AxoNet: A deep learning-based tool to count retinal ganglion cell axons

Mathematical definition and rules of the splitting/merging patterns in bundles of human peripheral nerve segment

Accurately measuring the spatial extension distance of nerve bundles in completing a split/merge is impossible because no clear mathematical definition exists for the starting and ending positions in nerve-bundle splitting/merging. We manually count the number of nerve-bundle splits/merges in long nerve segments, which is labor-intensive, inefficient, and prone to counting errors. Currently, the mathematics are unclear for the nerve-bundle diameter before and after splitting/merging. This paper explores these problems and proposes nerve-bundle splitting/merging rules. Based on the method of defining the beginning and ending positions of nerve-bundle splitting/merging, we explored the mathematical law of equivalent diameter of nerve bundles before and after splitting/merging. The experimental results revealed that the moving average of circularity of nerve bundle accurately defines the beginning and ending positions of nerve-bundle splitting/merging. The diameter of the nerve bundles before and after split/merge approximately conforms to the principles of the Da Vinci formula. The proposed automatic counting algorithm based on centroid offset matching obtains the number of split/merged nerve bundles in the sequence scan images with 100 % accuracy. The mathematical definition of the starting and ending positions of nerve-bundle splitting/merging proposed in this paper is accurate and strict and is the foundation of subsequent research. The proposed automatic counting algorithm based on centroid offset matching (ACA-COM) can accurately and efficiently count the number of times the nerve bundles split and merge in sequential images. The mathematical law satisfied by the diameter of the nerve bundles before and after splitting/merging reflects that the nerve bundles tend to have better capability to resist breaking.

Acriflavine, a HIF-1 inhibitor, preserves vision in an experimental autoimmune encephalomyelitis model of optic neuritis

Introduction

Optic neuritis (ON) is often an early sign of multiple sclerosis (MS), and recent studies show a link between HIF-1 pathway activation and inflammation. This study aimed to determine if inhibition of the HIF-1 pathway using the HIF-1a antagonist acriflavine (ACF) can reduce clinical progression and rescue the ocular phenotype in an experimental autoimmune encephalomyelitis (EAE) ON model.

Methods

EAE-related ON was induced in 60 female C57BL/6J mice by immunization with MOG33-55, and 20 EAE mice received daily systemic injections of ACF at 5 mg/kg. Changes in the visual function and structure of ACF-treated EAE mice were compared to those of placebo-injected EAE mice and naïve control mice.

Results

ACF treatment improved motor-sensory impairment along with preserving visual acuity and optic nerve function. Analysis of retinal ganglion cell complex alsoshowed preserved thickness correlating with increased survival of retinal ganglion cells and their axons. Optic nerve cell infiltration and magnitude of demyelination were decreased in ACF-treated EAE mice. Subsequent in vitro studies revealed improvements not only attributed to the inhibition of HIF-1 butalso to previously unappreciated interaction with the eIF2a/ATF4 axis in the unfolded protein response pathway.

Discussion

This study suggests that ACF treatment is effective in an animal model of MS via its pleiotropic effects on the inhibition of HIF-1 and UPR signaling, and it may be a viable approach to promote rehabilitation in MS.

AxonQuantifier: A Semi-Automated Program for Quantifying Axonal Density from Whole-mounted Optic Nerves

BACKGROUND

Here, we present a semi-automated method for quantifying retinal ganglion cell (RGC) axon density at different distances from the optic nerve crush site using longitudinal, confocal microscopy images taken from whole-mounted optic nerves. This method employs the algorithm AxonQuantifier which operates on the freely available program, ImageJ.

NEW METHOD

To validate this method, seven adult male Long Evans rats underwent optic nerve crush injury followed by in vivo treatment with electric fields of varying strengths for 30 days to produce optic nerves with a wide range of axon densities distal to the optic nerve crush site. Prior to euthanasia, RGC axons were labelled with intravitreal injections of cholera toxin B conjugated to Alexa Fluor 647. After dissection, optic nerves underwent tissue clearing, were whole-mounted, and imaged longitudinally using confocal microscopy.

COMPARISON WITH EXISTING METHODS

Five masked raters quantified RGC axon density at 250, 500, 750, 1000, 1250, 1500, 1750, and 2000 µm distances past the optic nerve crush site for the seven optic nerves manually and using AxonQuantifier. Agreement between these methods was assessed using Bland-Altman plots and linear regression. Inter-rater agreement was assessed using the intra-class coefficient.

RESULTS

Semi-automated quantification of RGC axon density demonstrated improved inter-rater agreement and reduced bias values as compared to manual quantification, while also increasing time efficiency 4-fold. Relative to manual quantification, AxonQuantifier tended to underestimate axon density.

CONCLUSIONS

AxonQuantifier is a reliable and efficient method for quantifying axon density from whole mount optic nerves.

Topical and systemic GLP-1R agonist administration both rescue retinal ganglion cells in hypertensive glaucoma

Glaucomatous neurodegeneration, a blinding disease affecting millions worldwide, has a need for the exploration of new and effective therapies. Previously, the glucagon-like peptide-1 receptor (GLP-1R) agonist NLY01 was shown to reduce microglia/macrophage activation, rescuing retinal ganglion cells after IOP elevation in an animal model of glaucoma. GLP-1R agonist use is also associated with a reduced risk for glaucoma in patients with diabetes. In this study, we demonstrate that several commercially available GLP-1R agonists, administered either systemically or topically, hold protective potential in a mouse model of hypertensive glaucoma. Further, the resulting neuroprotection likely occurs through the same pathways previously shown for NLY01. This work contributes to a growing body of evidence suggesting that GLP-1R agonists represent a viable therapeutic option for glaucoma.

RGC-Net: An Automatic Reconstruction and Quantification Algorithm for Retinal Ganglion Cells Based on Deep Learning

Purpose

The purpose of this study was to develop a deep learning-based fully automated reconstruction and quantification algorithm which automatically delineates the neurites and somas of retinal ganglion cells (RGCs).

Methods

We trained a deep learning-based multi-task image segmentation model, RGC-Net, that automatically segments the neurites and somas in RGC images. A total of 166 RGC scans with manual annotations from human experts were used to develop this model, whereas 132 scans were used for training, and the remaining 34 scans were reserved as testing data. Post-processing techniques removed speckles or dead cells in soma segmentation results to further improve the robustness of the model. Quantification analyses were also conducted to compare five different metrics obtained by our automated algorithm and manual annotations.

Results

Quantitatively, our segmentation model achieves average foreground accuracy, background accuracy, overall accuracy, and dice similarity coefficient of 0.692, 0.999, 0.997, and 0.691 for the neurite segmentation task, and 0.865, 0.999, 0.997, and 0.850 for the soma segmentation task, respectively.

Conclusions

The experimental results demonstrate that RGC-Net can accurately and reliably reconstruct neurites and somas in RGC images. We also demonstrate our algorithm is comparable to human manually curated annotations in quantification analyses.

Translational Relevance

Our deep learning model provides a new tool that can trace and analyze the RGC neurites and somas efficiently and faster than manual analysis.

AxoNet 2.0: A Deep Learning-Based Tool for Morphometric Analysis of Retinal Ganglion Cell Axons

Purpose

Assessment of glaucomatous damage in animal models is facilitated by rapid and accurate quantification of retinal ganglion cell (RGC) axonal loss and morphologic change. However, manual assessment is extremely time- and labor-intensive. Here, we developed AxoNet 2.0, an automated deep learning (DL) tool that (i) counts normal-appearing RGC axons and (ii) quantifies their morphometry from light micrographs.

Methods

A DL algorithm was trained to segment the axoplasm and myelin sheath of normal-appearing axons using manually-annotated rat optic nerve (ON) cross-sectional micrographs. Performance was quantified by various metrics (e.g., soft-Dice coefficient between predicted and ground-truth segmentations). We also quantified axon counts, axon density, and axon size distributions between hypertensive and control eyes and compared to literature reports.

Results

AxoNet 2.0 performed very well when compared to manual annotations of rat ON (R2 = 0.92 for automated vs. manual counts, soft-Dice coefficient = 0.81 ± 0.02, mean absolute percentage error in axonal morphometric outcomes < 15%). AxoNet 2.0 also showed promise for generalization, performing well on other animal models (R2 = 0.97 between automated versus manual counts for mice and 0.98 for non-human primates). As expected, the algorithm detected decreased in axon density in hypertensive rat eyes (P ≪ 0.001) with preferential loss of large axons (P < 0.001).

Conclusions

AxoNet 2.0 provides a fast and nonsubjective tool to quantify both RGC axon counts and morphological features, thus assisting with assessing axonal damage in animal models of glaucomatous optic neuropathy.

Translational Relevance

This deep learning approach will increase rigor of basic science studies designed to investigate RGC axon protection and regeneration.

Reverse translation of artificial intelligence in glaucoma: Connecting basic science with clinical applications

Artificial intelligence (AI) has been approved for biomedical research in diverse areas from bedside clinical studies to benchtop basic scientific research. For ophthalmic research, in particular glaucoma, AI applications are rapidly growing for potential clinical translation given the vast data available and the introduction of federated learning. Conversely, AI for basic science remains limited despite its useful power in providing mechanistic insight. In this perspective, we discuss recent progress, opportunities, and challenges in the application of AI in glaucoma for scientific discoveries. Specifically, we focus on the research paradigm of reverse translation, in which clinical data are first used for patient-centered hypothesis generation followed by transitioning into basic science studies for hypothesis validation. We elaborate on several distinctive areas of research opportunities for reverse translation of AI in glaucoma including disease risk and progression prediction, pathology characterization, and sub-phenotype identification. We conclude with current challenges and future opportunities for AI research in basic science for glaucoma such as inter-species diversity, AI model generalizability and explainability, as well as AI applications using advanced ocular imaging and genomic data.

Continuous Hypoxia Reduces Retinal Ganglion Cell Degeneration in a Mouse Model of Mitochondrial Optic Neuropathy

Purpose

To test whether continuous hypoxia is neuroprotective to retinal ganglion cells (RGCs) in a mouse model of mitochondrial optic neuropathy.

Methods

RGC degeneration was assessed in genetically modified mice in which the floxed gene for the complex I subunit NDUFS4 is deleted from RGCs using Vlgut2-driven Cre recombinase. Beginning at postnatal day 25 (P25), Vglut2-Cre;ndufs4loxP/loxP mice and control littermates were housed under hypoxia (11% oxygen) or kept under normoxia (21% oxygen). Survival of RGC somas and axons was assessed at P60 and P90 via histological analysis of retinal flatmounts and optic nerve cross-sections, respectively. Retinal tissue was also assessed for gliosis and neuroinflammation using western blot and immunofluorescence.

Results

Consistent with our previous characterization of this model, at least one-third of RGCs had degenerated by P60 in Vglut2-Cre;ndufs4loxP/loxP mice remaining under normoxia. However, continuous hypoxia resulted in complete rescue of RGC somas and axons at this time point, with normal axonal myelination observed on electron microscopy. Though only partial, hypoxia-mediated rescue of complex I-deficient RGC somas and axons remained significant at P90. Hypoxia prevented reactive gliosis at P60, but the retinal accumulation of Iba1+ mononuclear phagocytic cells was not substantially reduced.

Conclusions

Continuous hypoxia achieved dramatic rescue of early RGC degeneration in mice with severe mitochondrial dysfunction. Although complete rescue was not durable to P90, our observations suggest that investigating the mechanisms underlying hypoxia-mediated neuroprotection of RGCs may identify useful therapeutic strategies for optic neuropathies resulting from less profound mitochondrial impairment, such as Leber hereditary optic neuropathy.

Evaluation of Spatially Targeted Scleral Stiffening on Neuroprotection in a Rat Model of Glaucoma

Purpose

Scleral stiffening may protect against glaucomatous retinal ganglion cell (RGC) loss or dysfunction associated with ocular hypertension. Here, we assess the potential neuroprotective effects of two treatments designed to stiffen either the entire posterior sclera or only the sclera adjacent to the peripapillary sclera in an experimental model of glaucoma.

Methods

Rat sclerae were stiffened in vivo using either genipin (crosslinking the entire posterior sclera) or a regionally selective photosensitizer, methylene blue (stiffening only the juxtaperipapillary region surrounding the optic nerve). Ocular hypertension was induced using magnetic microbeads delivered to the anterior chamber. Morphological and functional outcomes, including optic nerve axon count and appearance, retinal thickness measured by optical coherence tomography, optomotor response, and electroretinography traces, were assessed.

Results

Both local (juxtaperipapillary) and global (whole posterior) scleral stiffening treatments were successful at increasing scleral stiffness, but neither provided demonstrable neuroprotection in hypertensive eyes as assessed by RGC axon counts and appearance, optomotor response, or electroretinography. There was a weak indication that scleral crosslinking protected against retinal thinning as assessed by optical coherence tomography.

Conclusions

Scleral stiffening was not demonstrated to be neuroprotective in ocular hypertensive rats. We hypothesize that the absence of benefit may in part be due to RGC loss associated with the scleral stiffening agents themselves (mild in the case of genipin, and moderate in the case of methylene blue), negating any potential benefit of scleral stiffening.

Translational Relevance

The development of scleral stiffening as a neuroprotective treatment will require the identification of better tolerated stiffening protocols and further preclinical testing.

AxonDeep: Automated Optic Nerve Axon Segmentation in Mice With Deep Learning

Purpose

Optic nerve damage is the principal feature of glaucoma and contributes to vision loss in many diseases. In animal models, nerve health has traditionally been assessed by human experts that grade damage qualitatively or manually quantify axons from sampling limited areas from histologic cross sections of nerve. Both approaches are prone to variability and are time consuming. First-generation automated approaches have begun to emerge, but all have significant shortcomings. Here, we seek improvements through use of deep-learning approaches for segmenting and quantifying axons from cross-sections of mouse optic nerve.

Methods

Two deep-learning approaches were developed and evaluated: (1) a traditional supervised approach using a fully convolutional network trained with only labeled data and (2) a semisupervised approach trained with both labeled and unlabeled data using a generative-adversarial-network framework.

Results

From comparisons with an independent test set of images with manually marked axon centers and boundaries, both deep-learning approaches outperformed an existing baseline automated approach and similarly to two independent experts. Performance of the semisupervised approach was superior and implemented into AxonDeep.

Conclusions

AxonDeep performs automated quantification and segmentation of axons from healthy-appearing nerves and those with mild to moderate degrees of damage, similar to that of experts without the variability and constraints associated with manual performance.

Translational Relevance

Use of deep learning for axon quantification provides rapid, objective, and higher throughput analysis of optic nerve that would otherwise not be possible.

Automating cell counting in fluorescent microscopy through deep learning with c-ResUnet

Counting cells in fluorescent microscopy is a tedious, time-consuming task that researchers have to accomplish to assess the effects of different experimental conditions on biological structures of interest. Although such objects are generally easy to identify, the process of manually annotating cells is sometimes subject to fatigue errors and suffers from arbitrariness due to the operator’s interpretation of the borderline cases. We propose a Deep Learning approach that exploits a fully-convolutional network in a binary segmentation fashion to localize the objects of interest. Counts are then retrieved as the number of detected items. Specifically, we introduce a Unet-like architecture, cell ResUnet (c-ResUnet), and compare its performance against 3 similar architectures. In addition, we evaluate through ablation studies the impact of two design choices, (i) artifacts oversampling and (ii) weight maps that penalize the errors on cells boundaries increasingly with overcrowding. In summary, the c-ResUnet outperforms the competitors with respect to both detection and counting metrics (respectively, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$F_1$$\end{document}F1 score = 0.81 and MAE = 3.09). Also, the introduction of weight maps contribute to enhance performances, especially in presence of clumping cells, artifacts and confounding biological structures. Posterior qualitative assessment by domain experts corroborates previous results, suggesting human-level performance inasmuch even erroneous predictions seem to fall within the limits of operator interpretation. Finally, we release the pre-trained model and the annotated dataset to foster research in this and related fields.

Age and intraocular pressure in murine experimental glaucoma

Age and intraocular pressure (IOP) are the two most important risk factors for the development and progression of open-angle glaucoma. While IOP is commonly considered in models of experimental glaucoma (EG), most studies use juvenile or adult animals and seldom older animals which are representative of the human disease. This paper provides a concise review of how retinal ganglion cell (RGC) loss, the hallmark of glaucoma, can be evaluated in EG with a special emphasis on serial in vivo imaging, a parallel approach used in clinical practice. It appraises the suitability of EG models for the purpose of in vivo imaging and argues for the use of models that provide a sustained elevation of IOP, without compromise of the ocular media. In a study with parallel cohorts of adult (3-month-old, equivalent to 20 human years) and old (2-year-old, equivalent to 70 human years) mice, we compare the effects of elevated IOP on serial ganglion cell complex thickness and individual RGC dendritic morphology changes obtained in vivo. We also evaluate how age modulates the impact of elevated IOP on RGC somal and axonal density in histological analysis as well the density of melanopsin RGCs. We discuss the challenges of using old animals and emphasize the potential of single RGC imaging for understanding the pathobiology of RGC loss and evaluating new therapeutic avenues.

Quantification and image-derived phenotyping of retinal ganglion cell nuclei in the nee mouse model of congenital glaucoma

The nee mouse model exhibits characteristic features of congenital glaucoma, a common cause of childhood blindness. The current study of nee mice had two components. First, the time course of neurodegeneration in nee retinal flat-mounts was studied over time using a retinal ganglion cell (RGC)-marker, BRN3A; a pan-nuclear marker, TO-PRO-3; and H&E staining. Based on segmentation of nuclei using ImageJ and RetFM-J, this analysis identified a rapid loss of BRN3A⁺ nuclei from 4 to 15 weeks of age, with the first statistically significant difference in average density compared to age-matched controls detected in 8-week-old cohorts (49% reduction in nee). Consistent with a model of glaucoma, no reductions in BRN3A^- nuclei were detected, but the combined analysis indicated that some RGCs lost BRN3A marker expression prior to actual cell loss. These results have a practical application in the design of experiments using nee mice to study mechanisms or potential therapies for congenital glaucoma. The second component of the study pertains to a discovery-based analysis of the large amount of image data with 748,782 segmented retinal nuclei. Using the automatedly collected region of interest feature data captured by ImageJ, we tested whether RGC density of glaucomatous mice was significantly correlated to average nuclear area, perimeter, Feret diameter, or MinFeret diameter. These results pointed to two events influencing nuclear size. For variations in RGC density above approximately 3000 nuclei/mm² apparent spreading was observed, in which BRN3A^- nuclei-regardless of genotype-became slightly larger as RGC density decreased. This same spreading occurred in BRN3A⁺ nuclei of wild-type mice. For variation in RGC density below 3000 nuclei/mm², which only occurred in glaucomatous nee mutants, BRN3A⁺ nuclei became smaller as disease was progressively severe. These observations have relevance to defining RGCs of relatively higher sensitivity to glaucomatous cell death and the nuclear dynamics occurring during their demise.

A Fair Assessment of Evaluation Tools for the Murine Microbead Occlusion Model of Glaucoma

Despite being one of the most studied eye diseases, clinical translation of glaucoma research is hampered, at least in part, by the lack of validated preclinical models and readouts. The most popular experimental glaucoma model is the murine microbead occlusion model, yet the observed mild phenotype, mixed success rate, and weak reproducibility urge for an expansion of available readout tools. For this purpose, we evaluated various measures that reflect early onset glaucomatous changes in the murine microbead occlusion model. Anterior chamber depth measurements and scotopic threshold response recordings were identified as an outstanding set of tools to assess the model’s success rate and to chart glaucomatous damage (or neuroprotection in future studies), respectively. Both are easy-to-measure, in vivo tools with a fast acquisition time and high translatability to the clinic and can be used, whenever judged beneficial, in combination with the more conventional measures in present-day glaucoma research (i.e., intraocular pressure measurements and post-mortem histological analyses). Furthermore, we highlighted the use of dendritic arbor analysis as an alternative histological readout for retinal ganglion cell density counts.

Transpupillary collagen photocrosslinking for targeted modulation of ocular biomechanics

The central vision-threatening event in glaucoma is dysfunction and loss of retinal ganglion cells (RGCs), thought to be promoted by local tissue deformations. Here, we sought to reduce tissue deformation near the optic nerve head by selectively stiffening the peripapillary sclera, i.e. the scleral region immediately adjacent to the optic nerve head. Previous scleral stiffening studies to treat glaucoma or myopia have used either pan-scleral stiffening (not regionally selective) or regionally selective stiffening with limited access to the posterior globe. We present a method for selectively stiffening the peripapillary sclera using a transpupillary annular light beam to activate methylene blue administered by retrobulbar injection. Unlike prior approaches to photocrosslinking in the eye, this approach avoids the damaging effects of ultraviolet light by employing red light. This targeted photocrosslinking approach successfully stiffened the peripapillary sclera at 6 weeks post-treatment, as measured by whole globe inflation testing. Specifically, strain was reduced by 47% when comparing treated vs. untreated sclera within the same eye (n = 7, p=0.0064) and by 54% when comparing the peripapillary sclera of treated vs. untreated eyes (n = 7, p<0.0001). Post-treatment characterization of RGCs (optic nerve axon counts/density, and grading), retinal function (electroretinography), and retinal histology revealed that photocrosslinking was associated with some ocular toxicity. We conclude that a transpupillary photocrosslinking approach enables selective scleral stiffening targeted to the peripapillary region that may be useful in future treatments of glaucoma.

A novel retinal ganglion cell quantification tool based on deep learning

Glaucoma is a disease associated with the loss of retinal ganglion cells (RGCs), and remains one of the primary causes of blindness worldwide. Major research efforts are presently directed towards the understanding of disease pathogenesis and the development of new therapies, with the help of rodent models as an important preclinical research tool. The ultimate goal is reaching neuroprotection of the RGCs, which requires a tool to reliably quantify RGC survival. Hence, we demonstrate a novel deep learning pipeline that enables fully automated RGC quantification in the entire murine retina. This software, called RGCode (Retinal Ganglion Cell quantification based On DEep learning), provides a user-friendly interface that requires the input of RBPMS-immunostained flatmounts and returns the total RGC count, retinal area and density, together with output images showing the computed counts and isodensity maps. The counting model was trained on RBPMS-stained healthy and glaucomatous retinas, obtained from mice subjected to microbead-induced ocular hypertension and optic nerve crush injury paradigms. RGCode demonstrates excellent performance in RGC quantification as compared to manual counts. Furthermore, we convincingly show that RGCode has potential for wider application, by retraining the model with a minimal set of training data to count FluoroGold-traced RGCs.

Using retinal function to define ischemic exclusion criteria for animal models of glaucoma

Most animal models of glaucoma rely on induction of ocular hypertension (OHT), yet such models can suffer from high IOPs leading to undesirable retinal ischemia. Thus, animals with IOPs exceeding a threshold (e.g. > 60 mmHg) are often excluded from studies. However, due to the intermittent nature of IOP measurements, this approach may fail to detect ischemia. Conversely, it may also inappropriately eliminate animals with IOP spikes that do not induce ischemic damage. It is known that acute ischemia selectively impairs inner retinal function, which results in a reduced b-wave amplitude. Here, we explore the potential of using electroretinography (ERG) to detect ischemic damage in OHT eyes. 74 Brown Norway rats received a unilateral injection of magnetic microbeads to induce OHT, while contralateral eyes served as controls. IOP was measured every 2-3 days for 14 days after microbead injection. Retinal function was evaluated using dark-adapted bright flash ERG (2.1 log cd•s/m²) prior to, and at 7 and 14 days after, injection. We investigated two criteria for excluding animals: (IOP Criterion) a single IOP measurement > 60 mmHg; or (ERG Criterion) a b-wave amplitude below the 99.5% confidence interval for naïve eyes. 49 of 74 rats passed both criteria, 7 of 74 failed both, and 18 passed one criterion but not the other. We suggest that ERG testing can detect unwelcome ischemic damage in animal models of OHT. Since brief IOP spikes do not necessarily lead to ischemic retinal damage, and because extended periods of elevated IOP can be missed, such ERG-based criteria may provide more objective and robust exclusion criteria in future glaucoma studies.

Assessment of Visual and Retinal Function Following In Vivo Genipin-Induced Scleral Crosslinking

Purpose

Genipin has been proposed as a possible neuroprotective therapy in myopia and glaucoma. Here, we aim to determine the effects of prolonged genipin-induced scleral stiffening on visual function.

Methods

Eyes from Brown Norway rats were treated in vivo with either a single 15 mM genipin retrobulbar injection or sham retrobulbar injection and were compared to naïve eyes. Intraocular pressure, optomotor response, and electroretinograms were repeatedly measured over 4 weeks following retrobulbar injections to determine visual and retinal function. At 4 weeks, we quantified retinal ganglion cell axon counts. Finally, molecular changes in gene and protein expression were analyzed via real-time polymerase chain reaction (RT-PCR) and proteomics.

Results

Retrobulbar injection of genipin did not affect intraocular pressure (IOP) or retinal function, nor have a sustained impact on visual function. Although genipin-treated eyes had a small decrease in retinal ganglion cell axon counts compared to contralateral sham-treated eyes (−8,558 ± 18,646; mean ± SD), this was not statistically significant (P = 0.206, n = 9). Last, we did not observe any changes in gene or protein expression due to genipin treatment.

Conclusions

Posterior scleral stiffening with a single retrobulbar injection of 15 mM genipin causes no sustained deficits in visual or retinal function or at the molecular level in the retina and sclera. Retinal ganglion cell axon morphology appeared normal.

Translational Significance

These results support future in vivo studies to determine the efficacy of genipin-induced posterior scleral stiffening to help treat ocular diseases, like myopia and glaucoma.

The Effects of Glaucoma on the Pressure-Induced Strain Response of the Human Lamina Cribrosa

Purpose

To measure the ex vivo pressure-induced strain response of the human optic nerve head and analyze for variations with glaucoma diagnosis and optic nerve axon damage.

Methods

The posterior sclera of 16 eyes from 8 diagnosed glaucoma donors and 10 eyes from 6 donors with no history of glaucoma were inflation tested between 5 and 45 mm Hg. The optic nerve from each donor was examined for degree of axon loss. The posterior volume of the lamina cribrosa (LC) was imaged with second harmonic generation and analyzed using volume correlation to calculate LC strains between 5 and 10 and 5 and 45 mm Hg.

Results

Eye length and LC area were larger in eyes diagnosed with glaucoma (P≤ 0.03). Nasal-temporal E_XX and circumferential E_θθ strains were lower in the LC of diagnosed glaucoma eyes at 10 mm Hg (P≤ 0.05) and 45 mm Hg (P≤ 0.07). E_XX was smaller in the LC of glaucoma eyes with <25% axon loss compared with undamaged normal eyes (P = 0.01, 45 mm Hg). In general, the strains were larger in the peripheral than central LC. The ratio of the maximum principal strain E_max in the peripheral to central LC was larger in glaucoma eyes with >25% axon loss than in glaucoma eyes with milder damage (P = 0.004, 10 mm Hg).

Conclusions

The stiffness of the LC pressure-strain response was greater in diagnosed glaucoma eyes and varied with glaucomatous axon damage. Lower LC strains in glaucoma eyes with milder damage may represent baseline biomechanical behavior that contributes to axon loss, whereas greater LC strain and altered radial LC strain variation in glaucoma eyes with more severe damage may be caused by glaucoma-related remodeling.