Figure 1

Theoretical models of how gRNA-specific efficiencies and frameshift gene editing outcome probabilities influence the cellular composition and percentage of protein knockout cells in a mosaic F₀ animal model. (A) There is a non-linear relationship between gRNA-specific probability of obtaining a frameshift gene editing outcome (x-axis) and the probability of obtaining a biallelic frameshift gene editing outcome in a single cell (y-axis). E.g. upon a gRNA-specific frameshift frequency of 80%, the probability of a single biallelic edited cells to be biallelic frameshift mutant is 64% (0.80*0.80). (Grey demarcation). (B) Examples of theoretical outcomes of gene editing (presuming 100% on-target efficiency) in an F₀ mosaic varying one parameter: gRNA-specific probability of frameshift editing. (C) Examples of theoretical outcomes of gene editing in an F₀ mosaic varying two parameters: gRNA-specific probability of frameshift editing and gRNA-specific on-target efficiency. E.g. for a 100% efficient gRNA with an 80% gRNA-specific probability of frameshift editing, we expect 64% of the cells to be biallelic frameshift mutant (see grey demarcation in A). Please note, blue circles represent cells that are biallelic gene edited, but retain at least one in-frame mutation and cannot be considered complete protein knock-out. (D) Flowchart representing the pipe-line for investigating the correlations between experimentally observed in vivo gene editing outcomes and gene editing outcomes projected by computational prediction models.