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 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.

Keywords: Huntington's disease; bromodomains; deep learning; organoids.

Graphical abstract

Figure 1

High-throughput screening strategy and validation (A) Schematic illustration of the high-throughput screening (HTS) strategy. Organoids carrying the mutation for Huntington’s disease (HD) are treated with drug compounds and then analyzed to establish whether they revert the phenotype to the wild type (WT). (B) A deep neural network can be used as an efficient classifier to distinguish between the phenotypes and to determine that degree of reversal. (C) Example wells of a 96-well plate for WT and HD organoids (diameter 700 μm) stained for the early neural progenitor marker PAX6 and the actin marker phalloidin. (D) Example WT and HD images and averaged images over all control organoids (diameter 700 μm). (E) Classification accuracy of the neural network trained on control WT and HD images. The accuracy is determined by validating on a set of images that were not used during training (individual images: n = 1,464, n_WT = 700, n_HD = 764; averaged per well: n = 58, n_WT = 29, n_HD = 29). (F) Comparison with other discrimination methods in terms of distance between WT and disease and the resulting Z′ factors show superior accuracy of the neural network approach compared with image clustering using UMAP or feature segmentation using random forest classification (each data point represents average per well, all n = 29). (G and H) Validation of the neural network approach for identifying phenotypic reversal using an HTT knockout and overexpression (OE) (n = 54, n_WT = 10, n_KO = 11, n_KO+OE = 33). The neural network correctly identifies the rescued HTT^−/− + HTT-OE as WT.

Figure 2

Strategy for determining adverse effect and phenotypic space (A) Schematic illustration of the method for quantifying the adverse effect of a compound. (B) Overview of the autoencoder architecture used for encoding organoid data into a latent vector. (C) Illustration of the phenotypic space spanned by the latent vectors of the autoencoder. (D) adverse effect estimation procedure and phenotypic space of a screen with 1,065 compounds (each data point is average of one well; n = 1,257, n_WT = 96, n_HD = 96, n_HD+compound = 1,065). (E) Comparison of adverse effect scores for different drugs and varying concentration obtained by the method described here and the conventional MTT assay shows high accuracy of our method (high scores correspond to low adverse effect, error bars indicate standard deviation over triplicates). (F) Example phalloidin stains for the drugs and concentrations shown in (E). Panels missing are associated with high toxic compound concentrations that resulted in cell death and no structure to be imaged. Micropattern diameter, 700 μm.

Figure 3

Screen for compounds that rescue the HD phenotype (A) Phenotypic rescue and adverse effect scores for screen on the bioactive compounds library. Each data point represents the average score per well. Hit compounds are identified as high rescue (>0.95) and low adverse effect (<3) (n = 1,481, n_WT = 96, n_HD = 96, n_HD+compound = 1,065, n_{additional HD control} = 224). (B) Phenotypic space used for estimating the adverse effect for compounds. (C) Control wells from 96-well compound plates show no substructure in the latent space, confirming high reproducibility across plates (each data point is average of one well; n = 224, n_{per plate} = 16, n_plates = 14). (D and E) Phenotypic rescue (D) and adverse effect (E) for the 14 individual 96-well plates. (F) Example images and details for hit compounds. (G) Dose response for rescue and adverse effect for the hit compound bromosporine. (H) Example images for the quantification of the dose response in (G). All micropattern diameters, 700 μm.

Figure 4

Screen of bromodomain inhibitors and additional validation assays (A) Target, rescue, and adverse effect for specific bromodomain inhibitors. (B) Dose response for rescue and adverse effect of several bromodomain inhibitors (associated z-AUC values are shown in Figure S4D). (C) Validation of efficacy of bromosporine and BI-7273 in 500-μm-diameter micropatterns. (D) Rescue efficiency determined using individual channels. (E) Rescue efficiency determined using a combination of channels (n = 350). (F) Phenotypic space obtained from the latent vectors of an autoencoder confirms phenotypic rescue by compounds as quantified in (E) (n = 435). (G and H) Validation in cortical neurons shows that BRD inhibitors rescue the effect of BDNF removal in HD neurons, with two concentrations tested per compound, 1 μM (blue) and 5 μM (red). Data points are individual fields of view collected in multiple wells; n = 185. p values were calculated using the two-tailed Mann-Whitney U test (Benjamini-Hochberg corrected); n.s. (not significant), p > 0.05; ∗∗∗p < 0.0005. Scale bar, 50 μm.