PDF(Pubmed)
Abstract：
Endocrine-disrupting chemicals (EDCs) pose a significant threat to human well-being and the ecosystem. However, in managing the many thousands of uncharacterized chemical entities, the high-throughput screening of EDCs using relevant biological endpoints remains challenging. Three-dimensional (3D) culture technology enables the development of more physiologically relevant systems in more realistic biochemical microenvironments. The high-content and quantitative imaging techniques enable quantifying endpoints associated with cell morphology, cell-cell interaction, and microtissue organization. In the present study, 3D microtissues formed by MCF-7 breast cancer cells were exposed to the model EDCs estradiol (E2) and propyl pyrazole triol (PPT). A 3D imaging and image analysis pipeline was established to extract quantitative image features from estrogen-exposed microtissues. Moreover, a machine-learning classification model was built using estrogenic-associated differential imaging features. Based on 140 common differential image features found between the E2 and PPT group, the classification model predicted E2 and PPT exposure with AUC-ROC at 0.9528 and 0.9513, respectively. Deep learning-assisted analysis software was developed to characterize microtissue gland lumen formation. The fully automated tool can accurately characterize the number of identified lumens and the total luminal volume of each microtissue. Overall, the current study established an integrated approach by combining non-supervised image feature profiling and supervised luminal volume characterization, which reflected the complexity of functional ER signaling and highlighted a promising conceptual framework for estrogenic EDC risk assessment.
摘要：
内分泌干扰化学物质（EDC）对人类福祉和生态系统构成重大威胁。然而,在管理成千上万个未表征的化学实体时，使用相关生物学终点对EDCs进行高通量筛选仍然具有挑战性.三维（3D）培养技术可以在更现实的生化微环境中开发更多生理相关的系统。高含量和定量成像技术能够定量与细胞形态相关的终点，细胞-细胞相互作用，和微组织组织。在本研究中,将MCF-7乳腺癌细胞形成的3D微组织暴露于模型EDC雌二醇(E2)和丙基吡唑三醇(PPT)。建立了3D成像和图像分析管道，以从暴露于雌激素的微组织中提取定量图像特征。此外，利用雌激素相关差异成像特征建立了机器学习分类模型.根据E2和PPT组之间找到的140个常见差分图像特征，分类模型预测E2和PPT暴露，AUC-ROC分别为0.9528和0.9513。开发了深度学习辅助分析软件来表征微组织腺腔形成。全自动工具可以准确地表征所识别的腔的数量和每个微组织的总腔体积。总的来说,当前的研究通过结合非监督图像特征分析和监督腔体积表征建立了一种集成方法，这反映了功能性ER信号的复杂性，并强调了一个有前途的雌激素EDC风险评估概念框架。
