5 research outputs found

    Deep phenotyping of PROM1-associated retinal degeneration

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    BACKGROUND/AIMS: The purpose of this study was to investigate retinal structure in detail of subjects with autosomal-dominant (AD) and autosomal-recessive (AR) PROM1-associated retinal degeneration (PROM1-RD), study design: institutional, cross-sectional study. METHODS: Four eyes from four subjects (three with AD and one with AR) PROM1-RD were investigated by ophthalmic examination including best-corrected visual acuity (BCVA) and multimodal retinal imaging: fundus autofluorescence (FAF), spectral-domain optical coherence tomography (SD-OCT) and adaptive optics scanning light ophthalmoscopy. Quantitative assessment of atrophic lesions determined by FAF, thickness of individual retinal layers and cone photoreceptor quantification was performed. RESULTS: BCVA ranged from 20/16 to 20/200. Initial pathological changes included the presence of hyperautofluorescent spots on FAF imaging, while later stages demonstrated discrete areas of atrophy. In all patients, thinning of the outer retinal layers on SD-OCT with varying degrees of atrophy could be detected depending on disease-causing variants and age. Cone density was quantified both in central and/or at different eccentricities from the fovea. Longitudinal assessments were possible in two patients. CONCLUSIONS: PROM1-RD comprises a wide range of clinical phenotypes. Depending on the stage of disease, the cone mosaic in PROM1-RD is relatively preserved and can potentially be targeted by cone-directed interventions

    Cone photoreceptor structure in patients with x-linked cone dysfunction and red-green color vision deficiency

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    PURPOSE: Mutations in the coding sequence of the L and M opsin genes are often associated with X-linked cone dysfunction (such as Bornholm Eye Disease, BED), though the exact color vision phenotype associated with these disorders is variable. We examined individuals with L/M opsin gene mutations to clarify the link between color vision deficiency and cone dysfunction. METHODS: We recruited 17 males for imaging. The thickness and integrity of the photoreceptor layers were evaluated using spectral-domain optical coherence tomography. Cone density was measured using high-resolution images of the cone mosaic obtained with adaptive optics scanning light ophthalmoscopy. The L/M opsin gene array was characterized in 16 subjects, including at least one subject from each family. RESULTS: There were six subjects with the LVAVA haplotype encoded by exon 3, seven with LIAVA, two with the Cys203Arg mutation encoded by exon 4, and two with a novel insertion in exon 2. Foveal cone structure and retinal thickness was disrupted to a variable degree, even among related individuals with the same L/M array. CONCLUSIONS: Our findings provide a direct link between disruption of the cone mosaic and L/M opsin variants. We hypothesize that, in addition to large phenotypic differences between different L/M opsin variants, the ratio of expression of first versus downstream genes in the L/M array contributes to phenotypic diversity. While the L/M opsin mutations underlie the cone dysfunction in all of the subjects tested, the color vision defect can be caused either by the same mutation or a gene rearrangement at the same locus

    Unsupervised identification of cone photoreceptors in non-confocal adaptive optics scanning light ophthalmoscope images

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    Precise measurements of photoreceptor numerosity and spatial arrangement are promising biomarkers for the early detection of retinal pathologies and may be valuable in the evaluation of retinal therapies. Adaptive optics scanning light ophthalmoscopy (AOSLO) is a method of imaging that corrects for aberrations of the eye to acquire high-resolution images that reveal the photoreceptor mosaic. These images are typically graded manually by experienced observers, obviating the robust, large-scale use of the technology. This paper addresses unsupervised automated detection of cones in non-confocal, split-detection AOSLO images. Our algorithm leverages the appearance of split-detection images to create a cone model that is used for classification. Results show that it compares favorably to the state-of-the-art, both for images of healthy retinas and for images from patients affected by Stargardt disease. The algorithm presented also compares well to manual annotation while excelling in speed
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