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 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.

The present article introduces RetFM-J, a semi-automated ImageJ-based module that detects, counts, and collects quantitative data on nuclei of the inner retina from H&E-stained whole-mounted retinas. To illustrate performance, computer-derived outputs were analyzed in inbred C57BL/6J mice. Automated characterization yielded computer-derived outputs that closely matched manual counts. As a method using open-source software that is freely available, inexpensive staining reagents that are robust, and imaging equipment that is routine to most laboratories, RetFM-J could be utilized in a wide variety of experiments benefiting from high-throughput, quantitative, uniform analyses of total cellularity in the inner retina.

MXD3 is a transcription factor that plays an important role in proliferation of human DAOY medulloblastoma cells. Here, we demonstrate that MXD3 is highly enriched in human precursor B acute lymphoblastic leukemia (preB ALL) samples compared to mobilized peripheral blood mononuclear cells, bone marrow, or hematopoietic stem cells from healthy donors. MXD3 knock-down in the preB ALL cell line Reh resulted in decreased cell numbers with no change in G0/G1, S or G2/M populations but increased apoptosis compared to control cells. Our results suggest that MXD3 is important for survival of Reh preB ALL cells, possibly as an anti-apoptotic factor.