The Focinator - a new open-source tool for high-throughput foci evaluation of DNA damage
- Sebastian Oeck†1,
- Nathalie M. Malewicz†1,
- Sebastian Hurst1,
- Justine Rudner1 and
- Verena Jendrossek1Email author
© Oeck et al. 2015
Received: 2 March 2015
Accepted: 5 July 2015
Published: 4 August 2015
The quantitative analysis of foci plays an important role in many cell biological methods such as counting of colonies or cells, organelles or vesicles, or the number of protein complexes. In radiation biology and molecular radiation oncology, DNA damage and DNA repair kinetics upon ionizing radiation (IR) are evaluated by counting protein clusters or accumulations of phosphorylated proteins recruited to DNA damage sites. Consistency in counting and interpretation of foci remains challenging. Many current software solutions describe instructions for time-consuming and error-prone manual analysis, provide incomplete algorithms for analysis or are expensive. Therefore, we aimed to develop a tool for costless, automated, quantitative and qualitative analysis of foci.
For this purpose we integrated a user-friendly interface into ImageJ and selected parameters to allow automated selection of regions of interest (ROIs) depending on their size and circularity. We added different export options and a batch analysis. The use of the Focinator was tested by analyzing γ-H2.AX foci in murine prostate adenocarcinoma cells (TRAMP-C1) at different time points after IR with 0.5 to 3 Gray (Gy). Additionally, measurements were performed by users with different backgrounds and experience.
The Focinator turned out to be an easily adjustable tool for automation of foci counting. It significantly reduced the analysis time of radiation-induced DNA-damage foci. Furthermore, different user groups were able to achieve a similar counting velocity. Importantly, there was no difference in nuclei detection between the Focinator and ImageJ alone.
The Focinator is a costless, user-friendly tool for fast high-throughput evaluation of DNA repair foci. The macro allows improved foci evaluation regarding accuracy, reproducibility and analysis speed compared to manual analysis. As innovative option, the macro offers a combination of multichannel evaluation including colocalization analysis and the possibility to run all analyses in a batch mode.
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 . 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) . 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 . 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 . 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 , and Du and colleagues developed a tool for foci picking without batch mode and automated foci selection . The FindFoci plugin for ImageJ supports self-learning parameters but does not support multi-channel analysis . 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 . 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 , DNA double strand break repair , cell size  and to quantify soft tissue in tomography images  or wound healing . 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.
Materials and methods
Chemicals, antibodies and drugs
Antibodies linked with Alexa Fluor 647 against γ-H2.AX protein were obtained from Becton Dickinson (Heidelberg, Germany). Hoechst 33342 from Invitrogen (Eugene, USA) and DAKO Fluorescent mounting medium from Dako North America Inc. (Carpinteria, USA) were used. All other chemicals were purchased from Sigma-Aldrich (Deisenhofen, Germany) if not otherwise specified.
Cell culture and irradiation
TRAMP-C1 murine prostatic adenocarcinoma cells (p53−/−, androgen-independent) were purchased from the ATCC (Bethesda, Maryland, USA). Cells were cultured in Dulbecco’s Modified Eagle Medium (DMEM) (Life Technologies, Germany) supplemented with 10 % (v/v) fetal calf serum (FCS; Biochrom, Berlin, Germany) and maintained in a humidified incubator at 37 °C and 5 % CO2 (C200, Labotect Incubator, Goettingen, Germany). Cells were irradiated using an X-RAD 320 X-Ray Biological Irradiator with a MIR-324 X-ray tube (Precision X-Ray Inc., North Branford, USA). Cell number and viability was quantified by counting cells using CASY cell counter (Innovatis, Reutlingen, Germany).
Cells were irradiated with 3 Gy and fixed and permeabilized (3 % para-Formaldehyde (PFA) and 0.2 % Triton X-100 in PBS buffer; 15 min; room temperature) at different time points (30 min, 1, 2, 4, 6, 8 and 24 h) after irradiation. After washing, cells were blocked overnight with 2 % goat serum in PBS buffer. Staining with the Alexa Fluor 647-conjugated anti-γ-H2.AX antibody was performed for one hour at a 1:75 dilution in blocking buffer. Samples were washed three times with PBS and stained for 30 min in the dark with 0.2 % (w/v) Hoechst 33342 in PBS. Samples were again washed three times with PBS, mounted with DAKO mounting medium and stored at 4 °C in the dark. Single layer fluorescence images were taken with a Zeiss AxioCam MRm (1388 × 1040 pixels) mounted at a Zeiss Axio Observer Z1 fluorescence microscope with Plan-Apochromat 63x/1.40 Oil M27 lens, 49 DAPI filter, 78 HE ms CFP/YFP filter (γ-H2.AX AF-647 detection) and “ApoTome” transmission grid (High Grid: PH/VH with 5 phase images) (Carl Zeiss, Goettingen, Germany). Images were taken with exposure times of 500 ms for the DAPI channel and 1500 ms for the Alexa Fluor 647 antibody. The pictures were saved as 16-bit Zeiss Vision Image ZVI files with no further editing.
Software and programming
The macro “Focinator” was programmed as a macro for automated quantitative and qualitative analysis of foci with the open-source software ImageJ, a public domain Java image processing program developed at the National Institutes of Health (NIH) [14, 32]. ImageJ is designed with an open architecture and provides extensibility via Java plugins and automation with macros. Custom-built tools can be developed to solve image processing or analysis problems. [21, 22]. ImageJ is available for Windows, Mac OS, Mac OS X and Linux. It has its own ImageJ macro language that is able to control ImageJ procedures and the automation of action series including variables and user-defined functions. The macro, instructions and support are obtainable at http://www.focinator.oeck.de.
In addition, a tool for batch mode and import of data from foci-count and ROI analysis was developed: the batch mode was programmed using R, a free software environment for programming . The R-script allows automated opening of images, foci evaluation and the direct export of foci data in a Microsoft Excel spreadsheet. Excel spreadsheets enable further statistical analysis as well as easy export into other statistical software.
Foci analysis methods
For evaluations of foci counting, the respective groups counted γ-H2.AX foci formed in TRAMP-C1 cells at different time points after exposure to 3 Gy. Additionally, one experiment was performed with 0.5 to 3 Gy. In total 24,858 nuclei in 3361 images were counted. Two trained investigators performed manual foci-counting using a standard manual counter and two trained investigators analyzed foci with ImageJ. Three different user groups (in total 6 investigators) tested the Focinator’s user-friendliness: two programmers of the Focinator with prior knowledge of ImageJ and image processing software, two biologists with basic knowledge of image processing in a scientific context and two users without scientific background or prior knowledge of image processing. All investigators used the same workstations. The investigators did not receive any prior training in this software, but had to read the software’s instruction manual (Additional file 1: Supplement). The images were fully blinded before analysis; therefore investigators had no information about exposure details, dose, time points or type of analyzed cells. Manual counting, analysis with ImageJ and the Focinator were performed independently. Results from manual counting were not available for the investigators performing computational counting.
The software-based analysis for accuracy, comparability, validity and velocity was done by the primary developers of the Focinator (SO and NMM). Consequently, these people had prior knowledge of the image processing software.
Data represent mean values of at least 3 independent experiments ± standard deviation (SD). Data analysis was performed by two-way ANOVA test with Bonferroni two pair comparison post-test and determination coefficient calculation using Prism5TM software (Graph pad Inc., La Jolla, USA). P values ≤ 0.05 were considered as significant.
Results and discussion
When running the Focinator, the selected ROIs are measured, including the area as well as mean, minimal and maximal grey values within the selection. The foci are detected based on the “Find maxima…” command of ImageJ.  Using the “Find maxima…” command, ImageJ identifies signal peaks of the 16-bit grey scale of an image compared to the grey scale values of the surrounding pixels. Testing of the Focinator was performed by capturing fluorescence images for detection of γH2AX foci. In this case higher fluorescence intensities of a putative focus correlate with an incensement of grey values in the image file. Importantly, the thresholds or contrasts of the foci images are not altered. The user might add a value for the noise level to the “Find maxima…” command, to disregard lower grey values caused by background noise. Background noise can be caused by unspecific staining due to unspecific antibody binding, insufficient blocking or washing. The maximal, mean and minimal densities as well as the localization and determination of ROIs size and intensity are also measured (Fig. 3).
The Focinator can either be run in an automated mode or in a semi-automated mode (shortcuts < F3 > to < F8>), respectively, with the possibility of manual addition or deletion of ROIs for better control and adjustments. After starting the Automated Mode via the button or < F1>, the macro selects the ROIs automatically using a preset threshold. When choosing “active separation”, it separates the cells. After this, the foci are counted and the results are saved in the chosen directory (Fig. 1.1 and 1.2). After having tested and adjusted the parameters on several pictures, it is possible to run a batch mode. We recommend testing parameters on multiple pictures before running the batch mode . The batch mode analyzes all pictures in a selected folder including all subfolders. After completion of the batch analysis all retrieved values are summarized and means are calculated.
Adjusting preferences in Focinator options
Preferences of the Focinator can be changed in Options < F2 > (Fig. 1.3). At first, the analysis mode has to be chosen, e.g. multi-channel analysis or separated pictures for each channel. The basic multi-channel analysis uses one channel for ROI selection and one or two foci channels, e.g. based on different stainings. The criteria for ROI selection can be defined in ROI Settings, a sub-paragraph of the Options window. The aforementioned window also includes preferences for the threshold level of the picture, the size and circularity of included particles, separation of overlapping cells or exclusion of areas being cut by the frame. For detection of foci a suitable noise level can be set. Finally, the last two dialogs of the options window offer the opportunity to change the saving directory and the file format.
For data export, we chose MS Excel because this program is widely used for spreadsheet calculation including the scientific context. Moreover, it enables further statistical analysis, presentation in graphs and charts, as well as easy export into other statistical software.
Comparison of the Focinator to manual analysis and counting with ImageJ without automation
The time for automated scoring in the Focinator’s batch mode and automated mode is mainly dependent on the computer performance. The batch mode can be executed in the user’s absence. Valente et al. reports about 30 s for loading setting parameters, the batch mode of the Focinator can be started after around 20 s. . These data reveal that the new macro allows for highly accurate ROI selection, meaning that the nuclei or cells were selected in a valid and fast way. Moreover, we provide evidence that the Focinator excels manual analysis concerning time, effort for image processing and informative content, thereby corroborating with findings by others. However, the integration of an automatic ROI selection proved to be a valid and time-saving step, which is what makes the Focinator superior compared to other software solutions [13, 17, 20].
Validation of the Focinator
Our results support the conclusion that computational analysis is well suited to replace manual analysis with high accuracy; moreover it is time-saving and offers the opportunity to acquire further valuable parameters, such as nuclei size or the intensity of the foci. These values can be used for further normalization as explained in other publications [18, 25, 39]. In contrast, manual analysis is highly dependent on the experience of the investigator and requires extensive training [9–13]. Potential errors in manual analysis include multiple counting of single foci, counting of regions without foci and rare selection of less intense foci.  Finally, manual counting is not always reproducible [9–11]. Therefore, we and others recommend automated analysis to overcome the limitations of manual evaluation [9–13].
Moreover, manual analysis provides only a quantification of the number of foci, and there is no possibility of gathering additional information such as the size of nuclei or the intensity of the foci.
Applicability of the Focinator for different users
Users with no scientific background needed 21 min for counting 553 nuclei in 90 images in the first try. However, after the second analysis round, the untrained user needed only 14 min and 7 s for 653 nuclei in 80 images. The fastest analysis was executed in 10 min and 1 s for 461 nuclei in 82 images and was performed by the programmers. Although the measurements were performed by users with different professional backgrounds all investigators were able to successfully perform the analysis rapidly and particularly faster than by manual analysis.
The results obtained were rather similar, confirming the reproducibility of data. Evidently the Focinator achieves user-friendliness by redundancy of controls, like buttons and shortcuts, as well as by a menu and an implemented manual instruction as shown in Fig. 1.1 and 1.2.
Taken together, the Focinator is a user-friendly program. Moreover, a high comparability and consistency is achieved by automated computer analysis at increased velocity. This is achieved by automated analysis independent of the investigator’s prior knowledge if parameter setting is performed by an experienced researcher.
Actual limitations of the Focinator and potential solutions
As outlined above, the Focinator is a valid open-source tool based on ImageJ for both the non-experienced and experienced user of scientific image processing alike. The Focinator offers advantages over manual analysis and already established software solutions. However, there are also limitations to its application. Since foci size and number varies depending on radiation dose and repair time, detection of overlapping foci can be difficult, a problem also recognized in manual analysis or when using an alternative software . Because computational analysis offers measuring of qualitative parameters, a correction of these overlapping foci is possible by taking the intensity of foci into account. The Focinator counts foci based on signal intensity. This provides the opportunity to set a noise level to exclude foci with low intensity for further calculation, thereby strengthening the results [12, 25]. Cai and colleagues also suggested to include watershedding of foci to separate overlapping foci . Watershedding is a procedure offered by ImageJ, which can be used for the segmentation of overlapping objects, like cells or even foci, in greyscale images . We decided against this procedure, because watershedding of foci requires 8-bit formatting, which would cause the information about the signal intensity to be lost. Plugins such as 3D Object Counter , top-hat filter in Fast Filters  and FociPicker 3D  are alternative approaches to solving the problem of overlapping foci by taking size, intensities and algorithms into account. Though these options are more suited for the advanced user, implementation of the FociPicker 3D into the Focinator’s source code is feasible. Nevertheless, it is still possible that a single focus is too small to be detected with a confocal microscope, because the resolution of the microscope might be too low to display small foci separately [12, 43, 44]. To counter this problem, the macro offers the option of noise level adjustment. Lowering the noise level might result in higher foci numbers, due to noise artifacts been recognized as foci. The real number of foci can be validated by measuring the intensities of foci and taking these into consideration. As other authors have shown, not only the foci count, but also their intensity plays an important role and correlates well with the absorbed radiation dose. [12, 25] The use of an implemented cut off can further improve the results by deleting foci with a value below a chosen intensity to eliminate background signals. Intensities and XY-localization of each focus are exported into Excel. Setting a cut off for each channel and performing colocalization analyses are possible with these exported values.
Another limitation of the Focinator can be high cell density, which might result in overlapping cells and, thus, in overlapping nuclei. Therefore, it is not recommended to use overcrowded images. The result without use of watershedding or manual correction of the selection would combine two overlapping cells into one ROI with a larger area size. However, when using the Focinator the overall foci count will not be affected by a high cell density, because the foci count can be normalized by the area of the ROI.
Though being intuitive, the user needs to adjust parameters, such as the noise level, on their own, in contrast to FindFoci, where the program is able to learn the parameters on its own . Although it is possible to perform analysis with predefined settings and an automated threshold, parameter setting for the individual cell type is a major step and can only be validly performed by people with sufficient background knowledge e.g. of the specific foci-related protein of interest. Parameters are supposed to be set according to image quality and the corresponding values found in published data such as 15–19 foci per nucleus per 1 Gy [35–38].
Another limitation of our macro is the restriction of multi-channel analysis to three channels only. This limits the macro to two foci channels with distinct fluorescence labeling (for example. using γ-H2.AX and 53BP1 antibodies with different secondary antibodies) since one channel is needed to select the ROI (e.g. using DAPI, 4′,6-diamidino-2-phenylindole to mark the nucleus).
Advantages of Focinator-based foci evaluation
The Focinator is an inexpensive alternative to commercial packages. In contrast to the limited file formats accepted by some of the commercially available software solutions, the Focinator supports all file formats of ImageJ including TIFF, PNG, GIF, JPEG, BMP, DICOM and FITS, as well as raw formats. It is also possible to use stacked images and device-specific formats such as Zeiss’ AxioVision ZVI or Leica’s LIF .
The Focinator provides the possibility of adjusting multiple parameters for better image processing. Automated selection of cells or nuclei as ROIs is possible. For advanced users with prior knowledge of image processing who wish to adjust their analysis, the software offers further preferences and the choice of running the analyses in an automated mode or a semi-automated mode with the possibility of adding or deleting ROIs manually. It is even possible to analyze overlapping ROIs, a problem occurring in other automated solutions [13, 20].
Adjustability of the Focinator parameters is achieved without manipulating the images. The Focinator ImageJ macro exports the intensity and XY-localization of each focus. This step allows further processing of exported foci values, such as setting a cut off for each channel and colocalization. The raw data of the pictures are not changed for analysis by filters like changing of contrast, blurring or sharpening. This prevents the results from being manipulated . We consider counting of one ROI per time and displaying the results as advantage and improvement in quality. Moreover, it is possible for the user to observe problems or incorrect preferences in selecting ROIs or foci counts. [13, 15] Being developed as a macro for ImageJ, the Focinator allows the implementation of new algorithms in order to customize the macro . This is an important advantage of the Focinator compared to the standalone solutions. Due to the open-source nature of ImageJ, it is also possible with the macro to change the source code, to use the functions and plugins of ImageJ, and to program additional macros to solve the array of problems associated with image processing in a scientific context [13, 21, 22].
Further advantages compared to other established software solutions include the option to run the analysis in a batch mode and a data export into Microsoft Excel spreadsheet for further statistical and graphical evaluation. The Focinator Batch is programmed with R enabling modification by the user.
The possibility of observing the tool while selecting ROIs and foci in the batch mode, is very useful for further analysis security. This enables the user to recognize aberrant data and to adjust the settings accordingly.
The Focinator is a costless, reliable and user-friendly open-source tool for fast automated high-throughput quantitative and qualitative analysis of DNA damage-induced foci formed by repair-associated proteins such as γ-H2.AX at the DNA damage sites with high accuracy and reproducibility. The Focinator is based on ImageJ with additional data export to Microsoft Excel. In comparison to manual analysis, it overcomes investigator-related bias and significantly reduces analyzing time. Moreover, it delivers a valid, fast and automated selection of nuclei and cells. Furthermore, it enhances the speed and reliability of analysis, and provides additional options for qualitative foci analysis like area size of nuclei and the intensity of foci. Importantly, the Focinator offers analysis of multi-channel pictures and colocalization. Its self-explanatory features make it possible to use the Focinator without prior training and the batch mode enables the user analyzing data in his absence. Data export into different output files with consecutive export into a spreadsheet is available, thus enabling further data processing and analysis. With the option to run data analysis in a batch mode, we think that the Focinator is a valid tool for efficient preclinical testing of the efficacy of new drugs targeting DNA repair alone and in combination with radio(chemo)therapy. For differing scientific aims, using further functions and plugins of ImageJ or programming of additional macros is possible.
The work was supported by a grant of the DFG (GRK1739/1). We thank Fabian Mörtl, Alexander Maharam, Denis Ahlemann and Carolin Koop for testing the Focinator and for evaluating the γ-H2.AX foci pictures. We also thank Emma Ococks for proof-reading the manuscript.
- Hašek J, Streiblová E. Fluorescence microscopy methods. In: Evans I, editor. Yeast protocols. Methods in molecular biology™. Totowa, NJ: Humana Press; 1996. p. 391–405.Google Scholar
- Olive PL, Banath JP. Phosphorylation of histone H2AX as a measure of radiosensitivity. Int J Radiat Oncol Biol Phys. 2004;58(2):331–5.PubMedView ArticleGoogle Scholar
- Pilch DR, Sedelnikova OA, Redon C, Celeste A, Nussenzweig A, Bonner WM. Characteristics of gamma-H2AX foci at DNA double-strand breaks sites. Biochem Cell Biol. 2003;81(3):123–9.PubMedView ArticleGoogle Scholar
- Lord CJ, Ashworth A. The DNA damage response and cancer therapy. Nature. 2012;481(7381):287–94.PubMedView ArticleGoogle Scholar
- DiTullio Jr RA, Mochan TA, Venere M, Bartkova J, Sehested M, Bartek J, et al. 53BP1 functions in an ATM-dependent checkpoint pathway that is constitutively activated in human cancer. Nat Cell Biol. 2002;4(12):998–1002.PubMedView ArticleGoogle Scholar
- Gupta A, Hunt CR, Chakraborty S, Pandita RK, Yordy J, Ramnarain DB, et al. Role of 53BP1 in the regulation of DNA double-strand break repair pathway choice. Radiat Res. 2014;181(1):1–8.PubMed CentralPubMedView ArticleGoogle Scholar
- Kinner A, Wu W, Staudt C, Iliakis G. Gamma-H2AX in recognition and signaling of DNA double-strand breaks in the context of chromatin. Nucleic Acids Res. 2008;36(17):5678–94.PubMed CentralPubMedView ArticleGoogle Scholar
- Rogakou EP, Pilch DR, Orr AH, Ivanova VS, Bonner WM. DNA double-stranded breaks induce histone H2AX phosphorylation on serine 139. J Biol Chem. 1998;273(10):5858–68.PubMedView ArticleGoogle Scholar
- Brugal G, editor. Pattern recognition, image processing, related data analysis and expert systems integrated in medical microscopy. Pattern Recognition, 1988., 9th International Conference on; 1988.Google Scholar
- Herbert AD, Carr AM, Hoffmann E. FindFoci: a focus detection algorithm with automated parameter training that closely matches human assignments, reduces human inconsistencies and increases speed of analysis. PLoS One. 2014;9:12.Google Scholar
- Runge R, Hiemann R, Wendisch M, Kasten-Pisula U, Storch K, Zoephel K, et al. Fully automated interpretation of ionizing radiation-induced γH2AX foci by the novel pattern recognition system AKLIDES®. Int J Radiat Biol. 2012;88(5):439–47. doi:10.3109/09553002.2012.658468.PubMedView ArticleGoogle Scholar
- Bocker W, Iliakis G. Computational methods for analysis of foci: validation for radiation-induced gamma-H2AX foci in human cells. Radiat Res. 2006;165(1):113–24.PubMedView ArticleGoogle Scholar
- Wiesmann V, Franz D, Held C, Munzenmayer C, Palmisano R, Wittenberg T. Review of free software tools for image analysis of fluorescence cell micrographs. J Microsc. 2015;257(1):39–53.PubMedView ArticleGoogle Scholar
- Girish V, Vijayalakshmi A. Affordable image analysis using NIH Image/ImageJ. Indian J Cancer. 2004;41(1):47.PubMedGoogle Scholar
- Haass-Koffler CL, Naeemuddin M, Bartlett SE. An analytical tool that quantifies cellular morphology changes from three-dimensional fluorescence images. J Vis Exp. 2012;31:66.Google Scholar
- Stuurman N, Swedlow JR. Software tools, data structures, and interfaces for microscope imaging. Cold Spring Harb Protoc. 2012;1(1):50–61.Google Scholar
- Jucha A, Wegierek-Ciuk A, Koza Z, Lisowska H, Wojcik A, Wojewodzka M, et al. FociCounter: a freely available PC programme for quantitative and qualitative analysis of gamma-H2AX foci. Mutat Res. 2010;696(1):16–20.PubMedView ArticleGoogle Scholar
- Barber PR, Locke RJ, Pierce GP, Rothkamm K, Vojnovic B, editors. Gamma-H2AX foci counting: image processing and control software for high-content screening. 2007.Google Scholar
- Valente M, Voisin P, Laloi P, Roy L, Roch-Lefèvre S. Automated gamma-H2AX focus scoring method for human lymphocytes after ionizing radiation exposure. Radiat Meas. 2011.Google Scholar
- Gonzalez JE, Lee M, Barquinero JF, Valente M, Roch-Lefevre S, Garcia O. Quantitative image analysis of gamma-H2AX foci induced by ionizing radiation applying open source programs. Anal Quant Cytol Histol. 2012;34(2):66–71.PubMedGoogle Scholar
- Irving BA, Weltman JY, Brock DW, Davis CK, Gaesser GA, Weltman A. NIH ImageJ and Slice-O-Matic computed tomography imaging software to quantify soft tissue. Obesity. 2007;15(2):370–6.PubMedView ArticleGoogle Scholar
- Collins T. Introduction to ImageJ for light microscopy. Microsc Microanal. 2007;13(SupplementS02):1674–5. doi:10.1017/S1431927607072911.Google Scholar
- Qvarnstrom OF, Simonsson M, Johansson KA, Nyman J, Turesson I. DNA double strand break quantification in skin biopsies. Radiother Oncol. 2004;72(3):311–7.PubMedView ArticleGoogle Scholar
- Light Microscopy Core Facility -Duke University and Duke University Medical Center http://microscopy.duke.edu/HOWTO/countfoci.html. 11.5.2015.
- Cai Z, Vallis KA, Reilly RM. Computational analysis of the number, area and density of gamma-H2AX foci in breast cancer cells exposed to (111)In-DTPA-hEGF or gamma-rays using Image-J software. Int J Radiat Biol. 2009;85(3):262–71.PubMedView ArticleGoogle Scholar
- Du G, Drexler GA, Friedland W, Greubel C, Hable V, Krucken R, et al. Spatial dynamics of DNA damage response protein foci along the ion trajectory of high-LET particles. Radiat Res. 2011;176(6):706–15.PubMedView ArticleGoogle Scholar
- Wu KH, Madigan MC, Billson FA, Penfold PL. Differential expression of GFAP in early v late AMD: a quantitative analysis. Br J Ophthalmol. 2003;87(9):1159–66.PubMed CentralPubMedView ArticleGoogle Scholar
- Woeffler-Maucler C, Beghin A, Ressnikoff D, Bezin L, Marinesco S. Automated immunohistochemical method to quantify neuronal density in brain sections: application to neuronal loss after status epilepticus. J Neurosci Methods. 2014;225:32–41.PubMedView ArticleGoogle Scholar
- Djuzenova CS, Elsner I, Katzer A, Worschech E, Distel LV, Flentje M, et al. Radiosensitivity in breast cancer assessed by the histone gamma-H2AX and 53BP1 foci. Radiat Oncol. 2013;8(98):8–98.Google Scholar
- Tchoukalova YD, Harteneck DA, Karwoski RA, Tarara J, Jensen MD. A quick, reliable, and automated method for fat cell sizing. J Lipid Res. 2003;44(9):1795–801.PubMedView ArticleGoogle Scholar
- Rifkin LH, Stojadinovic S, Stewart CH, Song KH, Maxted MC, Bell MH, et al. An athymic rat model of cutaneous radiation injury designed to study human tissue-based wound therapy. Radiat Oncol. 2012;7(68):7–68.Google Scholar
- Schneider CA, Rasband WS, Eliceiri KW. NIH Image to ImageJ: 25 years of image analysis. Nat Methods. 2012;9(7):671–5.PubMedView ArticleGoogle Scholar
- R Core Development Team. R: A language and environment for statistical computing. 2010. http://www.R-project.org.Google Scholar
- Moquet J, Barnard S, Rothkamm K. Gamma-H2AX biodosimetry for use in large scale radiation incidents: comparison of a rapid ‘96 well lyse/fix’ protocol with a routine method. PeerJ. 2014;6:2.Google Scholar
- Asaithamby A, Chen DJ. Cellular responses to DNA double-strand breaks after low-dose γ-irradiation. Nucleic Acids Res. 2009;37(12):3912–23. doi:10.1093/nar/gkp237.PubMed CentralPubMedView ArticleGoogle Scholar
- Rothkamm K, Löbrich M. Evidence for a lack of DNA double-strand break repair in human cells exposed to very low x-ray doses. Proc Natl Acad Sci USA. 2003;100(9):5057–62. doi:10.1073/pnas.0830918100.PubMed CentralPubMedView ArticleGoogle Scholar
- Schultz LB, Chehab NH, Malikzay A, Halazonetis TD. p53 binding protein 1 (53BP1) is an early participant in the cellular response to DNA double-strand breaks. J Cell Biol. 2000;151(7):1381–90.PubMed CentralPubMedView ArticleGoogle Scholar
- Markova E, Schultz N, Belyaev IY. Kinetics and dose–response of residual 53BP1/gamma-H2AX foci: co-localization, relationship with DSB repair and clonogenic survival. Int J Radiat Biol. 2007;83(5):319–29.PubMedView ArticleGoogle Scholar
- Hernandez L, Terradas M, Martin M, Tusell L, Genesca A. Highly sensitive automated method for DNA damage assessment: gamma-H2AX foci counting and cell cycle sorting. Int J Mol Sci. 2013;14(8):15810–26.PubMed CentralPubMedView ArticleGoogle Scholar
- Soille P, Vincent LM. Determining watersheds in digital pictures via flooding simulations, International society for optics and photonics. 1990.Google Scholar
- Bolte S, Cordelieres FP. A guided tour into subcellular colocalization analysis in light microscopy. J Microsc. 2006;224(Pt 3):213–32.PubMedView ArticleGoogle Scholar
- Schmid M. Fast filters. 2008. http://imagejdocu.tudor.lu/doku.php?id=plugin:filter:fast_filters:start.Google Scholar
- Bennett BT, Bewersdorf J, Knight KL. Immunofluorescence imaging of DNA damage response proteins: optimizing protocols for super-resolution microscopy. Methods. 2009;48(1):63–71.PubMed CentralPubMedView ArticleGoogle Scholar
- Darafsheh A, Guardiola C, Palovcak A, Finlay JC, Carabe A. Optical super-resolution imaging by high-index microspheres embedded in elastomers. Opt Lett. 2015;40(1):5–8.PubMedView ArticleGoogle Scholar
- Rossner M, Yamada KM. What’s in a picture? The temptation of image manipulation. J Cell Biol. 2004;166(1):11–5.PubMed CentralPubMedView ArticleGoogle Scholar
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.