Name: Mapping Microglial Parameters Software (MMPS): An Open-Source, User-Friendly Tool for Quantitative Microglia Morphology Analysis (Video) | JoVE Journal
Uploaded: 2026-07-24T12:55:00.000+00:00
Duration: 8 min 35 s
Description: Discover MMPS for quantitative microglial morphology analysis using Python-based image processing. This open-source tool enables semi-automated single-cell analysis without coding expertise, validated in rodent neuroinflammation models.

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This work was supported by the Alzheimer&#8217;s Disease and Related Dementias T32 Training Grant (T32AG052375) awarded to West Virginia University, the Stroke CoBRE Grant (P30GM159569) awarded to West Virginia University, and National Institutes of Health Grant R01NS19998 awarded to JDH and WJG.

The authors declare no conflicts of interest or financial interests related to this work.

The primary goal of this methodology is to expand accessibility to microglial morphology analysis by reducing common technical barriers while preserving the strengths of established morphological assessment approaches. MMPS enables detection of subtle microglial morphological alterations while maintaining compatibility with advanced analyses, including fractal, skeleton, hull, and Sholl analyses. Compared with previously published approaches, MMPS provides an accessible graphical user interface, integrated calculations for commonly used morphological parameters, and elimination of coding requirements while retaining compatibility with more advanced downstream analyses.
Several critical methodological steps strongly influence analytical accuracy and reproducibility. High-quality immunofluorescence (IF) imaging is essential for reliable mask generation and morphology calculations. Antibody specificity represents a major determinant of image quality, as nonspecific labeling increases background fluorescence and reduces signal separation between microglia and surrounding tissue. Similarly, suboptimal antibody concentrations can reduce fluorescence intensity and diminish image resolution. Acquisition of Z-stack images is also critical, as omission of Z-stack imaging can artificially alter apparent microglial branching complexity and process spread, thereby biasing morphological calculations.
Several additional considerations are important during MMPS processing. Although MMPS supports batch image processing, individual image optimization is often preferable because background characteristics and staining quality can vary substantially between images. Image quality directly influences mask generation accuracy; therefore, optimization during image preprocessing substantially improves reproducibility and robustness of downstream analyses. Careful selection of microglial somas is also important, particularly avoidance of cells contacting image borders, as partial-cell selection can artificially reduce soma measurements and distort mask generation. During mask quality-assurance review, masks that incorporate neighboring cells, excessive background, or image borders should be rejected. Conversely, smaller masks should not be rejected solely because they fail to capture all distal processes, as the iterative mask-generation algorithm intentionally adjusts local thresholding parameters according to mask size and regional pixel intensity.
This methodology is highly adaptable and can be modified to meet specific experimental requirements. Because MMPS is open source, the platform can be expanded to analyze individual Z-stack frames or incorporate automated cell-detection algorithms to generate a fully automated morphology-analysis pipeline. In addition, generated masks and soma outlines remain compatible with ImageJ-based workflows for fractal, hull, skeleton, and Sholl analyses. Because MMPS was developed in Python 3.11, additional analyses can also be incorporated directly into the software using commonly available scientific Python packages without requiring external software platforms.
Despite these advantages, several limitations should be considered. MMPS was validated using a neuroinflammation model in a specific brain region of male Sprague-Dawley rats, and different brain regions, disease models, species, or biological sex may influence morphological outcomes. Applicability of MMPS to alternative experimental paradigms should therefore be validated through pilot studies or supported through prior literature comparisons. In addition, IF-based imaging incompletely captures three-dimensional branching structures because processes extending directly above or below the focal plane may not be detected in maximum-projection images. Increasing the number of analyzed cells may help mitigate this limitation. MMPS also remains dependent on image-acquisition quality and therefore requires optimization for individual imaging systems, staining protocols, and antibody preparations. Low-resolution images can artificially inflate mask size because of blurred boundaries surrounding somas and tightly packed branches. For this reason, the present protocol incorporates free-floating IF methods and multiple image-processing options to maximize image quality.
MMPS additionally retains some potential for user-dependent variability because users manually select cells and outline somas. However, use of DAPI and Iba1 colocalization substantially reduces ambiguity during cell selection. Furthermore, blinded inter-user analysis demonstrated minimal variability between previously untrained users, supporting reproducibility of the analytical workflow.
Unlike several previously published microglial morphology platforms, MMPS is not fully automated. However, this design was selected to prioritize segmentation accuracy and mask quality assurance. Automated workflows could be incorporated through modification of the Python source code to enable automated DAPI/Iba1 colocalization detection, contour-based soma outlining, and machine-learning-based mask acceptance or rejection. In addition, MMPS requires substantial disk-storage capacity because generated masks and soma outlines are automatically saved as .tiff files for downstream analyses and quality assurance. Storage requirements could be reduced by modifying the software to generate masks sequentially on a cell-by-cell basis, followed by immediate quality assessment and parameter extraction.
In conclusion, MMPS expands access to quantitative analysis of microglial morphology by providing a standardized, open-source platform that requires no coding expertise or in vivo imaging capabilities. The platform facilitates reproducible analysis of IF-based microglial morphology while maintaining compatibility with advanced downstream analytical approaches.

Microglia are innate immune cells of the central nervous system (CNS) that continuously survey their surroundings for pathogens and tissue damage1. Upon activation, microglia retract their processes and transition from a homeostatic, ramified morphology to an amoeboid phenotype2,3. In neurodegenerative diseases such as ischemic stroke4,5 and Alzheimer&#8217;s disease6,7. Microglia often perpetuate neuroinflammation through sustained cytokine production, extensive tissue degradation, and phagocytosis2. These processes contribute to disruption of the neurovascular unit, further exacerbating microglial activation and CNS injury8, thereby making microglia promising therapeutic targets9,10.
One approach for identifying subtle differences among microglial subpopulations in neurodegenerative diseases is through morphological analysis. This is commonly performed using Cx3CR1-GFP animals with in vivo two-photon microscopy11 or through immunofluorescence (IF) labeling of established microglial markers such as ionized calcium-binding adaptor molecule-1 (Iba1) or P2Y purinoceptor receptor12. Although microglia frequently transition between homeostatic and amoeboid morphologies following injury, previous studies have demonstrated that microglia can also adopt additional phenotypes, including hyper-ramified microglia3,13, honeycomb microglia3,14, reactive microglia15, and rod microglia3,16. However, microglial morphology studies often rely on manual categorization of homeostatic and amoeboid phenotypes. These approaches require specialized analysis pipelines that frequently lack standardization between laboratories and make differentiation between similar morphologies, such as homeostatic versus hyper-ramified phenotypes, difficult even for trained personnel17. Furthermore, IF-based analysis of subtle morphological alterations requires high-resolution imaging, which can present additional technical challenges.
To address these limitations, several analytical platforms have been developed that semi-automatically11 or automatically18 detect microglia and generate multiple binary representations of the cells, referred to as &#8220;masks,&#8221; across user-defined target areas (e.g., 200&#8211;800 &#181;m2). These approaches reduce user bias, improve reproducibility, and increase analytical efficiency. Despite these advances, many currently available software platforms have limitations that restrict widespread adoption, including a lack of open-source accessibility17, requirements for coding expertise11,19,20, dependence on in vivo two-photon microscopy systems11,19, absence of intuitive graphical user interfaces11,21,22, slow processing speeds11, or reliance on commercial software platforms12,19,21. Although recent studies have begun to address several of these issues, many existing platforms remain poorly suited for IF-based workflows, which remain widely used for studying microglial morphology.
Presented here is a user-oriented microglial morphology analysis platform termed Mapping Microglial Parameters Software (MMPS). MMPS requires no coding expertise, integrates with established ImageJ-based workflows, and is designed specifically for use with IF images. The software was developed in Python 3.11 and incorporates concepts from previously published open-source morphology platforms11. MMPS features an intuitive graphical user interface that enables users to import and process large batches of IF images using multiple customizable image-processing options. The platform also incorporates manual cell selection to minimize oversampling artifacts introduced during IF imaging, as well as manual soma outlining to prevent the inclusion of cellular processes in soma measurements. In addition, MMPS includes customizable mask-generation parameters and multiple quality-assurance features to improve mask accuracy. The software calculates several commonly used morphological parameters, including perimeter, average centroid distance, mask area, eccentricity, roundness, and soma area. For more advanced studies, MMPS-generated outputs are compatible with multiple ImageJ plugins and can be further analyzed using accompanying plugins for fractal, skeleton, and hull analyses.
MMPS was validated using a rodent lipopolysaccharide (LPS)-induced neuroinflammation model by examining microglial alterations within cortical layer IV. Quantitative analysis demonstrated that LPS administration significantly increased microglial soma area while decreasing cell perimeter compared with vehicle-treated controls, indicating that MMPS accurately detects subtle morphological changes associated with microglial activation.

<table><tbody><tr><td>12-well plate</td><td>Fisher Scientific</td><td>07-200-82</td><td>Plate used for incubation and washing of tissue sections.</td></tr><tr><td>Aluminum foil</td><td>Fisher Scientific</td><td>01-213-101</td><td>Used to protect fluorescent samples from light exposure.</td></tr><tr><td>Antigen Retrieval Buffer (10&amp;times;)</td><td>ThermoFisher Scientific</td><td>00-4955-58</td><td>Used to improve antigen accessibility during immunofluorescence staining.</td></tr><tr><td>Anti-Iba1 primary antibody</td><td>FUJIFILM Wako Pure Chemical Corporation</td><td>019-19741</td><td>Primary antibody used for immunofluorescent labeling of microglia.</td></tr><tr><td>Cryostat</td><td>Fisher Scientific</td><td>95-710-0L</td><td>Instrument used to section frozen brain tissue at defined thicknesses.</td></tr><tr><td>Donkey anti-Rabbit IgG, Alexa Fluor 555</td><td>ThermoFisher Scientific</td><td>A-21428</td><td>Fluorescent secondary antibody used for visualization of rabbit primary antibodies.</td></tr><tr><td>Donkey Serum</td><td>Fisher Scientific</td><td>50-413-115</td><td>Blocking reagent used to reduce nonspecific antibody binding.</td></tr><tr><td>EVOS FL Auto 2 Imaging System</td><td>ThermoFisher Scientific</td><td>AMAFD2000</td><td>Fluorescence microscope used for image acquisition.</td></tr><tr><td>Fiji (ImageJ) 2.16.0</td><td>NIH</td><td>fiji.sc</td><td>Image-processing platform used for downstream morphology analyses and plugins.</td></tr><tr><td>Fluoromount-G</td><td>ThermoFisher Scientific</td><td>00-4959-52</td><td>Mounting medium used to preserve fluorescence and secure coverslips.</td></tr><tr><td>Isoflurane</td><td>EntirelyPets Pharmacy</td><td>MWI502017</td><td>Inhalation anesthetic used during animal procedures.</td></tr><tr><td>Lipopolysaccharide</td><td>Millipore Sigma</td><td>L2630</td><td>Used to induce systemic neuroinflammation and microglial activation in rodents.</td></tr><tr><td>Matplotlib</td><td>https://pypi.org/project/matplotlib/</td><td></td><td>Python plotting library used for data visualization and figure generation.</td></tr><tr><td>MMPS (Mapping Microglial Parameters Software) v1.00</td><td>This work</td><td>https://github.com/NeurodegenerativePharmaceuticalLab/Mapping-Microglia-Parameters-Software/releases/tag/v1.00</td><td>Open-source software platform developed for microglial morphology analysis.</td></tr><tr><td>Net wells</td><td>Millipore Sigma</td><td>CLS3477</td><td>Inserts used to hold free-floating tissue sections during staining procedures.</td></tr><tr><td>Numpy</td><td>https://pypi.org/project/numpy/</td><td></td><td>Python library used for numerical computations within MMPS.</td></tr><tr><td>Objective lens (4&amp;times;)</td><td>ThermoFisher Scientific</td><td>AMEP4922</td><td>Low-magnification objective used for anatomical orientation and region identification.</td></tr><tr><td>Objective lens (40&amp;times;)</td><td>ThermoFisher Scientific</td><td>AMEP4625</td><td>High-magnification objective used for microglial morphology imaging.</td></tr><tr><td>opencv-python</td><td>https://pypi.org/project/opencv-python/</td><td></td><td>Computer vision library used for image-processing functions within MMPS.</td></tr><tr><td>Paraformaldehyde</td><td>ThermoFisher Scientific</td><td>169650025</td><td>Fixative used for tissue perfusion and preservation prior to sectioning.</td></tr><tr><td>Permount</td><td>Fisher Scientific</td><td>SP15-100</td><td>Permanent mounting medium used for slide preservation.</td></tr><tr><td>Phosphate-buffered saline</td><td>Fisher Scientific</td><td>BP3991</td><td>Buffer solution used for tissue washing and reagent preparation.</td></tr><tr><td>Pillow</td><td>https://pypi.org/project/Pillow/</td><td></td><td>Python imaging library used for image processing operations.</td></tr><tr><td>PyQt5</td><td>https://pypi.org/project/PyQt5/</td><td></td><td>Python GUI framework used to generate the MMPS graphical user interface.</td></tr><tr><td>Python 3.11</td><td>Python Software Foundation</td><td>python.org</td><td>Programming language environment used to develop and run MMPS.</td></tr><tr><td>R 4.3.2</td><td>R Foundation for Statistical Computing</td><td>r-project.org</td><td>Statistical software used for data analysis and graph generation.</td></tr><tr><td>Scikit-image</td><td>https://pypi.org/project/scikit-image/</td><td></td><td>Python image-analysis library used for image processing and segmentation.</td></tr><tr><td>Scipy</td><td>https://pypi.org/project/scipy/</td><td></td><td>Python scientific-computing package used for filtering and image-processing operations.</td></tr><tr><td>Sprague-Dawley Rats</td><td>Evingo</td><td>002</td><td>Experimental animals used for validation of the neuroinflammation model.</td></tr><tr><td>Sucrose</td><td>Fisher Scientific</td><td>S5-3</td><td>Cryoprotectant used prior to freezing brain tissue.</td></tr><tr><td>Tifffile</td><td>https://pypi.org/project/tifffile/</td><td></td><td>Python package used for reading and writing TIFF image files.</td></tr><tr><td>Tissue-Tek O.C.T. Compound</td><td>Sakura Finetek</td><td>4583</td><td>Embedding medium used for cryosectioning frozen tissue samples.</td></tr><tr><td>Triton X-100</td><td>Fisher Scientific</td><td>BP151-100</td><td>Detergent used for membrane permeabilization during immunofluorescence.</td></tr></tbody></table>

mapping microglial parameters software (mmps): an open-source, user-friendly tool for quantitative microglia morphology analysis

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

Microglia are myeloid-derived immune cells of the central nervous system that mediate tissue degradation and remodeling following neurological injury and have emerged as promising therapeutic targets in several neurodegenerative diseases. Quantitative analysis of microglial morphology can reveal subtle differences among microglial subpopulations while preserving important in situ information. However, many currently available microglial morphology analysis platforms are either not publicly accessible or require specialized computational expertise, thereby limiting their widespread adoption. To address these limitations, an open-source microglial morphology analysis platform, termed Mapping Microglial Parameters Software (MMPS), using Python 3.11 was developed. MMPS is compatible with immunofluorescence-based workflows and requires no coding expertise for installation or operation. The software semi-automates single-cell microglial morphology analysis through an accessible graphical user interface and incorporates customizable image-processing and mask-generation workflows. The study involves validating MMPS using a rodent lipopolysaccharide (LPS)-induced neuroinflammation model. Quantitative animal-level analysis demonstrated that microglia from LPS-treated animals exhibited significantly increased soma area (p &#60; 0.01) and reduced cell perimeter (p &#60; 0.05) compared with vehicle-treated controls, consistent with morphological features associated with microglial activation and neuroinflammation. Overall, MMPS provides a standardized, reproducible platform for quantitative analysis of microglial morphology across laboratories.

An open-source, standalone microglial morphology analysis platform, termed Mapping Microglial Parameters Software (MMPS), was developed to provide an accessible, standardized workflow for IF-based microglial morphology analysis. The platform builds on previous morphology-analysis methodologies, requires no coding expertise, and incorporates an accessible graphical user interface. MMPS was designed to improve the detection of subtle microglial branching while incorporating multiple safeguards to minimize the overestimation of microglial masks.
MMPS was validated using an LPS-induced rodent neuroinflammation model. Following tissue processing and image acquisition (Figure 1), IF specificity was confirmed using negative-control sections lacking primary antibody staining (Supplementary Figure 1). MMPS processing begins with the importation of IF images into the software interface (Figure 2A). The software provides multiple image-processing options to improve microglial signal detection and background separation. Mandatory background subtraction is performed using the rolling-ball algorithm implemented in the Scikit-image package, while optional denoising and sharpening filters are available via SciPy-based Gaussian filtering. Following image processing, microglial somas are manually selected and identified by red selection markers (Figure 2B). Soma boundaries are subsequently outlined manually for downstream morphological calculations (Figure 2C).
<img alt="Mouse model workflow diagram; LPS injection, perfusion, cryoprotection, sectioning, MMPS analysis." class="xfigimg" src="/files/ftp_upload/71566/71566fig1.jpg" data-altupdatedat="1787025648000" data-alt="Figure 1"> 
Figure 1: Graphical overview of neuroinflammation model and processing. Rats were injected with LPS or PBS and collected 24 h with a cardiac perfusion followed by decapitation. Brains were extracted and sliced into 30 µm sections to then be used in IF for microglial marker Iba1 which were then processed with MMPS. Created with Biorender. <a href="https://www.jove.com/files/ftp_upload/71566/71566fig1large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Fluorescence microscopy images showing microglia (Iba1, DAPI) and cellular morphology analysis." class="xfigimg" src="/files/ftp_upload/71566/71566fig2.jpg" data-altupdatedat="1787025648000" data-alt="Figure 2"> 
Figure 2: Overview of MMPS usage. (A) Example of an image of cortical microglia (red, Iba1) with cell nuclei (blue, DAPI) to be referenced in MMPS (scale bar = 75 µm, 40x) . After image processing using MMPS, the background will be reduced and accurate morphologies assessed (B) After processing the image in MMPS, somas are manually selected with a red circle to be used in subsequent steps detailed in the methods (scale bar = 75 µm, 40x). (C) Two representative microglia were manually outlined, and the saved soma outline images are shown below. (D) Mask generation of selected cells automatically traces microglial branches up to 800 µm2, but it requires manual quality assurance. Here we display the inaccurate 800 µm2 mask followed by the more accurate 700 µm2 mask. <a href="https://www.jove.com/files/ftp_upload/71566/71566fig2large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
After soma outlining, MMPS generates masks iteratively across a size range of 200–800 µm2 using adaptive thresholding constrained by user-defined minimum-intensity thresholds. Smaller masks more closely follow the user-defined threshold, whereas larger masks progressively relax threshold restrictions to maximize branch incorporation. Generated masks undergo manual quality-assurance review to prevent inclusion of neighboring cells, excessive background incorporation, or truncation by image borders or scale bars. As shown in Figure 2D, the 800 µm2 mask for a representative microglial cell extended into a neighboring cell. Rejection of the 800 µm2 mask and selection of the 700 µm2 mask corrected the segmentation error while preserving accurate delineation of the target microglia.
Following MMPS processing, microglial activation was assessed using the largest accepted mask for each cell, as these masks most accurately captured distal branching while minimizing background incorporation. Calculated morphological parameters are summarized in Table 1 and represented in Figure 3. LPS administration significantly decreased average centroid distance and cell perimeter compared with vehicle-treated controls (p &lt; 0.05; Figure 3A,B), consistent with retraction of microglial processes during activation. No significant differences were observed in mask area, roundness, or eccentricity between treatment groups (Figure 3C–E), despite previous reports associating these parameters with activated microglial phenotypes3,23. The present study was not statistically powered to detect subtle differences in soma shape or mask area, and microglial subpopulations can adopt heterogeneous inflammatory morphologies that do not uniformly follow these trends3. In contrast, the soma area was significantly increased in LPS-treated animals compared with vehicle controls (p &lt; 0.01; Figure 3F), further supporting activation-associated morphological remodeling22.
<img alt="Box plot diagram comparing cell morphology metrics by treatment: centroid distance, area, roundness." class="xfigimg" src="/files/ftp_upload/71566/71566fig3v2.jpg" data-altupdatedat="1787025648000" data-alt="Figure 3"> 
Figure 3: Comparison of microglial morphological parameters. For all panels, tests for normality were conducted (Shapiro-Wilk and Levene) with nonsignificant values found in all cases (p&gt;0.05), leading to Student t-tests being employed. Dots represent individual animals (n=5 per treatment) (A) Average centroid distance was significantly greater in the vehicle group compared to the LPS treatment group. (B) Microglia cell perimeter was significantly greater in the vehicle group compared to the LPS treatment group. (C) There was no significant difference in mask area between treatment groups. (D) There was no significant difference in soma eccentricity between groups. (E) There is no significant difference in soma roundness between treatment groups. (F) There was a significant increase in soma area in the LPS group * p&lt;0.05, ** p&lt;0.01, *** p&lt;0.001. <a href="https://www.jove.com/files/ftp_upload/71566/71566fig3largev2.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>Parameter</td>
			<td>Definition</td>
			<td>Relation to Microglial Activation</td>
		</tr>
		<tr>
			<td>Average Centroid Distance</td>
			<td>Average distance of the furthest branch in the top, right, bottom, and left positions</td>
			<td>Decreases</td>
		</tr>
		<tr>
			<td>Perimeter</td>
			<td>Measurement of the perimeter of the cell</td>
			<td>Decreases</td>
		</tr>
		<tr>
			<td>Mask Area</td>
			<td>The actual area of the cell, not the target mask area</td>
			<td>Decreases</td>
		</tr>
		<tr>
			<td>Eccentricity</td>
			<td>A measurement of the approximate elongation of the cell soma (0: circular, 1: highly elongated)</td>
			<td>Decreases</td>
		</tr>
		<tr>
			<td>Roundness</td>
			<td>A measurement of the approximate circularity of the cell soma (0: elongated, 1: circular)</td>
			<td>Increases</td>
		</tr>
		<tr>
			<td>Soma Size</td>
			<td>The area of the cell soma</td>
			<td>Increases</td>
		</tr>
	</tbody>
</table>
Table 1: Calculated Parameters from MMPS and Predicted Change in Response to Microglia Activation. The table summarizes morphological parameters calculated by Mapping Microglial Parameters Software (MMPS), including perimeter, average centroid distance, mask area, eccentricity, roundness, and soma area.
Inter-user reproducibility was subsequently evaluated by providing the same image set to blinded, previously untrained users for independent analysis (Supplementary Figure 2). No statistically significant differences were detected between users, and all users reproduced morphological trends consistent with microglial activation in LPS-treated animals.
MMPS additionally supports downstream advanced morphological analyses. Correlative analysis of parameters generated from Figure 3 demonstrated relationships between distinct morphological features. Vehicle-treated rats demonstrated only a weak correlation between microglia roundness and perimeter (R2 = 0.17), whereas LPS-treated rats demonstrated a stronger correlation (R2 = 0.64), suggesting that increased microglial activation was associated with simultaneous reductions in roundness and perimeter (Figure 4A). In addition, the mask area demonstrated strong positive correlations with average centroid distance in both vehicle and LPS-treated groups (R2 = 0.71 and 0.88, respectively), consistent with changes in branching complexity associated with microglial activation status (Figure 4B).
<img alt="Roundness vs. Perimeter and Mask Area vs. Centroid Distance graphs show R² values, comparison of treatments." class="xfigimg" src="/files/ftp_upload/71566/71566fig4v2.jpg" data-altupdatedat="1787025648000" data-alt="Figure 4"> 
Figure 4: Advanced analysis of microglial parameters. All panels are animal-level lines of best fit computed by the ordinary least squares method displayed with their coefficient of determination (R2) and shaded with their 95% confidence interval. (A) Microglial soma roundness and total perimeter length trend weakly in microglia form the vehicle group (R2 = 0.17) but have stronger correlation after LPS exposure (R2 = 0.64). (B) Average centroid distance correlates with the area of the microglia in the vehicle (R2 = 0.71) and LPS (R2 = 0.88) groups. <a href="https://www.jove.com/files/ftp_upload/71566/71566fig4largev2.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
Supplementary Figure 1: Validation of IF Protocol Through Negative Control. One brain slice of the same region for each rat underwent the IF protocol without primary antibody addition to confirm lack of autofluorescence (scale bar = 75 µm) (A) Representative image of vehicle-stained tissue for DAPI (blue). (B) Representative image of vehicle-stained tissue for Iba1 (red). (C) Representative image of vehicle-stained tissue for DAPI (blue) and Iba1 (red). (D) Representative image of LPS-stained tissue for DAPI (blue). (E) Representative image of LPS-stained tissue for Iba1 (red). (F) Representative image of LPS-stained tissue for DAPI (blue) and Iba1 (red). <a href="https://www.jove.com/files/ftp_upload/71566/S1.tiff" target="_blank">Please click here to download this file.</a>
Supplementary Figure 2: Assessment of MMPS Inter-User Variation. Several blinded users utilized MMPS on the same samples from Figure 3 and the animal-level results were compared. For all, tests of normality (Shapiro-Wilk and Levene) were nonsignificant (p&gt;0.05), leading to the utilization of a repeated-means ANOVA with post-hoc Holm-corrected paired t-test (p&gt;0.05). (A) There was no significant difference in each group between users for average centroid distance (p&gt;0.05). (B) There was no significant difference in each group between users for perimeter (p&gt;0.05). (C) There was no significant difference in each group between users for mask area (p&gt;0.05). (D) There was no significant difference in each group between users for eccentricity (p&gt;0.05). (E) There was no significant difference in each group between users for roundness (p&gt;0.05). (F) There was no significant difference in each group between users for soma area (p&gt;0.05). <a href="https://www.jove.com/files/ftp_upload/71566/S2v2.tiff" target="_blank">Please click here to download this file.</a>

Mapping Microglial Parameters Software (MMPS) is a user-friendly analysis pipeline that requires no coding expertise and semi-automates single-cell microglial morphology analysis from immunofluorescence images. Validated in a rodent lipopolysaccharide-induced neuroinflammation model, MMPS reproducibly detects activation-associated morphological changes and enables standardized, scalable microglial phenotyping.

Microglia are myeloid-derived immune cells of the central nervous system that mediate tissue degradation and remodeling following neurological injury and ...

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.

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Research

JoVE Journal

Neuroscience

Source: Etienne, F. et al. Two-photon Imaging of Microglial Processes' Attraction Toward ATP or Serotonin in Acute Brain Slices. J. Vis. Exp. (2019)This video demonstrates the use of two-photon microscopy to study the attraction of microglial processes toward an injected compound in brain slices. It provides an overview of preparing mouse brain slices, injecting a chemoattractant test compound, and imaging fluorescently labeled microglia to visualize their responses.

Two-Photon Microscopy for Studying Microglial Process Attraction Toward a Compound in a Mouse Brain Slice

One key to successful investigation of microglial biology is the preservation of microglial immunofunction ex vivo during isolation from CNS tissue. Isolating microglia via rotary shaking results in highly pure and immunofunctional cell cultures as assessed by fluorescent imaging, immunocytochemistry, and ELISA following microglia activation with the proinflammatory stimuli lipopolysaccharide (LPS) and Pam3CSK4 (Pam).

Isolation of Cortical Microglia with Preserved Immunophenotype and Functionality From Murine Neonates

Microglia are the resident immune cells of the central nervous system (CNS) with a high capacity to phagocytose or engulf material in their extracellular environment. Here, a broadly applicable, reliable, and highly quantitative assay for visualizing and measuring microglia-mediated engulfment of synaptic components is described.

An Engulfment Assay: A Protocol to Assess Interactions Between CNS Phagocytes and Neurons

Microglia activation and microgliosis are key responses to chronic neurodegeneration. Here, we present methods for in vivo, long-term visualization of retinal CX3CR1-GFP+ microglial cells by confocal ophthalmoscopy, and for threshold and morphometric analyses to identify and quantify their activation. We monitor microglial changes during early stages of age-related glaucoma.

In Vivo Dynamics of Retinal Microglial Activation During Neurodegeneration: Confocal Ophthalmoscopic Imaging and Cell Morphometry in Mouse Glaucoma

This protocol provides an analysis of the macrophage subpopulations in the adult mouse central nervous system by flow cytometry and is helpful for the study of multiple markers expressed by these cells.

Analysis of Microglia and Monocyte-derived Macrophages from the Central Nervous System by Flow Cytometry

Microglia are brain immune cells that survey and react to altered brain physiology through morphologic changes which may be evaluated quantitatively. This protocol outlines an ImageJ based analysis protocol to represent microglia morphology as continuous data according to metrics such as cell ramification, complexity, and shape.

Quantifying Microglia Morphology from Photomicrographs of Immunohistochemistry Prepared Tissue Using ImageJ

This protocol describes a step-by-step workflow for immunofluorescent costaining of IBA1 and TMEM119, in addition to analysis of microglial density, distribution, and morphology, as well as peripheral myeloid cell infiltration in mouse brain tissue.

Immunofluorescence Staining Using IBA1 and TMEM119 for Microglial Density, Morphology and Peripheral Myeloid Cell Infiltration Analysis in Mouse Brain

Here, we describe a protocol combining adeno-associated virus injection with cranial window implantation for simultaneous imaging of microglial dynamics and neuronal activity in awake mice.

Simultaneous Imaging of Microglial Dynamics and Neuronal Activity in Awake Mice

Here we present two protocols to quantify microglial engulfment of vGLUT1-positive synapses and pHRodo Red-labeled crude synaptosomes using flow cytometry.

Quantification of Microglial Engulfment of Synaptic Material Using Flow Cytometry

This paper demonstrates a method for visualizing and quantifying microglia motility and contact with postsynaptic puncta in the retina using spinning disk confocal microscopy.

Live Imaging and Characterization of Microglia Dynamics and Interactions with Synapses in Diseased Murine Retina

This protocol combines flow cytometry and single-cell RNA sequencing to isolate and characterize microglial cell states in the cerebellum of early postnatal mouse brains. It uses enzymatic dissociation, Percoll centrifugation, and immunostaining to reveal microglia heterogeneity and improve understanding of their roles in cerebellar development and disease.

Flow Cytometry and Single-Cell Analysis for Characterizing Microglia Activation in Early Postnatal Mouse Brain Development

Discover MMPS for quantitative microglial morphology analysis using Python-based image processing. This open-source tool enables semi-automated single-cell analysis without coding expertise, validated in rodent neuroinflammation models.

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

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

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