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

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

Simon Memmel  1 , Dmitri Sisario  2 , Heiko Zimmermann  3   4   5 , Markus Sauer  2 , Vladimir L Sukhorukov  2 , Cholpon S Djuzenova  6 , Michael Flentje  7
Affiliations

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

Simon Memmel et al. BMC Bioinformatics. .

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.

Keywords: Automated analysis; DNA double-strand breaks; ImageJ plugin; Ionizing radiation; Open-source tool; Radiation biology; γH2AX-foci.

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Conflict of interest statement

The authors declare that they have no competing interests.

Figures

Fig. 1
Fig. 1
Flow chart demonstrating the main steps of the FocAn algorithm consisting of two independent components for nuclei (b-d) and foci (e-f) identification. In the first step (A), the raw image is normalized. Nucleus identification is then performed using a mean auto local threshold (ALT, B) followed by Gaussian blurring (c). Together, these steps result in gradual signal separation of nuclear and cytosolic areas (c), which is also illustrated in (g). The green, red and blue lines in (g) represent the intensity profiles of the corresponding colors in (a), (b) and (c), respectively. After that, mid-gray ALT creates a binary image, shown in (d). This is followed by watershed transformation for separation of overlapping nuclei, encircled with red lines in (d). The foci identification process starts with Gaussian blurring of the normalized images followed by median ALT (e). A 3D watershed transformation can be performed optionally, before finally the foci numbers per nucleus are determined (f)
Fig. 2
Fig. 2
Comparison of FocAn-, FoCo- and manual foci counting in the same image data set, consisting of a random mixture of non-irradiated and irradiated (2 Gy) DK-MG and SNB19 cells (N = 100 cells). The insets in a-c depict the regions of interest (either midsection a and c, or whole nucleus b) in which foci were counted. The data acquired by FocAn was plotted against data of either a manual point-and-click approach (a and b) or FoCo-based data (c). The dashed lines in a-c illustrate ideal 1:1 relationships between the compared counting methods. The linear regressions to the data (solid lines in a and c) deviate only slightly from the 1:1 relation (for detail, see text). Comparison of the total 2D foci numbers (FN2D, a and c) also reveals little difference (~ 2–3%) between the applied methods (d). The 3D foci number per nucleus (FN3D) determined with FocAn exceeds the number of manually detected foci by ~ 14% (b and d). Moreover, with increasing foci number (i.e. FN3D > ~ 50), FocAn yielded increasingly higher FN3D values as compared to manual counting (b), as illustrated in (b) by the upwardly curved linear-quadratic fit (solid line) diverging from the 1:1 relationship (dashed line). The bars shown in (d) are relative differences in foci numbers with respect to those detected by FocAn, calculated as RelDiff = (FN-FNFocAn)/FNFocAn) × 100%
Fig. 3
Fig. 3
Time-courses of DNA DSB induction and repair in two glioblastoma cell lines, DK-MG and SNB19 (red and blue symbols, respectively). The cells were irradiated with 2 Gy, fixed at the indicated time intervals after irradiation, immunolabeled for γH2AX and examined by 3D confocal microscopy. Each data point represents the mean (±SE) foci number per nucleus of at least 80 cells. The 3D foci numbers were acquired automatically from the image stacks using FocAn. The total computation time for the depicted data was ~ 30 h. The inset shows γH2AX foci counts during the first 3 h after irradiation in detail. The lines are best fits of the modified Mariotti-model (Eq. 4; for detail see text and [33]) to the experimental data
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