Tellu - an object-detector algorithm for automatic classification of intestinal organoids

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.

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.

Advanced 3D imaging and organoid bioprinting for biomedical research and therapeutic applications

Organoid cultures offer a valuable platform for studying organ-level biology, allowing for a closer mimicry of human physiology compared to traditional two-dimensional cell culture systems or non-primate animal models. While many organoid cultures use cell aggregates or decellularized extracellular matrices as scaffolds, they often lack precise biochemical and biophysical microenvironments. In contrast, three-dimensional (3D) bioprinting allows precise placement of organoids or spheroids, providing enhanced spatial control and facilitating the direct fusion for the formation of large-scale functional tissues in vitro. In addition, 3D bioprinting enables fine tuning of biochemical and biophysical cues to support organoid development and maturation. With advances in the organoid technology and its potential applications across diverse research fields such as cell biology, developmental biology, disease pathology, precision medicine, drug toxicology, and tissue engineering, organoid imaging has become a crucial aspect of physiological and pathological studies. This review highlights the recent advancements in imaging technologies that have significantly contributed to organoid research. Additionally, we discuss various bioprinting techniques, emphasizing their applications in organoid bioprinting. Integrating 3D imaging tools into a bioprinting platform allows real-time visualization while facilitating quality control, optimization, and comprehensive bioprinting assessment. Similarly, combining imaging technologies with organoid bioprinting can provide valuable insights into tissue formation, maturation, functions, and therapeutic responses. This approach not only improves the reproducibility of physiologically relevant tissues but also enhances understanding of complex biological processes. Thus, careful selection of bioprinting modalities, coupled with appropriate imaging techniques, holds the potential to create a versatile platform capable of addressing existing challenges and harnessing opportunities in these rapidly evolving fields.

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.

Suppression of epithelial proliferation and tumorigenesis by immunoglobulin A

Immunoglobulin A (IgA) is the most abundant antibody isotype produced across mammals and plays a specialized role in mucosal homeostasis 1 . Constantly secreted into the lumen of the intestine, IgA binds commensal microbiota to regulate their colonization and function 2,3 , with unclear implications for health. IgA deficiency is common in humans but is difficult to study due to its complex etiology and comorbidities 4-8 . Using genetically and environmentally controlled mice, here we show that IgA-deficient animals have a baseline alteration in the colon epithelium that increases susceptibility to multiple models of colorectal cancer. Transcriptome, imaging, and flow cytometry-based analyses revealed that, in the absence of IgA, colonic epithelial cells induce antibacterial factors and accelerate cell cycling in response to the microbiota. Oral treatment with IgA was sufficient to suppress aberrant epithelial proliferation independently of bacterial binding, suggesting that IgA provides a feedback signal to epithelial cells in parallel with its known roles in microbiome shaping. In a primary colonic organoid culture system, IgA directly suppresses epithelial growth. Conversely, the susceptibility of IgA-deficient mice to colorectal cancer was reversed by Notch inhibition to suppress the absorptive colonocyte developmental program, or by inhibition of the cytokine MIF, the receptor for which was upregulated in stem cells of IgA-deficient animals. These studies demonstrate a homeostatic function for IgA in tempering physiological epithelial responses to microbiota to maintain mucosal health.