Deep-learning analysis of micropattern-based organoids enables high-throughput drug screening of Huntington's disease models

Genomic characterization of Huntington's disease genetic modifiers informs drug target tractability

Huntington's disease is caused by a CAG repeat in the HTT gene. Repeat length correlates inversely with the age of onset but only explains part of the observed clinical variability. Genome-wide association studies highlight DNA repair genes in modifying disease onset, but further research is required to identify causal genes and evaluate their tractability as drug targets. To address these gaps and learn important preclinical information, we analysed genome-wide association study data from a large Huntington's disease age-of-onset study (n = 9064), prioritizing robust candidate Huntington's disease modifier genes using bioinformatic approaches and analysing related information for these genes from large-scale human genetic repositories. We supplemented this information with other Huntington's disease-related screens, including exome studies of Huntington's disease onset and high-throughput assessments of mHTT toxicity. To confirm whether Huntington's disease modifiers are shared across repeat expansion disorders, we also analysed age-of-onset genome-wide association study data from X-linked dystonia-parkinsonism caused by a (CCCTCT)n expansion. We also studied modifier-related associations with rare diseases to inform potential off-target therapeutic effects and conducted comprehensive phenome-wide studies to identify other traits linked to these genes. Finally, we evaluated the aggregated human genetic evidence and theoretical druggability of the prioritized Huntington's disease modifier genes, including characteristics recently associated with clinical trial stoppage due to safety concerns (i.e. human genetic constraint, number of interacting partners and RNA tissue expression specificity). In total, we annotated and assessed nine robust candidate Huntington's disease modifier genes. Notably, we detected a high correlation (R 2 = 0.78) in top age-of-onset genome-wide association study hits across repeat expansion disorders, emphasizing cross-disorder relevance. Clinical genetic repositories analysis showed DNA repair genes, such as MLH1, PMS2 and MSH3, are associated with cancer phenotypes, suggesting potential limitations as drug targets. LIG1 and RRM2B were both associated with neurofibrillary tangles, which may provide a link to a potential role in mHTT aggregates, while MSH3 was associated with several cortical morphology-related traits relevant to Huntington's disease. Finally, human genetic evidence and theoretical druggability analyses prioritized and ranked modifier genes, with PMS1 exhibiting the most favourable profile. Notably, HTT itself ranked poorly as a theoretical drug target, emphasizing the importance of exploring modifier-based alternative targets. In conclusion, our study highlights the importance of human genomic information to prioritize Huntington's disease modifier genes as drug targets, providing a basis for future therapeutic development in Huntington's disease and other repeat expansion disorders.

Discovery of CW-3308 as a Potent, Selective, and Orally Efficacious PROTAC Degrader of BRD9

The bromodomain-containing protein BRD9 has emerged as an attractive therapeutic target. In the present study, we successfully identified a number of highly potent BRD9 degraders by using two different cereblon ligands developed in our laboratory. Further optimization led to the discovery of CW-3308 as a potent, selective, and orally bioavailable BRD9 degrader. It displayed degradation potency (DC50) < 10 nM and efficiency (Dmax) > 90% against BRD9 in the G401 rhabdoid tumor and HS-SY-II synovial sarcoma cell lines and had a high degradation selectivity over BRD7 and BRD4 proteins. CW-3308 achieved 91% of oral bioavailability in mice. A single oral dose efficiently reduced the BRD9 protein by >90% in the synovial sarcoma HS-SY-II xenograft tumor tissue. Oral administration effectively inhibited HS-SY-II xenograft tumor growth in mice. CW-3308 is a promising lead compound for further optimization and extensive evaluation for the treatment of synovial sarcoma, rhabdoid tumor, and other BRD9-dependent human diseases.

Recent advances and applications of human brain models

Recent advances in human pluripotent stem cell (hPSC) technologies have prompted the emergence of new research fields and applications for human neurons and brain organoids. Brain organoids have gained attention as an in vitro model system that recapitulates the higher structure, cellular diversity and function of the brain to explore brain development, disease modeling, drug screening, and regenerative medicine. This progress has been accelerated by abundant interactions of brain organoid technology with various research fields. A cross-disciplinary approach with human brain organoid technology offers a higher-ordered advance for more accurately understanding the human brain. In this review, we summarize the status of neural induction in two- and three-dimensional culture systems from hPSCs and the modeling of neurodegenerative diseases using brain organoids. We also highlight the latest bioengineered technologies for the assembly of spatially higher-ordered neural tissues and prospects of brain organoid technology toward the understanding of the potential and abilities of the human brain.

Morphogenetic Designs, and Disease Models in Central Nervous System Organoids

Since the emergence of the first cerebral organoid (CO) in 2013, advancements have transformed central nervous system (CNS) research. Initial efforts focused on studying the morphogenesis of COs and creating reproducible models. Numerous methodologies have been proposed, enabling the design of the brain organoid to represent specific regions and spinal cord structures. CNS organoids now facilitate the study of a wide range of CNS diseases, from infections to tumors, which were previously difficult to investigate. We summarize the major advancements in CNS organoids, concerning morphogenetic designs and disease models. We examine the development of fabrication procedures and how these advancements have enabled the generation of region-specific brain organoids and spinal cord models. We highlight the application of these organoids in studying various CNS diseases, demonstrating the versatility and potential of organoid models in advancing our understanding of complex conditions. We discuss the current challenges in the field, including issues related to reproducibility, scalability, and the accurate recapitulation of the in vivo environment. We provide an outlook on prospective studies and future directions. This review aims to provide a comprehensive overview of the state-of-the-art CNS organoid research, highlighting key developments, current challenges, and prospects in the field.

Brain organoid as a model to study the role of mitochondria in neurodevelopmental disorders: achievements and weaknesses

Mitochondrial diseases are a group of severe pathologies that cause complex neurodegenerative disorders for which, in most cases, no therapy or treatment is available. These organelles are critical regulators of both neurogenesis and homeostasis of the neurological system. Consequently, mitochondrial damage or dysfunction can occur as a cause or consequence of neurodevelopmental or neurodegenerative diseases. As genetic knowledge of neurodevelopmental disorders advances, associations have been identified between genes that encode mitochondrial proteins and neurological symptoms, such as neuropathy, encephalomyopathy, ataxia, seizures, and developmental delays, among others. Understanding how mitochondrial dysfunction can alter these processes is essential in researching rare diseases. Three-dimensional (3D) cell cultures, which self-assemble to form specialized structures composed of different cell types, represent an accessible manner to model organogenesis and neurodevelopmental disorders. In particular, brain organoids are revolutionizing the study of mitochondrial-based neurological diseases since they are organ-specific and model-generated from a patient's cell, thereby overcoming some of the limitations of traditional animal and cell models. In this review, we have collected which neurological structures and functions recapitulate in the different types of reported brain organoids, focusing on those generated as models of mitochondrial diseases. In addition to advancements in the generation of brain organoids, techniques, and approaches for studying neuronal structures and physiology, drug screening and drug repositioning studies performed in brain organoids with mitochondrial damage and neurodevelopmental disorders have also been reviewed. This scope review will summarize the evidence on limitations in studying the function and dynamics of mitochondria in brain organoids.

Brain organoid protocols and limitations

Stem cell-derived organoid technology is a powerful tool that revolutionizes the field of biomedical research and extends the scope of our understanding of human biology and diseases. Brain organoids especially open an opportunity for human brain research and modeling many human neurological diseases, which have lagged due to the inaccessibility of human brain samples and lack of similarity with other animal models. Brain organoids can be generated through various protocols and mimic whole brain or region-specific. To provide an overview of brain organoid technology, we summarize currently available protocols and list several factors to consider before choosing protocols. We also outline the limitations of current protocols and challenges that need to be solved in future investigation of brain development and pathobiology.

Organoid intelligence: Integration of organoid technology and artificial intelligence in the new era of in vitro models

Organoid Intelligence ushers in a new era by seamlessly integrating cutting-edge organoid technology with the power of artificial intelligence. Organoids, three-dimensional miniature organ-like structures cultivated from stem cells, offer an unparalleled opportunity to simulate complex human organ systems in vitro. Through the convergence of organoid technology and AI, researchers gain the means to accelerate discoveries and insights across various disciplines. Artificial intelligence algorithms enable the comprehensive analysis of intricate organoid behaviors, intricate cellular interactions, and dynamic responses to stimuli. This synergy empowers the development of predictive models, precise disease simulations, and personalized medicine approaches, revolutionizing our understanding of human development, disease mechanisms, and therapeutic interventions. Organoid Intelligence holds the promise of reshaping how we perceive in vitro modeling, propelling us toward a future where these advanced systems play a pivotal role in biomedical research and drug development.

AI-organoid integrated systems for biomedical studies and applications

In this review, we explore the growing role of artificial intelligence (AI) in advancing the biomedical applications of human pluripotent stem cell (hPSC)-derived organoids. Stem cell-derived organoids, these miniature organ replicas, have become essential tools for disease modeling, drug discovery, and regenerative medicine. However, analyzing the vast and intricate datasets generated from these organoids can be inefficient and error-prone. AI techniques offer a promising solution to efficiently extract insights and make predictions from diverse data types generated from microscopy images, transcriptomics, metabolomics, and proteomics. This review offers a brief overview of organoid characterization and fundamental concepts in AI while focusing on a comprehensive exploration of AI applications in organoid-based disease modeling and drug evaluation. It provides insights into the future possibilities of AI in enhancing the quality control of organoid fabrication, label-free organoid recognition, and three-dimensional image reconstruction of complex organoid structures. This review presents the challenges and potential solutions in AI-organoid integration, focusing on the establishment of reliable AI model decision-making processes and the standardization of organoid research.

Angio-Net: deep learning-based label-free detection and morphometric analysis of in vitro angiogenesis

Despite significant advancements in three-dimensional (3D) cell culture technology and the acquisition of extensive data, there is an ongoing need for more effective and dependable data analysis methods. These concerns arise from the continued reliance on manual quantification techniques. In this study, we introduce a microphysiological system (MPS) that seamlessly integrates 3D cell culture to acquire large-scale imaging data and employs deep learning-based virtual staining for quantitative angiogenesis analysis. We utilize a standardized microfluidic device to obtain comprehensive angiogenesis data. Introducing Angio-Net, a novel solution that replaces conventional immunocytochemistry, we convert brightfield images into label-free virtual fluorescence images through the fusion of SegNet and cGAN. Moreover, we develop a tool capable of extracting morphological blood vessel features and automating their measurement, facilitating precise quantitative analysis. This integrated system proves to be invaluable for evaluating drug efficacy, including the assessment of anticancer drugs on targets such as the tumor microenvironment. Additionally, its unique ability to enable live cell imaging without the need for cell fixation promises to broaden the horizons of pharmaceutical and biological research. Our study pioneers a powerful approach to high-throughput angiogenesis analysis, marking a significant advancement in MPS.

The promise of genetic screens in human in vitro brain models

Advances of in vitro culture models have allowed unprecedented insights into human neurobiology. At the same time genetic screening has matured into a robust and accessible experimental strategy allowing for the simultaneous study of many genes in parallel. The combination of both technologies is a newly emerging tool for neuroscientists, opening the door to identifying causal cell- and tissue-specific developmental and disease mechanisms. However, with complex experimental genetic screening set-ups new challenges in data interpretation and experimental scope arise that require a deep understanding of the benefits and challenges of individual approaches. In this review, we summarize the literature that applies genetic screening to in vitro brain models, compare experimental strengths and weaknesses and point towards future directions of these promising approaches.

Highly reproducible and cost-effective one-pot organoid differentiation using a novel platform based on PF-127 triggered spheroid assembly

Organoid technology offers sophisticatedin vitrohuman models for basic research and drug development. However, low batch-to-batch reproducibility and high cost due to laborious procedures and materials prevent organoid culture standardization for automation and high-throughput applications. Here, using a novel platform based on the findings that Pluronic F-127 (PF-127) could trigger highly uniform spheroid assembly through a mechanism different from plate coating, we develop a one-pot organoid differentiation strategy. Using our strategy, we successfully generate cortical, nephron, hepatic, and lung organoids with improved reproducibility compared to previous methods while reducing the original costs by 80%-95%. In addition, we adapt our platform to microfluidic chips allowing automated culture. We showcase that our platform can be applied to tissue-specific screening, such as drug toxicity and transfection reagents testing. Finally, we generateNEAT1knockout tissue-specific organoids and showNEAT1modulates multiple signaling pathways fine-tuning the differentiation of nephron and hepatic organoids and suppresses immune responses in cortical organoids. In summary, our strategy provides a powerful platform for advancing organoid research and studying human development and diseases.