Machine learning-assisted neurotoxicity prediction in human midbrain organoids

Anna S. Monzel, Kathrin Hemmer, Tony Kaoma, Lisa M. Smits, Silvia Bolognin, Philippe Lucarelli, Isabel Rosety, Alise Zagare, Paul Antony, Sarah L. Nickels, Rejko Krueger, Francisco Azuaje, Jens C. Schwamborn*

*Corresponding author for this work

Research output: Contribution to journalArticleResearchpeer-review

17 Citations (Scopus)

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.

Original languageEnglish
Pages (from-to)105-109
Number of pages5
JournalParkinsonism and Related Disorders
Volume75
DOIs
Publication statusPublished - Jun 2020

Keywords

  • Machine learning
  • Midbrain organoids
  • Neurotoxicity
  • Parkinson's disease

Fingerprint

Dive into the research topics of 'Machine learning-assisted neurotoxicity prediction in human midbrain organoids'. Together they form a unique fingerprint.

Cite this