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

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.