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FocAn:通过共聚焦荧光显微镜获取的图像堆栈中 DNA 修复焦点的自动 3D 分析。

FocAn: automated 3D analysis of DNA repair foci in image stacks acquired by confocal fluorescence microscopy.

机构信息

Department of Radiation Oncology, University Hospital Würzburg, Josef-Schneider-Strasse 11, 97080, Würzburg, Germany.

Lehrstuhl für Biotechnologie und Biophysik, Biozentrum, Universität Würzburg, 97074, Würzburg, Germany.

出版信息

BMC Bioinformatics. 2020 Jan 28;21(1):27. doi: 10.1186/s12859-020-3370-8.

Abstract

BACKGROUND

Phosphorylated histone H2AX, also known as γH2AX, forms μm-sized nuclear foci at the sites of DNA double-strand breaks (DSBs) induced by ionizing radiation and other agents. Due to their specificity and sensitivity, γH2AX immunoassays have become the gold standard for studying DSB induction and repair. One of these assays relies on the immunofluorescent staining of γH2AX followed by microscopic imaging and foci counting. During the last years, semi- and fully automated image analysis, capable of fast detection and quantification of γH2AX foci in large datasets of fluorescence images, are gradually replacing the traditional method of manual foci counting. A major drawback of the non-commercial software for foci counting (available so far) is that they are restricted to 2D-image data. In practice, these algorithms are useful for counting the foci located close to the midsection plane of the nucleus, while the out-of-plane foci are neglected.

RESULTS

To overcome the limitations of 2D foci counting, we present a freely available ImageJ-based plugin (FocAn) for automated 3D analysis of γH2AX foci in z-image stacks acquired by confocal fluorescence microscopy. The image-stack processing algorithm implemented in FocAn is capable of automatic 3D recognition of individual cell nuclei and γH2AX foci, as well as evaluation of the total foci number per cell nucleus. The FocAn algorithm consists of two parts: nucleus identification and foci detection, each employing specific sequences of auto local thresholding in combination with watershed segmentation techniques. We validated the FocAn algorithm using fluorescence-labeled γH2AX in two glioblastoma cell lines, irradiated with 2 Gy and given up to 24 h post-irradiation for repair. We found that the data obtained with FocAn agreed well with those obtained with an already available software (FoCo) and manual counting. Moreover, FocAn was capable of identifying overlapping foci in 3D space, which ensured accurate foci counting even at high DSB density of up to ~ 200 DSB/nucleus.

CONCLUSIONS

FocAn is freely available an open-source 3D foci analyzer. The user-friendly algorithm FocAn requires little supervision and can automatically count the amount of DNA-DSBs, i.e. fluorescence-labeled γH2AX foci, in 3D image stacks acquired by laser-scanning microscopes without additional nuclei staining.

摘要

背景

磷酸化组蛋白 H2AX,也称为γH2AX,在电离辐射和其他试剂诱导的 DNA 双链断裂(DSB)部位形成微米大小的核焦点。由于其特异性和敏感性,γH2AX 免疫测定已成为研究 DSB 诱导和修复的金标准。这些测定之一依赖于 γH2AX 的免疫荧光染色,然后进行显微镜成像和焦点计数。在过去的几年中,能够快速检测和量化大量荧光图像数据集的半自动和全自动图像分析逐渐取代了传统的手动焦点计数方法。到目前为止,用于焦点计数的非商业软件的主要缺点是它们仅限于 2D 图像数据。在实践中,这些算法对于计数靠近细胞核中截面平面的焦点很有用,而忽略了离面焦点。

结果

为了克服 2D 焦点计数的限制,我们提出了一个基于免费的 ImageJ 的插件(FocAn),用于通过共聚焦荧光显微镜获取的 z 图像堆栈自动分析 γH2AX 焦点。在 FocAn 中实现的图像堆栈处理算法能够自动识别单个细胞核和 γH2AX 焦点,并评估每个细胞核的总焦点数。FocAn 算法由两部分组成:核识别和焦点检测,每个部分都使用特定的自动局部阈值序列结合分水岭分割技术。我们使用两种神经胶质瘤细胞系中的荧光标记 γH2AX 验证了 FocAn 算法,用 2Gy 照射,并在照射后最多 24 小时进行修复。我们发现,用 FocAn 获得的数据与已经可用的软件(FoCo)和手动计数获得的数据非常吻合。此外,FocAn 能够在 3D 空间中识别重叠的焦点,即使在高达 ~200 DSB/核的高 DSB 密度下,也能确保准确的焦点计数。

结论

FocAn 是一个免费的开源 3D 焦点分析器。用户友好的 FocAn 算法只需很少的监督,可以自动计数激光扫描显微镜获取的 3D 图像堆栈中的 DNA-DSB 数量,即荧光标记的 γH2AX 焦点,而无需额外的核染色。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/26e3/6986076/c68f2740a982/12859_2020_3370_Fig1_HTML.jpg

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