Our main research interest is an epigenetic mechanism that regulate human cellular plasticity and environmental adaptation. I am currently focusing on studying how changes in chromatin organization within nuclear condensates trigger pathological cellular conditions. I believe that studying the biophysical properties of chromatin organization opens a new field and offers unique opportunities for developing novel therapeutic approaches.
Immunofluorescence assays are valuable for screening protein organization in nuclear condensates. However, image analysis can be challenging due to parameter selection issues and large data volume when studying multiple conditions. We offer a comprehensive method with guidance on how to use the right parameters and an automated analysis pipeline to streamline the process.
Our protocol guides users from sample preparation to data analysis. We provide a semi-automated pipeline design for those with limited computational and image analysis expertise. The workflow is accessible to everyone at no cost, leveraging widely-adopted open source software like Imager in Python and the free cloud-based service Google Colab.
To begin, download and install the latest version of Fiji on a computer. After downloading the convert_to_TIFF. py script, drag and drop the script on Fiji and run the code.
Browse to the path where the experiment is stored. The converted TIFF files are created within the same experimental folder. Click on Plugins, then 3D Suite and open the 3D Manager Options.
Within the 3D Manager window, select the check boxes corresponding to the Volume in unit Mean Gray Value, Bounding Box in pixel, Standard Deviation Gray Value, Centroid in pixel and Centroid in unit. Tick the Exclude objects on edges XY and Exclude objects on edges Z.Then click OK.Open the fluorescent image of human T lymphocytes. Click Control Shift D to duplicate the protein channel, including the stack.
Check the hyperstack checkbox. Specify the appropriate channel number within the channels box and rename it accordingly. To launch the Fiji macro recorder, navigate to the Plugins, then Macros and select Record.
Then, to open the FindFoci plugin, sequentially, click on Plugins, GDSC, FindFoci and FindFoci GUI. From the image dropdown menu, select the image to be analyzed. Set the Gaussian blur to 1.5, Background method to standard deviation above mean, Background parameter to nine, search method to fraction of peak background, Search parameter to 0.7, Peak method to relative above background, Peak parameter to 0.2, and Minimum size to five.
Set the max peaks to high numbers to include all foci in the image. To enhance foci identification, adjust the Gaussian blur close to the foci diameter, and increase the values of the background parameter to impose more stringent thresholds. Then decrease the values of the search parameter to include areas farther away from the fluorescence peak and decrease the values of the peak parameter for the separation of peaks.
Run FindFoci and copy the string that appears in the recorder window. The string contains the selected parameters, excluding the quotation marks. After downloading the nuclear_prod_q.
py script, drag and drop the script on Fiji and click Run to execute the code. In the nucleus channel, enter the number corresponding to the channel of dapE. For nucleus Gaussian blur, enter the value of sigma needed to blur the image for segmentation.
Then enter the number corresponding to the channel of the staining of interest for the protein channel. In the FindFoci parameter, paste the string representing the macro recording of the selected parameters. Tick the visual checkbox and click Browse to select the directory where the images are stored.
Once all boxes are compiled, click OK to continue the execution. From the ROI list in the ROI manager 3D window, select the nuclei channel and click Live ROI to On.Select the ROI belonging to the same nucleus and press Merge. And for undesired nuclei, press Delete.
Again, click Live ROI to On, select all, and confirm that all nuclei are correctly segmented. From the quantification folder, open the TXT file with records of the parameters used for the analysis. Open Google Colab in a browser.
Then from the file, select Open Notebook and upload the final_nuclear_protein_metrics. ipymb script. Upload all the folders containing the csv files of each image field into a folder of preference in Google Drive.
In the notebook, indicate the path of the folder where the results sub folders are stored and run all cells. When the code has finished compiling, open the final spreadsheet file containing all the compiled data.
