MOrgAna: accessible quantitative analysis of organoids with machine learning

Integrated molecular-phenotypic profiling reveals metabolic control of morphological variation in a stem-cell-based embryo model

Considerable phenotypic variation under identical culture conditions limits the potential of stem-cell-based embryo models (SEMs) in basic and applied research. The biological processes causing this seemingly stochastic variation remain unclear. Here, we investigated the roots of phenotypic variation by parallel recording of transcriptomic states and morphological history in individual structures modeling embryonic trunk formation. Machine learning and integration of time-resolved single-cell RNA sequencing with imaging-based phenotypic profiling identified early features predictive of phenotypic end states. Leveraging this predictive power revealed that early imbalance of oxidative phosphorylation and glycolysis results in aberrant morphology and a neural lineage bias, which we confirmed by metabolic measurements. Accordingly, metabolic interventions improved phenotypic end states. Collectively, our work establishes divergent metabolic states as drivers of phenotypic variation and offers a broadly applicable framework to chart and predict phenotypic variation in organoids and SEMs. The strategy can be used to identify and control underlying biological processes, ultimately increasing reproducibility.

Glycolytic activity instructs germ layer proportions through regulation of Nodal and Wnt signaling

Metabolic pathways can influence cell fate decisions, yet their regulative role during embryonic development remains poorly understood. Here, we demonstrate an instructive role of glycolytic activity in regulating signaling pathways involved in mesoderm and endoderm specification. Using a mouse embryonic stem cell (mESC)-based in vitro model for gastrulation, we found that glycolysis inhibition increases ectodermal cell fates at the expense of mesodermal and endodermal lineages. We demonstrate that this relationship is dose dependent, enabling metabolic control of germ layer proportions through exogenous glucose levels. We further show that glycolysis acts as an upstream regulator of Nodal and Wnt signaling and that its influence on cell fate specification can be decoupled from its effects on growth. Finally, we confirm the generality of our findings using a human gastrulation model. Our work underscores the dependence of signaling pathways on metabolic conditions and provides mechanistic insight into the nutritional regulation of cell fate decision-making.

Bridging the gap: how patient-derived lung cancer organoids are transforming personalized medicine

Lung cancer is a major malignancy that poses a significant threat to human health, with its complex pathogenesis and molecular characteristics presenting substantial challenges for treatment. Traditional two-dimensional cell cultures and animal models are limited in their ability to accurately replicate the characteristics of different lung cancer patients, thereby hindering research on disease mechanisms and treatment strategies. The development of organoid technology has enabled the growth of patient-derived tumor cells in three-dimensional cultures, which can stably preserve the tumor's tissue morphology, genomic features, and drug response. There have been significant advancements in the field of patient-derived lung cancer organoids (PDLCOs), challenges remain in the reproducibility and standardization of PDLCOs models due to variations in specimen sources, subsequent processing techniques, culture medium formulations, and Matrigel batches. This review summarizes the cultivation and validation processes of PDLCOs and explores their clinical applications in personalized treatment, drug screening after resistance, PDLCOs biobanks construction, and drug development. Additionally, the integration of PDLCOs with cutting-edge technologies in various fields, such as tumor assembloid techniques, artificial intelligence, organoid-on-a-chip, 3D bioprinting, gene editing, and single-cell RNA sequencing, has greatly expanded their clinical potential. This review, incorporating the latest research developments in PDLCOs, provides an overview of their cultivation, clinical applications, and interdisciplinary integration, while also addressing the prospects and challenges of PDLCOs in precision medicine for lung cancer.

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.

Stem cell culture conditions affect in vitro differentiation potential and mouse gastruloid formation

Aggregating low numbers of mouse embryonic stem cells (mESCs) and inducing Wnt signalling generates 'gastruloids', self-organising complex structures that display an anteroposterior organisation of cell types derived from all three germ layers. Current gastruloid protocols display considerable heterogeneity between experiments in terms of morphology, elongation efficiency, and cell type composition. We therefore investigated whether altering the mESC pluripotency state would provide more consistent results. By growing three mESC lines from two different genetic backgrounds in different intervals of ESLIF and 2i medium the pluripotency state of cells was modulated, and mESC culture as well as the resulting gastruloids were analysed. Microscopic analysis showed a pre-culture-specific effect on gastruloid formation, in terms of aspect ratio and reproducibility. RNA-seq analysis of the mESC start population confirmed that short-term pulses of 2i and ESLIF modulate the pluripotency state, and result in different cellular states. Since multiple epigenetic regulators were detected among the top differentially expressed genes, we further analysed genome-wide DNA methylation and H3K27me3 distributions. We observed epigenetic differences between conditions, most dominantly in the promoter regions of developmental regulators. Lastly, when we investigated the cell type composition of gastruloids grown from these different pre-cultures, we observed that mESCs subjected to 2i-ESLIF preceding aggregation generated gastruloids more consistently, including more complex mesodermal contributions as compared to the ESLIF-only control. These results indicate that optimisation of the mESCs pluripotency state allows the modulation of cell differentiation during gastruloid formation.

A New Perspective on Precision Medicine: The Power of Digital Organoids

Precision medicine is a personalized medical model based on the individual's genome, phenotype, and lifestyle that provides tailored treatment plans for patients. In this context, tumor organoids, a 3-dimensional preclinical model based on patient-derived tumor cell self-organization, combined with digital analysis methods, such as high-throughput sequencing and image processing technology, can be used to analyze the genome, transcriptome, and cellular heterogeneity of tumors, so as to accurately track and assess the growth process, genetic characteristics, and drug responsiveness of tumor organoids, thereby facilitating the implementation of precision medicine. This interdisciplinary approach is expected to promote the innovation of cancer diagnosis and enhance personalized treatment. In this review, the characteristics and culture methods of tumor organoids are summarized, and the application of multi-omics, such as bioinformatics and artificial intelligence, and the digital methods of organoids in precision medicine research are discussed. Finally, this review explores the main causes and potential solutions for the bottleneck in the clinical translation of digital tumor organoids, proposes the prospects of multidisciplinary cooperation and clinical transformation to narrow the gap between laboratory and clinical settings, and provides references for research and development in this field.

Protocol for functional screening of CFTR-targeted genetic therapies in patient-derived organoids using DETECTOR deep-learning-based analysis

Here, we present a protocol for the rapid functional screening of gene editing and addition strategies in patient-derived organoids using the deep-learning-based tool DETECTOR (detection of targeted editing of cystic fibrosis transmembrane conductance regulator [CFTR] in organoids). We describe steps for wet-lab experiments, image acquisition, and CFTR function analysis by DETECTOR. We also detail procedures for applying pre-trained models and training custom models on new customized datasets. For complete details on the use and execution of this protocol, refer to Bulcaen et al.1.

Colorectal Organoids: Models, Imaging, Omics, Therapy, Immunology, and Ethics

Colorectal epithelium was the first long-term 3D organoid culture established in vitro. Identification of the key components essential for the long-term survival of the stem cell niche allowed an indefinite propagation of these cultures and the modulation of their differentiation into various lineages of mature intestinal epithelial cells. While these methods were eventually adapted to establish organoids from different organs, colorectal organoids remain a pioneering model for the development of new applications in health and disease. Several basic and applicative aspects of organoid culture, modeling, monitoring and testing are analyzed in this review. We also tackle the ethical problems of biobanking and distribution of these precious research tools, frequently confined in the laboratory of origin or condemned to destruction at the end of the project.

Exploring Mechanical Features of 3D Head and Neck Cancer Models

Head and neck squamous cell carcinoma (HNSCC) presents significant challenges in oncology due to its complex biology and poor prognosis. Traditional two-dimensional (2D) cell culture models cannot replicate the intricate tumor microenvironment, limiting their usefulness in studying disease mechanisms and testing therapies. In contrast, three-dimensional (3D) in vitro models provide more realistic platforms that better mimic the architecture, mechanical features, and cellular interactions of HNSCC. This review explores the mechanical properties of 3D in vitro models developed for HNSCC research. It highlights key 3D culture techniques, such as spheroids, organoids, and bioprinted tissues, emphasizing their ability to simulate critical tumor characteristics like hypoxia, drug resistance, and metastasis. Particular attention is given to stiffness, elasticity, and dynamic behavior, highlighting how these models emulate native tumor tissues. By enhancing the physiological relevance of in vitro studies, 3D models offer significant potential to revolutionize HNSCC research and facilitate the development of effective, personalized therapeutic strategies. This review bridges the gap between preclinical and clinical applications by summarizing the mechanical properties of 3D models and providing guidance for developing systems that replicate both biological and mechanical characteristics of tumor tissues, advancing innovation in cancer research and therapy.

OrganoIDNet: a deep learning tool for identification of therapeutic effects in PDAC organoid-PBMC co-cultures from time-resolved imaging data

PURPOSE

Pancreatic Ductal Adenocarcinoma (PDAC) remains a challenging disease due to its complex biology and aggressive behavior with an urgent need for efficient therapeutic strategies. To assess therapy response, pre-clinical PDAC organoid-based models in combination with accurate real-time monitoring are required.

METHODS

We established stable live-imaging organoid/peripheral blood mononuclear cells (PBMCs) co-cultures and introduced OrganoIDNet, a deep-learning-based algorithm, capable of analyzing bright-field images of murine and human patient-derived PDAC organoids acquired with live-cell imaging. We investigated the response to the chemotherapy gemcitabine in PDAC organoids and the PD-L1 inhibitor Atezolizumab, cultured with or without HLA-matched PBMCs over time. Results obtained with OrganoIDNet were validated with the endpoint proliferation assay CellTiter-Glo.

RESULTS

Live cell imaging in combination with OrganoIDNet accurately detected size-specific drug responses of organoids to gemcitabine over time, showing that large organoids were more prone to cytotoxic effects. This approach also allowed distinguishing between healthy and unhealthy status and measuring eccentricity as organoids' reaction to therapy. Furthermore, imaging of a new organoids/PBMCs sandwich-based co-culture enabled longitudinal analysis of organoid responses to Atezolizumab, showing an increased potency of PBMCs tumor-killing in an organoid-individual manner when Atezolizumab was added.

CONCLUSION

Optimized PDAC organoid imaging analyzed by OrganoIDNet represents a platform capable of accurately detecting organoid responses to standard PDAC chemotherapy over time. Moreover, organoid/immune cell co-cultures allow monitoring of organoid responses to immunotherapy, offering dynamic insights into treatment behavior within a co-culture setting with PBMCs. This setup holds promise for real-time assessment of immunotherapeutic effects in individual patient-derived PDAC organoids.

Exploring the effects of gut microbiota on cholangiocarcinoma progression by patient-derived organoids

BACKGROUND

Recent research indicates a role of gut microbiota in development and progression of life-threatening diseases such as cancer. Carcinomas of the biliary ducts, the so-called cholangiocarcinomas, are known for their aggressive tumor biology, implying poor prognosis of affected patients. An impact of the gut microbiota on cholangiocarcinoma development and progression is plausible due to the enterohepatic circulation and is therefore the subject of scientific debate, however evidence is still lacking. This review aimed to discuss the suitability of complex cell culture models to investigate the role of gut microbiota in cholangiocarcinoma progression.

MAIN BODY

Clinical research in this area is challenging due to poor comparability of patients and feasibility reasons, which is why translational models are needed to understand the basis of tumor progression in cholangiocarcinoma. A promising approach to investigate the influence of gut microbiota could be an organoid model. Organoids are 3D cell models cultivated in a modifiable and controlled condition, which can be grown from tumor tissue. 3D cell models are able to imitate physiological and pathological processes in the human body and thus contribute to a better understanding of health and disease.

CONCLUSION

The use of complex cell cultures such as organoids and organoid co-cultures might be powerful and valuable tools to study not only the growth behavior and growth of cholangiocarcinoma cells, but also the interaction with the tumor microenvironment and with components of the gut microbiota.

Harnessing 3D models to uncover the mechanisms driving infectious and inflammatory disease in the intestine

Representative models of intestinal diseases are transforming our knowledge of the molecular mechanisms of disease, facilitating effective drug screening and avenues for personalised medicine. Despite the emergence of 3D in vitro intestinal organoid culture systems that replicate the genetic and functional characteristics of the epithelial tissue of origin, there are still challenges in reproducing the human physiological tissue environment in a format that enables functional readouts. Here, we describe the latest platforms engineered to investigate environmental tissue impacts, host-microbe interactions and enable drug discovery. This highlights the potential to revolutionise knowledge on the impact of intestinal infection and inflammation and enable personalised disease modelling and clinical translation.

A spheroid whole mount drug testing pipeline with machine-learning based image analysis identifies cell-type specific differences in drug efficacy on a single-cell level

BACKGROUND

The growth and drug response of tumors are influenced by their stromal composition, both in vivo and 3D-cell culture models. Cell-type inherent features as well as mutual relationships between the different cell types in a tumor might affect drug susceptibility of the tumor as a whole and/or of its cell populations. However, a lack of single-cell procedures with sufficient detail has hampered the automated observation of cell-type-specific effects in three-dimensional stroma-tumor cell co-cultures.

METHODS

Here, we developed a high-content pipeline ranging from the setup of novel tumor-fibroblast spheroid co-cultures over optical tissue clearing, whole mount staining, and 3D confocal microscopy to optimized 3D-image segmentation and a 3D-deep-learning model to automate the analysis of a range of cell-type-specific processes, such as cell proliferation, apoptosis, necrosis, drug susceptibility, nuclear morphology, and cell density.

RESULTS

This demonstrated that co-cultures of KP-4 tumor cells with CCD-1137Sk fibroblasts exhibited a growth advantage compared to tumor cell mono-cultures, resulting in higher cell counts following cytostatic treatments with paclitaxel and doxorubicin. However, cell-type-specific single-cell analysis revealed that this apparent benefit of co-cultures was due to a higher resilience of fibroblasts against the drugs and did not indicate a higher drug resistance of the KP-4 cancer cells during co-culture. Conversely, cancer cells were partially even more susceptible in the presence of fibroblasts than in mono-cultures.

CONCLUSION

In summary, this underlines that a novel cell-type-specific single-cell analysis method can reveal critical insights regarding the mechanism of action of drug substances in three-dimensional cell culture models.

High-throughput platform for label-free sorting of 3D spheroids using deep learning

End-stage liver diseases have an increasing impact worldwide, exacerbated by the shortage of transplantable organs. Recognized as one of the promising solutions, tissue engineering aims at recreating functional tissues and organs in vitro. The integration of bioprinting technologies with biological 3D models, such as multi-cellular spheroids, has enabled the fabrication of tissue constructs that better mimic complex structures and in vivo functionality of organs. However, the lack of methods for large-scale production of homogeneous spheroids has hindered the upscaling of tissue fabrication. In this work, we introduce a fully automated platform, designed for high-throughput sorting of 3D spheroids based on label-free analysis of brightfield images. The compact platform is compatible with standard biosafety cabinets and includes a custom-made microscope and two fluidic systems that optimize single spheroid handling to enhance sorting speed. We use machine learning to classify spheroids based on their bioprinting compatibility. This approach enables complex morphological analysis, including assessing spheroid viability, without relying on invasive fluorescent labels. Furthermore, we demonstrate the efficacy of transfer learning for biological applications, for which acquiring large datasets remains challenging. Utilizing this platform, we efficiently sort mono-cellular and multi-cellular liver spheroids, the latter being used in bioprinting applications, and confirm that the sorting process preserves viability and functionality of the spheroids. By ensuring spheroid homogeneity, our sorting platform paves the way for standardized and scalable tissue fabrication, advancing regenerative medicine applications.

Harnessing the power of artificial intelligence for human living organoid research

As a powerful paradigm, artificial intelligence (AI) is rapidly impacting every aspect of our day-to-day life and scientific research through interdisciplinary transformations. Living human organoids (LOs) have a great potential for in vitro reshaping many aspects of in vivo true human organs, including organ development, disease occurrence, and drug responses. To date, AI has driven the revolutionary advances of human organoids in life science, precision medicine and pharmaceutical science in an unprecedented way. Herein, we provide a forward-looking review, the frontiers of LOs, covering the engineered construction strategies and multidisciplinary technologies for developing LOs, highlighting the cutting-edge achievements and the prospective applications of AI in LOs, particularly in biological study, disease occurrence, disease diagnosis and prediction and drug screening in preclinical assay. Moreover, we shed light on the new research trends harnessing the power of AI for LO research in the context of multidisciplinary technologies. The aim of this paper is to motivate researchers to explore organ function throughout the human life cycle, narrow the gap between in vitro microphysiological models and the real human body, accurately predict human-related responses to external stimuli (cues and drugs), accelerate the preclinical-to-clinical transformation, and ultimately enhance the health and well-being of patients.

Revolutionising oral organoids with artificial intelligence

The convergence of organoid technology and artificial intelligence (AI) is poised to revolutionise oral healthcare. Organoids - three-dimensional structures derived from human tissues - offer invaluable insights into the complex biology of diseases, allowing researchers to effectively study disease mechanisms and test therapeutic interventions in environments that closely mimic in vivo conditions. In this review, we first present the historical development of organoids and delve into the current types of oral organoids, focusing on their use in disease models, regeneration and microbiome intervention. We then compare single-source and multi-lineage oral organoids and assess the latest progress in bioprinted, vascularised and neural-integrated organoids. In the next part of the review, we highlight significant advancements in AI, emphasising how AI algorithms may potentially promote organoid development for early disease detection and diagnosis, personalised treatment, disease prediction and drug screening. However, our main finding is the identification of remaining challenges, such as data integration and the critical need for rigorous validation of AI algorithms to ensure their clinical reliability. Our main viewpoint is that current AI-enabled oral organoids are still limited in applications but, as we look to the future, we offer insights into the potential transformation of AI-integrated oral organoids in oral disease diagnosis, oral microbial interactions and drug discoveries. By synthesising these components, this review aims to provide a comprehensive perspective on the current state and future implications of AI-enabled oral organoids, emphasising their role in advancing oral healthcare and improving patient outcomes.

Advancing cancer research through organoid technology

The complexity of tumors and the challenges associated with treatment often stem from the limitations of existing models in accurately replicating authentic tumors. Recently, organoid technology has emerged as an innovative platform for tumor research. This bioengineering approach enables researchers to simulate, in vitro, the interactions between tumors and their microenvironment, thereby enhancing the intricate interplay between tumor cells and their surroundings. Organoids also integrate multidimensional data, providing a novel paradigm for understanding tumor development and progression while facilitating precision therapy. Furthermore, advancements in imaging and genetic editing techniques have significantly augmented the potential of organoids in tumor research. This review explores the application of organoid technology for more precise tumor simulations and its specific contributions to cancer research advancements. Additionally, we discuss the challenges and evolving trends in developing comprehensive tumor models utilizing organoid technology.

A Curated Cell Life Imaging Dataset of Immune-enriched Pancreatic Cancer Organoids with Pre-trained AI Models

Tumor organoids are three-dimensional in vitro models which can recapitulate the complex mutational landscape and tissue architecture observed in cancer patients, providing a realistic model for testing novel therapies, including immunotherapies. A significant challenge in organoid research in oncology lies in developing efficient and reliable methods for segmenting organoid images, quantifying organoid growth, regression and response to treatments, as well as predicting the behavior of organoid systems. Up to now, a curated dataset of organoids co-cultured with immune cells is not available. To address this gap, we present a new public dataset, comprising both phase-contrast images of murine and patient-derived tumor organoids of one of the deadliest cancer types, the Pancreatic Ductal Adenocarcinoma, co-cultured with immune cells, and state-of-the-art algorithms for object detection and segmentation. Our dataset, OrganoIDNetData, encompassing 180 images with 33906 organoids, can be a potential common benchmark for different organoids segmentation protocols, moving beyond the current practice of training and testing these algorithms on isolated datasets.

Three-Dimensionally Printed Agarose Micromold Supports Scaffold-Free Mouse Ex Vivo Follicle Growth, Ovulation, and Luteinization

Ex vivo follicle growth is an essential tool, enabling interrogation of folliculogenesis, ovulation, and luteinization. Though significant advancements have been made, existing follicle culture strategies can be technically challenging and laborious. In this study, we advanced the field through development of a custom agarose micromold, which enables scaffold-free follicle culture. We established an accessible and economical manufacturing method using 3D printing and silicone molding that generates biocompatible hydrogel molds without the risk of cytotoxicity from leachates. Each mold supports simultaneous culture of multiple multilayer secondary follicles in a single focal plane, allowing for constant timelapse monitoring and automated analysis. Mouse follicles cultured using this novel system exhibit significantly improved growth and ovulation outcomes with comparable survival, oocyte maturation, and hormone production profiles as established three-dimensional encapsulated in vitro follicle growth (eIVFG) systems. Additionally, follicles recapitulated aspects of in vivo ovulation physiology with respect to their architecture and spatial polarization, which has not been observed in eIVFG systems. This system offers simplicity, scalability, integration with morphokinetic analyses of follicle growth and ovulation, and compatibility with existing microphysiological platforms. This culture strategy has implications for fundamental follicle biology, fertility preservation strategies, reproductive toxicology, and contraceptive drug discovery.

Deciphering lineage specification during early embryogenesis in mouse gastruloids using multilayered proteomics

Gastrulation is a critical stage in embryonic development during which the germ layers are established. Advances in sequencing technologies led to the identification of gene regulatory programs that control the emergence of the germ layers and their derivatives. However, proteome-based studies of early mammalian development are scarce. To overcome this, we utilized gastruloids and a multilayered mass spectrometry-based proteomics approach to investigate the global dynamics of (phospho) protein expression during gastruloid differentiation. Our findings revealed many proteins with temporal expression and unique expression profiles for each germ layer, which we also validated using single-cell proteomics technology. Additionally, we profiled enhancer interaction landscapes using P300 proximity labeling, which revealed numerous gastruloid-specific transcription factors and chromatin remodelers. Subsequent degron-based perturbations combined with single-cell RNA sequencing (scRNA-seq) identified a critical role for ZEB2 in mouse and human somitogenesis. Overall, this study provides a rich resource for developmental and synthetic biology communities endeavoring to understand mammalian embryogenesis.

Lung Organoids: Systematic Review of Recent Advancements and its Future Perspectives

Organoids are essentially an in vitro (lab-grown) three-dimensional tissue culture system model that meticulously replicates the structure and physiology of human organs. A few of the present applications of organoids are in the basic biological research area, molecular medicine and pharmaceutical drug testing. Organoids are crucial in connecting the gap between animal models and human clinical trials during the drug discovery process, which significantly lowers the time duration and cost associated with each stage of testing. Likewise, they can be used to understand cell-to-cell interactions, a crucial aspect of tissue biology and regeneration, and to model disease pathogenesis at various stages of the disease. Lung organoids can be utilized to explore numerous pathophysiological activities of a lung since they share similarities with its function. Researchers have been trying to recreate the complex nature of the lung by developing various "Lung organoids" models.This article is a systematic review of various developments of lung organoids and their potential progenitors. It also covers the in-depth applications of lung organoids for the advancement of translational research. The review discusses the methodologies to establish different types of lung organoids for studying the regenerative capability of the respiratory system and comprehending various respiratory diseases.Respiratory diseases are among the most common worldwide, and the growing burden must be addressed instantaneously. Lung organoids along with diverse bio-engineering tools and technologies will serve as a novel model for studying the pathophysiology of various respiratory diseases and for drug screening purposes.

Prime editing functionally corrects cystic fibrosis-causing CFTR mutations in human organoids and airway epithelial cells

Prime editing is a recent, CRISPR-derived genome editing technology capable of introducing precise nucleotide substitutions, insertions, and deletions. Here, we present prime editing approaches to correct L227R- and N1303K-CFTR, two mutations that cause cystic fibrosis and are not eligible for current market-approved modulator therapies. We show that, upon DNA correction of the CFTR gene, the complex glycosylation, localization, and, most importantly, function of the CFTR protein are restored in HEK293T and 16HBE cell lines. These findings were subsequently validated in patient-derived rectal organoids and human nasal epithelial cells. Through analysis of predicted and experimentally identified candidate off-target sites in primary stem cells, we confirm previous reports on the high prime editor (PE) specificity and its potential for a curative CF gene editing therapy. To facilitate future screening of genetic strategies in a translational CF model, a machine learning algorithm was developed for dynamic quantification of CFTR function in organoids (DETECTOR: "detection of targeted editing of CFTR in organoids").

A large and diverse brain organoid dataset of 1,400 cross-laboratory images of 64 trackable brain organoids

Brain organoids represent a useful tool for modeling of neurodevelopmental disorders and can recapitulate brain volume alterations such as microcephaly. To monitor organoid growth, brightfield microscopy images are frequently used and evaluated manually which is time-consuming and prone to observer-bias. Recent software applications for organoid evaluation address this issue using classical or AI-based methods. These pipelines have distinct strengths and weaknesses that are not evident to external observers. We provide a dataset of more than 1,400 images of 64 trackable brain organoids from four clones differentiated from healthy and diseased patients. This dataset is especially powerful to test and compare organoid analysis pipelines because of (1) trackable organoids (2) frequent imaging during development (3) clone diversity (4) distinct clone development (5) cross sample imaging by two different labs (6) common imaging distractors, and (6) pixel-level ground truth organoid annotations. Therefore, this dataset allows to perform differentiated analyses to delineate strengths, weaknesses, and generalizability of automated organoid analysis pipelines as well as analysis of clone diversity and similarity.

Bioengineering toolkits for potentiating organoid therapeutics

Organoids are three-dimensional, multicellular constructs that recapitulate the structural and functional features of specific organs. Because of these characteristics, organoids have been widely applied in biomedical research in recent decades. Remarkable advancements in organoid technology have positioned them as promising candidates for regenerative medicine. However, current organoids still have limitations, such as the absence of internal vasculature, limited functionality, and a small size that is not commensurate with that of actual organs. These limitations hinder their survival and regenerative effects after transplantation. Another significant concern is the reliance on mouse tumor-derived matrix in organoid culture, which is unsuitable for clinical translation due to its tumor origin and safety issues. Therefore, our aim is to describe engineering strategies and alternative biocompatible materials that can facilitate the practical applications of organoids in regenerative medicine. Furthermore, we highlight meaningful progress in organoid transplantation, with a particular emphasis on the functional restoration of various organs.

Bridging the organoid translational gap: integrating standardization and micropatterning for drug screening in clinical and pharmaceutical medicine

Synthetic organ models such as organoids and organ-on-a-chip have been receiving recognition from administrative agencies. Despite the proven success of organoids in predicting drug efficacy on laboratory scales, their translational advances have not fully satisfied the expectations for both clinical implementation and commercial applications. The transition from laboratory settings to clinical applications continues to encounter challenges. Employing engineering methodologies to facilitate the bridging of this gap for organoids represents one of the key directions for future advancement. The main measures to bridge the gap include environmental and phenotypic recapitulation, 3D patterning, matrix engineering, and multi-modality information acquisition and processing. Pilot whole-process clinical/pharmaceutical applications with fast and standardized organoid models will continuously offer convincing frontline optimization clues and driving forces to the organoid community, which is a promising path to translational organoid technologies.

Revealing the clinical potential of high-resolution organoids

The symbiotic interplay of organoid technology and advanced imaging strategies yields innovative breakthroughs in research and clinical applications. Organoids, intricate three-dimensional cell cultures derived from pluripotent or adult stem/progenitor cells, have emerged as potent tools for in vitro modeling, reflecting in vivo organs and advancing our grasp of tissue physiology and disease. Concurrently, advanced imaging technologies such as confocal, light-sheet, and two-photon microscopy ignite fresh explorations, uncovering rich organoid information. Combined with advanced imaging technologies and the power of artificial intelligence, organoids provide new insights that bridge experimental models and real-world clinical scenarios. This review explores exemplary research that embodies this technological synergy and how organoids reshape personalized medicine and therapeutics.

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.

AI-enabled organoids: Construction, analysis, and application

Organoids, miniature and simplified in vitro model systems that mimic the structure and function of organs, have attracted considerable interest due to their promising applications in disease modeling, drug screening, personalized medicine, and tissue engineering. Despite the substantial success in cultivating physiologically relevant organoids, challenges remain concerning the complexities of their assembly and the difficulties associated with data analysis. The advent of AI-Enabled Organoids, which interfaces with artificial intelligence (AI), holds the potential to revolutionize the field by offering novel insights and methodologies that can expedite the development and clinical application of organoids. This review succinctly delineates the fundamental concepts and mechanisms underlying AI-Enabled Organoids, summarizing the prospective applications on rapid screening of construction strategies, cost-effective extraction of multiscale image features, streamlined analysis of multi-omics data, and precise preclinical evaluation and application. We also explore the challenges and limitations of interfacing organoids with AI, and discuss the future direction of the field. Taken together, the AI-Enabled Organoids hold significant promise for advancing our understanding of organ development and disease progression, ultimately laying the groundwork for clinical application.

High-throughput deconvolution of 3D organoid dynamics at cellular resolution for cancer pharmacology with Cellos

Three-dimensional (3D) organoid cultures are flexible systems to interrogate cellular growth, morphology, multicellular spatial architecture, and cellular interactions in response to treatment. However, computational methods for analysis of 3D organoids with sufficiently high-throughput and cellular resolution are needed. Here we report Cellos, an accurate, high-throughput pipeline for 3D organoid segmentation using classical algorithms and nuclear segmentation using a trained Stardist-3D convolutional neural network. To evaluate Cellos, we analyze ~100,000 organoids with ~2.35 million cells from multiple treatment experiments. Cellos segments dye-stained or fluorescently-labeled nuclei and accurately distinguishes distinct labeled cell populations within organoids. Cellos can recapitulate traditional luminescence-based drug response of cells with complex drug sensitivities, while also quantifying changes in organoid and nuclear morphologies caused by treatment as well as cell-cell spatial relationships that reflect ecological affinity. Cellos provides powerful tools to perform high-throughput analysis for pharmacological testing and biological investigation of organoids based on 3D imaging.

Mapping morphogenesis and mechanics in embryo models

Recent methodological advances in measurements of geometry and forces in the early embryo and its models are enabling a deeper understanding of the complex interplay of genetics, mechanics and geometry during development.

Engineered organoids for biomedical applications

As miniaturized and simplified stem cell-derived 3D organ-like structures, organoids are rapidly emerging as powerful tools for biomedical applications. With their potential for personalized therapeutic interventions and high-throughput drug screening, organoids have gained significant attention recently. In this review, we discuss the latest developments in engineering organoids and using materials engineering, biochemical modifications, and advanced manufacturing technologies to improve organoid culture and replicate vital anatomical structures and functions of human tissues. We then explore the diverse biomedical applications of organoids, including drug development and disease modeling, and highlight the tools and analytical techniques used to investigate organoids and their microenvironments. We also examine the latest clinical trials and patents related to organoids that show promise for future clinical translation. Finally, we discuss the challenges and future perspectives of using organoids to advance biomedical research and potentially transform personalized medicine.

Development of a deep learning based image processing tool for enhanced organoid analysis

Contrary to 2D cells, 3D organoid structures are composed of diverse cell types and exhibit morphologies of various sizes. Although researchers frequently monitor morphological changes, analyzing every structure with the naked eye is difficult. Given that deep learning (DL) has been used for 2D cell image segmentation, a trained DL model may assist researchers in organoid image recognition and analysis. In this study, we developed OrgaExtractor, an easy-to-use DL model based on multi-scale U-Net, to perform accurate segmentation of organoids of various sizes. OrgaExtractor achieved an average dice similarity coefficient of 0.853 from a post-processed output, which was finalized with noise removal. Correlation between CellTiter-Glo assay results and daily measured organoid images shows that OrgaExtractor can reflect the actual organoid culture conditions. The OrgaExtractor data can be used to determine the best time point for organoid subculture on the bench and to maintain organoids in the long term.

In-silico and in-vitro morphometric analysis of intestinal organoids

Organoids offer a powerful model to study cellular self-organisation, the growth of specific tissue morphologies in-vitro, and to assess potential medical therapies. However, the intrinsic mechanisms of these systems are not entirely understood yet, which can result in variability of organoids due to differences in culture conditions and basement membrane extracts used. Improving the standardisation of organoid cultures is essential for their implementation in clinical protocols. Developing tools to assess and predict the behaviour of these systems may produce a more robust and standardised biological model to perform accurate clinical studies. Here, we developed an algorithm to automate crypt-like structure counting on intestinal organoids in both in-vitro and in-silico images. In addition, we modified an existing two-dimensional agent-based mathematical model of intestinal organoids to better describe the system physiology, and evaluated its ability to replicate budding structures compared to new experimental data we generated. The crypt-counting algorithm proved useful in approximating the average number of budding structures found in our in-vitro intestinal organoid culture images on days 3 and 7 after seeding. Our changes to the in-silico model maintain the potential to produce simulations that replicate the number of budding structures found on days 5 and 7 of in-vitro data. The present study aims to aid in quantifying key morphological structures and provide a method to compare both in-vitro and in-silico experiments. Our results could be extended later to 3D in-silico models.

Balance between the cell viability and death in 3D

Cell death is a phenomenon, frequently perceived as an absolute event for cell, tissue and the organ. However, the rising popularity and complexity of such 3D multicellular 'tissue building blocks' as heterocellular spheroids, organoids, and 'assembloids' prompts to revise the definition and quantification of cell viability and death. It raises several questions on the overall viability of all the cells within 3D volume and on choosing the appropriate, continuous, and non-destructive viability assay enabling for a single-cell analysis. In this review, we look at cell viability and cell death modalities with attention to the intrinsic features of such 3D models as spheroids, organoids, and bioprints. Furthermore, we look at emerging and promising methodologies, which can help define and understand the balance between cell viability and death in dynamic and complex 3D environments. We conclude that the recent innovations in biofabrication, biosensor probe development, and fluorescence microscopy can help answer these questions.

Controlling human organoid symmetry breaking reveals signaling gradients drive segmentation clock waves

Axial development of mammals involves coordinated morphogenetic events, including axial elongation, somitogenesis, and neural tube formation. To gain insight into the signals controlling the dynamics of human axial morphogenesis, we generated axially elongating organoids by inducing anteroposterior symmetry breaking of spatially coupled epithelial cysts derived from human pluripotent stem cells. Each organoid was composed of a neural tube flanked by presomitic mesoderm sequentially segmented into somites. Periodic activation of the somite differentiation gene MESP2 coincided in space and time with anteriorly traveling segmentation clock waves in the presomitic mesoderm of the organoids, recapitulating critical aspects of somitogenesis. Timed perturbations demonstrated that FGF and WNT signaling play distinct roles in axial elongation and somitogenesis, and that FGF signaling gradients drive segmentation clock waves. By generating and perturbing organoids that robustly recapitulate the architecture of multiple axial tissues in human embryos, this work offers a means to dissect mechanisms underlying human embryogenesis.

Organoids revealed: morphological analysis of the profound next generation in-vitro model with artificial intelligence

In modern terminology, "organoids" refer to cells that grow in a specific three-dimensional (3D) environment in vitro, sharing similar structures with their source organs or tissues. Observing the morphology or growth characteristics of organoids through a microscope is a commonly used method of organoid analysis. However, it is difficult, time-consuming, and inaccurate to screen and analyze organoids only manually, a problem which cannot be easily solved with traditional technology. Artificial intelligence (AI) technology has proven to be effective in many biological and medical research fields, especially in the analysis of single-cell or hematoxylin/eosin stained tissue slices. When used to analyze organoids, AI should also provide more efficient, quantitative, accurate, and fast solutions. In this review, we will first briefly outline the application areas of organoids and then discuss the shortcomings of traditional organoid measurement and analysis methods. Secondly, we will summarize the development from machine learning to deep learning and the advantages of the latter, and then describe how to utilize a convolutional neural network to solve the challenges in organoid observation and analysis. Finally, we will discuss the limitations of current AI used in organoid research, as well as opportunities and future research directions.

Graphic abstract

Development of bioinformatics and multi-omics analyses in organoids

Pre-clinical models are critical in gaining mechanistic and biological insights into disease progression. Recently, patient-derived organoid models have been developed to facilitate our understanding of disease development and to improve the discovery of therapeutic options by faithfully recapitulating in vivo tissues or organs. As technological developments of organoid models are rapidly growing, computational methods are gaining attention in organoid researchers to improve the ability to systematically analyze experimental results. In this review, we summarize the recent advances in organoid models to recapitulate human diseases and computational advancements to analyze experimental results from organoids. [BMB Reports 2023; 56(1): 43-48].

Deep learning predicts the differentiation of kidney organoids derived from human induced pluripotent stem cells

BACKGROUND

Kidney organoids derived from human pluripotent stem cells (hPSCs) contain multilineage nephrogenic progenitor cells and can recapitulate the development of the kidney. Kidney organoids derived from hPSCs have the potential to be applied in regenerative medicine as well as renal disease modeling, drug screening, and nephrotoxicity testing. Despite biotechnological advances, individual differences in morphological and growth characteristics among kidney organoids need to be addressed before clinical and commercial application. In this study, we hypothesized that an automated noninvasive method based on deep learning of bright-field images of kidney organoids can predict their differentiation status.

METHODS

Bright-field images of kidney organoids were collected on day 18 after differentiation. To train convolutional neural networks (CNNs), we utilized a transfer learning approach. CNNs were trained to predict the differentiation of kidney organoids on bright-field images based on the messenger RNA expression of renal tubular epithelial cells as well as podocytes.

RESULTS

The best prediction model was DenseNet121 with a total Pearson correlation coefficient score of 0.783 on a test dataset. W classified the kidney organoids into two categories: organoids with above-average gene expression (Positive) and those with below-average gene expression (Negative). Comparing the best-performing CNN with human-based classifiers, the CNN algorithm had a receiver operating characteristic-area under the curve (AUC) score of 0.85, while the experts had an AUC score of 0.48.

CONCLUSION

These results confirmed our original hypothesis and demonstrated that our artificial intelligence algorithm can successfully recognize the differentiation status of kidney organoids.

Organoid Cultures In Silico: Tools or Toys?

The implementation of stem-cell-based organoid culture more than ten years ago started a development that created new avenues for diagnostic analyses and regenerative medicine. In parallel, computational modelling groups realized the potential of this culture system to support their theoretical approaches to study tissues in silico. These groups developed computational organoid models (COMs) that enabled testing consistency between cell biological data and developing theories of tissue self-organization. The models supported a mechanistic understanding of organoid growth and maturation and helped linking cell mechanics and tissue shape in general. What comes next? Can we use COMs as tools to complement the equipment of our biological and medical research? While these models already support experimental design, can they also quantitatively predict tissue behavior? Here, we review the current state of the art of COMs and discuss perspectives for their application.

Engineering approaches for cardiac organoid formation and their characterization

Cardiac organoids are 3-dimensional (3D) structures composed of tissue or niche-specific cells, obtained from diverse sources, encapsulated in either a naturally derived or synthetic, extracellular matrix scaffold, and include exogenous biochemical signals such as essential growth factors. The overarching goal of developing cardiac organoid models is to establish a functional integration of cardiomyocytes with physiologically relevant cells, tissues, and structures like capillary-like networks composed of endothelial cells. These organoids used to model human heart anatomy, physiology, and disease pathologies in vitro have the potential to solve many issues related to cardiovascular drug discovery and fundamental research. The advent of patient-specific human-induced pluripotent stem cell-derived cardiovascular cells provide a unique, single-source approach to study the complex process of cardiovascular disease progression through organoid formation and incorporation into relevant, controlled microenvironments such as microfluidic devices. Strategies that aim to accomplish such a feat include microfluidic technology-based approaches, microphysiological systems, microwells, microarray-based platforms, 3D bioprinted models, and electrospun fiber mat-based scaffolds. This article discusses the engineering or technology-driven practices for making cardiac organoid models in comparison with self-assembled or scaffold-free methods to generate organoids. We further discuss emerging strategies for characterization of the bio-assembled cardiac organoids including electrophysiology and machine-learning and conclude with prospective points of interest for engineering cardiac tissues in vitro.

Arrested coalescence of multicellular aggregates

Multicellular aggregates are known to exhibit liquid-like properties. The fusion process of two cell aggregates is commonly studied as the coalescence of two viscous drops. However, tissues are complex materials and can exhibit viscoelastic behaviour. It is known that elastic effects can prevent the complete fusion of two drops, a phenomenon known as arrested coalescence. Here we study this phenomenon in stem cell aggregates and provide a theoretical framework which agrees with the experiments. In addition, agent-based simulations show that active cell fluctuations can control a solid-to-fluid phase transition, revealing that arrested coalescence can be found in the vicinity of an unjamming transition. By analysing the dynamics of the fusion process and combining it with nanoindentation measurements, we obtain the effective viscosity, shear modulus and surface tension of the aggregates. More generally, our work provides a simple, fast and inexpensive method to characterize the mechanical properties of viscoelastic materials.

Periodic formation of epithelial somites from human pluripotent stem cells

During embryonic development, epithelial cell blocks called somites are periodically formed according to the segmentation clock, becoming the foundation for the segmental pattern of the vertebral column. The process of somitogenesis has recently been recapitulated with murine and human pluripotent stem cells. However, an in vitro model for human somitogenesis coupled with the segmentation clock and epithelialization is still missing. Here, we report the generation of human somitoids, organoids that periodically form pairs of epithelial somite-like structures. Somitoids display clear oscillations of the segmentation clock that coincide with the segmentation of the presomitic mesoderm. The resulting somites show anterior-posterior and apical-basal polarities. Matrigel is essential for epithelialization but dispensable for the differentiation into somite cells. The size of somites is rather constant, irrespective of the initial cell number. The amount of WNT signaling instructs the proportion of mesodermal lineages in somitoids. Somitoids provide a novel platform to study human somitogenesis.

Somitogenesis has been well characterized in model organisms, resulting in detailed description of the somite segmentation clock. Here they generate somitogenic organoids from human pluripotent stem cells that recapitulate somitogenesis, periodic segmentation, and proper polarity.

Imaging-Based Machine Learning Analysis of Patient-Derived Tumor Organoid Drug Response

Three-quarters of compounds that enter clinical trials fail to make it to market due to safety or efficacy concerns. This statistic strongly suggests a need for better screening methods that result in improved translatability of compounds during the preclinical testing period. Patient-derived organoids have been touted as a promising 3D preclinical model system to impact the drug discovery pipeline, particularly in oncology. However, assessing drug efficacy in such models poses its own set of challenges, and traditional cell viability readouts fail to leverage some of the advantages that the organoid systems provide. Consequently, phenotypically evaluating complex 3D cell culture models remains difficult due to intra- and inter-patient organoid size differences, cellular heterogeneities, and temporal response dynamics. Here, we present an image-based high-content assay that provides object level information on 3D patient-derived tumor organoids without the need for vital dyes. Leveraging computer vision, we segment and define organoids as independent regions of interest and obtain morphometric and textural information per organoid. By acquiring brightfield images at different timepoints in a robust, non-destructive manner, we can track the dynamic response of individual organoids to various drugs. Furthermore, to simplify the analysis of the resulting large, complex data files, we developed a web-based data visualization tool, the Organoizer, that is available for public use. Our work demonstrates the feasibility and utility of using imaging, computer vision and machine learning to determine the vital status of individual patient-derived organoids without relying upon vital dyes, thus taking advantage of the characteristics offered by this preclinical model system.