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

The overall goal of the following experiment is to characterize microglial dynamics in a mouse model of chronic glaucoma by in vivo confocal imaging of the retina. This is achieved by non-invasive monthly confocal ophthalmoscopy or CSLO to view the fluorescent microglia on the retina and optic nerve head. Live image analysis using threshold based cell SOMA segmentation and morpho quantifies changes in microglial cell number and activation.

The results show that monthly CSLO imaging in live mice can be used to monitor the kinetics of microglial cell density distribution and morphological activation during the early progression of chronic neurodegeneration. The main advantage of this technique over existing methods like two photon confocal imaging of GFP positive microglia mouse brain is that CSLO imaging of the retina allows direct observation and monitoring of microglial cells throughout the progression of neurodegenerative disorders of aging in the intact organism. I first had the idea for this method after we demonstrated exvivo, that microglia showed intense activation and clustering in the central readiness of young DBA through J mice at ages that preceded detectable neurodegeneration.

In order to ask whether these early innate immune changes that preceded detectable glaucoma did her neurodegeneration, we needed to establish an in vivo approach to track microglial changes. Demonstrating the procedure for live image acquisition will be Caesar Romero, a technician for my laboratory To begin operations, start the CSLO system and enter the session information. Next, prepare for imaging securely.

Attach a clean 55 degree wide field objective lens. Place a mouse over the imaging platform two minutes after administering the anesthetic and dilate both pupils using my tatic drops. Five minutes later, confirm that the mouse is anesthetized.

Next, using minimal pressure cover each eye with contact lenses. Orient the mouse so that the right eye meets the objective lens and the orbit is parallel to the lens. The animal does not need to be restrained but maintained at a constant distance from the objective lens.

With the eye aligned with it for the next 10 to 15 minutes, the mouse will not move or blink while its eye movements are tracked by the CSLO live imaging system. Start the imaging session by collecting a fundus view of the inner retina. For this.

Select the infrared mode, which corresponds to an excitation wavelength of 820 nanometers. Then adjust the laser power to 100%and the sensitivity to between 40 and 60%Next, working at high speed, locate the eye using the joystick to orient the ophthalmoscope and prepare to collect a fundus view of the optic disc and retinal vasculature. Inspect the cornea and lens for injury or opacity.

Exclude from the study eyes with defects or injuries that may affect image acquisition. Now by bringing the objective closer to the eye, locate the optic disc area, which is the surface of the optic nerve head or ONH. Then center the image on the ONH.

Positioning the ONH correctly is key to obtaining an even focus and de mission across the image. The next step is to visualize the inner planes of the retina as well as the ONH. For a reference focal plane, use major blood vessels located on the vitreal surface of the retina, which corresponds to 59 and 60 diopters, or even deeper at 55 diopters in excavated optic disc areas.

Next, adjust the image saturation using the knob on the touch panel until a white halo of illumination spans most of the fundus around the optic disc. Then select the lower saturation that renders a uniform halo indicative of an optimal contrast for that eye. It may be necessary to realign the camera if dark areas persist.

Now, collect a high resolution fundus image of the central retina by averaging 30 frames taken in real time to improve the signal to noise ratio. This corresponds to 4.7 frames per second, normalized immediately and at the same position prepared to collect a fluorescence image of the GFP positive cells. Switch to fluorescence imaging mode in the touchscreen panel, which selects the blue laser with 488 nanometer laser excitation and a 460 to 490 nanometer barrier filter set and set the acquisition to 100%laser power and 100 to 125%sensitivity.

Now collect a single XY point bi dimensional fluorescence image of the ONH averaging 100 x scans. Next, capture a multi-point image of the retina around the ONH select composite in the control panel, and then pan with the ophthalmoscope across the nasal temporal axis. The software automatically averages newly scanned areas and stitches them in real time.

If the image quality of areas of the retina is insufficient to allow averaging the green circle that identifies an area being scanned will become red. The resulting composite image covers an area of up to 1.7 by four millimeters. Care should be taken during acquisition of the composite image.

Images at high resolution can be lost by moving the objective too quickly before completion of scan averaging and image stitching. When the anesthesia begins to wear off, the imaging session must end because the mouse starts to breathe heavily and imaging becomes impracticable. Begin with sequentially aligning the fundus images for a time series using the vasculature and optic disc as landmarks.

Using conventional image editing software line all images up until their O hs overlap. The top image will be semi-transparent while being dragged. Then rotate each image in the sequence until its major blood vessels overlap with the vasculature on the previous image and save the arrangement for future reference.

Record the corrected angle for each image and apply it to the corresponding single XY point fluorescence image. Now it is possible to identify individual GFP positive microglial cells localized to the central retina by opening the aligned TIFF files in Fluor render. To visualize all cells, fill the screen with the image and define the render view composite orthogonal and interpolated.

Then define the properties 0.58 gamma 255 saturation point 255 luminance 195 alpha at 1.00 shading. Finish the selections by choosing both the RG channels as visible. Hide B channel by double clicking it in the workspace frame to segment individual cells.

Select and threshold the red channel by increasing the low threshold until the majority of cells are masked with minimum overlap and excluding cell processes while keeping the high threshold constant. Finally, save the file with masked cells using the capture command. Open the image with masked cells in the raster graphic editor there.

Invert the gray scale for direct observation and adjust the resolution to 300 DPI or more. The RGB image is saved as a TIF file. The next step of live image analysis is of microglial, so segmentation and morpho to identify SATA for area quantification.

Use software capable of intensity measurements and object counting based on threshold. Open the TIFF file with segmented cells and select the spline rescaling for maximal resolution and the red channel as black and white for better visualization. Next, calibrate the image by defining its horizontal diameter as 1.4 to 1.7 millimeters depending on age to segment individual cells.

So MA, apply intensity thresholding on the red channel. Open the threshold intensity function under object count and account update command. Use this function to track bi dimensional parameters for each threshold of region of interest representing individual.

So next, define the intensity threshold in the intensity histogram. Select the lowest 50 to 60%of the 255 levels and refine the final threshold value of the individual cells. Visually assess the overlap between the threshold color mask in blue and the cell soma perimeter in gray using the binary toolbar controls or the object catalog.

Manually identify somal regions of interest with multiple cells. Separate these elements by separating the regions of interest. While doing this, use the toggle to directly visualize the cells now by using area restriction.

Classify microglial cells as activated. If their somata areas are larger than 50 to 60 square microns. Classify the microglia as deactivated if they have smaller somata.

Finally, verify the process and check the branching complexity in each activated microglial cell by overlaying the somal mask and the single XY point image with the contrast increased 50%The described protocol was used to visualize microglial cells across a large area of the central retina. In young DBA two J mice heterozygous expressing CX three C one GFP, the high cell resolution combined with live image, thresholding and morphometric analysis allowed for automatic segmentation of individual GFP positive cells, and so the cells are discriminated based on area DBA two J mice model chronic glaucoma and were imaged monthly to monitor the development of local micro gliosis and microglia activation at ages proceeding detectable neurodegeneration consistent with the variable progression of neurodegeneration in individual eyes. This analysis revealed changes in retinal microglia activation and micro gliosis with time.

The analysis also detected dynamic changes in the density of total and activated microglia within individual retinas. The microglia population density was not coupled to changes in GFP expressing cell density consistent with other findings. Thus, CSLO analysis could be used as an indicator of early disease progression.

While attempting this procedure, it's important to remember to carefully optimize the saturation levels of the fluorescent image of GFP plus microglia, localized the inner retina, the reproducibility of the CSLO images of the central retina, as well as the consistent cellular detail detected across the central retina depends critically on this step After its development. This live imaging protocol is being applied in our laboratory, truly sedate the potential link between early changes in retinal and optic neur head microglia, and the later progression of glaucoma in DBA to JM and in other models of chronic retinal degeneration. This live imaging strategy also may allow early detection of other chronic pathologies that also target the retina and optic nerve for degeneration such as multiple sclerosis, Alzheimer's and Parkinson's disease.