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使用荧光染色和卷积神经网络预测球体细胞死亡。

Prediction of Spheroid Cell Death Using Fluorescence Staining and Convolutional Neural Networks.

机构信息

Division of Pharmaceutical Chemistry, Faculty of Pharmaceutical Sciences, Khon Kaen University, Khon Kaen 40002, Thailand.

Graduate School in the Program of Pharmaceutical Chemistry and Natural Products, Pharmaceutical Sciences, Faculty of Pharmaceutical Sciences, Khon Kaen University, Khon Kaen 40002, Thailand.

出版信息

Chem Res Toxicol. 2023 Dec 18;36(12):1980-1989. doi: 10.1021/acs.chemrestox.3c00257. Epub 2023 Dec 5.

DOI:10.1021/acs.chemrestox.3c00257
PMID:38052002
Abstract

Three-dimensional (3D) cell culture is emerging for drug design and drug screening. Skin toxicity is one of the most important assays for determining the toxicity of a compound before being used in skin application. Much work has been done to find an alternative assay without animal experiments. 3D cell culture is one of the methods that provides clinically relevant models with superior clinical translation compared to that of 2D cell culture. In this study, we developed a spheroid toxicity assay using keratinocyte HaCaT cells with propidium iodide and calcein AM. We also applied the transfer learning-containing convolutional neural network (CNN) to further determine spheroid cell death with fluorescence labeling. Our result shows that the morphologies of the spheroid are the key features in determining the apoptosis cell death of the HaCaT spheroid. Our CNN model provided good statistical measurement in terms of accuracy, precision, and recall in both validation and external test data sets. One can predict keratinocyte spheroid cell death if that spheroid image contains the fluorescence signals from propidium iodide and calcein AM. The CNN model can be accessed in the web application at https://qsarlabs.com/#spheroiddeath.

摘要

三维(3D)细胞培养技术在药物设计和药物筛选中崭露头角。皮肤毒性是在用于皮肤应用之前确定化合物毒性的最重要的测定之一。为了寻找替代动物实验的方法,已经做了很多工作。与 2D 细胞培养相比,3D 细胞培养是提供具有临床相关性模型的方法之一,具有更好的临床转化。在这项研究中,我们使用角质形成细胞 HaCaT 细胞和碘化丙啶(propidium iodide)和钙黄绿素 AM(calcein AM)开发了球体毒性测定法。我们还应用包含迁移学习的卷积神经网络(CNN)进一步用荧光标记来确定球体细胞死亡。我们的结果表明,球体的形态是确定 HaCaT 球体细胞凋亡的关键特征。我们的 CNN 模型在验证集和外部测试数据集的准确性、精度和召回率方面提供了良好的统计测量。如果该球体图像包含碘化丙啶和钙黄绿素 AM 的荧光信号,则可以预测角质形成细胞球体的细胞死亡。CNN 模型可以在网络应用程序 https://qsarlabs.com/#spheroiddeath 中访问。

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