Quantifier les cellules microgliales morphologie de microphotographies d’immunohistochimie préparé des tissus à l’aide de ImageJ

The overall goal of these skeleton and fractal analysis methods is to measure microglia morphology from IHC-prepared tissue, using quantitative methods that are both high-throughput in nature and sensitive to detect small differences in cell shapes. This method can help answer key questions in the neuroinflammmatory field, by defining the nuances of microglia form and function during health and disease. The main advantage of this technique is that it quantifies microglia morphology using continuous rather than categorical variables.

The methods described here do not require proprietary software, and it may be used for screening of brain regions or a cell-by cell analysis. Demonstrating the procedure will be Kimberly Young, a technician from my laboratory. Before beginning, ensure that the AnalayzeSkeleton plugin has been downloaded to ImageJ or Fiji.

Then open a 30 micron Z stack showing Iba1 immunostained brain. If using a fluorescence photomicrograph, ensure the image is 8-bit, and convert to gray scale to best visualize all positive staining. Use the toolbar and click Image, Lookup Tables, Grays.

If using a DAB bright field photograph, first use the FFT Bandpass filter plugin with the default settings. Click Process, FFT, Bandpass Filter, and then convert to gray scale. If the image is too dim to visualize the process of the microglia, 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. Adjusting the brightness produces the most variability in data analysis, and is therefore the most critical step. Next, run an Unsharp Mask filter to further increase contrast, by clicking Process, Filters, Unsharp Mask.

Perform a despeckle step to remove salt and pepper noise generated by the Unsharp Mask. Use the toolbar and click Process, Noise, Despeckle. Convert the image to binary by selecting Image, Adjust, Threshold.

Then apply the Despeckle function to the binary image to remove any remaining single pixel noise. Then apply the Close function by clicking Process, Binary, Close, to connect dark pixels separated by a maximum of two pixels. Next, remove outliers by clicking Process, Noise, Remove Outliers.

After saving the image as a separate file for future use, skeletonize the image using the toolbar, by clicking Process, Binary, Skeletonize. Select the skeletonized image and run the AnalyzeSkeleton plugin by clicking Plugins, Skeleton, Analyze Skeleton, and checking the branch information box. Copy the data from the results and branch information outputs, and paste the data into an Excel spreadsheet.

In Excel, trim the data to remove skeleton fragments that result from IHC and image acquisition. 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, which should be consistent throughout the dataset.

Custom sort the Excel spreadsheet by clicking Sort and Filter, Custom Sort. Sort by endpoint voxels from largest to smallest, and in a new level, by Mx branch PT from largest to smallest. Remove every row that contains two endpoints with a maximum branch length of less than the cutoff value.

Sum the data in the endpoints column to calculate the total number of endpoints collected from the image. Repeat for branch information data after sorting by branch length. After all data have been trimmed and summed, divide the data from each image by the number of microglia somas in the corresponding image.

Enter the final endpoint and cell and branch length per cell data into statistical software. Ensure that the FracLac plugin has been downloaded. Then 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. In the ROI Manager window, select Update to lock the ROI onto a randomly-selected cell in the photomicrograph. Open the binary image containing the selected 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.

Convert the binary cell to an outline using the toolbar, via Process, Binary, Outline. In the toolbar, open FracLac using the toolbar, by clicking Plugins, Fractal Analysis, FracLac, and select BC for box counting. In Grid Design, set Num G to four.

Under Graphics Options check the metrics box to analyze the convex hull and bounding circle of the cell. When finished select Okay and then select the Scan button to run a box counting scan on the selected image. In the Hull and Circle Results window, copy all desired data results.

Then do the same in the Box Count Summary window. Transfer the copied data to an Excel file or statistical software. This graph shows summary data of microglia endpoints per cell and processed length per cell in uninjured and injured cortical tissue.

This is the result of the analysis with the protocol applied. In both cases, statistical analysis was performed using Students T Test, and three images were analyzed. Care must be taken concerning inter-user variability in the application of the protocol.

Such differences are summarized here, where the same dataset was analyzed by two independent users applying an identical protocol. Although the trends are similar throughout, the inter-user variability affects the significance of the data. While attempting this procedure, it's important to remember that the goal is to obtain a skeleton or outline model that is representative of the original photomicrograph.

This means that the steps may be modifiable to the specific needs of the investigator. After watching this video, you should have a good understanding of how to quickly quantify microglia morphology from photomicrographs of immunohistochemistry tissue using readily available computer software.