Mapping Microglial Parameters Software (MMPS): An Open-Source, User-Friendly Tool for Quantitative Microglia Morphology Analysis

Mapping microglial parameter software, or MMPS, allows us to study how ferroptosis alters microglial subpopulations following ischemic stroke. MMPS addresses key bottlenecks within the microglial analysis field, bypassing cumbersome software requirements and requisite coding knowledge. To begin, open the GitHub repository on the computer and download the Mapping Microglial Parameters Software or MMPS standalone package.

In the downloads folder, double-click to launch the archive utility to automatically extract the compressed folder. Double-click the extracted MMPS application icon to verify and open the MMPS package. Click on select the image folder option and navigate through the computer's directory to locate the project files.

Grant folder access permissions and choose the image storage directory. Next, click select output folder, then choose the output directory. Select the image to be processed, and then choose image labeling.

Assign animal identifiers and treatment groups to the image and save the inputs. To calculate the pixel size of the image, select calibrate from scale bar. Select the beginning and end points of the scale bar, and enter the known scale bar distance in micrometers and click apply.

Select the fluorescence channel containing the microglial signal. Select images for processing by checking the box next to each image name, and process them individually if background fluorescence differs between images. Review the original, preview, processed, and masks tabs to compare image processing stages.

Select preview current image to adjust additional processing settings. For background subtraction, set the rolling ball value between five and 150 pixels. To reduce illumination artifacts, use a denoising value of three, and set the sharpening values between one and two to improve edge definition.

After optimization of image processing settings, click on process selected images. Next, select pick somas, all images. Click on show color C for RGB composite view.

Select channels, then deselect any unused channels if the image appears distorted in color. Click on display adjustments to adjust the channel brightness in the RGB view. Select microglia somas displaying DAPI and ionized calcium binding adapter molecule one, or IBA1 colocalization.

Remove incorrectly selected soma using the backspace or delete key. Press enter or return to proceed to the next image. Select outline somas all after soma selection is complete.

Outline the soma perimeter of each selected microglia cell with at least eight outline points, excluding the cellular processes. Remove incorrectly placed outline points using undo last point, delete, or backspace. Press enter, return, double-click, or right-click to finalize each soma outline.

Click generate all masks after completion of soma outlining. In mask generation settings, adjust minimum intensity to reduce background pixels and balance background suppression and branch preservation. Select QA all masks to begin mask quality assurance, and review all masks presented from largest to smallest areas.

Compare each mask to the corresponding microglia using the processed and mask tabs. To accept masks, use the A keyboard shortcut. To reject masks, use the R keyboard shortcut.

To undo an acceptance or rejection, press B.Use keyboard arrow keys to navigate through mask sizes. Select the largest acceptable mask that accurately captures microglial branching without including neighboring cells, excessive background, image borders, or scale bars. If most are inaccurate, use the clear all masks option to regenerate the masks with a different threshold value.

Next, select calculate simple characteristics to calculate various parameters for accepted masks, then click okay. For additional analysis, download fractalanalysisimagej. py, sholl.

py, and skeletonanalysisimagej. py plugins. For fractal and hull analyses, open and run fractalanalysisimagej.

py as a Fiji plugin. Then enter the output directory location. Input the pixel size and select the largest mask only option to analyze the largest accepted mask per cell.

For sholl analysis, open sholl. py as a Fiji plugin. Enter the output directory location, desired pixel size, step size.

Select use soma radius as start radius, or enter a fixed soma radius value if MMPS-derived soma radii are not used. Open skeletonanalysisimagej. py as a Fiji plugin for skeleton analysis.

Browse and enter the paths for the masks directory, output directory, and pixel size when prompted. Upon finishing, export all plugin generated results as comma-separated values files into the specific output directories. Validation study of MMPS using a lipopolysaccharide or LPS-induced rodent neuroinflammation model showed that LPS administration significantly decreased average centroid distance and cell perimeter compared with vehicle-treated controls.

No significant differences were observed in mask area, roundness, or eccentricity between treatment groups. The soma area was significantly increased in LPS-treated animals compared with vehicle controls consistent with activation-associated morphological remodeling of glial cells. Further downstream, advanced morphological analyses demonstrated only a weak correlation between microglia roundness and perimeter in vehicle-treated rats, and a strong correlation in LPS-treated rats, suggesting the association of increased microglial activation with reduced roundness and perimeter.

Additionally, the mask area demonstrated strong positive correlations with average centroid distance in both vehicle and LPS-treated groups consistent with changes in branching linked to microglial activation. This protocol allows researchers to study intricate microglial branching, morphological homogeneity, and structural complexity. Obtaining high resolution immunofluorescent images is critical for accurately using MMPS.

Future studies can utilize MMPS to study how ferroptotic inhibitors alter microglial morphology following different nervous system diseases such as stroke.