We thank the Flow Cytometry Facility team at the German Cancer Research Center (DKFZ) for their support.

1. Preparation
<ol>
	<li>Prepare &#8805;1 x 105 cells/sample in any type of culture vessel as starting material.
	<ol>
		<li>For example, conduct a time-course experiment after exposure of U87 glioblastoma cells to ionizing radiation: Irradiate sub-confluent U87 cells in T25 flasks in triplicates for each time point. Choose early time-points (15 min up to 8 h after irradiation) to follow the kinetics of DSB repair (&#947;H2AX level) and late time points (24 h up to 96 h) to assess residual DSB levels, cell cycle effects and apoptosis. 
		NOTE: The protocol is not restricted to irradiation experiments or any specific cell line. It has been tested with numerous cell lines of all types, from different species and for various treatment conditions.</li>
	</ol>
	</li>
	<li>Prepare the following solutions including 10% excess volume.
	<ol>
		<li>Prepare 2 mL per sample of fixation solution composed of 4.5% paraformaldehyde (PFA) in phosphate-buffered saline (PBS). Prepare the solution fresh. Dilute PFA in PBS by heating to 80 &#176;C with slow stirring under the fume hood. Cover the flask with aluminum foil to prevent heat loss. Let the solution cool to room temperature and adjust the final volume. Pass the solution through a folded cellulose filter, grade 3hw (see the Table of Materials). 
		CAUTION: PFA fumes are toxic. Perform this step under a fume hood and dispose PFA waste appropriately.</li>
		<li>Prepare 3 mL per sample of permeabilization solution composed of 70% ethanol in ice-cold H2O. Store at -20 &#176;C.</li>
		<li>Prepare 7 mL per sample&#160;of washing solution composed of 0.5% bovine serum albumin (BSA) in PBS.</li>
		<li>Prepare 100 &#181;L per sample of 3% BSA in PBS as antibody diluent.</li>
		<li>Prepare 100-250 &#181;L per sample of DNA staining solution composed of 1 &#956;g/mL 4&#39;,6-diamidin-2-phenylindol (DAPI) in PBS.</li>
	</ol>
	</li>
	<li>Set the centrifuge for 15 mL tubes to 5 min at 200 x g and 7 &#176;C. Let the centrifuge cool down and use these settings for all centrifugation steps.</li>
</ol>
2. Sample Collection
<ol>
	<li>If processing adherent cells (e.g., U87 glioblastoma cells grown in T25 flasks with 5 mL of Dulbecco&#39;s modified Eagle&#39;s medium supplemented with 10% fetal bovine serum at 37 &#176;C and 5% carbon dioxide atmosphere), continue with steps 2.1.1. and 2.1.2. For suspension cells proceed directly to step 2.1.2.
	<ol>
		<li>Collect the medium in a centrifugation tube. Detach the cells using a routine cell culture method, which may include the use of trypsin, ethylenediaminetetraacetic acid (EDTA) or other cell detachment agents.
		<ol>
			<li>For U87 cells, prewarm PBS and trypsin/EDTA (see the Table of Materials) to 37 &#176;C, wash the cell layer with 1 mL of PBS, incubate the cells for 1-2 min with 1 mL of trypsin/EDTA and support cell detachment by tapping at the flask. Collect all washing solution and the cell suspension in the tube with the medium.</li>
		</ol>
		</li>
		<li>Centrifuge the cells, discard the medium and resuspend the cells in 1 mL of PBS.</li>
	</ol>
	</li>
	<li>Pipet the cells up and down several times to ensure a single cell suspension and transfer the cell suspension into a tube with 2 mL of fixation solution (4.5% PFA/PBS, 3% final concentration). 
	CAUTION: PFA fumes are toxic. Perform this step under a fume hood and dispose PFA waste appropriately.</li>
	<li>Incubate the cells for 10 min at room temperature.</li>
	<li>Centrifuge the cells and discard the supernatant by decantation.</li>
	<li>Loosen the cell pellet by tapping onto the tube and resuspend the cells in 3 mL of 70% ethanol. Proceed directly with the next step or store the samples at 4 &#176;C for up to several weeks.</li>
</ol>
3. Washing and Staining
<ol>
	<li>Centrifuge the cells and discard the supernatant by decantation.</li>
	<li>Loosen the cell pellet by tapping onto the tube. Resuspend the cells in 3 mL of washing solution (0.5% BSA/PBS), centrifuge and discard the supernatant.</li>
	<li>Repeat the washing step 1x with 3 mL, and then 1x with 1 mL washing solution. In the last step, discard the supernatant carefully by pipetting. Take care not to aspirate the pellet.</li>
	<li>Dilute the antibodies against &#947;H2AX, phospho-histone H3 (Ser10) and caspase-3 (see the Table of Materials) in 100 &#181;L/sample with antibody diluent (3% BSA/PBS).</li>
	<li>Loosen the cell pellet by tapping onto the tube and resuspend the cells in 100 &#181;L of the antibody solution prepared in step 3.4. Keep the samples in the dark from this step onward.</li>
	<li>Incubate the samples for 1 h at room temperature.</li>
	<li>Centrifuge the cells and discard the supernatant carefully by pipetting. Take care not to aspirate the pellet.</li>
	<li>Loosen the cell pellet by tapping onto the tube and resuspend the cells in 100-250 &#181;L of DNA staining solution (1 &#181;g/mL DAPI/PBS).
	<ol>
		<li>Use 100 &#181;L if 1-2 x105 cells are present and increase the volume for higher cell numbers (250 &#181;L for &#8805;1 x 106 cells). Proceed directly with the next step or store the samples in the dark at 4 &#176;C for up to 2 weeks. 
		NOTE: For some cell types optimizing the DAPI concentration can help to improve the separation of the cell cycle phases.</li>
	</ol>
	</li>
	<li>Pipet the samples through the cell strainer cap of a sample tube with a mesh pore size of 35 &#181;m.</li>
</ol>
4. Measurement
<ol>
	<li>Place the samples on ice, start the flow cytometer (see the Table of Materials) configured with an optical setup according to Table 1 and press the Prime button. If required, switch on the ultraviolet laser (355 nm wavelength) separately and set the power to 20 mW using the appropriate software.</li>
	<li>Open the acquisition software (see Table of Materials), log in and create a new experiment by clicking the New Experiment button on the Browser toolbar.</li>
	<li>Use the Inspector window to customize the name of the experiment and choose &#39;5 Log Decades&#39;&#160;for plot display.</li>
	<li>Click the New Specimen button in the Browser toolbar and expand the new entry by clicking the &#39;+&#39;&#160;symbol at its left side to show the first Tube. Select the respective icon and type to rename the Specimen (e.g., cell type) and the Tube (sample identifier). Click the Tube Pointer of the first Tube (arrow-like symbol at its left) to turn it green (active).</li>
	<li>Open the Parameters tab in the Cytometer window and choose the parameters according to Table 1. Delete all unnecessary parameters. 
	NOTE: the parameter names may vary depending on the custom presets (e.g., Cy3 instead of Alexa555). Make sure that the selection matches the optical filters and detectors in Table 1. The light paths of all fluorophores are fully independent in this setup and compensation of spectral overlap is not required; however, it might be necessary if another optical setup is used.</li>
	<li>Open the Worksheet window and create plots according to Figure 1. Draw 2 dot plots and 4 histograms using the corresponding toolbar buttons and click the axis labels to choose the appropriate parameters (front scatter (FSC-A) versus side scatter (SSC-A), DAPI-W versus DAPI-A and a histogram for DAPI-A and each antibody-coupled fluorophore.</li>
	<li>Attach a control sample to the cytometer and press the Run button on the instrument. Select the first tube in the Browser window of the software and click the Acquire Data button in the Acquisition Dashboard. Adjust the sample injection volume using the Low, Mid or High buttons and the fine tuning wheel on the instrument. Preferably work at Low setting, but try to acquire at least 100 events/second (see Acquisition Dashboard).</li>
	<li>Adjust the detector voltages for FSC, SSC and DAPI in the Parameters tab of the Inspector window using the dot plots in Figure 1 as a guideline. Switch to logarithmic scale for the FSC and SSC parameters if the cell population appears too dispersed on a linear scale.</li>
	<li>Press the Standby button on the cytometer and continue with the worksheet setup in the software.
	<ol>
		<li>Use the Polygon Gate tool to define the Cells population in the FSC-A versus SSC-A plot and the Rectangle Gate tool to define the SingleCells population in the DAPI-W versus DAPI-A plot. Press Ctrl + G keys to show the Population Hierarchy and click on the default gate names to rename them.</li>
		<li>Subsequently right-click on all histograms and choose Show Populations | SingleCells from the context menu.</li>
	</ol>
	</li>
	<li>Optimize the detector voltages for the antibody-coupled fluorophores to cover the full dynamic range by subsequently acquiring control and treated samples. Maximize the signal-to-noise ratio and avoid detector saturation. Make sure that the Alexa488 peak in the SingleCells population is neither truncated in the control nor the treated sample.</li>
	<li>Press the Standby button on the cytometer and optionally perform steps 4.11.1-4.11.3 in the Worksheet window of the software to get a rough estimate of treatment effects during sample acquisition.
	<ol>
		<li>Select SingleCells in the Population Hierarchy and use the Rectangle Gate tool to define the G1 population in the DAPI-W versus DAPI-A plot. Right-click at the Alexa488 histogram and choose Show Populations | G1 from the context menu.</li>
		<li>Right-click at the Alexa488 histogram and choose Create Statistics View from the context menu. Right-click at the Statistics View and choose Edit Statistics View. Go to the Statistics tab and activate the checkbox for the median of the Alexa488 signal in the G1 population (deactivate all other options).</li>
		<li>Select SingleCells in the Population Hierarchy and use the Interval Gate tool to define the subG1, M and Casp3+ populations in the DAPI-A, Alexa555-A (Cy3-A in Figure 1) and Alexa647-A histograms.</li>
	</ol>
	</li>
	<li>Press the Run button on the cytometer and measure the samples using the Acquisition Dashboard in the software. Set the stopping gate to All events or the Cells gate (if numerous small particles are present) and the number of events to record to 10,000.</li>
	<li>Click Next Tube to create a new sample, rename it in the Browser window, click Acquire Data to start the acquisition and Record Data to start recording.</li>
	<li>Select File | Export | FCS files from the menu bar to export the data. Optionally select File | Export | Experiments to save the experiment as an additional zip file to enable reimporting the experiment at a later time point. 
	NOTE: The setup of existing experiments can be easily reused with Edit | Duplicate without data.</li>
</ol>
5. Data Evaluation
<ol>
	<li>Drag and drop the &#39;.fcs&#39;&#160;files into the sample browser of the flow cytometric analysis software (see Table of Materials). Apply the gating strategy shown in Figure 2. Make sure that the gates fit to the corresponding population in all of the samples before proceeding with the next daughter gate.
	<ol>
		<li>To apply changes to all samples, select the changed gate in the sample browser, copy it by pressing Ctrl + C, select the parent gate, press Ctrl + Shift + E to select the equivalent nodes in all samples and press Ctrl + V to paste or overwrite the gate. Do not use group gates.</li>
		<li>Double-click on the first sample in the browser to open the SSC-A vs. FSC-A plot. Use the Polygon tool in the toolbar to define the Cells population (Figure 2, plot 1), excluding debris from the analysis. Make sure that the gate is wide enough towards the upper right corner to accommodate treatment-related shifts, but restrict the border facing to the lower left corner of the plot to reliably exclude the cell.</li>
		<li>Double-click on the Cells gate to open a new plot window and change the axes to DAPI-W (vertical) vs. DAPI-A (horizontal) by clicking on the axis labels. Use the Rectangle tool to define the SingleCells population (Figure 2, plot 2), excluding cell doublets or clumps from the analysis (doublets of G1 cells have the same DAPI-A intensity as G2 and M cells, but a considerably higher DAPI-W value). 
		NOTE: Varying cell counts in different samples will cause shifts in the overall DAPI-A signal strength due to equilibrium binding of DAPI to DNA. This will not affect the cell cycle analysis, but it may be necessary to adjust the right border of the single cell gate sample by sample to account for these shifts.</li>
		<li>Double-click on the SingleCells gate to open a new plot window and change the axes to show the DAPI-A histogram. Use the Bisector tool to distinguish single cells with normal DNA content (CellCycle population) from apoptotic cells with degraded DNA (subG1 population). Subsequently select the new gates in the browser and press Ctrl + R to rename them accordingly (Figure 2, plot 3).</li>
		<li>Select the CellCycle gate in the browser and choose Tool | Biology | Cell Cycle... from the menu bar to open the cell cycle modelling tool. Choose Dean-Jett-Fox25,26 in the Model section to estimate the frequency of cells in G1, S and G2/M phase (Figure 2, plot 4). Use constraints only in case of poor modelling performance (minimize the Root Mean Square deviation between model and data).</li>
		<li>Create gates for G1 (Ellipse tool), S (Polygon tool) and G2 + M (Ellipse tool) phase in the DAPI-W vs. DAPI-A plot of the CellCycle population to enable cell cycle-specific &#947;H2AX measurement (Figure 2, plot 5). Do not use the modelling tool for automatic cell cycle gating based on the DAPI-A histogram, as this can be inaccurate. 
		NOTE: It may be necessary to move the gates along the DAPI-A axis sample by sample to account for the aforementioned shifts in overall DAPI signal strength. Only move the three gates as a group and do not change the shape of individual gates to avoid bias.</li>
		<li>Use the Bisector tool to distinguish phospho-histone H3-positive (M) and -negative (M-) cells in the Alexa555-A histogram of the CellCycle population (Figure 2, plot 6). Hold Ctrl and select the gates G2 + M and M-. Press Ctrl + Shift + A to create the G2 (G2+M &#38; M-) gate.</li>
		<li>Use the Bisector tool to distinguish caspase-3-positive (Casp3+) and -negative (Casp3-) cells in the Alexa647-A histogram of the SingleCells population (Figure 2, plot 7). Set the threshold such that the average of the Casp3+ population in the untreated controls amounts to ~0.8% to assure high sensitivity and minimize assay-to-assay variations.</li>
		<li>Press Ctrl + T to open the Table Editor and configure it according to Figure 3. Drag and drop the different populations from the sample browser into the Table Editor and double-click on the rows to change the statistic, parameter and name settings. Remove the unnecessary rows that are automatically added after the drag and drop of the cell cycle modelling icon by selecting the rows and pressing Del.</li>
		<li>Choose To File, the format and destination in the Output section of the menu ribbon and click Create Table to export the data as &#39;.xlsx&#39;&#160;file.</li>
	</ol>
	</li>
	<li>Use table calculation software (see Table of Materials) for further data analysis according to Supplementary File 1 (FACS_Analysis_Template_(1).xlsx).
	<ol>
		<li>Correct the frequencies of cells in the different cell cycle phases such that their sum amounts to 100% by applying the formula X&#39;&#160;= X * 100 / &#931;(all cell cycle phases), with X&#39;: corrected value, X: raw value, to each cell cycle phase. 
		NOTE: Deviations from a sum of 100% occur due to inaccuracies in the cell cycle modelling, but are usually small (&#60;5%).</li>
		<li>Normalize the median &#947;H2AX intensities to the DNA content in the different cell cycle phases by dividing the values in S phase by 1.5 and in G2 and M phase by 2.0.</li>
		<li>To calculate the combined normalized &#947;H2AX level in the whole cell population, use the formula IA = IG1 * G1 + IS * S + IG2 * G2 + IM * M, where IA, IG1, IS, IG2, IM are normalized median &#947;H2AX intensities of all, G1, S, G2, M cells respectively and G1, S, G2, M are corrected frequency of cells in the respective cell cycle phase.</li>
		<li>For the normalized &#947;H2AX levels and the frequency of subG1 and caspase-3-positive cells, subtract the average value of the untreated controls from each sample.</li>
		<li>Calculate the mean and standard deviation of each parameter from all replicate samples and plot the results into diagrams.</li>
	</ol>
	</li>
</ol>

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N., Jett, J. H. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Mathematical+analysis+of+DNA+distributions+derived+from+flow+microfluorometry.">Mathematical analysis of DNA distributions derived from flow microfluorometry.</a> Journal of Cell Biology. 60 (2), 523-527 (1974).</li><li>Fox, M. H. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+model+for+the+computer+analysis+of+synchronous+DNA+distributions+obtained+by+flow+cytometry.">A model for the computer analysis of synchronous DNA distributions obtained by flow cytometry.</a> Cytometry. 1 (1), 71-77 (1980).</li><li>Costes, S. V., Boissi&#232;re, A., Ravani, S., Romano, R., Parvin, B., Barcellos-Hoff, M. H. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Imaging+features+that+discriminate+between+foci+induced+by+high-+and+low-LET+radiation+in+human+fibroblasts.">Imaging features that discriminate between foci induced by high- and low-LET radiation in human fibroblasts.</a> Radiation Research. 165 (5), 505-515 (2006).</li><li>Meyer, B., Voss, K. -. O., Tobias, F., Jakob, B., Durante, M., Taucher-Scholz, G. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Clustered+DNA+damage+induces+pan-nuclear+H2AX+phosphorylation+mediated+by+ATM+and+DNA-PK.">Clustered DNA damage induces pan-nuclear H2AX phosphorylation mediated by ATM and DNA-PK.</a> Nucleic Acids Research. 41 (12), 6109-6118 (2013).</li><li>Lopez Perez, R., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Superresolution+light+microscopy+shows+nanostructure+of+carbon+ion+radiation-induced+DNA+double-strand+break+repair+foci.">Superresolution light microscopy shows nanostructure of carbon ion radiation-induced DNA double-strand break repair foci.</a> FASEB. 30 (8), 2767-2776 (2016).</li><li>Schipler, A., Iliakis, G. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=DNA+double-strand-break+complexity+levels+and+their+possible+contributions+to+the+probability+for+error-prone+processing+and+repair+pathway+choice.">DNA double-strand-break complexity levels and their possible contributions to the probability for error-prone processing and repair pathway choice.</a> Nucleic Acids Research. 41 (16), 7589-7605 (2013).</li><li>Stenerlow, B., Hoglund, E., Carlsson, J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=DNA+fragmentation+by+charged+particle+tracks.">DNA fragmentation by charged particle tracks.</a> Advances in Space Research. 30 (4), 859-863 (2002).</li><li>Friedland, W., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Comprehensive+track-structure+based+evaluation+of+DNA+damage+by+light+ions+from+radiotherapy-relevant+energies+down+to+stopping.">Comprehensive track-structure based evaluation of DNA damage by light ions from radiotherapy-relevant energies down to stopping.</a> Scientific Reports. 7, 45161 (2017).</li><li>Pang, D., Chasovskikh, S., Rodgers, J. E., Dritschilo, A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Short+DNA+Fragments+Are+a+Hallmark+of+Heavy+Charged-Particle+Irradiation+and+May+Underlie+Their+Greater+Therapeutic+Efficacy.">Short DNA Fragments Are a Hallmark of Heavy Charged-Particle Irradiation and May Underlie Their Greater Therapeutic Efficacy.</a> Frontiers in Oncology. 6, 130 (2016).</li><li>B&#246;cker, W., Iliakis, G. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Computational+Methods+for+analysis+of+foci:+validation+for+radiation-induced+gamma-H2AX+foci+in+human+cells.">Computational Methods for analysis of foci: validation for radiation-induced gamma-H2AX foci in human cells.</a> Radiation Research. 165 (1), 113-124 (2006).</li><li>L&#246;brich, M., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=&#947;H2AX+foci+analysis+for+monitoring+DNA+double-strand+break+repair:+Strengths,+limitations+and+optimization.">&#947;H2AX foci analysis for monitoring DNA double-strand break repair: Strengths, limitations and optimization.</a> Cell Cycle. 9 (4), 662-669 (2010).</li></ol>

The authors have nothing to disclose.

The featured method is easy to use and offers a fast, accurate and reproducible measurement of the DNA damage response including double-strand break (DSB) induction and repair, cell cycle effects and apoptotic cell death. The combination of these endpoints provides a more complete picture of their interrelations than individual assays. The method can be widely applied as a comprehensive genotoxicity assay in the fields of radiation biology, therapy and protection, and more generally in oncology (e.g., for environmental risk factor assessment, drug screening and evaluation of genetic instability in tumor cells).
The most critical step in this protocol is the fixation of the cells. To obtain a clean sample with a clearly defined cell population and optimal resolution of different cell cycle phases, it is important to give adherent cells enough time to detach from the culture vessel and form a completely round shape. The cells should be transferred into the fixation solution as a single cell suspension without cell clumps. To separate them, the cells must be pipetted up and down or passed through a fine needle several times in difficult cases. The fixation time should be kept constant between all samples and should not exceed 20 min including the time for centrifugation before resuspension of the cells in 70% ethanol. Prolonged fixation will lead to loss of accessible epitopes for antibody binding and will decrease the signal strength and sensitivity of the method.
The main limitation of the technique is that it does not provide information on the quality of the &#947;H2AX foci other than the intensity in the whole nucleus. Particularly large &#947;H2AX foci as well as the occurrence of a pan-nuclear &#947;H2AX staining have been linked to clustered DSBs1,27,28,29, which are highly complex lesions that are very difficult to repair30. Such clustered lesions seem to be characteristic for particle radiation such as clinically applied carbon ion radiation and may be the reason for the superior biological effectiveness of heavy ion radiation compared to photons31,32,33. Analysis of the &#947;H2AX foci size may therefore be of interest as an indicator of the damage complexity. Although it is possible to use the present protocol with an image stream cytometer to visualize the &#947;H2AX foci, the achievable resolution cannot compete with a classical microscopic approach. However, we have successfully prepared microscopic slides from the remaining samples after flow cytometric measurement (Supplementary Figure 1) and evaluated several features including &#947;H2AX foci count, size and pan-nuclear intensity using a semi-automated imaging and analysis system. To prepare the samples for microscopy, 30-50 &#181;L of the stained cells were spread over the surface of a 24 mm x 24 mm cover glass, air dried overnight at room temperature in the dark and then embedded with mounting medium on a glass slide.
In comparison with microscopic evaluation of DSB levels by &#947;H2AX foci counting, the flow cytometric measurement is superior in throughput, sampling rate, dynamic range and accuracy. The dynamic range of microscopic foci counting is limited by the optical resolution, leading to the inability to distinguish overlapping foci29,34. In practice, we have observed a linear relationship between radiation dose and &#947;H2AX foci numbers below 2 Gy, and a strong saturation effect above 2 Gy in U87 glioblastoma cells1. In contrast, the intensity of the &#947;H2AX signal measured by flow cytometry increased linearly with dose for the whole dose range tested (0-8 Gy).
The accuracy of microscopic foci counting is limited by the inability to distinguish between different cell cycle phases without additional stainings35. The number of DSBs induced in a cell is not only dependent on the radiation dose, but also on the DNA content, and hence the cell cycle stage. G2 cells for example have twice as much DNA as G1 cells and the average number of DSBs induced at a certain radiation dose is twice as high for G2 cells compared to G1. This is clearly reflected in the &#947;H2AX signal intensity of G2 versus G1 cells at early time points after radiation measured by flow cytometry. Therefore, it is important to evaluate DSB levels in a cell cycle-specific manner to obtain accurate results. A pooled analysis of cells in different cell cycle phases, like usual in microscopic approaches, can be biased by shifts in the cell cycle distribution particularly due to radiation-induced G2 arrest. To account for this effect and to compare DSB repair characteristics between different cell cycle stages, the &#947;H2AX levels can easily be normalized to the DNA content in the flow cytometry method as indicated in the protocol.
Extensions of the present protocol are possible with relatively little extra effort. Additional antibodies with compatible fluorophores labels can be included in step 3.4 without the need for any extra steps during sample preparation. If a more detailed analysis of apoptosis is desired, caspase-7, -9 and -8 antibodies could be included. As another extension, we have recently included a live and dead cell dye, a troponin T antibody and counting beads in the protocol to specifically analyze cardiomyocytes co-cultured with fibroblasts after irradiation. The addition of the live/dead cell stain and the counting beads expands the capability of the method to include fibroblast proliferation and non-apoptotic cell death of the cardiomyocytes as further endpoints that provide useful additional information on the cell fate after genotoxic stress. The extended protocol is available upon request.

The goal of oncology is to kill or to inactivate cancer cells without harming normal cells. Many therapies either directly or indirectly induce genotoxic stress in cancer cells, but also to some extend in normal cells. Chemotherapy or targeted drugs are often combined with radiotherapy to enhance the radiosensitivity of the irradiated tumor1,2,3,4,5, which allows for a reduction of the radiation dose to minimize normal tissue damage.
Ionizing radiation and other genotoxic agents induce different kinds of DNA damage, including base modifications, strand crosslinks and single- or double-strand breaks. DNA double-strand breaks (DSBs) are the most serious DNA lesions and their induction is key to the cell killing effect of ionizing radiation and various cytostatic drugs in radiochemotherapy. DSBs do not only harm the integrity of the genome, but also promote the formation of mutations6,7. Therefore, different DSB repair pathways, and mechanisms to eliminate irreparably damaged cells like apoptosis have developed during evolution. The entire DNA damage response (DDR) is regulated by a complex network of signaling pathways that reach from DNA damage recognition and cell cycle arrest to allow for DNA repair, to programmed cell death or inactivation in case of repair failure8.
The presented flow cytometric method has been developed to investigate the DDR after genotoxic stress in one comprehensive assay that covers DSB induction and repair, as well as consequences of repair failure. It combines the measurement of the widely applied DSB marker &#947;H2AX with analysis of the cell cycle and induction of apoptosis, using classical subG1 analysis and more specific evaluation of caspase-3 activation.
The combination of these endpoints in one assay not only reduces time, labor and cost expenses, but also enables cell cycle-specific measurement of DSB induction and repair, as well as caspase-3 activation. Such analyses would not be possible with independently conducted assays, but they are highly relevant for a comprehensive understanding of the DNA damage response after genotoxic stress. Many anti-cancer drugs, such as cytostatic compounds, are directed against dividing cells and their efficiency is strongly dependent on the cell cycle stage. The availability of different DSB repair processes is also dependent on the cell cycle stage and pathway choice which is critical for the repair accuracy, and in turn determines the fate of the cell9,10,11,12. In addition, cell cycle-specific measurement of DSB levels is more accurate than pooled analysis, because DSB levels are not only dependent on the dose of a genotoxic compound or radiation, but also on the DNA content of the cell.
The method has been used to compare the efficacy of different radiotherapies to overcome resistance mechanisms in glioblastoma13 and to dissect the interplay between ionizing radiation and targeted drugs in osteosarcoma14,15 and atypical teratoid rhabdoid cancers16. Additionally, the described method has been widely used to analyze side effects of radio- and chemotherapy on mesenchymal stem cells17,18,19,20,21,22,23,24, which are essential for the repair of treatment-induced normal tissue damage and have a potential application in regenerative medicine.

<table border="0" cellpadding="0" cellspacing="0" style="width:1064px;" width="1063">
	<colgroup>
		<col />
		<col />
		<col />
		<col />
	</colgroup>
	<tbody>
		<tr height="40">
			<td height="40" style="height:40px;width:405px;">1000 &#181;L filter tips</td>
			<td style="width:151px;">Nerbe plus</td>
			<td style="width:169px;">07-693-8300</td>
			<td style="width:339px;"></td>
		</tr>
		<tr height="96">
			<td height="96" style="height:97px;">100-1000 &#181;L pipette</td>
			<td>Eppendorf</td>
			<td>3123000063</td>
			<td style="width:339px;"></td>
		</tr>
		<tr height="40">
			<td height="40" style="height:40px;">12 x 75 mm Tubes with Cell Strainer Cap, 35 &#181;m mesh pore size</td>
			<td>BD Falcon</td>
			<td>352235</td>
			<td style="width:339px;"></td>
		</tr>
		<tr height="40">
			<td height="40" style="height:40px;">15 mL tubes</td>
			<td>BD Falcon</td>
			<td>352096</td>
			<td style="width:339px;"></td>
		</tr>
		<tr height="40">
			<td height="40" style="height:40px;">200 &#181;L filter tips</td>
			<td>Nerbe plus</td>
			<td>07-662-8300</td>
			<td style="width:339px;"></td>
		</tr>
		<tr height="40">
			<td height="40" style="height:40px;">20-200 &#181;L pipette</td>
			<td>Eppendorf</td>
			<td>3123000055</td>
			<td style="width:339px;"></td>
		</tr>
		<tr height="40">
			<td height="40" style="height:40px;">4&#8217;,6-Diamidin-2-phenylindol (DAPI)</td>
			<td>Sigma-Aldrich</td>
			<td>D9542</td>
			<td style="width:339px;">Dissolve in water at 200 &#181;g/ml and store aliquots at -20 &#176;C</td>
		</tr>
		<tr height="40">
			<td height="40" style="height:40px;width:405px;">Alexa Fluor 488 anti-H2A.X Phospho (Ser139) Antibody, RRID: AB_2248011</td>
			<td>BioLegend</td>
			<td>613406</td>
			<td style="width:339px;">Dilute 1:20</td>
		</tr>
		<tr height="40">
			<td height="40" style="height:40px;width:405px;">Alexa Fluor 647 Rabbit Anti-Active Caspase-3 Antibody, AB_1727414</td>
			<td>BD Pharmingen</td>
			<td>560626</td>
			<td style="width:339px;">Dilute 1:20</td>
		</tr>
		<tr height="40">
			<td height="40" style="height:40px;">BD FACSClean solution</td>
			<td>BD Biosciences</td>
			<td>340345</td>
			<td style="width:339px;">For cytometer cleaning routine after measurement</td>
		</tr>
		<tr height="40">
			<td height="40" style="height:40px;">BD FACSRinse solution</td>
			<td>BD Biosciences</td>
			<td>340346</td>
			<td style="width:339px;">For cytometer cleaning routine after measurement</td>
		</tr>
		<tr height="40">
			<td height="40" style="height:40px;">Dulbecco&#8217;s Phosphate Buffered Saline (PBS)</td>
			<td>Biochrom</td>
			<td>L 182</td>
			<td style="width:339px;">Dissolve in water to 1x concentration</td>
		</tr>
		<tr height="40">
			<td height="40" style="height:40px;">Dulbecco&#39;s Modified Eagle&#39;s Medium with stable glutamin</td>
			<td>Biochrom</td>
			<td>FG 0415</td>
			<td style="width:339px;">Routine cell culture material for the example cell line used in the protocol</td>
		</tr>
		<tr height="40">
			<td height="40" style="height:40px;">Ethanol absolute</td>
			<td>VWR</td>
			<td>20821.330</td>
			<td style="width:339px;"></td>
		</tr>
		<tr height="40">
			<td height="40" style="height:40px;">Excel software</td>
			<td>Microsoft</td>
			<td></td>
			<td style="width:339px;"></td>
		</tr>
		<tr height="40">
			<td height="40" style="height:40px;">FBS Superior (fetal bovine serum)</td>
			<td>Biochrom</td>
			<td>S 0615</td>
			<td style="width:339px;">Routine cell culture material for the example cell line used in the protocol</td>
		</tr>
		<tr height="40">
			<td height="40" style="height:40px;">FlowJo v10 software</td>
			<td>LLC</td>
			<td>online order</td>
			<td style="width:339px;"></td>
		</tr>
		<tr height="40">
			<td height="40" style="height:40px;">Fluoromount-G</td>
			<td>SouthernBiotech</td>
			<td>0100-01</td>
			<td style="width:339px;">Embedding medium for optional preparation of microscopic slides from stained samples</td>
		</tr>
		<tr height="40">
			<td height="40" style="height:40px;">folded cellulose filters, grade 3hw</td>
			<td>NeoLab</td>
			<td>11416</td>
			<td style="width:339px;"></td>
		</tr>
		<tr height="40">
			<td height="40" style="height:40px;">LSRII or LSRFortessa cytometer</td>
			<td>BD Biosciences</td>
			<td></td>
			<td style="width:339px;"></td>
		</tr>
		<tr height="40">
			<td height="40" style="height:40px;">MG132</td>
			<td>Calbiochem</td>
			<td>474787</td>
			<td style="width:339px;">optional drug for apoptosis positive control</td>
		</tr>
		<tr height="40">
			<td height="40" style="height:40px;">Multifuge 3SR+</td>
			<td>Heraeus</td>
			<td></td>
			<td style="width:339px;"></td>
		</tr>
		<tr height="40">
			<td height="40" style="height:40px;">Paraformaldehyde</td>
			<td>AppliChem</td>
			<td>A3813</td>
			<td style="width:339px;">Prepare 4.5% solution fresh. Dilute in PBS by heating to 80 &#176;C with slow stirring under the fume hood. Cover the flask with aluminium foil to prevent heat loss. Let the solution cool to room temperature and adjust the final volume. Pass the solution through a cellulose filter.</td>
		</tr>
		<tr height="40">
			<td height="40" style="height:40px;width:405px;">Phospho-Histone H3 (Ser10) (D2C8) XP Rabbit mAb (Alexa Fluor&#174; 555 Conjugate) RRID: AB_10694639</td>
			<td>Cell Signaling Technology</td>
			<td>#3475</td>
			<td style="width:339px;">Dilute 3:200</td>
		</tr>
		<tr height="40">
			<td height="40" style="height:40px;">PIPETBOY acu 2</td>
			<td>Integra Biosciences</td>
			<td>155 016</td>
			<td style="width:339px;"></td>
		</tr>
		<tr height="40">
			<td height="40" style="height:40px;">Serological pipettes, 10 mL</td>
			<td>Corning</td>
			<td>4488</td>
			<td style="width:339px;"></td>
		</tr>
		<tr height="40">
			<td height="40" style="height:40px;">Serological pipettes, 25 mL</td>
			<td>Corning</td>
			<td>4489</td>
			<td style="width:339px;"></td>
		</tr>
		<tr height="40">
			<td height="40" style="height:40px;">Serological pipettes, 5 mL</td>
			<td>Corning</td>
			<td>4487</td>
			<td style="width:339px;"></td>
		</tr>
		<tr height="40">
			<td height="40" style="height:40px;">SuperKillerTRAIL (modified TNF-related apoptosis-inducing ligand)</td>
			<td>Biomol</td>
			<td>AG-40T-0002-C020</td>
			<td style="width:339px;">optional drug for apoptosis positive control</td>
		</tr>
		<tr height="40">
			<td height="40" style="height:40px;">T25 cell culture flasks</td>
			<td>Greiner bio-one</td>
			<td>690160</td>
			<td style="width:339px;">Routine cell culture material for the example cell line used in the protocol</td>
		</tr>
		<tr height="40">
			<td height="40" style="height:40px;">Trypsin/EDTA</td>
			<td>PAN Biotech</td>
			<td>P10-025500</td>
			<td style="width:339px;">Routine cell culture material for the example cell line used in the protocol</td>
		</tr>
		<tr height="40">
			<td height="40" style="height:40px;">U87 MG glioblastoma cells</td>
			<td>ATCC</td>
			<td>ATCC-HTB-14</td>
			<td style="width:339px;">Example cell line used in the protocol</td>
		</tr>
	</tbody>
</table>

cell cycle-specific measurement of &#947;h2ax and apoptosis after genotoxic stress by flow cytometry

The presented method or slightly modified versions have been devised to study specific treatment responses and side effects of various anti-cancer treatments as used in clinical oncology. It enables a quantitative and longitudinal analysis of the DNA damage response after genotoxic stress, as induced by radiotherapy and a multitude of anti-cancer drugs. The method covers all stages of the DNA damage response, providing endpoints for induction and repair of DNA double-strand breaks (DSBs), cell cycle arrest and cell death by apoptosis in case of repair failure. Combining these measurements provides information about cell cycle-dependent treatment effects and thus allows an in-depth study of the interplay between cellular proliferation and coping mechanisms against DNA damage. As the effect of many cancer therapeutics including chemotherapeutic agents and ionizing radiation is limited to or strongly varies according to specific cell cycle phases, correlative analyses rely on a robust and feasible method to assess the treatment effects on the DNA in a cell cycle-specific manner. This is not possible with single-endpoint assays and an important advantage of the presented method. The method is not restricted to any particular cell line and has been thoroughly tested in a multitude of tumor and normal tissue cell lines. It can be widely applied as a comprehensive genotoxicity assay in many fields of oncology besides radio-oncology, including environmental risk factor assessment, drug screening and evaluation of genetic instability in tumor cells.

Human U87 or LN229 glioblastoma cells were irradiated with 4 Gy of photon or carbon ion radiation. Cell cycle-specific &#947;H2AX levels and apoptosis were measured at different time points up to 48 h after irradiation using the flow cytometric method presented here (Figure 3). In both cell lines, carbon ions induced higher &#947;H2AX peak levels that declined slower and remained significantly elevated at 24 to 48 h compared to photon radiation at the same physical dose (Figure 4A). This indicated that carbon ions induced higher peak levels of DNA double-strand breaks (DSBs) than photons that were repaired less efficiently. For both radiation types, the &#947;H2AX levels were highest in G1 cells, probably because DSB repair is limited to the pathway of non-homologous end-joining at this stage of the cell cycle.
In line with the higher DSB induction rate and slower repair kinetics, carbon ions induced a stronger and longer-lasting cell cycle arrest in G2 phase (Figure 3B) and a higher rate of apoptosis than photons (Figure 4C). Carbon ion radiation may therefore help to overcome radioresistance mechanisms observed in glioblastoma upon classical radiotherapy with photons (see Lopez Perez, et al.1 for detailed discussion).
The results shown in Figure 4 are examples of an optimal outcome of this method, showing clear differences between untreated and irradiated cells and statistically significant differential effects between different treatments. In cases where induction of DSBs and apoptosis is less clear, it is important to include positive controls. As shown here, cells fixed 1 h after photon irradiation are good positive controls for &#947;H2AX induction. Photon radiation is also known to efficiently induce apoptosis in lymphocytes, which makes them useful as positive controls for apoptosis 2-4 days after irradiation. Another possibility is to use drugs for apoptosis induction, such as the proteasome inhibitor MG132 or a death receptor ligand (Figure 5).
Another important aspect for an optimal outcome of the assay is the quality of the cell cycle profiles. In Figure 4B the G1 and G2 peaks were narrow and clearly separated, making it easy to apply cell cycle modelling and to define the gates for cell-cycle-specific measurement of &#947;H2AX. Depending on the cell type and the strength of the treatment effect (Figure 6A,B), the resolution of the different cell cycle phases can be significantly worse. In some cases, the DAPI concentration has a big impact on the quality of the cell cycle profile and needs to be adjusted (Figure 6C). In cases where no clear G1 peak is detectable due to the treatment, the cell cycle modelling tool cannot be applied accurately. It is advisable then to only use manual gating in the DAPI-A versus DAPI-W plot based on controls with a well-defined cell cycle profile, as described in the protocol.
The analysis of a cell cycle profile without a clear G1 peak can be further complicated if the treatment leads to low cell numbers that result in large shifts in the overall DAPI signal strength. In this situation, it can be difficult to judge, if a peak is the G1 or the G2 peak. To avoid this problem, the cells can be counted with a Neubauer chamber or by other means after the last washing step (step 3.3) and the cell numbers can be adjusted. The peak positions in the treated cells will then match with the controls.
<img alt="Figure 1" class="xfigimg" src="/files/ftp_upload/59968/59968fig1.jpg" /> 
Figure 1: Worksheet setup for sample acquisition. Plots and gates for signal display in the Worksheet window of the flow cytometer software (see the Table of Materials). <a href="https://www.jove.com/files/ftp_upload/59968/59968fig1large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 2" class="xfigimg" src="/files/ftp_upload/59968/59968fig2v2.jpg" /> 
Figure 2: Gating strategy for data evaluation. Population tree and diagrams showing how the gates are set to define the different sub-populations in the flow cytometric analysis software. DJF refers to the cell cycle modelling tool using the Dean-Jett-Fox method25,26. <a href="https://www.jove.com/files/ftp_upload/59968/59968fig2v2large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 3" class="xfigimg" src="/files/ftp_upload/59968/59968fig3.jpg" /> 
Figure 3: Raw data export table. Setup of the &#39;Table Editor&#39;&#160;for raw data export in the flow cytometric analysis software. <a href="https://www.jove.com/files/ftp_upload/59968/59968fig3large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 4" class="xfigimg" src="/files/ftp_upload/59968/59968fig4v2.jpg" /> 
Figure 4: DNA damage response of human U87 and LN229 glioblastoma cells after irradiation with 4 Gy of photon versus carbon ion radiation. (A) DSB induction and repair measured by &#947;H2AX levels. The median fluorescence intensity was normalized to the relative DNA content in each cell cycle phase (G1 = 1.0, S = 1.5, G2 = 2.0) and control levels were subtracted. Symbols indicate mean and error bars indicate SD values. Solid lines represent fits according to the function I(t) = (p1*t&#178; + p2*t) / (t&#178; + q1*t + q2) | t &#8805; 0, p1 &#60; 0, p1, q1, q2 &#62; 0. (B) Cell cycle distribution (mean and SD) and representative histograms of the DNA content marker DAPI at 24 h after photon or carbon ion irradiation. (C) Apoptosis induction, as measured by caspase-3 and subG1 positive cells (mean and SD). *P &#60; 0.05, **P &#60; 0.01, ***P &#60; 0.001 (two-sided Student&#39;s t test). This figure has been modified from Lopez, et al.1 with permission. <a href="https://www.jove.com/files/ftp_upload/59968/59968fig4v2large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 5" class="xfigimg" src="/files/ftp_upload/59968/59968fig5.jpg" /> 
Figure 5: Drug-mediated apoptosis-induction in U87 glioblastoma cells. U87 cells were treated with 5 ng/mL of a modified TNF-related apoptosis-inducing ligand (see Table of Materials) plus 2.5 &#181;M MG132 for 20 h before fixation to validate the apoptosis measurement by active caspase-3 and subG1 analysis. (A) Histogram of the caspase-3 signal of treated versus untreated cells. (B) DAPI histogram of treated cells with a subG1 population, indicating DNA degradation. The histogram of caspase-3-positive events is overlaid in red, demonstrating that most of the apoptotic cells had not yet degraded their DNA at this time point. <a href="https://www.jove.com/files/ftp_upload/59968/59968fig5large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 6" class="xfigimg" src="/files/ftp_upload/59968/59968fig6.jpg" /> 
Figure 6: Factors influencing the quality of cell cycle profiles. Too harsh treatments complicate cell cycle analysis. (A) No G1 peak is detectable 48 h after treatment of LLC cells (Lewis lung carcinoma) with 20 Gy of photon radiation or (B) 96 h after treatment of HS68 fibroblasts with 100 mJ of UV radiation. (C) DAPI concentration in different cell lines: In HS68 fibroblasts (upper panel) the G2/M peak (see arrows) was equally well-separated from the G1 peak for DAPI concentrations between 0.01 and 1.0 &#181;g/mL. In contrast, DAPI concentrations below 0.75 &#181;g/mL resulted in poor separation and shape definition of the G2/M peak (see arrows) in MRC-5 fibroblasts (lower panel). <a href="https://www.jove.com/files/ftp_upload/59968/59968fig6large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>

<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>Parameter</td>
			<td>Significance</td>
			<td colspan="2">Excitation laser</td>
			<td colspan="2">Detector</td>
			<td colspan="4">Signal</td>
		</tr>
		<tr>
			<td></td>
			<td></td>
			<td>&#955; (nm)</td>
			<td>P (mW)</td>
			<td>Filter</td>
			<td>U (V)</td>
			<td>log</td>
			<td>-A</td>
			<td>-H</td>
			<td>-W</td>
		</tr>
		<tr>
			<td>FSC</td>
			<td>forward scatter (size)</td>
			<td>488</td>
			<td>100</td>
			<td>488/10</td>
			<td>230</td>
			<td></td>
			<td>&#9679;</td>
			<td>&#9679;</td>
			<td></td>
		</tr>
		<tr>
			<td>SSC</td>
			<td>side scatter (granularity)</td>
			<td>488</td>
			<td>100</td>
			<td>488/10</td>
			<td>210</td>
			<td></td>
			<td>&#9679;</td>
			<td>&#9679;</td>
			<td></td>
		</tr>
		<tr>
			<td>Alexa488</td>
			<td>&#947;H2AX (double-strand breaks)</td>
			<td>488</td>
			<td>100</td>
			<td>525/50</td>
			<td>275</td>
			<td>&#9679;</td>
			<td>&#9679;</td>
			<td>&#9679;</td>
			<td></td>
		</tr>
		<tr>
			<td>Alexa555</td>
			<td>phospho-histone H3 (M phase)</td>
			<td>561</td>
			<td>150</td>
			<td>586/15</td>
			<td>360</td>
			<td>&#9679;</td>
			<td>&#9679;</td>
			<td>&#9679;</td>
			<td></td>
		</tr>
		<tr>
			<td>Alexa647</td>
			<td>active caspase-3 (apoptosis)</td>
			<td>640</td>
			<td>40</td>
			<td>670/14</td>
			<td>410</td>
			<td>&#9679;</td>
			<td>&#9679;</td>
			<td>&#9679;</td>
			<td></td>
		</tr>
		<tr>
			<td>DAPI</td>
			<td>DNA (cell cycle, apoptosis)</td>
			<td>350</td>
			<td>20</td>
			<td>450/50</td>
			<td>350</td>
			<td></td>
			<td>&#9679;</td>
			<td>&#9679;</td>
			<td>&#9679;</td>
		</tr>
	</tbody>
</table>
Table 1: Typical flow cytometer configuration. &#955; = wavelength of the excitation laser, U = detector voltage, log = logarithmic scale (otherwise linear), -A = signal area, -H = maximum signal height, -W = signal width (duration), FSC = front scatter, SSC = side scatter. Optical filter specifications are given as central wavelength/bandwidth in nanometers. Settings may vary for different flow cytometers and detector voltages need to be adjusted for different cell lines. <a href="https://www.jove.com/files/ftp_upload/59968/table1.xlsx" target="_blank">Please click here to view this table (Right click to download).</a>
<img alt="Figure 1" src="/files/ftp_upload/59968/59968supfig1.jpg" /> 
Supplementary Figure 1: Microscopic images of &#947;H2AX foci. U87 cells were irradiated with different doses of photon radiation, fixed and stained after 30 min according to the presented protocol and prepared for microscopy as described in the discussion. At 2 Gy, individual foci could be easily distinguished, while at 8 Gy foci counting was biased due to considerable overlap. <a href="https://www.jove.com/files/ftp_upload/59968/59968supfig1large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
Supplementary File 1:&#160;<a href="https://www.jove.com/files/ftp_upload/59968/FACS_Analysis_Template_(1).xlsx" target="_blank">Please click here to download this file.</a>

Watch this Scientific Journal Video about Cell Cycle-specific Measurement of ÃŽÂ³H2AX and Apoptosis After Genotoxic Stress by Flow Cytometry at JoVE.com

Cell Cycle-specific Measurement of &#947;H2AX and Apoptosis After Genotoxic Stress by Flow Cytometry

The presented method combines the quantitative analysis of DNA double-strand breaks (DSBs), cell cycle distribution and apoptosis to enable cell cycle-specific evaluation of DSB induction and repair as well as the consequences of repair failure.

The presented method or slightly modified versions have been devised to study specific treatment responses and side effects of various anti-cancer ...

cell-cycle-specific-measurement-h2ax-apoptosis-after-genotoxic-stress

Nanyang Technological University

Research

JoVE Journal

Cancer Research

13.3K Views.  German Cancer Research Center. The presented method combines the quantitative analysis of DNA double-strand breaks (DSBs), cell cycle distribution and apoptosis to enable cell cycle-specific evaluation of DSB induction and repair as well as the consequences of repair failure.

Video: Cell Cycle-specific Measurement of γH2AX and Apoptosis After Genotoxic Stress by Flow Cytometry

The Drosophila Imaginal Disc Tumor Model: Visualization and Quantification of Gene Expression and Tumor Invasiveness Using Genetic Mosaics

Drosophila melanogaster has emerged as a powerful experimental system for functional and mechanistic studies of tumor development and progression in the context of a whole organism. Sophisticated techniques to generate genetic mosaics facilitate induction of visually marked, genetically defined clones surrounded by normal tissue. The clones can be analyzed through diverse molecular, cellular and omics approaches. This study describes how to generate fluorescently labeled clonal tumors of varying malignancy in the eye/antennal imaginal discs (EAD) of Drosophila larvae using the Mosaic Analysis with a Repressible Cell Marker (MARCM) technique. It describes procedures how to recover the mosaic EAD and brain from the larvae and how to process them for simultaneous imaging of fluorescent transgenic reporters and antibody staining. To facilitate molecular characterization of the mosaic tissue, we describe a protocol for isolation of total RNA from the EAD. The dissection procedure is suitable to recover EAD and brains from any larval stage. The fixation and staining protocol for imaginal discs works with a number of transgenic reporters and antibodies that recognize Drosophila proteins. The protocol for RNA isolation can be applied to various larval organs, whole larvae, and adult flies. Total RNA can be used for profiling of gene expression changes using candidate or genome-wide approaches. Finally, we detail a method for quantifying invasiveness of the clonal tumors. Although this method has limited use, its underlying concept is broadly applicable to other quantitative studies where cognitive bias must be avoided.

The Drosophila Imaginal Disc Tumor Model: Visualization and Quantification of Gene Expression and Tumor Invasiveness Using Genetic Mosaics

This protocol demonstrates how to generate fluorescently marked, genetically defined clonal tumors in the Drosophila eye/antennal imaginal discs (EAD). It describes how to dissect the EAD and brain from the third instar larvae and how to process them to visualize and quantify gene expression changes and tumor invasiveness.

Drosophila melanogaster has emerged as a powerful experimental system for functional and mechanistic studies of tumor development and progression in the ...

Detection of Mitochondria Membrane Potential to Study CLIC4 Knockdown-induced HN4 Cell Apoptosis In Vitro

Depletion of the mitochondrial membrane potential (MMP, &#916;&#936;m) is considered the earliest event in the apoptotic cascade. It even occurs ahead of nuclear apoptotic characteristics, including chromatin condensation and DNA breakage. Once the MMP collapses, cell apoptosis will initiate irreversibly. A series of lipophilic cationic dyes can pass through the cell membrane and aggregate inside the matrix of mitochondrion, and serve as fluorescence marker to evaluate MMP change. As one of the six members of the Cl- intracellular channel (CLIC) family, CLIC4 participates in the cell apoptotic process mainly through the mitochondrial pathway. Here we describe a detailed protocol to measure MMP via monitoring the fluorescence fluctuation of Rhodamine 123 (Rh123), through which we study apoptosis induced by CLIC4 knockdown. We discuss the advantages and limitations of the application of confocal laser scanning and normal fluorescence microscope in detail, and also compare it with other methods.

Detection of Mitochondria Membrane Potential to Study CLIC4 Knockdown-induced HN4 Cell Apoptosis In Vitro

Here we present a detailed protocol for the application of rhodamine 123 to identify the mitochondrial membrane potential (MMP) and study CLIC4 knockdown-induced HN4 cell apoptosis in vitro. Under common fluorescence microscope and confocal laser scanning fluorescence microscope, the real-time change of the MMP was recorded.

Depletion of the mitochondrial membrane potential (MMP, &#916;&#936;m) is considered the earliest event in the apoptotic cascade. It even occurs ahead of ...

Immunophenotyping of Orthotopic Homograft (Syngeneic) of Murine Primary KPC Pancreatic Ductal Adenocarcinoma by Flow Cytometry

Homograft (syngeneic) tumors are the workhorse of today&#39;s immuno-oncology (I/O) preclinical research. The tumor microenvironment (TME), particularly its immune-components, is vital to the prognosis and prediction of treatment outcomes, especially those of immunotherapy. TME immune-components are composed of different subsets of tumor-infiltrating immune cells assessable by multi-color FACS. Pancreatic ductal adenocarcinoma (PDAC) is among the deadliest malignances lacking good treatment options, thus an urgent and unmet medical need. One important reason for its non-responsiveness to various therapies (chemo-, targeted, I/O) has been its abundant TME, consisting of fibroblasts and leukocytes that protect tumor cells from these therapies. Orthotopically implanted PDAC is believed to more accurately recapture the TME of human pancreatic cancers than conventional subcutaneous (SC) models.
Homograft tumors (KPC) are transplants of mouse spontaneous PDAC originating from genetically engineered KPC-mice (KrasG12D/+/P53-/-/Pdx1-Cre) (KPC-GEMM). The primary tumor tissue is cut into small fragments (~2 mm3) and transplanted subcutaneously (SC) to the syngeneic recipients (C57BL/6, 7&#8211;9 weeks old). The homografts were then surgically orthotopically transplanted onto the pancreas of new C57BL/6 mice, along with SC-implantation, which reached tumor volumes of 300&#8211;1,000 mm3 by 17 days. Only tumors of 400&#8211;600 mm3 were harvested per approved autopsy procedure and cleaned to remove the adjacent non-tumor tissues. They were dissociated per protocol using a tissue dissociator into single-cell suspensions, followed by staining with designated panels of fluorescently-labeled antibodies for various markers of different immune cells (lymphoid, myeloid and NK, DCs). The stained samples were analyzed using multi-color FACS to determine numbers of immune cells of different lineages, as well as their relative percentage within tumors. The immune profiles of orthotopic tumors were then compared to those of SC tumors. The preliminary data demonstrated significantly elevated infiltrating TILs/TAMs in tumors over the pancreas, and higher B-cell infiltration into orthotopic rather than SC tumors.

Immunophenotyping of Orthotopic Homograft Syngeneic of Murine Primary KPC Pancreatic Ductal Adenocarcinoma by Flow Cytometry

The experimental procedure on the immunophenotyping of murine orthotopic PDAC homografts aims at profiling the tumor immuno-microenvironment. Tumors are orthotopically implanted via surgery. Tumors of 200&#8211;600 mm3 in size were harvested and dissociated to prepare single-cell suspensions, followed by multi-immune marker FACS analysis using different fluorescently-labeled antibodies.

Homograft (syngeneic) tumors are the workhorse of today&#39;s immuno-oncology (I/O) preclinical research. The tumor microenvironment (TME), particularly ...

Phospho Flow Cytometry with Fluorescent Cell Barcoding for Single Cell Signaling Analysis and Biomarker Discovery

Aberrant cell signaling plays a central role in cancer development and progression. Most novel targeted therapies are indeed directed at proteins and protein functions, and cell signaling aberrations may therefore serve as biomarkers to indicate personalized treatment options. As opposed to DNA and RNA analyses, changes in protein activity can more efficiently evaluate the mechanisms underlying drug sensitivity and resistance. Phospho flow cytometry is a powerful technique that measures protein phosphorylation events at the cellular level, an important feature that distinguishes this method from other antibody-based approaches. The method allows for simultaneous analysis of multiple signaling proteins. In combination with fluorescent cell barcoding, larger medium- to high-throughput data-sets can be acquired by standard cytometer hardware in short time. Phospho flow cytometry has applications both in studies of basic biology and in clinical research, including signaling analysis, biomarker discovery and assessment of pharmacodynamics. Here, a detailed experimental protocol is provided for phospho flow analysis of purified peripheral blood mononuclear cells, using chronic lymphocytic leukemia cells as an example.

Here, a protocol for medium- to high-throughput analysis of protein phosphorylation events at the cellular level is presented. Phospho flow cytometry is a powerful approach to characterize signaling aberrations, identify and validate biomarkers, and assess pharmacodynamics.

Aberrant cell signaling plays a central role in cancer development and progression. Most novel targeted therapies are indeed directed at proteins and ...

Flow Cytometric Detection of Newly-formed Breast Cancer Stem Cell-like Cells After Apoptosis Reversal

Cancer recurrence has long been studied by oncologists while the underlying mechanisms remain unclear. Recently, we and others found that a phenomenon named apoptosis reversal leads to increased tumorigenicity in various cell models under different stimuli. Previous studies have been focused on tracking this process in vitro and in vivo; however, the isolation of real reversed cells has yet to be achieved, which limits our understanding on the consequences of apoptosis reversal. Here, we take advantage of a Caspase-3/7 Green Detection dye to label cells with activated caspases after apoptotic induction. Cells with positive signals are further sorted out by fluorescence-activated cell sorting (FACS) for recovery. Morphological examination under confocal microscopy helps confirm the apoptotic status before FACS. An increase in tumorigenicity can often be attributed to the elevation in the percentage of cancer stem cell (CSC)-like cells. Also, given the heterogeneity of breast cancer, identifying the origin of these CSC-like cells would be critical to cancer treatment. Thus, we prepare breast non-stem cancer cells before triggering apoptosis, isolating caspase-activated cells and performing the apoptosis reversal procedure. Flow cytometry analysis reveals that breast CSC-like cells re-appear in the reversed group, indicating breast CSC-like cells are transited from breast non-stem cancer cells during apoptosis reversal. In summary, this protocol includes the isolation of apoptotic breast cancer cells and detection of changes in CSC percentage in reversed cells by flow cytometry.

Here, we present a protocol to isolate apoptotic breast cancer cells by fluorescence-activated cell sorting and further detect the transition of breast non-stem cancer cells to breast cancer stem cell-like cells after apoptosis reversal by flow cytometry.

Cancer recurrence has long been studied by oncologists while the underlying mechanisms remain unclear. Recently, we and others found that a phenomenon ...

Real Time Detection of In Vitro Tumor Cell Apoptosis Induced by CD8+ T Cells to Study Immune Suppressive Functions of Tumor-infiltrating Myeloid Cells

Potentiation of the tumor-killing ability of CD8+ T cells in tumors, along with their efficient tumor infiltration, is a key element of successful immunotherapies. Several studies have indicated that tumor infiltrating myeloid cells (e.g., myeloid-derived suppressor cells (MDSCs) and tumor-associated macrophages (TAMs)) suppress cytotoxicity of CD8+ T cells in the tumor microenvironment, and that targeting these regulatory myeloid cells can improve immunotherapies. Here, we present an in vitro assay system to evaluate immune suppressive effects of monocytic-MDSCs and TAMs on the tumor-killing ability of CD8+ T cells. To this end, we first cultured na&#239;ve splenic CD8+ T cells with anti-CD3/CD28 activating antibodies in the presence or absence of suppressor cells, and then co-cultured the pre-activated T cells with target cancer cells in the presence of a fluorogenic caspase-3 substrate. Fluorescence from the substrate in cancer cells was detected by real-time fluorescence microscopy as an indicator of T-cell induced tumor cell apoptosis. In this assay, we can successfully detect the increase of tumor cell apoptosis by CD8+ T cells and its suppression by pre-culture with TAMs or MDSCs. This functional assay is useful for investigating CD8+ T cell suppression mechanisms by regulatory myeloid cells and identifying druggable targets to overcome it via high throughput screening.

Real Time Detection of In Vitro Tumor Cell Apoptosis Induced by CD8+ T Cells to Study Immune Suppressive Functions of Tumor-infiltrating Myeloid Cells

We describe here a protocol to investigate cytotoxicity of pre-activated CD8+ T cells against cancer cells by detecting apoptotic cancer cells via real-time microscopy. This protocol can investigate mechanisms behind myeloid cell-induced T cell suppression and evaluate compounds aimed at replenishing T cells via blockade of immune suppressive myeloid cells.

Potentiation of the tumor-killing ability of CD8+ T cells in tumors, along with their efficient tumor infiltration, is a key element of successful ...

In Vivo Immunofluorescence Localization for Assessment of Therapeutic and Diagnostic Antibody Biodistribution in Cancer Research

Monoclonal antibodies (mAbs) are important tools in cancer detection, diagnosis, and treatment. They are used to unravel the role of proteins in tumorigenesis, can be directed to cancer biomarkers enabling tumor detection and characterization, and can be used for cancer therapy as mAbs or antibody-drug conjugates to activate immune effector cells, to inhibit signaling pathways, or directly kill cells carrying the specific antigen. Despite clinical advancements in the development and production of novel and highly specific mAbs, diagnostic and therapeutic applications can be impaired by the complexity and heterogeneity of the tumor microenvironment. Thus, for the development of efficient antibody-based therapies and diagnostics, it is crucial to assess the biodistribution and interaction of the antibody-based conjugate with the living tumor microenvironment. Here, we describe In Vivo Immunofluorescence Localization (IVIL) as a new approach to study interactions of antibody-based therapeutics and diagnostics in the in vivo physiological and pathological conditions. In this technique, a therapeutic or diagnostic antigen-specific antibody is intravenously injected in&#160;vivo and localized ex vivo with a secondary antibody in isolated tumors. IVIL, therefore, reflects the in vivo biodistribution of antibody-based drugs and targeting agents. Two IVIL applications are described assessing the biodistribution and accessibility of antibody-based contrast agents for molecular imaging of breast cancer. This protocol will allow future users to adapt the IVIL method for their own antibody-based research applications.

The in vivo immunofluorescence localization (IVIL) method can be used to examine in vivo biodistribution of antibodies and antibody conjugates for oncological purposes in living organisms using a combination of in vivo tumor targeting and ex vivo immunostaining methods.

Monoclonal antibodies (mAbs) are important tools in cancer detection, diagnosis, and treatment. They are used to unravel the role of proteins in ...

Impedance-based Real-time Measurement of Cancer Cell Migration and Invasion

Cancer arises due to uncontrolled proliferation of cells initiated by genetic instability, mutations, and environmental and other stress factors. These acquired abnormalities in complex, multilayered molecular signaling networks induce aberrant cell proliferation and survival, extracellular matrix degradation, and metastasis to distant organs. Approximately 90% of cancer-related deaths are estimated to be caused by the direct or indirect effects of metastatic dissemination. Therefore, it is important to establish a highly reliable, comprehensive system to characterize cancer cell behaviors upon genetic and environmental manipulations. Such a system can give a clear understanding of the molecular regulation of cancer metastasis and the opportunity for successful development of stratified, precise therapeutic strategies. Hence, accurate determination of cancer cell behaviors such as migration and invasion with gain or loss of function of gene(s) allows assessment of the aggressive nature of cancer cells. The real-time measurement system based on cell impedance enables researchers to continually acquire data during a whole experiment and instantly compare and quantify the results under various experimental conditions. Unlike conventional methods, this method does not require fixation, staining, and sample processing to analyze cells that migrate or invade. This method paper emphasizes detailed procedures for real-time determination of migration and invasion of glioblastoma cancer cells.

Cancer is a lethal disease due to its ability to metastasize to different organs. Determining the ability of cancer cells to migrate and invade under various treatment conditions is crucial to assessing therapeutic strategies. This protocol presents a method to assess the real-time metastatic abilities of a glioblastoma cancer cell line.&#160;

Cancer arises due to uncontrolled proliferation of cells initiated by genetic instability, mutations, and environmental and other stress factors. These ...

In Vivo Immunogenicity Screening of Tumor-Derived Extracellular Vesicles by Flow Cytometry of Splenic T Cells

Immunogenic cell death of tumors, caused by chemotherapy or irradiation, can trigger tumor-specific T cell responses by releasing danger-associated molecular patterns and inducing the production of type I interferon. Immunotherapies, including checkpoint inhibition, primarily rely on preexisting tumor-specific T cells to unfold a therapeutic effect. Thus, synergistic therapeutic approaches that exploit immunogenic cell death as an intrinsic anti-cancer vaccine may improve their responsiveness. However, the spectrum of immunogenic factors released by cells under therapy-induced stress remains incompletely characterized, especially regarding extracellular vesicles (EVs). EVs, nano-scale membranous particles emitted from virtually all cells, are considered to facilitate intercellular communication and, in cancer, have been shown to mediate cross-priming against tumor antigens. To assess the immunogenic effect of EVs derived from tumors under various conditions, adaptable, scalable, and valid methods are sought-for. Therefore, herein a relatively easy and robust approach is presented to assess EVs' in vivo immunogenicity. The protocol is based on flow cytometry analysis of splenic T cells after in vivo immunization of mice with EVs, isolated by precipitation-based assays from tumor cell cultures under therapy or steady-state conditions. For example, this work shows that oxaliplatin exposure of B16-OVA murine melanoma cells resulted in the release of immunogenic EVs that can mediate the activation of tumor-reactive cytotoxic T cells. Hence, screening of EVs via in vivo immunization and flow cytometry identifies conditions under which immunogenic EVs can emerge. Identifying conditions of immunogenic EV release provides an essential prerequisite to testing EVs' therapeutic efficacy against cancer and exploring the underlying molecular mechanisms to ultimately unveil new insights into EVs' role in cancer immunology.

In Vivo Immunogenicity Screening of Tumor-Derived Extracellular Vesicles by Flow Cytometry of Splenic T Cells

This manuscript describes how to assess in vivo immunogenicity of tumor cell-derived extracellular vesicles (EVs) using flow cytometry. EVs derived from tumors undergoing treatment-induced immunogenic cell death seem particularly relevant in tumor immunosurveillance. This protocol exemplifies the assessment of oxaliplatin-induced immunostimulatory tumor EVs but can be adapted to various settings.

Immunogenic cell death of tumors, caused by chemotherapy or irradiation, can trigger tumor-specific T cell responses by releasing danger-associated ...

Simultaneous Imaging and Flow-Cytometry-based Detection of Multiple Fluorescent Senescence Markers in Therapy-Induced Senescent Cancer Cells

Chemotherapeutic drugs can induce irreparable DNA damage in cancer cells, leading to apoptosis or premature senescence. Unlike apoptotic cell death, senescence is a fundamentally different machinery restraining&#160;propagation of cancer cells. Decades of scientific studies have revealed the complex pathological effects of senescent cancer cells in tumors and microenvironments that modulate cancer cells and stromal cells. New evidence suggests that senescence is a potent prognostic factor during cancer treatment, and therefore rapid and accurate detection of senescent cells in cancer samples is essential. This paper presents a method to visualize and detect therapy-induced senescence (TIS) in cancer cells. Diffuse large B-cell lymphoma (DLBCL) cell lines were treated with mafosfamide (MAF) or daunorubicin (DN) and examined for the senescence marker, senescence-associated &#946;-galactosidase (SA-&#946;-gal), the DNA synthesis marker 5-ethynyl-2&#8242;-deoxyuridine (EdU), and the DNA damage marker gamma-H2AX (&#947;H2AX). Flow cytometer imaging can help generate high-resolution single-cell images in a short period of time to simultaneously visualize and quantify the three markers in cancer cells.

Here we present a flow cytometry-based method for visualization and quantification of multiple senescence-associated markers in single cells.

Chemotherapeutic drugs can induce irreparable DNA damage in cancer cells, leading to apoptosis or premature senescence. Unlike apoptotic cell death, ...