-ABE8e: 7540, SpCas9-ABEmax: 9702, ABE combined: 12000