Organoids, Volume 4, Issue 4 (December 2025) – 12 articles

Three-dimensional cell model systems such as tumour organoids allow for in vitro modelling of self-organized tissue with functional and histologic similarity to in vivo tissue. However, there is a need for standard protocols and techniques to confirm the presence of cancer within organoids derived from tumour tissue. The aim of this study was to assess the utility of a Nanostring gene expression-based machine learning classifier to determine the presence of cancer or normal organoids in cultures developed from both benign and cancerous stomach biopsies. A prospective cohort of normal and cancer stomach biopsies were collected from 2019 to 2022. Tissue specimens were processed for formalin-fixed paraffin-embedding (FFPE) and a subset of specimens were established in organoid cultures. Specimens were labelled as normal or cancer according to analysis of the FFPE tissue by two pathologists. The gene expression in FFPE and organoid tissue was measured using a 107 gene Nanostring codeset and normalized using the Removal of Unwanted Variation III algorithm. Our machine learning model was developed using five-fold nested cross-validation to classify normal or cancer gastric tissue from publicly available Asian Cancer Research Group (ACRG) gene expression data. The models were externally validated using the Cancer Genome Atlas (TCGA), as well as our own FFPE and organoid gene expression data. A total of 60 samples were collected, including 38 cancer FFPE specimens, 5 normal FFPE specimens, 12 cancer organoids, and 5 normal organoids. The optimal model design used a Least Absolute Shrinkage and Selection Operator model for feature selection and an ElasticNet model for classification, yielding area under the curve (AUC) values of 0.99 [95% CI: 0.99–1], 0.90 [95% CI: 0.87–0.93], and 0.79 [95% CI: 0.74–0.84] for ACRG (internal test), FFPE, and organoid (external test) data, respectively. The performance of our final model on external data achieved AUC values of 0.99 [95% CI: 0.98–1], 0.94 [95% CI: 0.86–1], and 0.85 [95% CI: 0.63–1] for TCGA, FFPE, and organoid specimens, respectively. Using a public database to create a machine learning model in combination with a Nanostring gene expression assay allows us to allocate organoids and their paired whole tissue samples. This platform yielded reasonable accuracy for FFPE and organoid specimens, with the former being more accurate. This study re-affirms that although organoids are a high-fidelity model, there are still limitations in validating the recapitulation of cancer in vitro. Full article

Ensuring access to safe drinking water is a fundamental public health priority, yet the growing diversity of contaminants demands more human-relevant toxicity assessment frameworks. Conventional models based on immortalized cell lines or sentinel species, while informative, lack the tissue complexity and inter-individual variability required to capture realistic human responses. Organoids, three-dimensional epithelial structures derived from adult or pluripotent stem cells, retain the genomic, histological, and functional characteristics of their original tissue, enabling assessment of contaminant-induced toxicity, short-term peak exposures, and inter-donor variability within a single system. This study examined whether current international drinking water guidelines remain protective or if recent organoid-based findings reveal toxicity at differing concentrations. Comparative synthesis indicates that per- and polyfluoroalkyl substances (PFAS) often display organoid toxicity at concentrations above current thresholds, suggesting conservative guidelines, whereas most metals are properly regulated. However, some metals exhibit toxicity at concentrations that include levels below guideline values, highlighting the need for further investigation. Emerging contaminants, including pesticides, nanoparticles, microplastics, and endocrine disruptors, induce adverse effects at environmentally relevant concentrations, despite limited or absent regulatory limits. Integrating organoid-based toxicology with high-frequency monitoring and dynamic exposure modeling could refine water quality guidelines and support adaptive regulatory frameworks that better reflect real-world exposure patterns and human diversity. Full article

Cancer remains a leading cause of mortality worldwide. Patient-derived organoids (PDOs) are three-dimensional (3D) cultures that recapitulate tumor histology, genetics, and cellular heterogeneity, providing physiologically relevant preclinical models. Integrating PDOs with artificial intelligence (AI) and machine learning (ML) enables scalable analysis of high-dimensional datasets, including imaging, transcriptomics, proteomics, and pharmacological readouts. These approaches support prediction of drug sensitivity, biomarker discovery, and patient stratification. Recent advances—such as deep learning (DL), transfer learning, federated learning, and self-supervised learning—enhance phenotypic profiling, cross-institutional model training, and translational prediction. In this review, we summarize the current state of AI-driven PDO research, highlighting methodological approaches, preclinical and clinical applications, challenges, and emerging trends. We also propose strategies for standardization, validation, and multi-modal integration to accelerate patient-specific therapeutic strategies. Full article

As brain organoids and organoid-based computational models grow in complexity, they increasingly exhibit electrophysiological patterns consistent with plasticity and information processing. This article explores a central question at the intersection of neuroscience, synthetic biology, and philosophy of mind: Can intelligent behavior be meaningfully separated from an intelligent being? In other words, can adaptive, goal-directed behavior exist independently of subjective awareness—a question that challenges conventional definitions of cognition and consciousness. Drawing from neuroscience, artificial intelligence, and philosophy, I propose a tiered framework based on neural complexity and environmental responsiveness. This includes a simple level analysis and a context-sensitive benchmark for evaluating intelligence in organoid systems without presupposing sentience. Ethical and ontological implications are also addressed, particularly the risk of anthropomorphizing synthetic cognition and the importance of developing context-aware definitions of intelligence. By distinguishing functional sophistication from subjective experience, the framework aims to guide responsible scientific inquiry while clarifying the boundaries of synthetic cognition. Full article

This review aims to highlight how the study of kidney organoids combined with proteomic analysis can deepen our understanding of renal physiology and disease. Proteomics quantifies proteins in a sample, allowing us to determine which proteins are present, how abundant they are, and how they are modified. These data may reveal the pathways that are active in the kidney organoids and how they change in disease, helping to pinpoint candidate biomarkers. Kidney organoids are three-dimensional structures derived from induced pluripotent stem cells (iPS) that recapitulate many architectural and functional features of the adult organ. Because they can be generated in large numbers under defined conditions, organoids provide a promising platform for testing how genetic mutations, environmental stresses, or drugs affect kidney development and pathology. When proteomic profiles are obtained from mature organoids, researchers can directly link protein-level changes to phenotypic outcomes observed in the model. This integration makes it possible to map disease-related networks at the molecular level and to assess the impact of therapeutic interventions in a system that more closely resembles human kidney tissue than traditional cell lines. A current limitation is that many kidney organoids do not reach the full maturation seen in vivo; they often lack complete segmental differentiation and the functional robustness of adult nephrons. Improving the maturation state of organoids will be essential for accurately modeling chronic kidney diseases and for translating findings into clinically relevant therapies. Full article

Human retinal organoids derived from pluripotent stem cells represent a robust in vitro model for investigating retinal development and disease mechanisms of retinal disorders. However, achieving structural maturation that faithfully recapitulates the intricate architecture of photoreceptors within a feasible and cost-efficient culture timeframe remains a significant challenge. Here, we present an optimized differentiation protocol that enables the generation of retinal organoids exhibiting advanced photoreceptor maturation within 140 days. Photoreceptors in the retinal organoids displayed compartmentalized architecture, including distinct inner and outer segments and connecting cilia. Notably, we observed the emergence of budding calyceal process-like structures—a feature not previously emphasized in photoreceptors derived from pluripotent stem cells. These results suggest that our protocol may promote advanced photoreceptor maturation within a relatively shortened culture period. Thus, this method could serve as a useful model for investigating retinal development and related pathologies, building upon previous protocols. Full article

Breast cancer remains one of the leading causes of cancer morbidity and mortality among women worldwide. Conventional two-dimensional (2D) cell culture models and animal studies often fail to accurately recapitulate the complex tumor microenvironment and heterogeneous nature of breast cancer. Recent advancements in tissue engineering have enabled the development of more physiologically relevant models using three-dimensional (3D) bioprinting and organoid technology. This study focuses on integrating 3D bioprinting with patient-derived organoid models to replicate breast cancer tissue architecture, cellular heterogeneity, and tumor-stroma interactions. Utilizing biomimetic bioinks and customized bioprinting protocols, we successfully fabricated breast cancer tissue constructs embedded with stromal and immune components. These engineered models demonstrated high fidelity in mimicking in vivo tumor pathophysiology, including angiogenesis, epithelial–mesenchymal transition, and extracellular matrix remodeling. Furthermore, the platform allowed for high-throughput drug screening and evaluation of therapeutic responses, revealing differential sensitivities to chemotherapeutics and targeted therapies. Our findings highlight the potential of bioprinted organoid models as powerful tools for personalized medicine, enabling more predictive and reliable cancer research and drug development. Full article

Obesity is an epidemic with myriad health effects, but little is understood regarding individual obese phenotypes and how they may respond to therapy. Epigenetic changes associated with obesity have been detected in blood, liver, pancreas, and adipose tissues. Previous work using human organoids found that dietary glucose hyperabsorption is a steadfast trait in cultures derived from some obese subjects, but detailed transcriptional or epigenomic features of the intestinal epithelia associated with this persistent phenotype are unknown. This study evaluated differentially expressed genes and relative chromatin accessibility in intestinal organoids established from donors classified as non-obese, obese, or obese hyperabsorptive by body mass index and glucose transport assays. Transcriptomic analysis indicated that obese hyperabsorptive subject organoids have significantly upregulated dietary nutrient absorption transcripts and downregulated type I interferon targets. Chromatin accessibility and transcription factor footprinting predicted that enhanced HNF4G binding may promote the obese hyperabsorption phenotype. Quantitative RT-PCR assessment in organoids representing a larger subject cohort suggested that intestinal epithelial expression of CUBN, GIP, SLC5A11, and SLC2A5 were highly correlated with hyperabsorption. Thus, the obese hyperabsorption phenotype was characterized by transcriptional changes that support increased nutrient uptake by intestinal epithelia, potentially driven by differentially accessible chromatin. Recognizing unique intestinal phenotypes in obesity provides a new perspective in considering therapeutic targets and options with which to manage the disease. Full article

First-trimester placental development comprises many critical yet understudied cellular events that determine pregnancy outcomes. Improper placentation leads to a host of health issues that not only impact the fetal period but also influence later-life offspring health. Thus, an experimental paradigm for studying early placental development is necessary and has spurred the development of new in vitro models. Organoid model systems are three-dimensional structures comprising multiple differentiated cell types that originate from a progenitor population. Trophoblasts are the progenitor cells that serve as the proliferative base for the differentiation and maintenance of the placenta. Due to research constraints, experimental studies on the causal mechanisms underlying pathological pregnancies cannot readily be performed in human subjects. The nonhuman primate (NHP) offers a solution to this problem as it circumvents the limitations of human pregnancy sampling. Importantly, NHPs share many developmental features of human pregnancy, including placenta type and a similar fetal growth trajectory, making longitudinal pregnancy studies feasible and relevant. Since perturbations made in vivo can be validated in vitro, an NHP model of early pregnancy would facilitate mechanistic studies of pregnancy disorders. Herein, we describe the methodology for the establishment of a first-trimester NHP placenta trophoblast organoid model system. Full article

Organoid technology has emerged as a revolutionary tool in cancer research, offering physiologically accurate, three-dimensional models that preserve the histoarchitecture, genetic stability, and phenotypic complexity of primary tumors. These self-organizing structures, derived from adult stem cells, induced pluripotent stem cells, or patient tumor biopsies, recapitulate critical aspects of tumor heterogeneity, clonal evolution, and microenvironmental interactions. Organoids serve as powerful systems for modeling tumor progression, assessing drug sensitivity and resistance, and guiding precision oncology strategies. Recent innovations have extended organoid capabilities beyond static culture systems. Integration with microfluidic organoid-on-chip platforms, high-throughput CRISPR-based functional genomics, and AI-driven phenotypic analytics has enhanced mechanistic insight and translational relevance. Co-culture systems incorporating immune, stromal, and endothelial components now permit dynamic modeling of tumor–host interactions, immunotherapeutic responses, and metastatic behavior. Comparative analyses with conventional platforms, 2D monolayers, spheroids, and patient-derived xenografts emphasize the superior fidelity and clinical potential of organoids. Despite these advances, several challenges remain, such as protocol variability, incomplete recapitulation of systemic physiology, and limitations in scalability, standardization, and regulatory alignment. Addressing these gaps with unified workflows, synthetic matrices, vascularized and innervated co-cultures, and GMP-compliant manufacturing will be crucial for clinical integration. Proactive engagement with regulatory frameworks and ethical guidelines will be pivotal to ensuring safe, responsible, and equitable clinical translation. With the convergence of bioengineering, multi-omics, and computational modeling, organoids are poised to become indispensable tools in next-generation oncology, driving mechanistic discovery, predictive diagnostics, and personalized therapy optimization. Full article

In many cancers, tumorigenesis is determined in part by the cell type in the tissue that transforms, which has been called the cell of origin. In intestinal cancer, previous observations suggested that transformation can occur from both stem cells and more differentiated cells; in the latter case, this is provided that NF-kB is activated and apoptosis is blocked. However, whether these distinct transformation trajectories yield similar types of cancer remains unresolved. In this study the effect of APC loss within different cellular backgrounds was analyzed. Transformation of either stem-like cells or secretory-like cells, as defined by CD24 or c-KIT expression, by deleting the APC function in organoids in vitro, led to WNT-independent growth of organoids in both cellular populations. Importantly, transformed cultures derived from secretory-like cells had significantly distinct gene expression profiles as compared to the more stem cell-derived (CD44^high cells) APC mutant cultures and in fact preserved a level of gene expression that relates back to their original cell lineage. Our data highlights the influence of different cellular backgrounds on the initiation of intestinal cancer and suggests that the cell of origin could be a defining factor in colorectal cancer heterogeneity. Full article