BOMA, a machine-learning framework for comparative gene expression analysis across brains and organoids

Application of human cardiac organoids in cardiovascular disease research

With the progression of cardiovascular disease (CVD) treatment technologies, conventional animal models face limitations in clinical translation due to interspecies variations. Recently, human cardiac organoids (hCOs) have emerged as an innovative platform for CVD research. This review provides a comprehensive overview of the definition, characteristics, classifications, application and development of hCOs. Furthermore, this review examines the application of hCOs in models of myocardial infarction, heart failure, arrhythmias, and congenital heart diseases, highlighting their significance in replicating disease mechanisms and pathophysiological processes. It also explores their potential utility in drug screening and the development of therapeutic strategies. Although challenges persist regarding technical complexity and the standardization of models, the integration of multi-omics and artificial intelligence (AI) technologies offers a promising avenue for the clinical translation of hCOs.

Cell Type-Agnostic Transcriptomic Signatures Enable Uniform Comparisons of Neurodevelopment

Single-cell transcriptomics has revolutionized our understanding of neurodevelopmental cell identities, yet, predicting a cell type's developmental state from its transcriptome remains a challenge. We perform a meta-analysis of developing human brain datasets comprising over 2.8 million cells, identifying both tissue-level and cell-autonomous predictors of developmental age. While tissue composition predicts age within individual studies, it fails to generalize, whereas specific cell type proportions reliably track developmental time across datasets. Training regularized regression models to infer cell-autonomous maturation, we find that a cell type-agnostic model achieves the highest accuracy (error = 2.6 weeks), robustly capturing developmental dynamics across diverse cell types and datasets. This model generalizes to human neural organoids, accurately predicting normal developmental trajectories (R = 0.91) and disease-induced shifts in vitro. Furthermore, it extends to the developing mouse brain, revealing an accelerated developmental tempo relative to humans. Our work provides a unified framework for comparing neurodevelopment across contexts, model systems, and species.

Deliod a lightweight detection model for intestinal organoids based on deep learning

Intestinal organoids are indispensable tools for exploring intestinal disorders. Deep learning methodologies are often employed in morphological analysis to evaluate the condition of these organoids. Nonetheless, prevailing analytical techniques face obstacles such as many organisational overlaps and tiny targets lead to a high incidence of errors and limited applicability. This paper presents Deliod, a streamlined intestinal organoid detection model founded on YOLOv8 and designed to automate the identification of organoid morphology. Deliod performed excellently compared to leading detection models when applied to an intestinal organoid dataset, attaining an mAP50 of 87.5%. Ablation experiments verified the module's efficacy in improving detection performance. Furthermore, Deliod features a modest parameter count of 5.41 M and a computational load of 16.6 GFLOPs, facilitating the broader application of the detection model in the realm of intestinal organoid image recognition. This streamlined model not only enables efficient and accurate recognition of organoid morphology but also minimizes hardware deployment requirements, broadening its range of potential applications.

Consequences of trisomy 21 for brain development in Down syndrome

The appearance of cognitive deficits and altered brain morphology in newborns with Down syndrome (DS) suggests that these features are driven by disruptions at the earliest stages of brain development. Despite its high prevalence and extensively characterized cognitive phenotypes, relatively little is known about the cellular and molecular mechanisms that drive the changes seen in DS. Recent technical advances, such as single-cell omics and the development of induced pluripotent stem cell (iPSC) models of DS, now enable in-depth analyses of the biochemical and molecular drivers of altered brain development in DS. Here, we review the current state of knowledge on brain development in DS, focusing primarily on data from human post-mortem brain tissue. We explore the biological mechanisms that have been proposed to lead to intellectual disability in DS, assess the extent to which data from studies using iPSC models supports these hypotheses, and identify current gaps in the field.

From wings to whiskers to stem cells: why every model matters in fragile X syndrome research

Fragile X syndrome (FXS) is caused by epigenetic silencing of the X-linked fragile X messenger ribonucleoprotein 1 (FMR1) gene located on chromosome Xq27.3, which leads to the loss of its protein product, fragile X messenger ribonucleoprotein (FMRP). It is the most prevalent inherited form of intellectual disability and the highest single genetic cause of autism. Since the discovery of the genetic basis of FXS, extensive studies using animal models and human pluripotent stem cells have unveiled the functions of FMRP and mechanisms underlying FXS. However, clinical trials have not yielded successful treatment. Here we review what we have learned from commonly used models for FXS, potential limitations of these models, and recommendations for future steps.

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.

Cross-site reproducibility of human cortical organoids reveals consistent cell type composition and architecture

Background

Reproducibility of human cortical organoid (hCO) phenotypes remains a concern for modeling neurodevelopmental disorders. While guided hCO protocols reproducibly generate cortical cell types in multiple cell lines at one site, variability across sites using a harmonized protocol has not yet been evaluated. We present an hCO cross-site reproducibility study examining multiple phenotypes.

Methods

Three independent research groups generated hCOs from one induced pluripotent stem cell (iPSC) line using a harmonized miniaturized spinning bioreactor protocol. scRNA-seq, 3D fluorescent imaging, phase contrast imaging, qPCR, and flow cytometry were used to characterize the 3 month differentiations across sites.

Results

In all sites, hCOs were mostly cortical progenitor and neuronal cell types in reproducible proportions with moderate to high fidelity to the in vivo brain that were consistently organized in cortical wall-like buds. Cross-site differences were detected in hCO size and morphology. Differential gene expression showed differences in metabolism and cellular stress across sites. Although iPSC culture conditions were consistent and iPSCs remained undifferentiated, primed stem cell marker expression prior to differentiation correlated with cell type proportions in hCOs.

Conclusions

We identified hCO phenotypes that are reproducible across sites using a harmonized differentiation protocol. Previously described limitations of hCO models were also reproduced including off-target differentiations, necrotic cores, and cellular stress. Improving our understanding of how stem cell states influence early hCO cell types may increase reliability of hCO differentiations. Cross-site reproducibility of hCO cell type proportions and organization lays the foundation for future collaborative prospective meta-analytic studies modeling neurodevelopmental disorders in hCOs.