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

Cornell Affiliated Author(s)

Author

J.J. Metzger
C. Pereda
A. Adhikari
T. Haremaki
S. Galgoczi
E.D. Siggia
A.H. Brivanlou
F. Etoc

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 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. © 2022 The Authors

Date Published

Journal

Cell Reports Methods

Volume

2

Issue

9

URL

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85138098276&doi=10.1016%2fj.crmeth.2022.100297&partnerID=40&md5=1482b483732c3bb447c023d967941f43

DOI

10.1016/j.crmeth.2022.100297

Research Area

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