D-CryptO: deep learning-based analysis of colon organoid morphology from brightfield images

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.

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.

Construction organoid model of ovarian endometriosis and the function of estrogen and progesterone in the model

Endometriosis is a refractory estrogen-dependent gynecological disease in which ovarian endometriosis(OE) is the most common, and the main cell components are endometrial epithelial cells and stromal cells. However, constructing ectopic endometrial epithelial cell models in basic studies is still challenging. In this study, we explored the feasibility and influencing factors of constructing and validating eutopic and ectopic endometrial organoid models of OE as in-vitro models. Eutopic and ectopic endometrial tissues of OE patients were selected to establish organoids. Morphologically, the organoids showed a three-dimensional glandular structure with vacuoles or cystic irregularities, and the histological features of the epithelial organoids in endometriosis were well preserved. Immunofluorescence showed positive expression of epithelial markers and estrogen/progesterone receptors. Genetic identification revealed a 100% match between endometriosis epithelial organoids and endometrial tissue, indicating a common origin. The effects of estrogen and progesterone on the proliferation and secretion of organoids differed with the change in concentration. The successful construction of ectopic endometrial organoids provides a new in vitro model for drug intervention and mechanism study of ovarian endometriosis.

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.

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.

Understanding patient-derived tumor organoid growth through an integrated imaging and mathematical modeling framework

Patient-derived tumor organoids (PDTOs) are novel cellular models that maintain the genetic, phenotypic and structural features of patient tumor tissue and are useful for studying tumorigenesis and drug response. When integrated with advanced 3D imaging and analysis techniques, PDTOs can be used to establish physiologically relevant high-throughput and high-content drug screening platforms that support the development of patient-specific treatment strategies. However, in order to effectively leverage high-throughput PDTO observations for clinical predictions, it is critical to establish a quantitative understanding of the basic properties and variability of organoid growth dynamics. In this work, we introduced an innovative workflow for analyzing and understanding PDTO growth dynamics, by integrating a high-throughput imaging deep learning platform with mathematical modeling, incorporating flexible growth laws and variable dormancy times. We applied the workflow to colon cancer organoids and demonstrated that organoid growth is well-described by the Gompertz model of growth. Our analysis showed significant intrapatient heterogeneity in PDTO growth dynamics, with the initial exponential growth rate of an organoid following a lognormal distribution within each dataset. The level of intrapatient heterogeneity varied between patients, as did organoid growth rates and dormancy times of single seeded cells. Our work contributes to an emerging understanding of the basic growth characteristics of PDTOs, and it highlights the heterogeneity in organoid growth both within and between patients. These results pave the way for further modeling efforts aimed at predicting treatment response dynamics and drug resistance timing.

From Organ-on-a-Chip to Human-on-a-Chip: A Review of Research Progress and Latest Applications

Organ-on-a-Chip (OOC) technology, which emulates the physiological environment and functionality of human organs on a microfluidic chip, is undergoing significant technological advancements. Despite its rapid evolution, this technology is also facing notable challenges, such as the lack of vascularization, the development of multiorgan-on-a-chip systems, and the replication of the human body on a single chip. The progress of microfluidic technology has played a crucial role in steering OOC toward mimicking the human microenvironment, including vascularization, microenvironment replication, and the development of multiorgan microphysiological systems. Additionally, advancements in detection, analysis, and organoid imaging technologies have enhanced the functionality and efficiency of Organs-on-Chips (OOCs). In particular, the integration of artificial intelligence has revolutionized organoid imaging, significantly enhancing high-throughput drug screening. Consequently, this review covers the research progress of OOC toward Human-on-a-chip, the integration of sensors in OOCs, and the latest applications of organoid imaging technologies in the biomedical field.

Design optimization of geometrically confined cardiac organoids enabled by machine learning techniques

Stem cell organoids are powerful models for studying organ development, disease modeling, drug screening, and regenerative medicine applications. The convergence of organoid technology, tissue engineering, and artificial intelligence (AI) could potentially enhance our understanding of the design principles for organoid engineering. In this study, we utilized micropatterning techniques to create a designer library of 230 cardiac organoids with 7 geometric designs. We employed manifold learning techniques to analyze single organoid heterogeneity based on 10 physiological parameters. We clustered and refined the cardiac organoids based on their functional similarity using unsupervised machine learning approaches, thus elucidating unique functionalities associated with geometric designs. We also highlighted the critical role of calcium transient rising time in distinguishing organoids based on geometric patterns and clustering results. This integration of organoid engineering and machine learning enhances our understanding of structure-function relationships in cardiac organoids, paving the way for more controlled and optimized organoid design.

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.

Vascularized organoid-on-a-chip: design, imaging, and analysis

Vascularized organoid-on-a-chip (VOoC) models achieve substance exchange in deep layers of organoids and provide a more physiologically relevant system in vitro. Common designs for VOoC primarily involve two categories: self-assembly of endothelial cells (ECs) to form microvessels and pre-patterned vessel lumens, both of which include the hydrogel region for EC growth and allow for controlled fluid perfusion on the chip. Characterizing the vasculature of VOoC often relies on high-resolution microscopic imaging. However, the high scattering of turbid tissues can limit optical imaging depth. To overcome this limitation, tissue optical clearing (TOC) techniques have emerged, allowing for 3D visualization of VOoC in conjunction with optical imaging techniques. The acquisition of large-scale imaging data, coupled with high-resolution imaging in whole-mount preparations, necessitates the development of highly efficient analysis methods. In this review, we provide an overview of the chip designs and culturing strategies employed for VOoC, as well as the applicable optical imaging and TOC methods. Furthermore, we summarize the vascular analysis techniques employed in VOoC, including deep learning. Finally, we discuss the existing challenges in VOoC and vascular analysis methods and provide an outlook for future development.

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.

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.

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.

Nondestructive, quantitative viability analysis of 3D tissue cultures using machine learning image segmentation

Ascertaining the collective viability of cells in different cell culture conditions has typically relied on averaging colorimetric indicators and is often reported out in simple binary readouts. Recent research has combined viability assessment techniques with image-based deep-learning models to automate the characterization of cellular properties. However, further development of viability measurements to assess the continuity of possible cellular states and responses to perturbation across cell culture conditions is needed. In this work, we demonstrate an image processing algorithm for quantifying features associated with cellular viability in 3D cultures without the need for assay-based indicators. We show that our algorithm performs similarly to a pair of human experts in whole-well images over a range of days and culture matrix compositions. To demonstrate potential utility, we perform a longitudinal study investigating the impact of a known therapeutic on pancreatic cancer spheroids. Using images taken with a high content imaging system, the algorithm successfully tracks viability at the individual spheroid and whole-well level. The method we propose reduces analysis time by 97% in comparison with the experts. Because the method is independent of the microscope or imaging system used, this approach lays the foundation for accelerating progress in and for improving the robustness and reproducibility of 3D culture analysis across biological and clinical research.

Deep-Orga: An improved deep learning-based lightweight model for intestinal organoid detection

PROBLEM

Organoids are 3D cultures that are commonly used for biological and medical research in vitro due to their functional and structural similarity to source organs. The development of organoids can be assessed by morphological tests. However, manual analysis of organoid morphology requires intensive labor from professionals and is prone to observer discrepancies.

AIM

Computer-assisted methods alleviate the pressure of manual labor, especially with the development of deep learning, the performance of morphological detection has been further improved. The aim of this paper is to automate the assessment of organoid morphology using deep learning techniques to reduce the labor pressure of professionals.

METHODS

Based on the lightweight model YOLOX, a lightweight intestinal organoid detection model named Deep-Orga is proposed. First, the performance of the Deep-Orga model is compared with other classical models on the intestinal organoids dataset. Then, ablation experiments are used to validate the improvement of the model detection performance by the improved module. Finally, Deep-Orga is compared with other methods.

RESULTS

Deep-Orga achieves optimal organoid detection with a partial increase in computational effort. Using Deep-Orga to replace the manual analysis process provides a new automated method for organoid morphology evaluation.

CONCLUSION

Deep-Orga proposed in this paper is able to accurately assess organoid development, effectively relieving the labor pressure of professionals and avoiding the subjectivity of assessment. This paper demonstrates the potential application of deep learning in the field of organoid morphology analysis.

Analysis of organoid and immune cell co-cultures by machine learning-empowered image cytometry

Organoids are three-dimensional (3D) structures that can be derived from stem cells or adult tissue progenitor cells and exhibit an extraordinary ability to autonomously organize and resemble the cellular composition and architectural integrity of specific tissue segments. This feature makes them a useful tool for analyzing therapeutical relevant aspects, including organ development, wound healing, immune disorders and drug discovery. Most organoid models do not contain cells that mimic the neighboring tissue’s microenvironment, which could potentially hinder deeper mechanistic studies. However, to use organoid models in mechanistic studies, which would enable us to better understand pathophysiological processes, it is necessary to emulate the in situ microenvironment. This can be accomplished by incorporating selected cells of interest from neighboring tissues into the organoid culture. Nevertheless, the detection and quantification of organoids in such co-cultures remains a major technical challenge. These imaging analysis approaches would require an accurate separation of organoids from the other cell types in the co-culture. To efficiently detect and analyze 3D organoids in co-cultures, we developed a high-throughput imaging analysis platform. This method integrates automated imaging techniques and advanced image processing tools such as grayscale conversion, contrast enhancement, membrane detection and structure separation. Based on machine learning algorithms, we were able to identify and classify 3D organoids within dense co-cultures of immune cells. This procedure allows a high-throughput analysis of organoid-associated parameters such as quantity, size, and shape. Therefore, the technology has significant potential to advance contextualized research using organoid co-cultures and their potential applications in translational medicine.

Organoid assessment technologies

Despite enormous advances in the generation of organoids, robust and stable protocols of organoids are still a major challenge to researchers. Research for assessing structures of organoids and the evaluations of their functions on in vitro or in vivo is often limited by precision strategies. A growing interest in assessing organoids has arisen, aimed at standardizing the process of obtaining organoids to accurately resemble human-derived tissue. The complex microenvironment of organoids, intricate cellular crosstalk, organ-specific architectures and further complicate functions urgently quest for high-through schemes. By utilizing multi-omics analysis and single-cell analysis, cell-cell interaction mechanisms can be deciphered, and their structures can be investigated in a detailed view by histological analysis. In this review, we will conclude the novel approaches to study the molecular mechanism and cell heterogeneity of organoids and discuss the histological and morphological similarity of organoids in comparison to the human body. Future perspectives on functional analysis will be developed and the organoids will become mature models.

Label-free tumor cells classification using deep learning and high-content imaging

Many studies have shown that cellular morphology can be used to distinguish spiked-in tumor cells in blood sample background. However, most validation experiments included only homogeneous cell lines and inadequately captured the broad morphological heterogeneity of cancer cells. Furthermore, normal, non-blood cells could be erroneously classified as cancer because their morphology differ from blood cells. Here, we constructed a dataset of microscopic images of organoid-derived cancer and normal cell with diverse morphology and developed a proof-of-concept deep learning model that can distinguish cancer cells from normal cells within an unlabeled microscopy image. In total, more than 75,000 organoid-drived cells from 3 cholangiocarcinoma patients were collected. The model achieved an area under the receiver operating characteristics curve (AUROC) of 0.78 and can generalize to cell images from an unseen patient. These resources serve as a foundation for an automated, robust platform for circulating tumor cell detection.

Automated detection and growth tracking of 3D bio-printed organoid clusters using optical coherence tomography with deep convolutional neural networks

Organoids are advancing the development of accurate prediction of drug efficacy and toxicity in vitro. These advancements are attributed to the ability of organoids to recapitulate key structural and functional features of organs and parent tumor. Specifically, organoids are self-organized assembly with a multi-scale structure of 30-800 μm, which exacerbates the difficulty of non-destructive three-dimensional (3D) imaging, tracking and classification analysis for organoid clusters by traditional microscopy techniques. Here, we devise a 3D imaging, segmentation and analysis method based on Optical coherence tomography (OCT) technology and deep convolutional neural networks (CNNs) for printed organoid clusters (Organoid Printing and optical coherence tomography-based analysis, OPO). The results demonstrate that the organoid scale influences the segmentation effect of the neural network. The multi-scale information-guided optimized EGO-Net we designed achieves the best results, especially showing better recognition workout for the biologically significant organoid with diameter ≥50 μm than other neural networks. Moreover, OPO achieves to reconstruct the multiscale structure of organoid clusters within printed microbeads and calibrate the printing errors by segmenting the printed microbeads edges. Overall, the classification, tracking and quantitative analysis based on image reveal that the growth process of organoid undergoes morphological changes such as volume growth, cavity creation and fusion, and quantitative calculation of the volume demonstrates that the growth rate of organoid is associated with the initial scale. The new method we proposed enable the study of growth, structural evolution and heterogeneity for the organoid cluster, which is valuable for drug screening and tumor drug sensitivity detection based on organoids.