Semi-automated workflows to quantify AAV transduction in various brain areas and predict gene editing outcome for neurological disorders

Abstract

One obstacle to the development of gene therapies for the central nervous system is the lack of workflows for quantifying transduction efficiency in affected neural networks and ultimately predicting therapeutic potential. We integrated data from a brain cell atlas with 3D or 2D semi-automated quantification of transduced cells in segmented images to predict AAV transduction efficiency in multiple brain regions. We used this workflow to estimate the transduction efficiency of AAV2/rh.10 and AAV2.retro co-injection in the corticostriatal network affected in Huntington’s disease. We then validated our pipeline in gene editing experiments targeting both human and mouse huntingtin genes in transgenic and wild-type mice, respectively. Our analysis predicted that 54% of striatal cells and 7% of cortical cells would be edited in highly transduced areas. Remarkably, in the treated animals, huntingtin gene inactivation reached 54.5% and 9.6%, respectively. These results demonstrate the power of this workflow to predict transduction efficiency and the therapeutic potential of gene therapies in the central nervous system.

Keywords

• CNS
• Gene Therapy
• Gene Editing
• Semi-automated
• Transduction Efficiency
• Huntington’s Disease