Combining Automated Organoid Workflows with Artificial Intelligence‐Based Analyses: Opportunities to Build a New Generation of Interdisciplinary High‐Throughput Screens for Parkinson's Disease and Beyond
Объединение автоматизированных процессов получения органоидов с анализом на основе искусственного интеллекта: возможности создания нового поколения междисциплинарных высокопроизводительных скринингов при болезни Паркинсона и за её пределами
2021-09-08
SCID: 54.1/kevuxv78
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Parkinson's diseaseartificial intelligenceautomated midbrain organoidshigh-throughput screeningphenotypic drug discovery
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Abstract (AI)
Parkinson's disease (PD) is the second most common neurodegenerative disease and primarily characterized by the loss of dopaminergic neurons in the substantia nigra pars compacta of the midbrain. Despite decades of research and the development of various disease model systems, there is no curative treatment. This could be due to current model systems, including cell culture and animal models, not adequately recapitulating human PD etiology. More complex human disease models, including human midbrain organoids, are maturing technologies that increasingly enable the strategic incorporation of the missing components needed to model PD in vitro. The resulting organoid-based biological complexity provides new opportunities and challenges in data analysis of rich multimodal data sets. Emerging artificial intelligence (AI) capabilities can take advantage of large, broad data sets and even correlate results across disciplines. Current organoid technologies no longer lack the prerequisites for large-scale high-throughput screening (HTS) and can generate complex yet reproducible data suitable for AI-based data mining. We have recently developed a fully scalable and HTS-compatible workflow for the generation, maintenance, and analysis of three-dimensional (3D) microtissues mimicking key characteristics of the human midbrain (called "automated midbrain organoids," AMOs). AMOs build a reproducible, scalable foundation for creating next-generation 3D models of human neural disease that can fuel mechanism-agnostic phenotypic drug discovery in human in vitro PD models and beyond. Here, we explore the opportunities and challenges resulting from the convergence of organoid HTS and AI-driven data analytics and outline potential future avenues toward the discovery of novel mechanisms and drugs in PD research. © 2021 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.
Key Findings
1
AMOs reproducibly mimic key characteristics of the human midbrain and provide a foundation for mechanism-agnostic phenotypic drug discovery.
2
Combining organoid high-throughput workflows with AI may enable interdisciplinary discovery of novel Parkinson’s disease mechanisms and therapeutics, while introducing new data-analysis challenges.
3
Human midbrain organoids offer more complex in vitro Parkinson’s disease models that may better recapitulate missing aspects of human disease etiology.
4
Organoid-based high-throughput screening can generate complex, reproducible multimodal datasets suitable for artificial-intelligence-driven analysis.
5
The authors developed a fully scalable, high-throughput workflow for generating, maintaining, and analyzing automated midbrain organoids (AMOs).
Research Object
Automated human midbrain organoids (AMOs) and their scalable high-throughput workflows for in vitro Parkinson’s disease modeling
Research Subject
The integration of organoid-based high-throughput screening with AI-driven multimodal data analysis for reproducible phenotypic drug discovery and mechanism identification in Parkinson’s disease
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2021-09-08
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