Revealing the clinical potential of high-resolution organoids

Enhancing organoid technology with carbon-based nanomaterial biosensors: Advancements, challenges, and future directions

Various carbon-based nanomaterials (CBNs) have been utilized to develop nano- and microscale biosensors that enable real-time and continuous monitoring of biochemical and biophysical changes in living biological systems. The integration of CBN-based biosensors into organoids has recently provided valuable insights into organoid development, disease modeling, and drug responses, enhancing their functionality and expanding their applications in diverse biomedical fields. These biosensors have been particularly transformative in studying neurological disorders, cardiovascular diseases, cancer progression, and liver toxicity, where precise, non-invasive monitoring is crucial for understanding pathophysiological mechanisms and assessing therapeutic efficacy. This review introduces intra- and extracellular biosensors incorporating CBNs such as graphene, carbon nanotubes (CNTs), graphene oxide (GO), reduced graphene oxide (rGO), carbon dots (CDs), and fullerenes. Additionally, it discusses strategies for improving the biocompatibility of CBN-based biosensors and minimizing their potential toxicity to ensure long-term organoid viability. Key challenges such as biosensor integration, data accuracy, and functional compatibility with specific organoid models are also addressed. Furthermore, this review highlights how CBN-based biosensors enhance the precision and relevance of organoid models in biomedical research, particularly in organ-specific applications such as brain-on-a-chip systems for neurodegenerative disease studies, liver-on-a-chip platforms for hepatotoxicity screening, and cardiac organoids for assessing cardiotoxicity in drug development. Finally, it explores how biosensing technologies could revolutionize personalized medicine by enabling high throughput drug screening, patient-specific disease modeling, and integrated sensing platforms for early diagnostics. By capturing current advancements and future directions, this review underscores the transformative potential of carbon-based nanotechnology in organoid research and its broader impact on medical science.

Proximity adjusted centroid mapping for accurate detection of nuclei in dense 3D cell systems

In the past decade, deep learning algorithms have surpassed the performance of many conventional image segmentation pipelines. Powerful models are now available for segmenting cells and nuclei in diverse 2D image types, but segmentation in 3D cell systems remains challenging due to the high cell density, the heterogenous resolution and contrast across the image volume, and the difficulty in generating reliable and sufficient ground truth data for model training. Reasoning that most image processing applications rely on nuclear segmentation but do not necessarily require an accurate delineation of their shapes, we implemented Proximity Adjusted Centroid MAPping (PAC-MAP), a 3D U-net based method that predicts the position of nuclear centroids and their proximity to other nuclei. We show that our model outperforms existing methods, predominantly by boosting recall, especially in conditions of high cell density. When trained from scratch with limited expert annotations (30 images), PAC-MAP attained an average F1 score of 0.793 for nuclei centroid prediction in dense spheroids. When pretraining using weakly supervised bulk data (>2300 images) followed by finetuning with the available expert annotations, the average F1 score could be significantly improved to 0.816. We demonstrate the utility of our method for quantifying the absolute cell content of spheroids and comprehensively mapping the infiltration pattern of patient-derived glioblastoma cells in cerebral organoids.

A Large Model-Derived Algorithm for Complex Organoids with Internal Morphogenesis and Digital Marker Derivation

Automated segmentation and evaluation algorithms have been demonstrated to enhance the simplicity and translational utility of organoid technology. However, there is a pressing need for the development of complex organoids that possess epithelium environmental elements, dense regional cell aggregation, and intraorganoid morphologies. Nevertheless, there has been limited progress, including both the construction of data sets and the development of algorithms, in the use of user-friendly microscopy to address such complex organoids. In this study, a data set of bright-field and living cell fluorescence images in paired forms and with temporal variance was constructed using droplet-engineered lung organoids. Additionally, a large model-based algorithm was developed. Both the organoid contours and intraorganoid morphologies were included in the data set, and their physical parameters were included and screened to form multiplex digital markers for organoid evaluation. The algorithm has been demonstrated to outperform existing methods and is therefore suitable for the evaluation of complex organoids. It is expected that the algorithm will facilitate the successful demonstration of AI in organoid evaluation and decision-making regarding their status.

Segmentation and Multi-Timepoint Tracking of 3D Cancer Organoids from Optical Coherence Tomography Images Using Deep Neural Networks

Recent years have ushered in a transformative era in in vitro modeling with the advent of organoids, three-dimensional structures derived from stem cells or patient tumor cells. Still, fully harnessing the potential of organoids requires advanced imaging technologies and analytical tools to quantitatively monitor organoid growth. Optical coherence tomography (OCT) is a promising imaging modality for organoid analysis due to its high-resolution, label-free, non-destructive, and real-time 3D imaging capabilities, but accurately identifying and quantifying organoids in OCT images remain challenging due to various factors. Here, we propose an automatic deep learning-based pipeline with convolutional neural networks that synergistically includes optimized preprocessing steps, the implementation of a state-of-the-art deep learning model, and ad-hoc postprocessing methods, showcasing good generalizability and tracking capabilities over an extended period of 13 days. The proposed tracking algorithm thoroughly documents organoid evolution, utilizing reference volumes, a dual branch analysis, key attribute evaluation, and probability scoring for match identification. The proposed comprehensive approach enables the accurate tracking of organoid growth and morphological changes over time, advancing organoid analysis and serving as a solid foundation for future studies for drug screening and tumor drug sensitivity detection based on organoids.