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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.

Purpose: Despite popularity of optical coherence tomography (OCT) in glaucoma studies, it's unclear how well OCT-derived metrics compare to traditional measures of retinal ganglion cell (RGC) abundance. Here, Diversity Outbred (J:DO) mice are used to directly compare ganglion cell complex (GCC) thickness measured by OCT to metrics of retinal anatomy measured ex vivo with retinal wholemounts and optic nerve histology., Methods: J:DO mice (n = 48) underwent fundoscopic and OCT examinations, with automated segmentation of GCC thickness. RGC axons were quantified from para-phenylenediamine-stained optic nerve cross-sections and somas from BRN3A-immunolabeled retinal wholemounts, with total inner retinal cellularity assessed by TO-PRO and subsequent hematoxylin staining., Results: J:DO tissues lacked overt disease. GCC thickness, RGC abundance, and total cell abundance varied broadly across individuals. GCC thickness correlated significantly to RGC somal density (r = 0.58) and axon number (r = 0.44), but not total cell density. Retinal area and nerve cross-sectional area varied widely. No metrics were significantly influenced by sex. In bilateral comparisons, GCC thickness (r = 0.95), axon (r = 0.72), and total cell density (r = 0.47) correlated significantly within individuals., Conclusions: Amongst outbred mice, OCT-derived measurements of GCC thickness correlate significantly to RGC somal and axon abundance. Factors limiting correlation are likely both biological and methodological, including differences in retinal area that distort sampling-based estimates of RGC abundance., Translational Relevance: There are significant-but imperfect-correlations between GCC thickness and RGC abundance across genetic contexts in mice, highlighting valid uses and ongoing challenges for meaningful use of OCT-derived metrics.

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.

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/mm 2 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/mm 2 , 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., (Published by Elsevier Ltd.)

Glaucoma is the leading cause of irreversible blindness worldwide. Although most glaucoma patients are elderly, congenital glaucoma and glaucomas of childhood are also important causes of visual disability. Primary congenital glaucoma (PCG) is isolated, non-syndromic glaucoma that occurs in the first three years of life and is a major cause of childhood blindness. Other early-onset glaucomas may arise secondary to developmental abnormalities, such as glaucomas that occur with aniridia or as part of Axenfeld-Rieger syndrome. Congenital and childhood glaucomas have strong genetic bases and disease-causing mutations have been discovered in several genes. Mutations in three genes (CYP1B1, LTBP2, TEK) have been reported in PCG patients. Axenfeld-Rieger syndrome is caused by mutations in PITX2 or FOXC1 and aniridia is caused by PAX6 mutations. This review discusses the roles of these genes in primary congenital glaucoma and glaucomas of childhood., (© The Author 2017. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.)

Duplication of the TBK1 gene is associated with 1-2% of normal tension glaucoma, a common cause of vision loss and blindness that occurs without grossly abnormal intraocular pressure. We generated a transgenic mouse that has one copy of the human TBK1 gene (native promoter and gene structure) incorporated into the mouse genome (Tg-TBK1). Expression of the TBK1 transgene in the retinae of these mice was demonstrated by real-time PCR. Using immunohistochemistry TBK1 protein was predominantly localized to the ganglion cell layer of the retina, the cell type most affected by glaucoma. More intense TBK1 labelling was detected in the retinal ganglion cells (RGCs) of Tg-TBK1 mice than in wild-type littermates. Tg-TBK1 mice exhibit the cardinal sign of glaucoma, a progressive loss of RGCs. Hemizygous Tg-TBK1 mice (with one TBK1 transgene per genome) had a 13% loss of RGCs by 18 months of age (P = 1.5 × 10-8). Homozygous Tg-TBK1 mice had 7.6% fewer RGCs than hemizygous Tg-TBK1 mice and 20% fewer RGCs than wild-type mice (P = 1.9 × 10-5) at 6 months of age. No difference in intraocular pressures was detected between Tg-TBK1 mice and wild-type littermates as they aged (P > 0.05). Tg-TBK1 mice with extra doses of the TBK1 gene recapitulate the phenotype of normal tension glaucoma in human patients with a TBK1 gene duplication. Together, these studies confirm the pathogenicity of the TBK1 gene duplication in human glaucoma and suggest that excess production of TBK1 kinase may have a role in the pathology of glaucoma., (© The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.)

We have developed a publicly available tool, AxonJ, which quantifies the axons in optic nerve sections of rodents stained with paraphenylenediamine (PPD). In this study, we compare AxonJ's performance to human experts on 100x and 40x images of optic nerve sections obtained from multiple strains of mice, including mice with defects relevant to glaucoma. AxonJ produced reliable axon counts with high sensitivity of 0.959 and high precision of 0.907, high repeatability of 0.95 when compared to a gold-standard of manual assessments and high correlation of 0.882 to the glaucoma damage staging of a previously published dataset. AxonJ allows analyses that are quantitative, consistent, fully-automated, parameter-free, and rapid on whole optic nerve sections at 40x. As a freely available ImageJ plugin that requires no highly specialized equipment to utilize, AxonJ represents a powerful new community resource augmenting studies of the optic nerve using mice.

The inner surface of the retina contains a complex mixture of neurons, glia, and vasculature, including retinal ganglion cells (RGCs), the final output neurons of the retina and primary neurons that are damaged in several blinding diseases. The goal of the current work was two-fold: to assess the feasibility of using computer-assisted detection of nuclei and random forest classification to automate the quantification of RGCs in hematoxylin/eosin (H&E)-stained retinal whole-mounts; and if possible, to use the approach to examine how nuclear size influences disease susceptibility among RGC populations. To achieve this, data from RetFM-J, a semi-automated ImageJ-based module that detects, counts, and collects quantitative data on nuclei of H&E-stained whole-mounted retinas, were used in conjunction with a manually curated set of images to train a random forest classifier. To test performance, computer-derived outputs were compared to previously published features of several well-characterized mouse models of ophthalmic disease and their controls: normal C57BL/6J mice; Jun-sufficient and Jun-deficient mice subjected to controlled optic nerve crush (CONC); and DBA/2J mice with naturally occurring glaucoma. The result of these efforts was development of RetFM-Class, a command-line-based tool that uses data output from RetFM-J to perform random forest classification of cell type. Comparative testing revealed that manual and automated classifications by RetFM-Class correlated well, with 83.2% classification accuracy for RGCs. Automated characterization of C57BL/6J retinas predicted 54,642 RGCs per normal retina, and identified a 48.3% Jun-dependent loss of cells at 35 days post CONC and a 71.2% loss of RGCs among 16-month-old DBA/2J mice with glaucoma. Output from automated analyses was used to compare nuclear area among large numbers of RGCs from DBA/2J mice (n = 127,361). In aged DBA/2J mice with glaucoma, RetFM-Class detected a decrease in median and mean nucleus size of cells classified into the RGC category, as did an independent confirmation study using manual measurements of nuclear area demarcated by BRN3A-immunoreactivity. In conclusion, we have demonstrated that histology-based random forest classification is feasible and can be utilized to study RGCs in a high-throughput fashion. Despite having some limitations, this approach demonstrated a significant association between the size of the RGC nucleus and the DBA/2J form of glaucoma., (Published by Elsevier Ltd.)

Purpose: Mutations in SH3PXD2B cause Frank-Ter Haar syndrome, a rare condition characterized by congenital glaucoma, as well as craniofacial, skeletal, and cardiac anomalies. The nee strain of mice carries a spontaneously arising mutation in Sh3pxd2b. The purpose of this study was to test whether nee mice develop glaucoma., Methods: Eyes of nee mutants and strain-matched controls were comparatively analyzed at multiple ages by slit lamp examination, intraocular pressure recording, and histologic analysis. Cross sections of the optic nerve were analyzed to confirm glaucomatous progression., Results: Slit lamp examination showed that, from an early age, nee mice uniformly exhibited severe iridocorneal adhesions around the entire circumference of the eye. Presumably as a consequence of aqueous humor outflow blockage, they rapidly developed multiple indices of glaucoma. By 3 to 4 months of age, they exhibited high intraocular pressure (30.8 ± 12.5 mm Hg; mean ± SD), corneal opacity, and enlarged anterior chambers. Although histologic analyses at P17 did not reveal any indices of damage, similar analysis at 3 to 4 months of age revealed a course of progressive retinal ganglion cell loss, optic nerve head excavation, and axon loss., Conclusions: Eyes of nee mice exhibit anterior segment dysgenesis and early-onset glaucoma. Because SH3PXD2B is predicted to be a podosome adaptor protein, these findings implicate podosomes in normal development of the iridocorneal angle and the genes influencing podosomes as candidates in glaucoma. Because of the early-onset, high-penetrance glaucoma, nee mice offer many potential advantages as a new mouse model of the disease.