Machine learning-assisted neurotoxicity prediction in human midbrain organoids. (June 2020) - Monzel, Anna S.; Hemmer, Kathrin; Kaoma, Tony; Smits, Lisa M.; Bolognin, Silvia; Lucarelli, Philippe; Rosety, Isabel; Zagare, Alise; Antony, Paul; Nickels, Sarah L.; Krueger, Rejko; Azuaje, Francisco; et al.
Abstract: Introduction: Brain organoids are highly complex multi-cellular tissue proxies, which have recently risen as novel tools to study neurodegenerative diseases such as Parkinson's disease (PD). However, with increasing complexity of the system, usage of quantitative tools becomes challenging. Objectives: The primary objective of this study was to develop a neurotoxin-induced PD organoid model and to assess the neurotoxic effect on dopaminergic neurons using microscopy-based phenotyping in a high-content fashion. Methods: We describe a pipeline for a machine learning-based analytical method, allowing for detailed image-based cell profiling and toxicity prediction in brain organoids treated with the neurotoxic compound 6-hydroxydopamine (6-OHDA). Results: We quantified features such as dopaminergic neuron count and neuronal complexity and built a machine learning classifier with the data to optimize data processing strategies and to discriminate between different treatment conditions. We validated the approach with high content imaging data from PD patient derived midbrain organoids. Conclusions: The here described model is a valuable tool for advanced in vitro PD modeling and to test putative neurotoxic compounds. Highlights: We describe an interdisciplinary approach to assess the effect of 6-OHDA on dopaminergic neurons in midbrain organoids. 3D high-content screening of organoids was used to extract morphometric features of dopaminergic neurons. Machine learning classifier was applied on the high-content data to predict neurotoxin-induced perturbations.