PurposeMicroglia represent the primary resident immune cells in the CNS, and have been implicated in the pathology of neurodegenerative diseases. Under basal or “resting” conditions, microglia possess ramified morphologies and exhibit dynamic surveying movements in their processes. Despite the prominence of this phenomenon, the function and regulation of microglial morphology and dynamic behavior are incompletely understood. We investigate here whether and how neurotransmission regulates “resting” microglial morphology and behavior.MethodsWe employed an ex vivo mouse retinal explant system in which endogenous neurotransmission and dynamic microglial behavior are present. We utilized live-cell time-lapse confocal imaging to study the morphology and behavior of GFP-labeled retinal microglia in response to neurotransmitter agonists and antagonists. Patch clamp electrophysiology and immunohistochemical localization of glutamate receptors were also used to investigate direct-versus-indirect effects of neurotransmission by microglia.ResultsRetinal microglial morphology and dynamic behavior were not cell-autonomously regulated but are instead modulated by endogenous neurotransmission. Morphological parameters and process motility were differentially regulated by different modes of neurotransmission and were increased by ionotropic glutamatergic neurotransmission and decreased by ionotropic GABAergic neurotransmission. These neurotransmitter influences on retinal microglia were however unlikely to be directly mediated; local applications of neurotransmitters were unable to elicit electrical responses on microglia patch-clamp recordings and ionotropic glutamatergic receptors were not located on microglial cell bodies or processes by immunofluorescent labeling. Instead, these influences were mediated indirectly via extracellular ATP, released in response to glutamatergic neurotransmission through probenecid-sensitive pannexin hemichannels.ConclusionsOur results demonstrate that neurotransmission plays an endogenous role in regulating the morphology and behavior of “resting” microglia in the retina. These findings illustrate a mode of constitutive signaling between the neural and immune compartments of the CNS through which immune cells may be regulated in concert with levels of neural activity.

We present a fully automatic algorithm to identify fluid-filled regions and seven retinal layers on spectral domain optical coherence tomography images of eyes with diabetic macular edema (DME). To achieve this, we developed a kernel regression (KR)-based classification method to estimate fluid and retinal layer positions. We then used these classification estimates as a guide to more accurately segment the retinal layer boundaries using our previously described graph theory and dynamic programming (GTDP) framework. We validated our algorithm on 110 B-scans from ten patients with severe DME pathology, showing an overall mean Dice coefficient of 0.78 when comparing our KR + GTDP algorithm to an expert grader. This is comparable to the inter-observer Dice coefficient of 0.79. The entire data set is available online, including our automatic and manual segmentation results. To the best of our knowledge, this is the first validated, fully-automated, seven-layer and fluid segmentation method which has been applied to real-world images containing severe DME.

Abstract:We present a novel fully automated algorithm for the detection of retinal diseases via optical coherence tomography (OCT) imaging. Our algorithm utilizes multiscale histograms of oriented gradient descriptors as feature vectors of a support vector machine based classifier. The spectral domain OCT data sets used for cross-validation consisted of volumetric scans acquired from 45 subjects: 15 normal subjects, 15 patients with dry age-related macular degeneration (AMD), and 15 patients with diabetic macular edema (DME). Our classifier correctly identified 100% of cases with AMD, 100% cases with DME, and 86.67% cases of normal subjects. This algorithm is a potentially impactful tool for the remote diagnosis of ophthalmic diseases.

These studies demonstrate that LPA and S1P, the physiological agonists of Edg receptors, decrease outflow facility in perfused porcine eyes in association with increased MLC phosphorylation and Rho guanosine triphosphatase (GTPase) activation. These data provide evidence for a novel mechanism for negative regulation of outflow facility, which may contribute to overall physiological homeostasis of aqueous humor outflow facility.

Macular sensitivity and fixation quality undergo progressive change during the GA progression, reflecting alterations in macular function extending beyond the GA lesion proper. Microperimetric measurements may provide useful functional outcome measures for the clinical study of GA.

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We propose a novel, graph-theoretic framework for distinguishing arteries from veins in a fundus image. We make use of the underlying vessel topology to better classify small and midsized vessels. We extend our previously proposed tree topology estimation framework by incorporating expert, domain-specific features to construct a simple, yet powerful global likelihood model. We efficiently maximize this model by iteratively exploring the space of possible solutions consistent with the projected vessels. We tested our method on four retinal datasets and achieved classification accuracies of 91.0%, 93.5%, 91.7%, and 90.9%, outperforming existing methods. Our results show the effectiveness of our approach, which is capable of analyzing the entire vasculature, including peripheral vessels, in wide field-of-view fundus photographs. This topology-based method is a potentially important tool for diagnosing diseases with retinal vascular manifestation.

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