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

Abstract

Summary: 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 discovered that bromodomain inhibitors revert complex phenotypes induced by the HD mutation. This work demonstrates the power of combining machine learning with phenotypic drug screening and its successful application to reveal a potentially new druggable target for HD. Motivation: Organoids are promising tools for modeling complex disease phenotypes. However, they often lack the reproducibility and scalability that would allow their use in high-throughput screening assays. We therefore sought to develop a method of using reproducible and scalable micropatterned neural organoids for drug screening, to develop associated deep-learning analysis methods for efficiently classifying wild-type and disease organoids, and to carry out a drug screen to identify targets that can rescue developmental phenotypes in organoids derived from stem cells that carry mutations for Huntington’s disease.

Keywords

• organoids
• deep learning
• bromodomains
• Huntington's disease