Three-dimensional (3D) tissue models such as epithelial spheroids or organoids have become popular for pre-clinical drug studies. However, different from 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.

中文翻译：

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深度腔分析–使用深度学习从明场图像中对人肺上皮球状体进行分类

诸如上皮球体或类器官的三维（3D）组织模型已在临床前药物研究中流行。但是，与2D单层培养不同，根据非侵入性明场图像表征3D组织模型是一项重大挑战。为了解决这个问题，我们在这里报告了一种深度学习的上皮网络发现测量方法（Deep-LUMEN）。Deep-LUMEN是一种对象检测算法，经过微调可自动从明场图像中发现上皮球状体形态的细微差异。该算法可以跟踪组织球体的腔结构变化，并区分极化和非极化的肺上皮球体。通过筛查响应不同细胞外基质和药物治疗的球状上皮结构的变化，验证了Deep-LUMEN分析的有效性。具体而言，我们发现，如果不考虑药物对组织形态的影响，则可以低估环孢菌素的剂量依赖性毒性。因此，Deep-LUMEN可用于以非侵入性方式评估药物效果并捕获3D球体模型中的形态变化。