Our overarching goal is to study how cells of the immune system, and glial cells within the central nervous system contribute to the development, and progression of neurological disorders such as multiple sclerosis. Immunofluorescent microscopy is a standard, and widely used technique across biological disciplines, and is essential to neuroscience, and the immunology research. One of the main issues that plague researchers is that immunofluorescent microscopy is typically limited in the number of probes that can be imaged at a single time due to the limitations of conventional microscopes.
In this protocol, we describe a method to expand the number of probes that many microscopes can image at a single time, and provide an analysis pipeline to process information-dense images. The advantage of this protocol is that it can be adapted to many widely available microscopes, and is easy to execute, which would make multiplex histology analysis available to a greater number of labs. To begin, set up a fluorescent microscope for imaging.
Insert a fully stained slide with immunostain tissue sections. Once the objective has been set, press the live button, and then focus on the sample. Use the autoscale icon to evaluate if the channels are adequately saturated with signal.
After finalizing the microscope settings, load the microscope with the single color control slides. Focus below the sample, and press the begin option in the Zs stack menu to start setting up a Z stack. Then focus above the sample, and press the end option.
For fluorescence compensation of the images, open one of the single control images. Observe the gray scale windows for each channel for a signal in an inappropriate channel. To remove the bleed through, manually input numbers into the matrix then press apply to test whether it has been adequately removed.
Next, assemble all the values from each control into a single matrix. Apply this matrix to a fully stained sample. Launch the ilastik software, and open the TIFF file of the tissues.
Click on the training tab, then use the paintbrush tool to highlight individual cells on the images. Ensure to highlight the cells such that the interior and cell membrane are included. Next, use label two to highlight everything that is not the cells of interest.
Now launch software three and open an IMS image of the tissue containing all the fluorescent stains. Choose the mask over nuclei option as the source channel for nuclei detection, then select the advanced option for splitting nuclei by seed points. Set a value for the nucleus diameter.
Examine the image for fused multiple cells or missing cells. Then click on the created cells object, followed by the creation tab, and then the store parameters for batch option to save the use settings. Launch Anaconda, and then launch Jupyter Lab within Anaconda.
Open the supplementary coding file one, then press on run all cells or run selected cells in the run menu. When a prompt appears, input the file directory for the exported fluorescent values. Then input the file directory for any location suitable for saving the processed files.
Run the next section of the code to annotate the data with the names of each channel. Once a gating strategy is established, right-click on population in the menu, then choose export. Export the parameters as a CSV file with the headers included.
Launch Jupyter Lab in Anaconda again. Then open the script in supplementary coding file two, and run the code. When prompted to input the location of the software four file, input the file directory of the exported CSV files.
Next, when prompted to add the output location, choose a file directory of choice, and make sure that the file directory includes the name of the file at the end. Run the rest of the code. Now copy the text in a txt file, and open the corresponding IMS file in software three.
Click on sell object in the file. Switch to the statistics tab, then paste the text into the search bar, and begin the search. All cells of interest will now be highlighted.
Spectrally overlapping fluorescent dyes were effectively separated in the tissue that was stained with multiple dyes. The separation of the dyes clearly differentiated different cell types in the lymph tissue. User-defined machine learning was used to separate the cells even when closely compacted together.
