Deep-learning analysis of micropattern-based organoids enables high-throughput drug screening of Huntington's disease models.

ABSTRACT: Organoids are carrying the promise of modeling complex disease phenotypes and serving as a powerful basis for unbiased drug screens, potentially offering a more efficient drug-discovery route. However, unsolved technical bottlenecks of reproducibility and scalability have prevented the use of current organoids for high-throughput screening. Here, we present a method that overcomes these limitations by using deep-learning-driven analysis for phenotypic drug screens based on highly standardized micropattern-based neural organoids. This allows us to distinguish between disease and wild-type phenotypes in complex tissues with extremely high accuracy as well as quantify two predictors of drug success: efficacy and adverse effects. We applied our approach to Huntington's disease (HD) and discove

SUBMITTER: Metzger JJ 

PROVIDER: S-EPMC9500000 | biostudies-literature | 2022 Sep

REPOSITORIES: biostudies-literature