Radiotherapy (RT) is a mainstay in modern cancer treatment. To evaluate the efficacy of IR alone or in combination with chemotherapy or drugs inducing DNA damage and targeting DNA repair, radiation biologists usually count fluorescence-labeled protein-foci in the nucleus using fluorescence microscopy. For this purpose the proteins of interest or their specific phosphorylated isoform are visualized by immunofluorescence using protein-specific (e.g. p53 binding protein 1 (53BP1)) or phospho-protein-specific (e.g. phospho-histone 2.AX (γ-H2.AX)) antibodies directly linked to a fluorophore or detected by using a secondary fluorophore-labeled antibody. Another possibility is to fuse the proteins of interest with fluorescent proteins, such as green fluorescence protein GFP [
1]. This method takes advantage of the fact that many repair proteins and repair associated proteins, such as γ-H2.AX, 53BP1 and RAD51, accumulate and co-localize at the site of DNA damage [
2‐
8]. To evaluate formation and processing of these DNA damage foci, a reliable and accurate image analysis is required.
Due to the wide use of methods retrieving images of foci and cells, multiple evaluation procedures have been developed. However, the programs currently available for counting and analysis of nuclei are often based on manual analysis. Several publications showed that manual counting of foci is time consuming, frequently inaccurate and subjected to investigator-related bias. Conversely, automated computer-based foci analysis is considered to yield better sensitivity, comparability and consistency of the data [
9‐
13]. However, some current software solutions for automated analysis are unsatisfactory as they provide limited algorithms, are stand-alone tools or are simply expensive [
13,
14]. For example, Böcker
et al. developed a software based on a cost-intensive program, ImageProPlus (Media Cybernetics Inc., US) [
12]. Another commercially available package is IMARIS (Bitplane AG) [
13,
15]. Moreover, not all existing tools support the complete range of file formats commonly used for image acquisition [
16]. The FociCounter, a freely available, non-customizable stand-alone tool, does not support all formats, for example files used by Zeiss (CZI and ZVI) and by Leica (LIF). Moreover, the FociCounter only allows manual selection of cells [
17]. However, integration of automated cell selection and a batch mode performing automated analysis of various pictures would result in desirable time-saving steps for data analysis. TRI2 and CellProfiler are stand-alone tools written with the programming language Python [
18‐
20]. One disadvantage of stand-alone tools can be the lack of updates by an established platform. In contrast, the platform of ImageJ offers support, frequent updates and the possibility to change the source code or to link it with additional programming tools [
9‐
13,
21,
22]. ImageJ-based solutions have already been described by several authors and institutions, but these solutions frequently provide incomplete algorithms or macros not suited for immediate use [
23,
24]. For example, Cai and colleagues published the source code for an ImageJ macro without interface, like a menu and buttons [
25], and Du and colleagues developed a tool for foci picking without batch mode and automated foci selection [
26]. The FindFoci plugin for ImageJ supports self-learning parameters but does not support multi-channel analysis [
10]. Thus, there was a demand for the development of easy-to-use, customizable and reliable software solutions with an intuitive interface combined with an automated open-source platform, like ImageJ [
13]. To overcome these limitations, we have developed an automated, adjustable and user friendly macro based on ImageJ named “Focinator” for quantitative and qualitative analysis of nuclei, γ-H2.AX foci and other biological foci with the possibility of easy data export and processing. In addition, we integrated an option for multi-channel analysis, e.g. 53BP1 foci and γ-H2.AX foci in one image file and implemented the option for colocalization studies. This option enables the determination of absolute numbers and the percentages of colocalized foci. We used ImageJ as an established platform, as it is an image processing software that is routinely used by many investigators to analyze western blots, fluorescence cell images [
13,
27], immunohistochemical probes [
28], DNA double strand break repair [
29], cell size [
30] and to quantify soft tissue in tomography images [
21] or wound healing [
31]. We adapted the Focinator based on algorithms published by the Light Microscopy Core Facility -Duke University and Duke University Medical Center by adding additional setting preferences [
24,
25]. To further facilitate data analysis, a program for automated analysis and data export into a spreadsheet was integrated.