Name: quantifying microglia morphology from photomicrographs of immunohistochemistry prepared tissue using imagej
Uploaded: 2018-06-05T14:00:00.000+00:00
Duration: 8 min 44 s
Description: Watch this Scientific Journal Video about Quantifying Microglia Morphology from Photomicrographs of Immunohistochemistry Prepared Tissue Using ImageJ at JoVE.com

This study received financial support from NINR (F32NR013611). We would like to further acknowledge and thank the developers of AnalyzeSkeleton(2D/3D) and FracLac (Arganda-Carreras et al. and Karperien et al., respectively) without which the analysis described herein would not be possible.

All experiments were approved by and performed in accordance with the guidelines established by the University of Arizona Institutional Animal Care and Use Committee and the NIH guidelines for the care and use of laboratory animals. Care was taken to minimize animal pain and discomfort. Euthanasia methods are according to an approved protocol and consist of cervical decapitation under isoflurane anesthesia.
1. Tissue Preparation
NOTE: Carry out microglia morphology analysis on fixed, cryoprotected tissue samples to preserve cell morphology. The following is a standard protocol to prepare and directly slice fixed tissue for fluorescence IHC.
<ol>
	<li>Remove mouse or rat brains from a euthanized animal after the desired experiment and according to a standard laboratory protocol. Place the brain into a 10 mL vial containing 5 mL of a 4% paraformaldehyde solution for 24 h at 4&#160;&#176;C. Then rinse and place into 5-10 mL of a 30% sucrose phosphate buffered solution (PBS, 0.01 M) for 72 h at 4&#160;&#176;C. Store whole brains at -80 &#176;C until tissue sectioning with a cryostat, or at 4&#160;&#176;C if sectioning with a microtome. 
	NOTE: This protocol has not yet been tested using tissue sliced from paraffin-embedded tissue.</li>
	<li>Section the brain tissue to the desired section thickness and orientation using either a cryostat or microtome and store free-floating sections in a cryoprotection solution (50 mM PBS, ethylene glycol, glycerol) at -20&#160;&#176;C. 
	NOTE: This protocol has been successfully carried out on coronal tissue sections ranging from 50 &#181;m to 200 &#181;m in thickness. Tissue sections less than 50 &#181;m may not capture the full span of microglia processes, while in thick tissue sections, IHC staining may be imperfect due to antibody penetration into tissue. Tissue may be sectioned either in a coronal or sagittal orientation and this choice will depend on the experimental goal and brain region(s) to be studied.</li>
</ol>
2. Immunohistochemistry
NOTE: Skeleton and fractal analysis methods can be applied to either fluorescence or 3,3&#8242;-diaminobenzidine (DAB) IHC. The following is a standard fluorescence IHC protocol and may be substituted as needed. Fluorescence IHC yields superior visualization of cell processes when compared to DAB IHC.
<ol>
	<li>Place the tissue sections into a 4 mL glass vial (up to 15 mouse brain sections/vial) and incubate with 1 mL of solution containing up to 10% horse serum, PBS (0.01 M), 0.5% Triton, 0.04% NaN3 at room temperature (23&#160;&#176;C) for 1&#160;h.</li>
	<li>Wash for 5 min with PBS (0.01&#160;M) three times at room temperature.</li>
	<li>Incubate with the primary antibody (Iba1, 1:1,000) at room temperature for 72 h in 1 mL of solution containing PBS (0.01&#160;M), 0.5% Triton, 0.04% NaN3, and covered (to preserve the NaN3 effectiveness).</li>
	<li>Wash for 5 min with PBS (0.01 M) three times at room temperature.</li>
	<li>Incubate with the secondary antibody (anti-rabbit 488, 1:250) covered at room temperature for 4 h in 1 mL of solution containing PBS (0.01&#160;M), 0.5% Triton, 0.04% NaN3 </li>
	<li>Wash for 5 min with PBS (0.01 M) three times at room temperature.</li>
	<li>Mount the brain tissue sections (number and orientation based on preference) to subbed slides, apply a soft-set mounting medium to the slide, and place the coverslip over the tissue. Store the slides at 4&#160;&#176;C. 
	NOTE: A 1.5 glass coverslip thickness is required for confocal imaging. Soft-set is a preferred mounting medium because the high viscosity does not compress the tissue and best retains the morphology when compared to hard-set mounting media.</li>
</ol>
3. Imaging
<ol>
	<li>Image Iba1 positive cells in the brain tissue section using a bright-field or confocal microscope that has a z-stack acquisition capability using a 20X objective or greater. 
	NOTE: Imaging parameters and software set-up should be constant for all photomicrograph acquisition in an experiment. Excellent process detail is obtained using a 40X or 63X objective. It is possible to apply the skeleton analysis protocol described herein to an entire photomicrograph or a multi-cell ROI within a larger photomicrograph.
	<ol>
		<li>Acquire 8-bit images by using the appropriate software settings specific to the microscope software. 
		NOTE: The conversion of files to 8-bit post-acquisition may distort the data collection.</li>
		<li>Acquire at least a 30 &#181;m z-stack with no more than a 2 &#181;m interval between images using the appropriate software settings specific to the microscope software. 
		NOTE: The microscope and software should allow for image acquisition in an X, Y, and Z axis. A z-stack may be increased, and the interval may be decreased to provide additional microglia detail. In exchange, imaging time will increase. Use Nyquist sampling where possible for fluorescence microscopy.</li>
	</ol>
	</li>
	<li>Save all files as Tif files or as required by the microscope software.</li>
	<li>Open the Tif files in ImageJ and use the toolbar to split channels by clicking Image | Color | Split Channels, and stack images by clicking Image | Stacks | X Project | Maximum Intensity Projection where appropriate. Save as .tif files.</li>
</ol>
4. Skeleton Analysis
<ol>
	<li>Download FIJI or ImageJ from &#60;https://imagej.net/Fiji/Downloads&#62;. For the individual plugins, download AnalyzeSkeleton(2D/3D) from &#60;http://imagej.net/AnalyzeSkeleton&#62;15 and download FracLac from &#60;https://imagej.nih.gov/ij/plugins/fraclac/fraclac.html&#62;16. 
	NOTE: The entire process of converting a photomicrograph to a binary skeletonized image takes less than 1 min.</li>
	<li>If using a fluorescence photomicrograph, ensure the image is 8-bit and convert to grayscale to best visualize all positive staining. Use the toolbar and click Image | Lookup Tables | Greys. If using a DAB bright-field photograph, first use the FFT bandpass filter plugin (using the toolbar by clicking Process | FFT | bandpass filter) and then convert to grayscale. 
	NOTE: For the purposes of this protocol, ImageJ default settings for an FFT Bandpass Filter are sufficient (filter up to 3 pixels, down to 40, no stripe suppression). Applying an FFT Bandpass filter removes noise (small features) while preserving the overall larger aspects of the image. This is particularly useful in bright field images, where splits and cracks in the tissue can appear as background and thus complicate the skeleton analysis18.</li>
	<li>Adjust the brightness and contrast if the image is too dim and the microglia process cannot be visualized. Use the toolbar and click Image | Adjust | Brightness/Contrast. Adjust the minimum or maximum sliders as needed, up to the edges of the histogram but no further. 
	NOTE: In ImageJ, brightness and contrast are changed by updating the image&#39;s lookup table (LUT), so pixel values are unchanged. Max and min sliders control the lower and upper limits of the display range, with pixel values above 255 appearing white and pixel values below 0 appearing black18. In the case of fluorescence photomicrographs, the maximum slider should be used, whereas the minimum slider will be used for photomicrographs of DAB stained microglia.</li>
	<li>Run an Unsharp Mask filter to further increase contrast using the toolbar by clicking Process | Filters | Unsharp Mask. For the purposes of this protocol, ImageJ&#39;s default settings (a pixel radius of 3 and mask weight of 0.6) are used. 
	NOTE: Unsharp Mask sharpens and enhances the edge features of an image by subtracting a blurred version of the image (the Unsharp Mask) from the original, and then merging the resulting image with a high-contrast version of the original image. Therefore, the Unsharp Mask filter does not create details, but rather clarifies the existing detail in an image. The radius setting changes how blurry the Unsharp Mask will be (and thus how much blur will be removed), and the mask weight setting changes the degree of contrast that the Unsharp Mask will be merged with (and thus adjusts the contrast of the final image).</li>
	<li>Perform a despeckle step to remove salt-and-pepper noise generated by the Unsharp Mask. Use the toolbar and click Process | Noise | Despeckle. 
	NOTE: Despeckle removes salt-and-pepper noise by replacing each pixel with the median value in its 3 &#215; 3 neighborhood. The effect is that outliers in intensity are replaced by the median pixel without affecting the sharpness of edges18.</li>
	<li>Convert the image to binary using the toolbar by clicking Image | Adjust | Threshold. 
	NOTE: Thresholding stratifies the grayscale images into features of interest versus background and converts the image into binary18.</li>
	<li>Apply the despeckle, close-, and remove outliers functions: In the resulting binary image, there may be single-pixel background noise and gaps between processes.
	<ol>
		<li>Apply the despeckle function using the toolbar by clicking Process | Noise | Despeckle. 
		NOTE: Applying despeckle to the binary image removes any remaining single-pixel noise.</li>
		<li>Apply the close function using the toolbar by clicking Process | Binary | Close. 
		NOTE: This plugin connects two dark pixels if they are separated by up to 2 pixels.</li>
		<li>Apply the remove outliers function using the toolbar by clicking Process | Noise | Remove Outliers. 
		NOTE: For the purposes of this protocol, bright outliers are targeted with a pixel radius of 2 and a threshold of 50. This plugin replaces a bright or dark outlier pixel by the median pixels in the surrounding area if it deviates by more than a certain value (the threshold)18.</li>
	</ol>
	</li>
	<li>Save the image as a separate file for future use and/or fractal analysis and skeletonize the image using the toolbar by clicking Process | Binary | Skeletonize.</li>
	<li>Select the skeletonized image and run the plugin AnalyzeSkeleton(2D/3D) using the toolbar by clicking Plugins | Skeleton | Analyze Skeleton, and checking the Branch Information box. 
	NOTE: It is likely that the image processing will require optimization with the addition or deletion of the above suggested steps. In this process, skeletonized images are assessed for accuracy by creating an overlay of the skeleton and the original image. Somas should be single origin points with processes emanating from the center; circular somas confound the data and should be avoided through protocol adjustment. An example of a single origin point versus circular somas is illustrated in Figure 1.</li>
</ol>
Common problems resulting in non-representative skeletons and suggested solutions:
<ul>
	<li>Image too dim: convert to greyscale, adjust brightness/contrast sliders, and/or apply Unsharp Mask</li>
	<li>Too much background: adjust brightness/contrast sliders, apply Despeckle, and/or Remove outliers</li>
	<li>Circular somas in skeletonized image (particularly for fluorescence images): apply FFT Bandpass filter, and/or Unsharp Mask</li>
	<li>Cracks in tissue (particularly for bright-field images): apply FFT Bandpass filter, and/or Despeckle</li>
</ul>
<ol start="10">
	<li>Copy all of the data from the Results and Branch information outputs and paste the data into an Excel spreadsheet.</li>
	<li>In Excel, trim the data to remove skeleton fragments that result from IHC and image acquisition.
	<ol>
		<li>Duplicate the experiment workbook with the raw data output from skeleton analysis and add TRIM to the filename. All subsequent data trimming should occur in the duplicated workbook to preserve the raw data for future use and reference.</li>
		<li>Determine which length of fragments will be trimmed from the dataset by opening the skeletonized image in ImageJ and selecting the Line tool. Measure several fragments, taking note of the average length, and decide on a cutoff value. 
		NOTE: For the purposes of the data presented here, the cutoff length for undesired fragments is 0.5. This value should be consistent throughout a dataset.</li>
		<li>Custom sort the Excel spreadsheet by clicking Sort &#38; Filter | Custom sort. Sort by &#34;endpoint voxels&#34; from largest to smallest and, in a new level, by &#34;Mx branch pt&#34; from largest to smallest.</li>
		<li>Remove every row that contains 2 endpoints with a maximum branch length of less than the cutoff value (i.e., 0.5). Sum the data in the endpoints column to calculate the total number of endpoints collected from the image.</li>
		<li>Repeat for Branch information data: sort by &#39;branch length&#39; from largest to smallest. Scroll through the data and remove every row that has a branch length of less than the cutoff value(i.e., 0.5). Sum the values in the branch length column to calculate the summed length of all branches collected from the image.</li>
		<li>Repeat steps 4.11.3-4.11.5 for every image/sheet until all data have been trimmed and summed.</li>
		<li>Divide the data from each image (summed number of endpoints and summed branch length) by the number of microglia somas in the corresponding image. Enter the final data (endpoints/cell &#38; branch length/cell) into statistical software. 
		NOTE: The summed branch length/cell data may require conversion from length in pixels to microns.</li>
	</ol>
	</li>
</ol>
5. Fractal Analysis
NOTE: FracLac is able to run a number of different shape analyses that are not covered in this protocol. For a more detailed explanation of FracLac&#39;s various functions, see the FracLac manual at &#60;https://imagej.nih.gov/ij/plugins/fraclac/FLHelp/Introduction.htm&#62; and associated references 2,16,19. Fractal analysis utilizes the protocol steps 4.1-4.7 described above.
<ol>
	<li>Determine the size of the ROI that will be used for all fractal analysis. Use the rectangle tool to draw the ROI. Ensure that the box is large enough to capture the entire cell and can remain consistent throughout the dataset. 
	NOTE: Use the rectangle selection rather than the freehand selection to ensure that all ROIs are the same sized rectangle, and therefore, the cells have the same scale. This would not be the case if the freehand selection were used because ImageJ will auto-scale a square ROI to fit different sized rectangular windows resulting in cells with different scales. While fractal shapes are scale-independent, the fractal analysis process using FracLac for ImageJ is dependent on scale16. Thus, there is the need for a consistently sized ROI throughout data collection.</li>
	<li>Randomly select microglia for fractal analysis in each photomicrograph and corresponding binary image. In the ROI manager window, select Update to lock the ROI onto the cell&#39;s position. Use Ctrl + Shift + D to duplicate the area within the ROI as a new window, and then save the cropped cell. On the corresponding binary image (from the skeleton analysis), duplicate the area with the same cell using the ROI manager. Repeat until a sufficient number of cells have been randomly selected for fractal analysis and save all files. 
	NOTE: A random number generator and a numbered grid can be used to randomly select cells.</li>
	<li>Open the binary image with the individual cell. Double-click the paintbrush tool, set the color to black, and adjust the brush width as needed. Using the matching photomicrograph as a reference, use the paintbrush to remove adjacent cell processes, connect fragmented processes, and isolate the cell of interest. Once the binary cell has been isolated, save the binary file. 
	NOTE: Holding &#39;alt&#39; will switch the paintbrush from the foreground color (black) to the background color (white).</li>
	<li>Convert the binary cell to an outline using the toolbar via Process | Binary | Outline. 
	NOTE: FracLac for Image J can be used on either solid shapes or shape outlines, however, current convention is to use shape outlines16.</li>
	<li>In the toolbar, open FracLac using the toolbar by clicking Plugins | Fractal Analysis | FracLac and select BC (box counting). In the Grid Design settings, set Num G to 4. Under Graphics options, check the Metrics box to analyze the convex hull and bounding circle of the cell. When finished, select OK. 
	NOTE: Num G is the number of box counting grid orientations used during the scan and the recommended range for Num G is 4-12. The Num G setting and the suggested range is thoroughly reviewed in the FracLac manual16. Increasing Num G can significantly slow calculation times. FracLac settings will only need to be set up once per session. Once the settings have been entered, the Scan button will become available.</li>
	<li>Select the Scan button to run a box-counting scan on the selected image. 
	NOTE: The scan will generate three windows with data outputs: Hull and Circle Results, Box Count Summary File, and Scan Types. The Scan Types window contains a log of the settings used, as well as standard deviations for certain measures. For the purposes of this protocol, the Scan Types window is not used and may be closed or saved for future reference.</li>
	<li>In the Hull and Circle Results window, copy all desired data (i.e., density, span ratio, and circularity) results. In the Box Count Summary window, copy the desired data (i.e., fractal dimension and lacunarity) results. Transfer the copied data to an Excel file or statistical software. 
	NOTE: A full listing of the FracLac output data are provided and thoroughly explained in the FracLac for ImageJ manual16.</li>
</ol>

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The authors have nothing to disclose.

Microglia cells are finely tuned to the physiology and pathology within their micro-domains and display a diverse range of morphologies2 in both subtle7,14 and gross injury8. The use of ImageJ protocols makes microglia morphology quantification accessible to all laboratories as the platform and plugins are an open-source image processing software. While the described protocol is focused on image processing and analysis using this software, the consistency of data collection, validity, and reliability begins with excellent IHC and microscopy. This protocol is used to improve binary, skeleton, and outline representations of entire photomicrographs and single cells but cannot take the place of poor IHC staining and microscopy that results in low contrast, blurred, or distorted images. As an additional consideration, care must be taken not to flatten the brain tissue during storage, prior to sectioning, which irrevocably alters microglia morphology. Lastly, within each experiment, microglia must be imaged using the same scale as well as the same microscope. Instrumentation, objectives, and software vary amongst microscopes which will result in different sized photomicrographs despite similar objectives and change the detail as well as the number of cells within each frame. For example, image acquisition using a 40X objective on a Leica SPII results in twice the number of cells and less detail than acquisition using a Zeiss 880. This is particularly important for cell ramification data collected from the entire frame rather than a single cell, as this becomes an issue of data sampling.
In general, skeleton analysis which utilizes the entire photomicrograph precedes the single cell fractal analysis for two reasons. Determining cell ramification of all cells in a photomicrograph is rapid when compared to the single cell fractal analysis and may be considered as a screening tool if time is a factor. In addition, the binary images derived during skeleton analysis are used for fractal analysis. Once imaged, there are a number of critical steps that may influence skeleton analysis results and introduce user-influence. The protocol steps that are most variable between users are step 4.2 (increasing image brightness) and step 4.5 (determining the threshold). Where possible, an optimal number to increase brightness (max or min slider between 0-255) is determined and held constant for all images and users. Where image variability is great, the user can instead choose a brightness that will vary between images. Alternatively, if images are bright and contrast is high, then increasing brightness can be omitted and thresholding can be standardized by using a specialty threshold filter (e.g., Huang) rather than the more variable default. Once optimized, the parameters should be adhered to in order to minimize additional user-influence.
An example of user variability is presented in Figure 5. Data values were increased in User 1 versus User 2 and therefore variability would be increased if both User 1 and User 2 contributed to data collection. An example of the differences in User 1 and User 2 binary and skeletonized images are highlighted by colored circles (Figure 5). In this instance, both users were briefly trained undergraduate students with limited expertise in microglia. Regular oversight and mentoring by a microglia expert along with increased protocol training2 can reduce inter-user variability. Although not assessed here, fractal analysis is less subject to inter-user variability because binary cells are manually and individually isolated from a photomicrograph rather than relying solely on thresholding to determine microglia shapes. However, all methods possess some variability between users. Therefore, a single user (ideally, trained by some expertise in microglia cells) should complete the data collection for an entire dataset.
Additional modifications can be easily made to this protocol and will depend on image quality, and the efforts taken to reduce noise and ensure process connectivity. For example, if contrast is adequate, then unsharp mask is not necessary and can be omitted. It is prudent to optimize and finalize the protocol for a specific set of images, both experimental cases and controls, prior to collecting data from an entire set. Lastly, additional plugins may be used in place of others to clarify or sharpen images that were not described in this protocol such as dilate or sharpen.
Advantages of this protocol are its universal availability and adaptability. In addition, assessing cell ramification using AnalyzeSkeleton is quick and applicable to an entire photomicrograph. A benefit of multi-cell analysis approach is the focus on an entire region rather than single cells. Therefore, it is possible to quickly assess the average ramification (in terms of endpoints and process length) of all microglia within the image. Skeleton analysis provides an analysis of multiple cells: a data sampling in terms of cell numbers that cannot be matched by fractal analysis due to the required time investment to isolate single cells from photomicrographs. An instance where this might be best suited would be in screening microglia morphologies in proximities to a focal injury. One limitation is the whole field image rendering to create skeleton models of IHC photomicrographs is imperfect when compared to the more time consuming single cell approach. In addition, a region analysis is not appropriate to circumstances where microglia morphologies are drastically different within the same field. Lastly, this analysis method is dependent on cell count, a parameter that may differ between experimental conditions.
Fractal analysis is conducted on a single cell and therefore complements the average cell ramification data output resulting from the skeleton analysis. Although much more time consuming, this investment yields a broad range of morphometric data. For example, cell density, span ratio, and circularity data describe the size, elongation, and shape of the cell outline, respectively. Fractal dimension and lacunarity summarize the cell complexity and shape heterogeneity, respectively. A more in-depth summary of how each parameter is calculated and how data may be interpreted is provided in the interactive manual16 and such detail should be considered in relation to the specific research question. The described protocol results in sensitive tools to quantify small changes in 2D microglia morphologies that may occur in physiologic and pathologic conditions. Additional morphometric analysis such as solidity, convexity, and form factor16,20 may be possible if generating 3D shapes.
Protocol development and adaptation is continuous and user-driven. It has been extended from fluorescence8 to DAB/bright-field images7 but not yet to paraffin embedded tissue. It addition, it can be used in conjunction with proprietary software such as Imaris for additional analysis. This protocol can be applied to a variety of physiology and is not limited to microglia but may be applied to any cell or tissue with particular patterns or shapes that can be identified using IHC methods. Lastly, with sufficient sample size, a multivariate or cluster analysis can be applied to stratify microglia according to morphology12,21; this is meaningful information as microglia morphology is a vital indicator of microglia functions and responses to their surroundings. The appreciation for microglial morphologic diversity is expanding and important to fully understand neuron-glia-vascular interactions during health and disease. Growth in this field is enhanced by well-developed, easy to use, and reproducible protocols to quantify and summarize microglia morphology using multiple continuous variables.

Microglia have an immediate and diverse morphologic response to alterations in brain physiology1 along a continuum of possibilities that range from hyper-ramification and highly complex morphologies to de-ramified and amoeboid morphologies2. Microglia may also become polarized and rod-shaped3. Microglia cell ramification is commonly defined as a complex shape having multiple processes and is often reported as the number of endpoints per cell and the length of cell processes. Since microglia are finely tuned to neuronal and glial function through continuous cell-cell cross-talk and in vivo motility4,5, microglia morphologies may serve as indicators of diverse cell functions and dysfunctions in the brain. A quantitative approach is necessary to adequately describe the diversity of these morphologic changes and to distinguish the differences among ramified cells that occur with subtle physiologic perturbations (such as epilepsy5,6 and concussion7) in addition to gross injury (such as stroke8). An increased use of morphology quantification7,8,9,10,11,12,13,14 will reveal the full diversity of microglia morphologies during health and disease. The present study details the stepwise use of ImageJ plugins necessary to summarize microglia morphology from fluorescent or non-fluorescent photomicrographs of microglia acquired in fixed rodent tissue after immunohistochemistry (IHC).
Central to the analysis techniques described here are the ImageJ plugins AnalyzeSkeleton (2D/3D)15, developed in 2010 to quantify large mammary structures, and FracLac16, developed in 2014 to integrate ImageJ and fractal analysis to quantify individual microglia shapes. These plugins provide a rapid analysis of microglia ramification within entire photomicrographs or multiple microglia of a defined ROI within a photomicrograph. This analysis may be used alone or in complement with fractal analysis. The single-cell fractal analysis (FracLac) requires an investment of time but provides multiple morphology outputs regarding microglia complexity, shape, and size. The use of both tools is not redundant, as cell ramification is complementary to cell complexity, and the combination of multiple parameters may be used to distinguish between diverse microglia morphologies within datasets12,17.

<table><tbody><tr><td>primary antibody anti-IBA1</td><td>Wako&amp;nbsp;</td><td>019-19741</td><td>rabbit host</td></tr><tr><td>Vectashield soft mount</td><td>Vector Labs</td><td>H-1000</td><td></td></tr><tr><td>Secondary antibody</td><td>Jackson ImmunorResearch</td><td>711-545-152</td><td>donkey host</td></tr><tr><td>4 mL glass vial</td><td>Wheaton</td><td>UX-08923-11</td><td></td></tr><tr><td>Triton X-100</td><td>Fisher Scientific&amp;nbsp;</td><td>BP151</td><td></td></tr><tr><td>Sodium Azide (NaN&lt;sub&gt;3&lt;/sub&gt;)</td><td>Sigma</td><td>S-8032</td><td></td></tr><tr><td>glass coverslip</td><td>Fisher Scientific&amp;nbsp;</td><td>12-544-G</td><td></td></tr></tbody></table>

quantifying microglia morphology from photomicrographs of immunohistochemistry prepared tissue using imagej

Microglia are brain phagocytes that participate in brain homeostasis and continuously survey their environment for dysfunction, injury, and disease. As the first responders, microglia have important functions to mitigate neuron and glia dysfunction, and in this process, they undergo a broad range of morphologic changes. Microglia morphologies can be categorized descriptively or, alternatively, can be quantified as a continuous variable for parameters such as cell ramification, complexity, and shape. While methods for quantifying microglia are applied to single cells, few techniques apply to multiple microglia in an entire photomicrograph. The purpose of this method is to quantify multiple and single cells using readily available ImageJ protocols. This protocol is a summary of the steps and ImageJ plugins recommended to convert fluorescence and bright-field photomicrographs into representative binary and skeletonized images and to analyze them using software plugins AnalyzeSkeleton (2D/3D) and FracLac for morphology data collection. The outputs of these plugins summarize cell morphology in terms of process endpoints, junctions, and length as well as complexity, cell shape, and size descriptors. The skeleton analysis protocol described herein is well suited for a regional analysis of multiple microglia within an entire photomicrograph or region of interest (ROI) whereas FracLac provides a complementary individual cell analysis. Combined, the protocol provides an objective, sensitive, and comprehensive assessment tool that can be used to stratify between diverse microglia morphologies present in the healthy and injured brain.

The microglia morphology analysis protocols described herein summarize steps helpful in processing fluorescent and DAB photomicrographs for morphometric analysis. These steps are visually summarized in Figure 2 and Figure 3. The goal of these steps is to create a representative binary and skeletonized image that appropriately models the original photomicrograph such that the accumulated data are valid. After protocol application, the AnalyzeSkeleton plugin results in a tagged skeleton image from which the number of endpoints and branch (i.e., process) lengthcan be summarized from resulting output files. Endpoints and process length data are then used to estimate the extent of microglia ramification in the photomicrograph or ROI. Figure 4 summarizes the resulting data (endpoints/cell and process length/cell) collected with and without the protocol application. While similar trends exist, the data summarized in Figure 4F are less variable than those in Figure 4E. In addition, these data illustrate increased sensitivity to detect differences between groups when the protocol is applied. Lastly, care must be taken concerning inter-user variability in the application of the protocol. Such differences are summarized by Figure 5 where the same data set was analyzed by two independent users applying an identical protocol as summarized above.
Additional morphology data are collected from single cells isolated from the binary images created during the protocol application. The protocol steps to analyze microglia morphology prior to and using the FracLac plugin are summarized in Figure 6. We illustrate this analysis in both uninjured (Figure 6A) and injured (Figure 6B) tissue. Representative images of binary, outlined, convex hull/encapsulating circle, and box counting examples for each cell analyzed with and without the protocol application are shown in Figure 6C-F. These images help to illustrate the origins of differences in morphology data which are summarized in Figure 6G.
<img alt="Neuron morphology diagrams and overlay; circular soma, single origin, fluorescence labeling." class="xfigimg" src="/files/ftp_upload/57648/57648fig1.jpg" data-altupdatedat="1747911167000" data-alt="Figure 1"> 
Figure 1. Illustrations of skeletonized microglia with a circular soma (suboptimal) versus a single origin soma (optimal) and the corresponding overlay between the skeletonized cell and the original photomicrograph. Scale bar = 10 µm. <a href="/files/ftp_upload/57648/57648fig1large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Image processing workflow; grayscale to skeletonize; microscopy image analysis steps." class="xfigimg" src="/files/ftp_upload/57648/57648fig2.jpg" data-altupdatedat="1747911167000" data-alt="Figure 2"> 
Figure 2. Protocol application to fluorescent photomicrographs. Illustrations of the Skeleton Analysis protocol applied to a fluorescent photomicrograph with a single cell cropped to show details. Scale bar = 10 µm. <a href="/files/ftp_upload/57648/57648fig2large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Image processing workflow diagram; steps: filtering, grayscale, contrast adjustment, despekling." class="xfigimg" src="/files/ftp_upload/57648/57648fig3.jpg" data-altupdatedat="1747911167000" data-alt="Figure 3"> 
Figure 3. Protocol application to bright-field DAB photomicrographs. Illustrations of the Skeleton Analysis protocol applied to a bright-field DAB photomicrograph with a single cell cropped to show details. Scale bar = 10 µm. <a href="/files/ftp_upload/57648/57648fig3large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Neuronal growth comparison; fluorescence microscopy images, bar graphs; protocol impact analysis." class="xfigimg" src="/files/ftp_upload/57648/57648fig4.jpg" data-altupdatedat="1747911167000" data-alt="Figure 4"> 
Figure 4. Data analysis with and without the protocol application. (A) An example photomicrograph of fluorescent IHC and cropped cell corresponding to the yellow box in (B). Example binary and skeletonized images with (C) and without (D) the protocol applied as described. Summary data of microglia endpoints/cell and process length/cell in uninjured (white) and injured (grey) cortical tissue with (E) and without (F) the protocol applied. Statistical analysis using student's t-test and n = 3, ** denotes p &lt; 0.01. Scale bar = 10 µm. <a href="/files/ftp_upload/57648/57648fig4large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Microglia analysis, original image to binary and skeleton; bar graphs of endpoint and process length data." class="xfigimg" src="/files/ftp_upload/57648/57648fig5.jpg" data-altupdatedat="1747911167000" data-alt="Figure 5"> 
Figure 5. User differences with protocol application. An example of an original image and protocol conversion to binary and skeleton images by User 1 and User 2. Differences between the two images are highlighted with matching colored circles. Summary graphs of microglia endpoints/cell and process length/cell data in uninjured and injured brain regions by User 1 and User 2. Statistical analysis with ANOVA and sample size is n = 3; *p &lt; 0.05, **p &lt; 0.01, ***p &lt; 0.001. Scale bar = 10 µm. <a href="/files/ftp_upload/57648/57648fig5large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Neural morphology analysis, microscopy images comparing uninjured and injured states with protocol variations." class="xfigimg" src="/files/ftp_upload/57648/57648fig6.jpg" data-altupdatedat="1747911167000" data-alt="Figure 6"> 
Figure 6. Fractal analysis with and without the protocol application. Example of cropped photomicrographs of microglia in the uninjured (A) and injured (B) cortex with corresponding binary (C), and outline (D) images that result with and without the protocol applied. The associated convex hull (blue) and enclosing circle (pink) for corresponding outline shapes (E) are used to calculate shape density, span ratio, and circularity (G). The box counting method is illustrated in (F), and used for fractal dimension (DB) and lacunarity (λ) calculations (G). Scale bar = 10 µm. <a href="/files/ftp_upload/57648/57648fig6large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>

Watch this Scientific Journal Video about Quantifying Microglia Morphology from Photomicrographs of Immunohistochemistry Prepared Tissue Using ImageJ at JoVE.com

Quantifying Microglia Morphology from Photomicrographs of Immunohistochemistry Prepared Tissue Using ImageJ

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.

Microglia are brain phagocytes that participate in brain homeostasis and continuously survey their environment for dysfunction, injury, and disease. As ...

quantifying-microglia-morphology-from-photomicrographs

Research

JoVE Journal

Neuroscience

66.7K Views.  University of Arizona. 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.

Video: Quantifying Microglia Morphology from Photomicrographs of Immunohistochemistry Prepared Tissue Using ImageJ

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

Phagocytosis is a process in which a cell engulfs material (entire cell, parts of a cell, debris, etc.) in its surrounding extracellular environment and subsequently digests this material, commonly through lysosomal degradation. Microglia are the resident immune cells of the central nervous system (CNS) whose phagocytic function has been described in a broad range of conditions from neurodegenerative disease (e.g., beta-amyloid clearance in Alzheimer&#8217;s disease) to development of the healthy brain (e.g., synaptic pruning)1-6. The following protocol is an engulfment assay developed to visualize and quantify microglia-mediated engulfment of presynaptic inputs in the developing mouse retinogeniculate system7. While this assay was used to assess microglia function in this particular context, a similar approach may be used to assess other phagocytes throughout the brain (e.g., astrocytes) and the rest of the body (e.g., peripheral macrophages) as well as other contexts in which synaptic remodeling occurs (e.g. ,brain injury/disease).

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.

Phagocytosis is a process in which a cell engulfs material (entire cell, parts of a cell, debris, etc.) in its surrounding extracellular environment and ...

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

Microglia, which are CNS-resident neuroimmune cells, transform their morphology and size in response to CNS damage, switching to an activated state with distinct functions and gene expression profiles. The roles of microglial activation in health, injury and disease remain incompletely understood due to their dynamic and complex regulation in response to changes in their microenvironment. Thus, it is critical to non-invasively monitor and analyze changes in microglial activation over time in the intact organism. In vivo studies of microglial activation have been delayed by technical limitations to tracking microglial behavior without altering the CNS environment. This has been particularly challenging during chronic neurodegeneration, where long-term changes must be tracked. The retina, a CNS organ amenable to non-invasive live imaging, offers a powerful system to visualize and characterize the dynamics of microglia activation during chronic disorders.
This protocol outlines methods for long-term, in vivo imaging of retinal microglia, using confocal ophthalmoscopy (cSLO) and CX3CR1GFP/+ reporter mice, to visualize microglia with cellular resolution. Also, we describe methods to quantify monthly changes in cell activation and density in large cell subsets (200-300 cells per retina). We confirm the use of somal area as a useful metric for live tracking of microglial activation in the retina by applying automated threshold-based morphometric analysis of in vivo images. We use these live image acquisition and analyses strategies to monitor the dynamic changes in microglial activation and microgliosis during early stages of retinal neurodegeneration in a mouse model of chronic glaucoma. This approach should be useful to investigate the contributions of microglia to neuronal and axonal decline in chronic CNS disorders that affect the retina and optic nerve.

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

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.

Microglia, which are CNS-resident neuroimmune cells, transform their morphology and size in response to CNS damage, switching to an activated state with ...

Medicine

Two Algorithms for High-throughput and Multi-parametric Quantification of Drosophila Neuromuscular Junction Morphology

Synaptic morphology is tightly related to synaptic efficacy, and in many cases morphological synapse defects ultimately lead to synaptic malfunction. The Drosophila larval neuromuscular junction (NMJ), a well-established model for glutamatergic synapses, has been extensively studied for decades. Identification of mutations causing NMJ morphological defects revealed a repertoire of genes that regulate synapse development and function. Many of these were identified in large-scale studies that focused on qualitative approaches to detect morphological abnormalities of the Drosophila NMJ. A drawback of qualitative analyses is that many subtle players contributing to NMJ morphology likely remain unnoticed. Whereas quantitative analyses are required to detect the subtler morphological differences, such analyses are not yet commonly performed because they are laborious. This protocol describes in detail two image analysis algorithms "Drosophila NMJ Morphometrics" and "Drosophila NMJ Bouton Morphometrics", available as Fiji-compatible macros, for quantitative, accurate and objective morphometric analysis of the Drosophila NMJ. This methodology is developed to analyze NMJ terminals immunolabeled with the commonly used markers Dlg-1 and Brp. Additionally, its wider application to other markers such as Hrp, Csp and Syt is presented in this protocol. The macros are able to assess nine morphological NMJ features: NMJ area, NMJ perimeter, number of boutons, NMJ length, NMJ longest branch length, number of islands, number of branches, number of branching points and number of active zones in the NMJ terminal.

Two Algorithms for High-throughput and Multi-parametric Quantification of Drosophila Neuromuscular Junction Morphology

Two image analysis algorithms, "Drosophila NMJ Morphometrics" and "Drosophila NMJ Bouton Morphometrics" were created, to automatically quantify nine morphological features of the Drosophila neuromuscular junction (NMJ).

Synaptic morphology is tightly related to synaptic efficacy, and in many cases morphological synapse defects ultimately lead to synaptic malfunction. The ...

Area-based Image Analysis Algorithm for Quantification of Macrophage-fibroblast Cocultures

Quantification of cells is necessary for a wide range of biological and biochemical studies. Conventional image analysis of cells typically employs either fluorescence detection approaches, such as immunofluorescent staining or transfection with fluorescent proteins or edge detection techniques, which are often error-prone due to noise and other non-idealities in the image background. 
We designed a new algorithm that could accurately count and distinguish macrophages and fibroblasts, cells of different phenotypes that often colocalize during tissue regeneration. MATLAB was used to implement the algorithm, which differentiated distinct cell types based on differences in height from the background. A primary algorithm was developed using an area-based method to account for variations in cell size/structure and high-density seeding conditions. 
Non-idealities in cell structures were accounted for with a secondary, iterative algorithm utilizing internal parameters such as cell coverage computed using experimental data for a given cell type. Finally, an analysis of coculture environments was carried out using an isolation algorithm in which various cell types were selectively excluded based on the evaluation of relative height differences within the image. This approach was found to accurately count cells within a 5% error margin for monocultured cells and within a 10% error margin for cocultured cells.

We present a method, which utilizes a generalizable area-based image analysis approach to identify cell counts. Analysis of different cell populations exploited the significant cell height and structure differences between distinct cell types within an adaptive algorithm.

Quantification of cells is necessary for a wide range of biological and biochemical studies. Conventional image analysis of cells typically employs either ...

Bioengineering

Analyzing the Size, Shape, and Directionality of Networks of Coupled Astrocytes

It has become increasingly clear that astrocytes modulate neuronal function not only at the synaptic and single-cell levels, but also at the network level. Astrocytes are strongly connected to each other through gap junctions and coupling through these junctions is dynamic and highly regulated. An emerging concept is that astrocytic functions are specialized and adapted to the functions of the neuronal circuit with which they are associated. Therefore, methods to measure various parameters of astrocytic networks are needed to better describe the rules governing their communication and coupling and to further understand their functions.
Here, using the image analysis software (e.g., ImageJFIJI), we describe a method to analyze confocal images of astrocytic networks revealed by dye-coupling. These methods allow for 1) an automated and unbiased detection of labeled cells, 2) calculation of the size of the network, 3) computation of the preferential orientation of dye spread within the network, and 4) repositioning of the network within the area of interest.
This analysis can be used to characterize astrocytic networks of a particular area, compare networks of different areas associated to different functions, or compare networks obtained under different conditions that have different effects on coupling. These observations may lead to important functional considerations. For instance, we analyze the astrocytic networks of a trigeminal nucleus, where we have previously shown that astrocytic coupling is essential for the ability of neurons to switch their firing patterns from tonic to rhythmic bursting1. By measuring the size, confinement, and preferential orientation of astrocytic networks in this nucleus, we can build hypotheses about functional domains that they circumscribe. Several studies suggest that several other brain areas, including the barrel cortex, lateral superior olive, olfactory glomeruli, and sensory nuclei in the thalamus and visual cortex, to name a few, may benefit from a similar analysis.

Here we present a protocol to assess the organization of astrocytic networks. The described method minimizes bias to provide descriptive measures of these networks such as cell count, size, area, and position within a nucleus. Anisotropy is assessed with a vectorial analysis.

It has become increasingly clear that astrocytes modulate neuronal function not only at the synaptic and single-cell levels, but also at the network ...

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

This is a protocol for the dual visualization of microglia and infiltrating macrophages in mouse brain tissue. TMEM119 (which labels microglia selectively), when combined with IBA1 (which provides an exceptional visualization of their morphology), allows investigation of changes in density, distribution, and morphology. Quantifying these parameters is important in providing insights into the roles exerted by microglia, the resident macrophages of the brain. Under normal physiological conditions, microglia are regularly distributed in a mosaic-like pattern and present a small soma with ramified processes. Nevertheless, as a response to environmental factors (i.e., trauma, infection, disease, or injury), microglial density, distribution, and morphology are altered in various manners, depending on the insult. Additionally, the described double-staining method allows visualization of infiltrating macrophages in the brain based on their expression of IBA1 and without colocalization with TMEM119. This approach thus allows discrimination between microglia and infiltrating macrophages, which is required to provide functional insights into their distinct involvement in brain homeostasis across various contexts of health and disease. This protocol integrates the latest findings in neuroimmunology that pertain to the identification of selective markers. It also serves as a useful tool for both experienced neuroimmunologists and researchers seeking to integrate neuroimmunology into projects.

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.

This is a protocol for the dual visualization of microglia and infiltrating macrophages in mouse brain tissue. TMEM119 (which labels microglia ...

Immunology and Infection

Two-photon Imaging of Microglial Processes' Attraction Toward ATP or Serotonin in Acute Brain Slices

Microglial cells are resident innate immune cells of the brain that constantly scan their environment with their long processes and, upon disruption of homeostasis, undergo rapid morphological changes. For example, a laser lesion induces in a few minutes an oriented growth of microglial processes, also called &#34;directional motility&#34;, toward the site of injury. A similar effect can be obtained by delivering locally ATP or serotonin (5-hydroxytryptamine [5-HT]). In this article, we describe a protocol to induce a directional growth of microglial processes toward a local application of ATP or 5-HT in acute brain slices of young and adult mice and to image this attraction over time by multiphoton microscopy. A simple method of quantification with free and open-source image analysis software is proposed. A challenge that still characterizes acute brain slices is the limited time, decreasing with age, during which the cells remain in a physiological state. This protocol, thus, highlights some technical improvements (medium, air-liquid interface chamber, imaging chamber with a double perfusion) aimed at optimizing the viability of microglial cells over several hours, especially in slices from adult mice.

Microglia, the resident immune cells of the brain, respond quickly with morphological changes to modifications of their environment. This protocol describes how to use two-photon microscopy to study the attraction of microglial processes toward serotonin or ATP in acute brain slices of mice.

Microglial cells are resident innate immune cells of the brain that constantly scan their environment with their long processes and, upon disruption of ...

In Vivo Chronic Two-Photon Imaging of Microglia in the Mouse Hippocampus

Microglia, the only immune cells resident in the brain, actively participate in neural circuit maintenance by modifying synapses and neuronal excitability. Recent studies have revealed the differential gene expression and functional heterogeneity of microglia in different brain regions. The unique functions of the hippocampal neural network in learning and memory may be associated with the active roles of microglia in synapse remodeling. However, inflammatory responses induced by surgical procedures have been problematic in the two-photon microscopic analysis of hippocampal microglia. Here, a method is presented that enables the chronic observation of microglia in all layers of the hippocampal CA1 through an imaging window. This method allows the analysis of morphological changes in microglial processes for more than 1 month. Long-term and high-resolution imaging of the resting microglia requires minimally invasive surgical procedures, appropriate objective lens selection, and optimized imaging techniques. The transient inflammatory response of hippocampal microglia may prevent imaging immediately after surgery, but the microglia restore their quiescent morphology within a few weeks. Furthermore, imaging neurons simultaneously with microglia allows us to analyze the interactions of multiple cell types in the hippocampus. This technique may provide essential information about microglial function in the hippocampus.

In Vivo Chronic Two-Photon Imaging of Microglia in the Mouse Hippocampus

This paper describes a method for chronic in vivo observation of the resting microglia in the mouse hippocampal CA1 using precisely controlled surgery and two-photon microscopy.

Microglia, the only immune cells resident in the brain, actively participate in neural circuit maintenance by modifying synapses and neuronal ...

Human Microglia-like Cells: Differentiation from Induced Pluripotent Stem Cells and In Vitro Live-cell Phagocytosis Assay using Human Synaptosomes

Microglia are the resident immune cells of myeloid origin that maintain homeostasis in the brain microenvironment and have become a key player in multiple neurological diseases. Studying human microglia in health and disease represents a challenge due to the extremely limited supply of human cells. Induced pluripotent stem cells (iPSCs) derived from human individuals can be used to circumvent this barrier. Here, it is demonstrated how to differentiate human iPSCs into microglia-like cells (iMGs) for in vitro experimentation. These iMGs exhibit the expected and physiological properties of microglia, including microglia-like morphology, expression of proper markers, and active phagocytosis. Additionally, documentation for isolating and labeling synaptosome substrates derived from human iPSC-derived lower motor neurons (i3LMNs) is provided. A live-cell, longitudinal imaging assay is used to monitor engulfment of human synaptosomes labeled with a pH-sensitive dye, allowing for investigations of iMG's phagocytic capacity. The protocols described herein are broadly applicable to different fields that are investigating human microglia biology and the contribution of microglia to disease.

Human Microglia-like Cells: Differentiation from Induced Pluripotent Stem Cells and In Vitro Live-cell Phagocytosis Assay using Human Synaptosomes

This protocol describes the differentiation process of human induced pluripotent stem cells (iPSCs) into microglia-like cells for in vitro experimentation. We also include a detailed procedure for generating human synaptosomes from iPSC-derived lower motor neurons that can be used as a substrate for in vitro phagocytosis assays using live-cell imaging systems.

Microglia are the resident immune cells of myeloid origin that maintain homeostasis in the brain microenvironment and have become a key player in multiple ...

Flow Cytometry-Based Assays for Microglial Engulfment of Synaptic Materials

Quantification of Microglial Engulfment of Synaptic Material Using Flow Cytometry

Microglia play a pivotal role in synaptic refinement in the brain. Analysis of microglial engulfment of synapses is essential for comprehending this process; however, currently available methods for identifying microglial engulfment of synapses, such as immunohistochemistry (IHC) and imaging, are laborious and time-intensive. To address this challenge, herein we present in vitro and in vivo* assays that allow fast and high-throughput quantification of microglial engulfment of synapses using flow cytometry.
In the in vivo* approach, we performed intracellular vGLUT1 staining following fresh cell isolation from adult mouse brains to quantify engulfment of vGLUT1+ synapses by microglia. In the in vitro synaptosome engulfment assay, we used freshly isolated cells from the adult mouse brain to quantify the engulfment of pHrodo&#160;Red-labeled synaptosomes by microglia. These protocols together provide a time-efficient approach to quantifying microglial engulfment of synapses and represent promising alternatives to labor-intensive image analysis-based methods. By streamlining the analysis, these assays can contribute to a better understanding of the role of microglia in synaptic refinement in different disease models.

Author Spotlight: Advancing Understanding Through Technological Innovations in Psychoneuroimmunology

Here we present two protocols to quantify microglial engulfment of vGLUT1-positive synapses and pHRodo&#160;Red-labeled crude synaptosomes using flow cytometry.

Microglia play a pivotal role in synaptic refinement in the brain. Analysis of microglial engulfment of synapses is essential for comprehending this ...

Quantifying Microglia Morphology from Photomicrographs of Immunohistochemistry Prepared Tissue Using ImageJ

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Chapters in this video

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

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

Two Algorithms for High-throughput and Multi-parametric Quantification of Drosophila Neuromuscular Junction Morphology

Area-based Image Analysis Algorithm for Quantification of Macrophage-fibroblast Cocultures

Analyzing the Size, Shape, and Directionality of Networks of Coupled Astrocytes

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

Two-photon Imaging of Microglial Processes' Attraction Toward ATP or Serotonin in Acute Brain Slices

In Vivo Chronic Two-Photon Imaging of Microglia in the Mouse Hippocampus

Human Microglia-like Cells: Differentiation from Induced Pluripotent Stem Cells and In Vitro Live-cell Phagocytosis Assay using Human Synaptosomes

Quantification of Microglial Engulfment of Synaptic Material Using Flow Cytometry