Pharmaceutical Informatics Institute, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, Zhejiang 310058, China.
State Key Lab of CAD&CG, Zhejiang University, Hangzhou, Zhejiang 310058, China.
Anal Chem. 2020 Oct 20;92(20):14267-14277. doi: 10.1021/acs.analchem.0c03741. Epub 2020 Sep 28.
DNA damage is one of major culprits in many complex diseases; thus, there is great interest in the discovery of novel lead compounds regulating DNA damage. However, there remain plenty of challenges to evaluate DNA damage through counting the amount of intranuclear foci. Herein, a deep-learning-based open-source pipeline, FociNet, was developed to automatically segment full-field fluorescent images and dissect DNA damage of each cell. We annotated 6000 single-nucleus images to train the classification ability of the proposed computational pipeline. Results showed that FociNet achieved satisfying performance in classifying a single cell into a normal, damaged, or nonsignaling (no fusion-protein expression) state and exhibited excellent compatibility in the assessment of DNA damage based on fluorescent foci images from various imaging platforms. Furthermore, FociNet was employed to analyze a data set of over 5000 foci images from a high-content screening of 315 natural compounds from traditional Chinese medicine. It was successfully applied to identify several novel active compounds including evodiamine, isoliquiritigenin, and herbacetin, which were found to reduce 53BP1 foci for the first time. Among them, isoliquiritigenin from Fisch. exerts a significant effect on attenuating double strand breaks as indicated by the comet assay. In conclusion, this work provides an artificial intelligence tool to evaluate DNA damage on the basis of microscopy images as well as a potential strategy for high-content screening of active compounds.
DNA 损伤是许多复杂疾病的主要罪魁祸首之一；因此，人们对发现新型调节 DNA 损伤的先导化合物非常感兴趣。然而，通过计数核内焦点的数量来评估 DNA 损伤仍然存在许多挑战。在此，开发了一种基于深度学习的开源流水线 FociNet，用于自动分割全场荧光图像并剖析每个细胞的 DNA 损伤。我们对 6000 个单核图像进行注释，以训练所提出的计算流水线的分类能力。结果表明，FociNet 在将单个细胞分类为正常、损伤或无信号（无融合蛋白表达）状态方面表现出令人满意的性能，并且在基于来自各种成像平台的荧光焦点图像评估 DNA 损伤方面表现出出色的兼容性。此外，FociNet 被用于分析来自中药 315 种天然化合物高通量筛选的超过 5000 个焦点图像数据集。它成功地用于鉴定几种新型活性化合物，包括吴茱萸碱、异甘草素和草丙酮，这是首次发现它们可减少 53BP1 焦点。其中，来自 Fisch. 的异甘草素通过彗星试验显示出显著的减轻双链断裂的效果。总之，这项工作提供了一种基于显微镜图像评估 DNA 损伤的人工智能工具，以及一种用于活性化合物高通量筛选的潜在策略。
