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

Stem cell-derived organoids are a promising tool to model native human tissues as they resemble human organs functionally and structurally compared to traditional monolayer cell-based assays. For instance, colon organoids can spontaneously develop crypt-like structures similar to those found in the native colon. While analyzing the structural development of organoids can be a valuable readout, using traditional image analysis tools makes it challenging because of the heterogeneities and the abstract nature of organoid morphologies. To address this limitation, we developed and validated a deep learning-based image analysis tool, named D-CryptO, for the classification of organoid morphology. D-CryptO can automatically assess the crypt formation and opacity of colorectal organoids from brightfield images to determine the extent of organoid structural maturity. To validate this tool, changes in organoid morphology were analyzed during organoid passaging and short-term forskolin stimulation. To further demonstrate the potential of D-CryptO for drug testing, organoid structures were analyzed following treatments with a panel of chemotherapeutic drugs. With D-CryptO, subtle variations in how colon organoids responded to the different chemotherapeutic drugs were detected, which suggest potentially distinct mechanisms of action. This tool could be expanded to other organoid types, like intestinal organoids, to facilitate 3D tissue morphological analysis.

Correction: Deep-LUMEN assay - human lung epithelial spheroid classification from brightfield images using deep learning.

Correction for 'Deep-LUMEN assay - human lung epithelial spheroid classification from brightfield images using deep learning' by Lyan Abdul et al., Lab Chip, 2020, DOI: .

Deep-LUMEN assay - human lung epithelial spheroid classification from brightfield images using deep learning.

Three-dimensional (3D) tissue models such as epithelial spheroids or organoids have become popular for pre-clinical drug studies. In contrast to 2D monolayer culture, the characterization of 3D tissue models from non-invasive brightfield images is a significant challenge. To address this issue, here we report a deep-learning uncovered measurement of epithelial networks (Deep-LUMEN) assay. Deep-LUMEN is an object detection algorithm that has been fine-tuned to automatically uncover subtle differences in epithelial spheroid morphology from brightfield images. This algorithm can track changes in the luminal structure of tissue spheroids and distinguish between polarized and non-polarized lung epithelial spheroids. The Deep-LUMEN assay was validated by screening for changes in spheroid epithelial architecture in response to different extracellular matrices and drug treatments. Specifically, we found the dose-dependent toxicity of cyclosporin can be underestimated if the effect of the drug on tissue morphology is not considered. Hence, Deep-LUMEN could be used to assess drug effects and capture morphological changes in 3D spheroid models in a non-invasive manner.

IFlowPlate-A Customized 384-Well Plate for the Culture of Perfusable Vascularized Colon Organoids.

Despite the complexity and structural sophistication that 3D organoid models provide, their lack of vascularization and perfusion limit the capability of these models to recapitulate organ physiology effectively. A microfluidic platform named IFlowPlate is engineered, which can be used to culture up to 128 independently perfused and vascularized colon organoids in vitro. Unlike traditional microfluidic devices, the vascularized organoid-on-chip device with an "open-well" design does not require any external pumping systems and allows tissue extraction for downstream analyses, such as histochemistry or even in vivo transplantation. By optimizing both the extracellular matrix (ECM) and the culture media formulation, patient-derived colon organoids are co-cultured successfully within a self-assembled vascular network, and it is found that the colon organoids grow significantly better in the platform under constant perfusion versus conventional static condition. Furthermore, a colon inflammation model with an innate immune function where circulating monocytes can be recruited from the vasculature, differentiate into macrophage, and infiltrate the colon organoids in response to tumor necrosis factor (TNF)- inflammatory cytokine stimulation is developed using the platform. With the ability to grow vascularized colon organoids under intravascular perfusion, the IFlowPlate platform could unlock new possibilities for screening potential therapeutic targets or modeling relevant diseases.