In Vivo Vascular Injury Readouts in Mouse Retina to Promote Reproducibility

Summary

Here, we present three data analysis protocols for fluorescein angiography (FA) and optical coherence tomography (OCT) images in the study of Retinal Vein Occlusion (RVO).

Abstract

Advancements in ophthalmic imaging tools offer an unprecedented level of access to researchers working with animal models of neurovascular injury. To properly leverage this greater translatability, there is a need to devise reproducible methods of drawing quantitative data from these images. Optical coherence tomography (OCT) imaging can resolve retinal histology at micrometer resolution and reveal functional differences in vascular blood flow. Here, we delineate noninvasive vascular readouts that we use to characterize pathological damage post vascular insult in an optimized mouse model of retinal vein occlusion (RVO). These readouts include live imaging analysis of retinal morphology, disorganization of retinal inner layers (DRIL) measure of capillary ischemia, and fluorescein angiography measures of retinal edema and vascular density. These techniques correspond directly to those used to examine patients with retinal disease in the clinic. Standardizing these methods enables direct and reproducible comparison of animal models with clinical phenotypes of ophthalmic disease, increasing the translational power of vascular injury models.

Introduction

Neurovascular disease is a major healthcare problem responsible for ischemic strokes, a leading cause of mortality and morbidity, and retinal vascular diseases that lead to vision loss^1,2. To model neurovascular disease, we employ a mouse model of retinal vein occlusion (RVO). This model is noninvasive and utilizes similar in vivo imaging techniques to those used to examine people with retinal vascular disease in a clinical setting. The use of this model thus increases the translational potential of studies utilizing this model. As with all mouse models, it is critical to maximize reproducibility of the model.

Retinal vascular diseases are a major cause of vision loss in people under the age of 70. RVO is the second most common retinal vascular disease after diabetic retinopathy³. Clinical features characteristic of RVO include ischemic injury, retinal edema, and vision loss as a consequence of neuronal loss^3,4. Mouse models of RVO using laser photocoagulation of major vessels have been developed and refined to replicate key clinical pathologies observed in human RVO^5,6,7. Advancements in ophthalmic imaging also allow for replication of noninvasive diagnostic tools used in humans, namely, fluorescein angiography (FA) and optical coherence tomography (OCT)⁶. Fluorescein Angiography allows for the observation of leakage due to the breakdown of the blood-retinal barrier (BRB) as well as blood flow dynamics in the retina, including sites of occlusion, using the injection of fluorescein, a small fluorescent dye^8,9. OCT imaging allows for the acquisition of high-resolution cross-sectional images of the retina and the study of the thickness and organization of retinal layers¹⁰. Analysis of FA images has historically been largely qualitative, which limits the potential for direct and reproducible comparison between studies. Recently, a number of methods have been developed for the quantification of layer thickness in OCT imaging, though there is currently no standardized analysis protocol and the site of OCT image acquisition varies¹¹. In order to properly leverage these tools, standardized, quantitative, and replicable data analysis methodology are needed. In this paper, we present three such vascular readouts used to evaluate pathological damage in a mouse model of RVO-fluorescein leakage, OCT layer thickness, and disorganization of retinal layers.

Protocol

This protocol follows the Association for Research in Vision and Ophthalmology (ARVO) statement for the use of animals in ophthalmic and vision research. Rodent experiments were approved and monitored by the Institutional Animal Care and Use Committee (IACUC) of Columbia University.

NOTE: Imaging was done on 2 month old C57BL/6J male mice that weighed approximately 23 g.

1. Preparation of reagents for retinal imaging

1. Preparation of injectable fluorescein solution.
   NOTE: Fluorescein is very light-sensitive. Protect from light and use it shortly after preparation.
   1. Dilute fluorescein to a concentration of 1% in sterile saline.
2. Preparation of Ketamine/Xylazine
   1. Dilute Ketamine and Xylazine in sterile saline accordingly for the following concentration: Ketamine (80-100 mg/kg) and Xylazine (5-10 mg/kg).
3. Sterile saline
   1. Prepare a 5 mL syringe with a 26 G needle with sterile saline.

2. OCT and fluorescein imaging

1. Turn the retinal imaging microscope lightbox, the OCT machine, and the heated mouse platform ON.
2. Turn the computer ON and open the imaging program.
3. Add one drop of phenylephrine and tropicamide to each eye.
4. Inject 150 µL of anesthesia (Ketamine (80-100 mg/kg) and Xylazine (5-10 mg/kg)) intraperitoneally (IP). Determine the depth of anesthesia by toe pinch and wait until the animal is unresponsive. Apply ophthalmic ointment or artificial tears to both the eyes.
5. Accommodate the mouse on the platform.
6. Adjust the height and angle of the platform until the view of the retinal fundus is clear and focused. Take a picture of the fundus.
7. Open the imaging and OCT software. In the OCT program, adjust nudge to 5.
8. Take an OCT image at 75 µm distal from the burn. Repeat for the other three quadrants of the retina.
9. Inject 100 µL of 1% fluorescein IP.
10. Switch the camera to a 488 nm filter. Increase the camera gain to 5.
11. Take a picture of the fundus at exactly 5 min after fluorescein injection.
    ​NOTE: Avoid prolonged exposure of the eye to the camera light at maximum setting, as fluorescein can exacerbate retinal photodamage. Keep light source off until the 5 min wait time has elapsed and the mouse is ready for imaging.

3. Aftercare

1. Inject 1 mL of sterile saline IP. Apply lubricant eye drops to both the eyes. Apply ophthalmic ointment or artificial tears to both the eyes.
2. Observe the mouse as it recovers from anesthesia. Return to the cage with other animals only when fully recovered, generally after around 40 min.

4. Assessment for exclusion criteria

1. Open the fundus image taken at 24 h post-procedure to assess for exclusion criteria. Exclude the eye if any of the following criteria are identified.
2. Assess whether the image has zero occlusions
   1. Evaluate the image for the number of occluded vessels.
      NOTE: A successful occlusion usually has some purple pigmentation on or around the burn, very thin or discontinuous vessel through the burn, faint or non-existent vessel appearance outside the burn area, and retinal discoloration from hypoxia. If the entire vessel can be seen through the white burn by the laser, the vessel failed to occlude. Sometimes the vessel will appear partially obstructed, but if it looks uninterrupted outside the burn, the vessel likely didn't occlude.
   2. For ambiguous cases, use FA imaging at the same time point to evaluate occlusions. In these images, an occlusion will appear as a break in the continuity of a vessel, often with a tapering of the surrounding vessel.
   3. If zero occlusions are identified, exclude the eye from analysis, as the RVO is considered ineffective.
      NOTE: Occlusions typically resolve by 48-72 h post-RVO, and the presence of occlusions should no longer be used as an exclusion criterion at these time points.
3. Assess the fundus and OCT images for excessive retinal detachment
   NOTE: Subretinal fluid accumulation is common after induction of RVO, and causes separation of neural retina from RPE. Exclusionary criteria for excessive retinal detachment are defined as follows: OCT will either be completely unviewable, or some layers will appear incredibly distorted. Image quality is poor, with a loss of resolution of outer plexiform and RPE layers. The separation between the neural retina and the choroid is greater than what the OCT field of view allows. On the fundus image, the retina tone will be nearly completely white, with some purple blotching. Part of the retina may appear distorted and out of focus. This is because it has detached and is at a different focal distance than the rest of the retina.
   1. If the assessment of the images from an eye determines peripheral or complete detachment of the retina, exclude the eye from the analysis.
4. Exclude images with evidence of corneal cataract
   NOTE: A corneal cataract appears as an opaque white dot on the mouse's cornea. Cataracts typically occur due to insufficient lubrication of the eyes while the animal is anesthetized and can be largely avoided by taking care to apply eye ointment generously. Cataracts can generally be identified before imaging by inspecting the animal. Mice that have developed cataracts should be excluded from the dataset without needing to undergo the imaging process. In imaging, cataracts will obscure the retina from the camera, and the OCT will appear warped.
5. Assess the image for excessive hemorrhage
   ​NOTE: Excessive hemorrhage can be identified as amounts of red fluid in the image, usually obscuring retinal background, vessel, and burn. These areas of red fluid will be a brighter, opaquer red than the purple splotches that are normal in successful RVO. Hemorrhages show up at the ganglion cell layer on OCT imaging and interfere with the ability to visualize other retinal layers beneath the hemorrhage.
   1. If the image is determined to have an excessive hemorrhage, exclude the eye from the analysis.

5. Fluorescein image processing

1. Open the fluorescein image in the image processing software.
2. Duplicate the image
3. Using a selection tool, carefully trace the major vessels.
   1. The major vessels are the thicker veins and arteries radiating out from the optic disc. Ignore any vessels branching out from these vessels.
   2. If leakage prevents the outline of the vessel from being seen near the occlusion site, trace through the leakage in the approximate location of the vessel (maintain thickness, connect the last visible point to the next visible point).
4. In the first image, delete the selection, leaving only the background. Save this masked image.
5. Move the selection to the second image, invert the selection and delete, isolating the vessels. Save this masked image.
6. Open the two images in ImageJ. Open the background image and measure the integrated density.
7. Open the vessel's image, select the outline of the vessels, and then measure the mean intensity.
8. Divide the integrated density of the background by the mean intensity of the vessels, generating the leakage ratio for the eye.
9. Record this leakage ratio for each eye in an experimental cohort.
10. To further control for background, normalize experimental eyes to the mean leakage ratio of uninjured control eyes.
    ​NOTE: In order to create a standardized quantification of fluorescein leakage in the FA image, this calculation uses a ratio of the background density (where the leakage will be present) with the brightness of the major vessels to create results that control for the variation in brightness from image to image and can be reliably quantified. Eyes that are undamaged have no leakage and should theoretically have ratios of zero. The ratios calculated from these undamaged control eyes, therefore, represent background noise, and this value is used to further normalize experimental values.

6. Retinal layer thickness

1. Open the OCT image in the image processing software.
2. Trace the borders of the ganglion cell layer, inner plexiform layer, inner nuclear layer, outer plexiform layer, photoreceptor layer, and RPE layer. Measure the mean thickness of each layer.
3. Repeat for OCT images from the other three quadrants of the retina. Average the mean layer thicknesses across the four quadrants to obtain the mean thickness of each retinal layer for the eye.
4. Repeat for each eye in the experimental cohort.

7. Disorganization of retinal inner layers (DRIL)

1. Open the OCT image in ImageJ.
2. Using the line tool, measure the distance where the upper border of the outer plexiform layer is indistinct.
   NOTE: It is important to differentiate between DRIL and areas of poor layer visibility caused by imaging artifacts. Poor OCT image quality may invalidate an eye for DRIL analysis if sufficient image resolution is not possible. Images with DRIL will typically have other regions or retinal layers that are clearly resolved and organized, which can be a good indicator of sufficient image quality.
   1. Measure horizontally from the latitude where the disorganization begins to the latitude where the upper border of the outer plexiform layer becomes visible again, if at all. Even if the outer plexiform layer shifts upward or downward vertically, measure perfectly horizontally.
   2. There may be multiple areas of disorganization separated by areas with no disorganization. Measure these individually and calculate the sum of the distances.
3. Divide the length of disorganization by the total length of the retina visible in each OCT image to obtain the ratio of disorganization for the image.
4. Repeat the measurement and calculation for OCT images from the other three quadrants of the retina.
5. Take the mean of the ratios of disorganization from the four OCT images. This number represents the average disorganization for the whole retina. Repeat for each eye in the experimental cohort.

Representative Results

These analysis methods allow for the quantification of retinal pathology captured by FA and OCT imaging. The experiments from which the representative data is extracted used C57BL/6J male mice who either served as uninjured controls or underwent the RVO procedure and received either Pen1-XBir3 treatment eyedrops or Pen1-Saline vehicle eyedrops. The RVO injury model involved the laser irradiation (532 nm) of the major veins in each eye of an anesthetized mouse following a tail-vein injection of rose bengal, a photoactivator dye¹². Three laser pulses were delivered at an average distance of 375 µm from the optic nerve center to induce photocoagulation and occlude the vessels¹². Effective use of the RVO procedure is demonstrated in Avrutsky et al.¹², and further details on RVO method optimization are detailed in Colón Ortiz et al.¹³. Figure 1A shows examples of FA and OCT images from both the groups. Due to the variable nature of occlusion formation and stabilization through the photocoagulation process, differing degrees of damage can be observed. In some retinas, the damage induced by the RVO procedure introduces ophthalmic pathologies that render the retinal images unsuitable for analysis. After the acquisition, images should first be evaluated for exclusion criteria to ensure optimal analysis and reliable results. These exclusion criteria, delineated in Figure 1B, include retinal detachment, hemorrhage, and cataracts. As can be observed in the example fundus and OCT images, these pathologies prevent clear OCT imaging, making the retinas unsuitable for data analysis. Additionally, it is possible that some retinas will contain no stable occlusions; these images do not accurately model ischemic-hypoxic damage and should be excluded from the analysis.

The breakdown of the blood-retinal barrier contributes to the pathogenesis of RVO^14,15. Evaluating the amount of leakage from vessels is a useful indicator of injury-induced vessel permeability. FA imaging allows for visualization of this leakage, but a number of factors, such as differences in the rate of circulation, affect the raw intensity of FA images and make consistent quantification^16,17. Our method controls for this variability by normalizing the intensity observed in the retina to the mean intensity of the major vasculature. This provides a ratio of leakage for each retinal image that can be compared to others and analyzed. Figure 2A demonstrates the masked images used for this calculation, separating the major vasculature from the other areas of the retina. The ability to quantify fluorescein allows for the comparison of injury severity and treatment efficacy, as well as the study of changes in leakage over the injury time course (Figure 2B), which may be too subtle an effect to demonstrate with qualitative reporting alone.

OCT imaging allows for the analysis of the impact of RVO on individual retinal layers and overall retinal thickness. Figure 3A shows a delineation of the layers of the retina in an OCT image. Tracing the boundaries of each layer (Figure 3B) allows for several avenues of analysis. The quantification of thickness for each retinal layer proves useful, as the initial edematous response has a more profound effect on the inner retinal layers. Traces also allow for the study of total retina thickness and segregated analysis of the inner versus outer retinal layers. Figure 3C provides an analysis of a time course of RVO damage, where the initial inflammatory swelling of retinal layers and the eventual degenerative thinning can be observed. Plotting the thickness of each layer over time reveals different dynamics for the inner plexiform and inner nuclear layers, where the inner nuclear layer experiences a much greater response to the initial injury, but the inner plexiform layer demonstrates more severe thinning after the initial edema has been stabilized and returns to baseline (Figure 3D). This grants a more precise understanding of the drivers of response at different time points. We also tested the effectiveness of a caspase inhibitor in mitigating swelling and protecting against eventual degeneration, with analysis revealing differing effects in individual layers.

The disorganization of inner retinal layers (DRIL) is another OCT feature used as a diagnostic measure of ischemia in diabetic retinopathy, as well as a predictive measure of visual acuity in RVO^18,19. In OCT imaging, DRIL manifests as a disappearance of the upper boundary of the outer-plexiform layer¹², blending the outer-plexiform and inner nuclear layers together (Figure 4A). Figure 4B shows two examples of OCT images with highlighted areas of DRIL. We express DRIL as a proportion of total retinal length, averaging across four OCT cross-sections. This measure allows us to quantitatively compare experimental groups; Figure 4C presents an example analysis, where the retinal disorganization of two experimental groups were compared to investigate the efficacy of an inhibitor in mitigating retinal damage in RVO.

Figure 1: Images obtained from fluorescein angiography (FA) and optical coherence tomography (OCT) imaging. (A) Examples of FA and OCT images from retinas 24 h post-RVO and uninjured controls. (B) Fundus and OCT imaging of the different exclusion criteria: excessive retinal detachment, hemorrhage, corneal cataract, and no occlusions. Distance of OCT acquisition is indicated by the green guideline. Please click here to view a larger version of this figure.

Figure 2: Quantification of fluorescein leakage. (A) Separation of the FA image into the vessels and background for analysis (B) Fluorescein leakage quantification from eyes of C57BL/6J retinal vein occluded (RVO) mice receiving either 10 mg of Pen1-XBir3 inhibitor eyedrops (N = 17) or Pen1-Saline vehicle eyedrops (N = 13) at 24 h and 48 h post-procedure. Intensity reading of the background image is normalized to the mean intensity reading from the vessel's image. The mean of the intensity reading for RVO mice is further normalized to uninjured controls. Error bars show mean with SEM. Please click here to view a larger version of this figure.

Figure 3: Quantification of retinal layer thickness in OCT images. (A) Uninjured retina with the individual retinal layers labeled: Ganglion Cell Layer, Inner Plexiform Layer, Inner Nuclear Layer, Outer Plexiform Layer, Photoreceptor Layer, RPE, and Choroid. (B) Example of layer traces of OCT images taken from uninjured control and 24 h post-RVO C57/BL6 mice. (C) Quantification of change in total retinal thickness and intraretinal thickness observed in OCT imaging of C57BL/6J mice retinas at 4 h, 24 h, 48 h, 72 h, and 8 days post-RVO. (D) Quantification of thickness change in inner plexiform and inner nuclear layers of C57BL/6J mice retinas at 24 h, 48 h, and 8 days post-RVO for C57BL/6J mice receiving either 10 mg of Pen1-XBir3 inhibitor eyedrops (N = 14) or Pen1-Saline vehicle eyedrops (N = 15) immediately post RVO procedure and 24-h post-RVO. Error bars show mean with SEM. Please click here to view a larger version of this figure.

Figure 4: Quantification of the disorganization of the inner retinal layers (DRIL) observed in OCT images post-RVO. In OCT images, DRIL is indicated by the loss of a clear delineation between the inner nuclear and outer plexiform layers. (A) Examples of sections of the retina with and without DRIL in OCT imaging. (B) Areas of DRIL in OCT imaging of two regions in a C57BL/6J mouse 24 h post-RVO, indicated by white lines. DRIL is measured horizontally across the image instead of following the shape of the retina. (C) Quantification of the proportion of the retinal length where DRIL was observed at 24 h and 48 h post-RVO for the eyes of C57BL/6J mice receiving either 2.5 mg of Pen1-XBir3 inhibitor eyedrops (N = 19) or Pen1-Saline vehicle eyedrops (N = 21) after the RVO procedure. Error bars show mean with SEM. Please click here to view a larger version of this figure.

Discussion

Noninvasive rodent retinal imaging presents an avenue to study pathology and develop interventions. Previous studies have developed and optimized a mouse model of RVO, limiting variability and allowing for reliable translation of common clinical pathologies in the murine retina^5,7,13. Developments in ophthalmic imaging technology further allow for the use of clinical in vivo imaging techniques such as FA and OCT in experimental animals, granting the ability to compare mouse models with profiles of human disease^6,12,15. However, to maximize the information that can be extracted from these images and the overall translational potential of the model, there is a need for standardized, reproducible, and rigorous quantitative methods for analyzing images. Here we present analysis methods that allow for quantitative representations of damage severity, allowing for more precise and reliable comparisons between mice and across experimental groups. These analyses include leakage quantification in FA images, quantification of mean layer thickness, and areas of DRIL in OCT images.

A critical factor in successful analysis lies in the quality of the acquired images. Poorly resolved OCT images can lead to difficulty tracing individual layers and an inability to distinguish inner retinal disorganization from poor image quality. When imaging, it is important to take care in the positioning of the mouse on the platform, ensuring that the fundus image is in focus, the optic nerve is relatively centered, and the retinal cross-section is horizontal across the image. Consistent lubrication of the eyes while the animal is anesthetized is also important, especially when the same animal is imaged multiple days. Insufficient lubrication may result in corneal cataracts, which will obscure the retina and render it unsuitable for imaging. Various retinal pathologies may occur in RVO imaging, rendering images unsuitable for analysis. These include excessive retinal detachment and excessive hemorrhage, which, along with greatly compromising the quality of imaging, also represent a degree of damage that is too severe to use as a model of RVO. It is additionally possible for all occluded vessels to fully reperfuse shortly after injury, which will not accurately model RVO damage and should be used as an exclusion criterion. However, it is important to note that successful occlusions will naturally resolve by 48-72 h post injury, and the presence of occlusions as an exclusion criterion is best used at or before 24 h post-procedure. Colón Ortiz et al.¹³ detail best practices for limiting variability and calibrating injury in an optimized model for RVO procedure. The identification and judgment of exclusion criteria is also a critical step to image analysis. As this is largely up to the discretion of the evaluator, it is important that evaluators are blinded to treatment groups and practice consistency in the judgment of pathology severity. Some limitations in the application of these methods exist, particularly in the practice of imaging the same mouse at multiple time points. There is a limit to the frequency at which a mouse can be anesthetized for imaging, necessitating the testing and adjustment of time points to determine optimal time course. Our studies employ imaging time points at 4 h, 24 h, 48 h, and 8 days, which we have found capture stages of initial injury, acute inflammatory response, and longer-term injury¹². Additionally, certain mouse strains are more prone to the development of corneal cataracts, which include various diabetic mouse models, which may lead to a large number of exclusions or incomplete time courses^20,21. Studies utilizing such mouse lines may need to tailor experimental group size or imaging time points depending on the sensitivity of the cornea.

Fluorescein angiography imaging has largely been used qualitatively to observe and grade retinal pathologies such as leakage, as well as patterns of altered blood flow RVO⁶. Recently, there have been efforts to develop a quantitative analysis of FA in animal models, such as calculation of vascular area and tortuosity¹⁶ and linear regression analysis of image intensity temporality¹⁷. Segmentation of the major vessels from the fundus background has previously been used, but in a pixel analysis of fill and decay dynamics, testifying to the variability in image intensity in different mice¹⁷. Additionally, the potential for bias was noted in the interpretation of fluorescein pooling¹⁷. The quantitative method discussed here targets the leakage of fluorescein from the major retinal vasculature, indicative of the breakdown of the BRB, which has been demonstrated to play a role in RVO injury^11,12,14. An alternative analysis of leakage quantifies dye leakage on retinal flat mounts²². However, invasive post-mortem analyses are less suitable for studies of the timeline of RVO injury within a single mouse, where the leakage is studied at multiple timepoints. Analyses of fluorescein leakage area at different stages of the retinal disease have previously been used in clinical studies and correlated with other observed disease pathologies²³. This method allows for similar leveraging of FA images to study vessel leakage in vivo, allowing for the study of leakage dynamics within the timeline of RVO injury. As the selection of leakage area relies on evaluator selection of a region, it potentially introduces a greater amount of variability via subjectivity. Further, since the studies of the RVO injury model discussed here investigate leakage throughout the retina, we have instead opted to use a masking technique for calculation. This leakage method reflects a different facet of RVO damage from those revealed by DRIL and OCT layer tracing analysis, and correlation with these measures allows for the creation of a more accurate disease profile.

We present two methods for the evaluation of OCT images. Acute inflammation and subsequent degeneration of the retinal layers is a hallmark of RVO injury^6,12. The OCT layer tracing methodology detailed here allows for the precise study of individual layers and reveals more subtle effects and differences in dynamics in different regions of the retina. This analysis technique builds upon other commonly used protocols for the quantification of retinal layer thickness in OCT imaging. This method addresses the variation across protocols in the area used to estimate layer thickness, as well as the number of measurements taken across the image¹¹. As thinning is not uniform within each retinal layer, methods using fewer point measurements are unlikely to give a complete picture of injury effects. Meta-analysis of multiple measurement strategies for retinal layer thickness reported that protocols averaging across larger areas of the OCT image showed a higher correlation with disease severity, as well as greater repeatability¹¹. By averaging across the whole image, this method captures a more accurate representation of the retinal thinning present in long-term RVO injury. Studies also differ in terms of the location where OCT images are taken-many studies center imaging on the optic nerve. By contrast, the presented method centers relative to the occlusions. A recent development in the analysis of human OCT imaging is the usage of machine learning algorithms to classify and quantify features²⁴. Such analyses could be a promising future direction for the analysis of animal retinal imaging.

Additionally, we present a translation of DRIL, a clinical measure of capillary ischemia, into a rodent model. In humans, DRIL has been found to be a predictor of visual acuity loss and retinal thickness differences and has demonstrated high diagnostic sensitivity and specificity^18,19. Quantifying the DRIL in mice by measuring the proportion of the retina that is disorganized has shown correlation with the fraction of occluded veins, ERG b wave amplitude at 7 days post-RVO, and retinal thinning at 8 days post-RVO¹². An alternative to DRIL measurement is the usage of HYPOX-4 to measure retinal hypoxia and ischemic damage. HYPOX-4 joins pimonidazole anime hydrochloride, a hypoxia marker, with a fluorescent probe to detect retinal hypoxia²⁵. Most protocols using HYPOX-4 are invasive and require retinal flat mount analysis, which may be less suitable for the building of injury timelines, though an in vivo imaging protocol using a HYPOX-4 probe has recently been piloted²⁵. DRIL analysis is also useful as a quick readout of retinal damage, as single measurements in each OCT image are more time-efficient than analyses such as retinal layer tracing. However, it should be noted that these measures are not interchangeable and reveal different retinal pathologies. Rather, they should be used in concert, where DRIL can be used as an initial readout for effect size or intervention efficacy, and layer tracing can be subsequently employed for a thorough analysis of more subtle effects in the retinal layers.

These methods are orthogonal in nature, which allows for the creation of a disease profile for each experimental subject. As the pathologies reported by each of these methods are distinct, they are not guaranteed to scale proportionally, and obtaining a more holistic picture of pathology will allow for a more rigorous investigation of the varying manifestation configurations of RVO damage. The ability to maximize the amount of information that can be extracted from the imaging of each experimental animal will reduce the number of animals necessary to draw significant conclusions, enhancing the efficiency of the experimental process. Applying these methods on recently refined RVO protocols allow for greater reproducibility and study of the translation of clinical phenotypes into animal models. Beyond the study of RVO models, the usage of these methods has applications to other models of retinal diseases that employ FA and OCT imaging. Examples of such mouse models include those for age-related macular edema (AMD)²⁶, diabetic macular edema (DME)²³, choroidal neovascularization (CNV)²⁷, experimental autoimmune uveoretinitis (EAU)²⁸, and retinopathy of prematurity (ROP)¹⁵. These methods can further be generalized to studies using FA and OCT imaging in studying models of these diseases in other species. These quantifications are also sensitive to more subtle changes in disease mechanism, making them useful in the evaluation of treatment efficacy, such as in Figure 3D and Figure 4C. Utility also extends to the use of imaging in toxicity testing in tolerability studies of drug compounds. The standardization and reproducibility of these analysis protocols can serve to improve the translational validity of animal models and expand our understanding of the pathogenesis and pathophysiology of the retinovascular disease.

Materials

Name	Company	Catalog Number	Comments
AK-Fluor 10%	Akorn	NDC: 17478-253-10	light-sensitive
Carprofen	Rimadyl	NADA #141-199	keep at 4 °C
GenTeal	Alcon	00658 06401
Image J	NIH
InSight 2D	Phoenix Technology Group		OCT analysis software
Ketamine Hydrochloride	Henry Schein	NDC: 11695-0702-1
Phenylephrine	Akorn	NDCL174478-201-15
Phoenix Micron IV	Phoenix Technology Group		Retinal imaging microscope
Phoenix Micron Meridian Module	Phoenix Technology Group		Laser photocoagulator software
Phoenix Micron Optical Coherence Tomography Module	Phoenix Technology Group		OCT imaging software
Phoenix Micron StreamPix Module	Phoenix Technology Group		Fundus imaging and acquisition targeting
Photoshop	Adobe
Refresh	Allergan	94170
Tropicamide	Akorn	NDC: 174478-102-12
Xylazine	Akorn	NDCL 59399-110-20