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The Focinator - a new open-source tool for high-throughput foci evaluation of DNA damage.

Oeck S, Malewicz NM, Hurst S, Rudner J, Jendrossek V - Radiat Oncol (2015)

Bottom Line: It significantly reduced the analysis time of radiation-induced DNA-damage 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.

View Article: PubMed Central - PubMed

Affiliation: Institute of Cell Biology (Cancer Research), University of Duisburg-Essen, Medical School, Virchowstrasse 173, 45122, Essen, Germany. sebastian.oeck@uk-essen.de.

ABSTRACT

Background: 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.

Methods: 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.

Results: 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.

Conclusions: 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.

No MeSH data available.


Related in: MedlinePlus

Use of the Focinator macro reduces counting times compared to ImageJ-based counting and manual evaluation. TRAMP-C1 cells were irradiated with 3 Gy. The cells were fixed and permeabilized for 15 min with 3 % PFA and 0.2 % Triton X-100 at different time points after irradiation. The nuclei were stained with Hoechst 33342. DSB foci were labeled with Alexa Fluor 647-linked anti- γ-H2.AX antibodies. The evaluation time for the same 35 multi-channel images containing 439 nuclei was compared between the analysis with the Focinator, ImageJ-based counting via manual ROI marking and “Find Maxima…” function or manual counting. a Evaluation times using the different counting methods. b Comparison of detected nuclei numbers by ImageJ-based analysis, Focinator batch mode and manual counting shown as overall ROI count
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Fig4: Use of the Focinator macro reduces counting times compared to ImageJ-based counting and manual evaluation. TRAMP-C1 cells were irradiated with 3 Gy. The cells were fixed and permeabilized for 15 min with 3 % PFA and 0.2 % Triton X-100 at different time points after irradiation. The nuclei were stained with Hoechst 33342. DSB foci were labeled with Alexa Fluor 647-linked anti- γ-H2.AX antibodies. The evaluation time for the same 35 multi-channel images containing 439 nuclei was compared between the analysis with the Focinator, ImageJ-based counting via manual ROI marking and “Find Maxima…” function or manual counting. a Evaluation times using the different counting methods. b Comparison of detected nuclei numbers by ImageJ-based analysis, Focinator batch mode and manual counting shown as overall ROI count

Mentions: We tested the Focinator by counting radiation-induced γ-H2.AX foci in TRAMP-C1 cells at different time points after exposing the cells to 3 Gy. The results of the Focinator-analysis were compared to manual analysis as visual method and ImageJ-based counting via manual ROI marking and “Find Maxima” function as described by the Light Microscopy Core Facility -Duke University and Duke University Medical Center (Fig. 4) [24]. Manual counting of foci from images was chosen in the present study. By processing 35 multi-channel images, we counted 439 nuclei. Our software significantly reduced the analyzing time by a factor of approximately 23, from 132.07 ± 13.44 min for manual analysis to 5.61 ± 0.67 min with the Focinator (Fig. 4a). Surprisingly, evaluation with ImageJ without automation via macro needed more time than analysis with the Focinator or even the manual analysis (Fig. 4a). Nevertheless, analysis by ImageJ allowed the acquisition of more information about foci and nuclei than manual analysis. Importantly, there was no difference in nuclei detection between ImageJ-based methods and manual counting (Fig. 4b). Image acquisition was not part of the analyzing time; as fluorescent stainings are not stable, it is necessary to save image files for permanent documentation of the results with different counting methods, manual and automated. Moreover, image files can be used for more convenient manual foci counting with the option to mark counted foci with the software to avoid mistakes. Manual counting from images was chosen in the present study. In their routine protocol, Moquet et al. reported 1.5 h for counting non-irradiated cells and thus 4.68 s per cell to 6.1 h for irradiated cells and thus 19.06 s per cell for scoring of 20 cells in 96 samples. Thus, in comparison to Moquet et al. our manual scoring took about 59 min longer with an average of 30 s per cell. One explanation for our slower manual scoring is the higher radiation dose used - 3 Gy in our study compared to 0.5 to 1.0 Gy used by Moquet et al. and therefore a higher foci number per cell that amounted up to 70 foci per cell in our study compared to an average of 7 foci per cell in irradiated cells in the study of Moquet et al. Nevertheless, the Focinator would still be 13 times faster compared to the manual count of Moquet et al. [34].Fig. 4


The Focinator - a new open-source tool for high-throughput foci evaluation of DNA damage.

Oeck S, Malewicz NM, Hurst S, Rudner J, Jendrossek V - Radiat Oncol (2015)

Use of the Focinator macro reduces counting times compared to ImageJ-based counting and manual evaluation. TRAMP-C1 cells were irradiated with 3 Gy. The cells were fixed and permeabilized for 15 min with 3 % PFA and 0.2 % Triton X-100 at different time points after irradiation. The nuclei were stained with Hoechst 33342. DSB foci were labeled with Alexa Fluor 647-linked anti- γ-H2.AX antibodies. The evaluation time for the same 35 multi-channel images containing 439 nuclei was compared between the analysis with the Focinator, ImageJ-based counting via manual ROI marking and “Find Maxima…” function or manual counting. a Evaluation times using the different counting methods. b Comparison of detected nuclei numbers by ImageJ-based analysis, Focinator batch mode and manual counting shown as overall ROI count
© Copyright Policy - open-access
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC4554354&req=5

Fig4: Use of the Focinator macro reduces counting times compared to ImageJ-based counting and manual evaluation. TRAMP-C1 cells were irradiated with 3 Gy. The cells were fixed and permeabilized for 15 min with 3 % PFA and 0.2 % Triton X-100 at different time points after irradiation. The nuclei were stained with Hoechst 33342. DSB foci were labeled with Alexa Fluor 647-linked anti- γ-H2.AX antibodies. The evaluation time for the same 35 multi-channel images containing 439 nuclei was compared between the analysis with the Focinator, ImageJ-based counting via manual ROI marking and “Find Maxima…” function or manual counting. a Evaluation times using the different counting methods. b Comparison of detected nuclei numbers by ImageJ-based analysis, Focinator batch mode and manual counting shown as overall ROI count
Mentions: We tested the Focinator by counting radiation-induced γ-H2.AX foci in TRAMP-C1 cells at different time points after exposing the cells to 3 Gy. The results of the Focinator-analysis were compared to manual analysis as visual method and ImageJ-based counting via manual ROI marking and “Find Maxima” function as described by the Light Microscopy Core Facility -Duke University and Duke University Medical Center (Fig. 4) [24]. Manual counting of foci from images was chosen in the present study. By processing 35 multi-channel images, we counted 439 nuclei. Our software significantly reduced the analyzing time by a factor of approximately 23, from 132.07 ± 13.44 min for manual analysis to 5.61 ± 0.67 min with the Focinator (Fig. 4a). Surprisingly, evaluation with ImageJ without automation via macro needed more time than analysis with the Focinator or even the manual analysis (Fig. 4a). Nevertheless, analysis by ImageJ allowed the acquisition of more information about foci and nuclei than manual analysis. Importantly, there was no difference in nuclei detection between ImageJ-based methods and manual counting (Fig. 4b). Image acquisition was not part of the analyzing time; as fluorescent stainings are not stable, it is necessary to save image files for permanent documentation of the results with different counting methods, manual and automated. Moreover, image files can be used for more convenient manual foci counting with the option to mark counted foci with the software to avoid mistakes. Manual counting from images was chosen in the present study. In their routine protocol, Moquet et al. reported 1.5 h for counting non-irradiated cells and thus 4.68 s per cell to 6.1 h for irradiated cells and thus 19.06 s per cell for scoring of 20 cells in 96 samples. Thus, in comparison to Moquet et al. our manual scoring took about 59 min longer with an average of 30 s per cell. One explanation for our slower manual scoring is the higher radiation dose used - 3 Gy in our study compared to 0.5 to 1.0 Gy used by Moquet et al. and therefore a higher foci number per cell that amounted up to 70 foci per cell in our study compared to an average of 7 foci per cell in irradiated cells in the study of Moquet et al. Nevertheless, the Focinator would still be 13 times faster compared to the manual count of Moquet et al. [34].Fig. 4

Bottom Line: It significantly reduced the analysis time of radiation-induced DNA-damage 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.

View Article: PubMed Central - PubMed

Affiliation: Institute of Cell Biology (Cancer Research), University of Duisburg-Essen, Medical School, Virchowstrasse 173, 45122, Essen, Germany. sebastian.oeck@uk-essen.de.

ABSTRACT

Background: 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.

Methods: 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.

Results: 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.

Conclusions: 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.

No MeSH data available.


Related in: MedlinePlus