Retinal ganglion cells (RGCs) degenerate in diseases like glaucoma and are not replaced in adult mammals. Here we investigate whether transplanted RGCs can integrate into the mature retina. We have transplanted GFP-labelled RGCs into uninjured rat retinas in vivo by intravitreal injection. Transplanted RGCs acquire the general morphology of endogenous RGCs, with axons orienting towards the optic nerve head of the host retina and dendrites growing into the inner plexiform layer. Preliminary data show in some cases GFP+ axons extending within the host optic nerves and optic tract, reaching usual synaptic targets in the brain, including the lateral geniculate nucleus and superior colliculus. Electrophysiological recordings from transplanted RGCs demonstrate the cells' electrical excitability and light responses similar to host ON, ON–OFF and OFF RGCs, although less rapid and with greater adaptation. These data present a promising approach to develop cell replacement strategies in diseased retinas with degenerating RGCs.

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Neurons in the adult mammalian CNS decrease in intrinsic axon growth capacity during development in concert with changes in Krüppel-like transcription factors (KLFs). KLFs regulate axon growth in CNS neurons including retinal ganglion cells (RGCs). Here, we found that knock-down of KLF9, an axon growth suppressor that is normally upregulated 250-fold in RGC development, promotes long-distance optic nerve regeneration in adult rats of both sexes. We identified a novel binding partner, MAPK10/JNK3 kinase, and found that JNK3 (c-Jun N-terminal kinase 3) is critical for KLF9's axon-growth-suppressive activity. Interfering with a JNK3-binding domain or mutating two newly discovered serine phosphorylation acceptor sites, Ser106 and Ser110, effectively abolished KLF9's neurite growth suppression and promoted axon regeneration These findings demonstrate a novel, physiologic role for the interaction of KLF9 and JNK3 in regenerative failure in the optic nerve and suggest new therapeutic strategies to promote axon regeneration in the adult CNS. Injured CNS nerves fail to regenerate spontaneously. Promoting intrinsic axon growth capacity has been a major challenge in the field. Here, we demonstrate that knocking down Krüppel-like transcription factor 9 (KLF9) via shRNA promotes long-distance axon regeneration after optic nerve injury and uncover a novel and important KLF9-JNK3 interaction that contributes to axon growth suppression and regenerative failure These studies suggest potential therapeutic approaches to promote axon regeneration in injury and other degenerative diseases in the adult CNS.

Purpose: To develop and validate a deep learning (DL) algorithm that predicts referable glaucomatous optic neuropathy (GON) and optic nerve head (ONH) features from color fundus images, to determine the relative importance of these features in referral decisions by glaucoma specialists (GSs) and the algorithm, and to compare the performance of the algorithm with eye care providers.Design: Development and validation of an algorithm.Participants: Fundus images from screening programs, studies, and a glaucoma clinic. Methods: A DL algorithm was trained using a retrospective dataset of 86 618 images, assessed for glaucomatous ONH features and referable GON (defined as ONH appearance worrisome enough to justify referral for comprehensive examination) by 43 graders. The algorithm was validated using 3 datasets: dataset A (1205 images, 1 image/patient; 18.1% referable), images adjudicated by panels of GSs; dataset B (9642 images, 1 image/ patient; 9.2% referable), images from a diabetic teleretinal screening program; and dataset C (346 images, 1 image/patient; 81.7% referable), images from a glaucoma clinic.Main Outcome Measures: The algorithm was evaluated using the area under the receiver operating characteristic curve (AUC), sensitivity, and specificity for referable GON and glaucomatous ONH features.Results: The algorithm's AUC for referable GON was 0.945 (95% confidence interval [CI], 0.929e0.960) in dataset A, 0.855 (95% CI, 0.841e0.870) in dataset B, and 0.881 (95% CI, 0.838e0.918) in dataset C. Algorithm AUCs ranged between 0.661 and 0.973 for glaucomatous ONH features. The algorithm showed significantly higher sensitivity than 7 of 10 graders not involved in determining the reference standard, including 2 of 3 GSs, and showed higher specificity than 3 graders (including 1 GS), while remaining comparable to others. For both GSs and the algorithm, the most crucial features related to referable GON were: presence of vertical cup-to-disc ratio of 0.7 or more, neuroretinal rim notching, retinal nerve fiber layer defect, and bared circumlinear vessels.Conclusions: A DL algorithm trained on fundus images alone can detect referable GON with higher sensitivity than and comparable specificity to eye care providers. The algorithm maintained good performance on an independent dataset with diagnoses based on a full glaucoma workup.