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 okay. Open the fluorescent image of human T-lymphocytes.
Click Control Shift D to duplicate the protein channel, including the stack. Check the hyper stack 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 DAPI.
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 check box and click browse to select the directory where the images are stored. Once all boxes are compiled, click okay 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.
ipynb 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.
