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

Abstract

A BS TRACT: 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 highthroughput screening (HTS) and can generate complex yet reproducible data suitable for AI-based data mining.We have recently developed a fully scalable and HTScompatible 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.

AI‐organoid integrated systems for biomedical studies and applications

“…Through disease stratification, ML‐based integration of multimodal data was utilized to improve Parkinson's disease (PD) modeling based on hPSC‐derived brain organoids 125 . This could potentially comprise in vitro data produced by the organoids from PD patients, which can be integrated with in vivo data of demographics, magnetic resonance imaging (MRI), genetics, and other clinical information 126 . Notably, the integration of Brain–Computer Interface (BCI) feedback with brain organoid modeling can enable dynamic closed‐loop control by combining ML algorithms and organoid technology 127 …”

Digital Technologies: Advancing Individualized Treatments through Gene and Cell Therapies, Pharmacogenetics, and Disease Detection and Diagnostics

“…Another cell therapy platform is the organoid. Organoids are 3D multicellular tissue constructs that closely resemble functional organs, and their biological complexity provides new opportunities and challenges in data analytics [ 34 ], as well as chances to reduce the reliance on animal models [ 35 ], with higher physiological relevance [ 36 ] and automation [ 37 ].…”

Application and prospects of high-throughput screening for in vitro neurogenesis

“…Although high-content imaging analysis has been a powerful tool to evaluate organoid generation, for brain organoids, it is probable that the evaluation of neural circuit dynamics, such as that through 3D microelectrode arrays, could become a standard in upcoming studies[ 262 , 263 ]. Furthermore, combinations with the automated workflow of organoid culture and artificial intelligence can shed light on CNS disease modeling and drug discovery for clinical trials[ 264 ].…”