Immunostaining for DNA Modifications: Computational Analysis of Confocal Images

The overall goal of this technique is to provide a computational tool for assessing the spatial distribution and signal intensity of DNA modifications, visualized by immunostaining within nuclei. This method can help answer key questions in the field of epigenetics, allowing the examination of nuclear localisation and relative levels of DNA modifications in different model systems. The main advantage of this technique is that the DNA modifications are measured by bot signal magnitude and then distribution.

One of the implications of this technique is that it facilitates our understanding of potential functional rows of DNA modifications in various biological systems. This technique also has the added application of computational analysis of other epitopes detectable by immunohistochemistry. To begin, open the LSM formatted files of the confocal microscopy image.

Select image processing and then choose the desired image for analysis. Take note, that when comparing intensity profiles between samples, the laser power and gain must be consistent to make direct comparisons. Next, click on show all, to make all the graphical controls visible, then select the graphics tab and choose the rectangle tool to select a region containing the nuclei of interest.

Then use the cut region command to isolate the region of interest. Now save the image under a new name and either an LSM or CZI format and reopen the file in the Zen Blue software. Open the ZEN desk in Zen Blue, then use the send to ZEN command in Zen Black to open the file in Zen Blue.

Next, activate the tab labeled 2.5d to enable visualization of the image in 2.5d. Click show all, to make all graphical controls visible and alter the settings using the four gray bars. The bars control the zoom, rotation, axis tilt and expansion and contraction of scale bars.

Select the render mode to best visualize the individual peaks, then alter the great distance by changing the percentage. The lower the percentage, the more defined each individual peak. Now before saving the 2.5d image, hide the file list and the graphical tools by clicking on the gray arrow below the 2.5d plot.

Then save the plot as a screenshot by using the print screen key on the keyboard. Open the image processing option in the software and open the file of interest. Next, select the profile tab and select the show all tab to access all formatting options.

From the profile tab, choose the table option and the arrow button. Now use the mouse to select a start point and draw a line over a continuous number of cells an intensity plot for the cells along this line, is then generated along with the table of intensity measurements. Take note that the table also provides the distance at which pixel intensity is red for the red green and blue fluorescence.

The unit is microns. To export this data, right click on the table, select save table and save it as a text file. To save the intensity profile, choose the export option, in the pop up window, toggle two options, tagged image file, format and contents of image window, single pane, data.

Then follow the prompts to save the file. Now repeat this process for each intensity profile needed. In the software, select image processing and choose the image file of interest.

Then select the tab labeled coloc for co-localization analysis and select show all to access all the formatting options. From the four tabs in the lower half of the screen, choose co-localisation and choose the table and image options. Then choose the third icon along the top of the tab, identified by the pop up text, closed besier.

Use this tool to encircle a single nucleus. Values will then appear in the table and a scatter plot is produced for red versus green fluorescence. The axis of this plot is movable and this controls the gating of detection.

All the pixel intensities within the nucleus should be considered positive signals. As the levels of DNA modifications vary across the nucleus, to take weak signals into account, we do not gate the data. We should emphasize here, that is debatable if the background values should be included in the analysis.

Importantly, the overlap coefficient is independent from the differences in signal intensities between the two channels. While person correlation coefficient assumes a linear relationship between the signals. Now repeat this process of encircling nuclei in order to complete the data set.

Then to export the data, right click on the table and follow the options to save the data. To save a co-localisation image, use the export command with the two options, tagged image file, format and contents of image window single pane, data, then follow the options to save the file. The spatial distribution of two oxidized deriratives of five methyl cytosine was examined in differentiating haphadic progenitors, using the described protocol.

Red and green signals represent the two distinct DNA modifications. The signals are well defined with limited overlap. Consistent with expectations, the profiles of the two signal intensities and the corresponding cell, do not strongly coincide with each other.

The distinct pattern suggest that tap dependent oxidation of five methyl cytosine can generate different oxidized derivatives in specific chromatin regions. A similar comparison of the two DNA modifications was made in Daoy medulloblastoma cells and in BXD ependymoma cells. Both cell lineages are from pediatric brain tumors.

Quantification reveal that BXD cells exhibit significantly lower levels of both signals compared to Daoy cells. Next co-localization of the oxidized derivatives of 5MC, was examined in undifferentiated hiPSCs. In 24 hours post induction of hepatic endoderm differentiation and hiPSCs.

In this analysis, signal co-localisation changed significantly with induction of differentiation, which maybe attributable to a reduced magnitude of five CAC in the undifferentiated cells. After watching this video, you should have a good understanding of how to generate 2.5d intensity plots and profiles and how to produce co-localization data. The image analysis and visualization tools described here, contributes to epigenetic research, providing a relatively quick way to compare signals of the different DNA modifications in different experimental groups.

Once mastered, this technique can be done in about one hour if it is performed properly. It is important to avoid overexposure and use the same settings for different experimental groups during microscopy. Air bubbles in the mount and insufficient immersion can cause local loss of fluorescence, therefore, a visual inspection of the scanning area is critical.

The procedure can be further automated by segmenting the nuclei and creating regions of interest using Zen Blue. These regions can be imported to Zen Black, to perform co-localization analysis on multiple nuclei.