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).
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.
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 loss1,2. 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 loss3,4. Mouse models of RVO using laser photocoagulation of major vessels have been developed and refined to replicate key clinical pathologies observed in human RVO5,6,7. 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 dye8,9. 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.
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
2. OCT and fluorescein imaging
3. Aftercare
4. Assessment for exclusion criteria
5. Fluorescein image processing
6. Retinal layer thickness
7. Disorganization of retinal inner layers (DRIL)
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 RVO14,15. 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 quantification16,17. 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 RVO18,19. 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.
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 retina5,7,13. 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 disease6,12,15. 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 courses20,21. 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 injury11,12,14. 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 injury6,12. 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 specificity18,19. 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.
The authors have nothing to disclose.
This work was supported by the National Science Foundation Graduate Research Fellowship Program (NSF-GRFP) grant DGE – 1644869(to CKCO), the National Eye Institute (NEI) 5T32EY013933 (to AMP), the National Institute of Neurological Disorders and Stroke (RO1 NS081333, R03 NS099920 to CMT), and the Department of Defense Army/Air Force (DURIP to CMT).
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 |