Photoreceptor-Based Biomarkers in AOSLO Retinal Imaging

Abstract

Improved understanding of the mechanisms underlying inherited retinal degenerations has created the possibility of developing much needed treatments for these relentless, blinding diseases. However, standard clinical indicators of retinal health (such as visual acuity and visual field sensitivity) are insensitive measures of photoreceptor survival. In many retinal degenerations, significant photoreceptor loss must occur before measurable differences in visual function are observed. Thus, there is a recognized need for more sensitive outcome measures to assess therapeutic efficacy as numerous clinical trials are getting underway. Adaptive optics (AO) retinal imaging techniques correct for the monochromatic aberrations of the eye and can be used to provide nearly diffraction-limited images of the retina. Many groups routinely are using AO imaging tools to obtain in vivo images of the rod and cone photoreceptor mosaic, and it now is possible to monitor photoreceptor structure over time with single cell resolution. Highlighting recent work using AO scanning light ophthalmoscopy (AOSLO) across a range of patient populations, we review the development of photoreceptor-based metrics (e.g., density/geometry, reflectivity, and size) as candidate biomarkers. Going forward, there is a need for further development of automated tools and normative databases, with the latter facilitating the comparison of data sets across research groups and devices. Ongoing and future clinical trials for inherited retinal diseases will benefit from the improved resolution and sensitivity that multimodal AO retinal imaging affords to evaluate safety and efficacy of emerging therapies.

Figure 1

Variability of the foveal cone mosaic in achromatopsia. Split-detector AOSLO images from the right eye of two different subjects with CNGB3-associated achromatopsia (and no cone function). (A) 16-year-old female with low peak cone density (9917 cones/mm²). (B) 37-year-old male with relatively high peak cone density (44,959 cones/mm²). Peak cone density was measured as reported by Langlo et al. Implications of this level of interindividual variability in remnant cone structure for defining the therapeutic potential of a given retina remain to be elucidated, though it is worth noting that the visual acuity of these two subjects was markedly different (20/800 for the subject in [A] and 20/100 for the subject in [B]). Scale bar: 100 μm.

Figure 2

Confocal AOSLO images from a normal retina, displayed on a logarithmic scale. (A) Tightly packed cones in the fovea and (B) Cone and rod mosaic at 10° temporal to fixation. Right eye from a 27-year-old female (JC_11142). Scale bar: 50 μm.

Figure 3

Resolving cone inner and outer segment structure with AOSLO. Shown are confocal (A) and split-detection (B) images from the parafoveal retina of a patient with CNGA3-associated ACHM. The color-merged image (C) has the confocal image displayed in green and the split-detection image in red. Scale bar: 50 μm. (D) Photoreceptor schematic based off of a model presented by Jonnal et al. – the signal (I) requires intact photoreceptors^, and can vary as a result of small perturbations in photoreceptor structure. Multiply-scattered light from the RPE and inner segments is rejected by confocal AOSLO.

Figure 4

A schematic of a hexagonally arranged patch of cones illustrating the relationship between the distance measurements used by Cooper et al. A single cone (red circle) and its six closest neighbors (open circles) are highlighted for clarity. The NND is defined as the distance from a given cone to its closest neighbor (orange dashed line). The FND is defined as the distance from each cone to its most distant neighbor (blue dashed line), and ICD is defined as the average distance between a cone and all of its neighbors (dashed lines). To mitigate boundary effects, only cones with bound Voronoi regions (shaded region) are included when calculating each metric. The regularity of each of these metrics (M) is defined as the mean (μ_M) of the metric for all cones with bounded Voronoi cells, divided by the metric's SD (σ_M). Reprinted from Cooper RF, Wilk MA, Tarima S, Carroll J. Evaluating descriptive metrics of the human cone mosaic. Invest Ophthalmol Vis Sci. 2016;57:2992–3001. Licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Figure 5

Sensitivity and robustness of metrics in detecting cone loss. An illustration of the effect of cone undersampling on histograms of cell distances (NND, ICD, FND) and the DRPD from a single subject (JC_10145). A region of interest (37 × 37 μm sampling window) at 200 μm from the fovea was selected from confocal AOSLO, and cone coordinates were seminautomatically identified. For undersampled mosaics, 40% and 80% of the cone coordinates from the normal mosaic were removed by random distribution using the randperm MATLAB function of cone coordinate list. These mosaics are illustrated in the first column. In each plot, the blue dashed line is the mean of the histogram from the complete mosaic, while the orange dashed line is the mean of the histograms from the 40% (middle row) and the 80% undersampled mosaics (bottom row). On all plots, the y-axis is the number of cells within each histogram bin. The NND histogram is only marginally affected (indicated by the similarity in the blue and orange dashed lines), even with an 80% loss. Similarly, the DRPD is largely unaffected by cell loss; its estimated spacing is only affected when the bin size increases (bottom right) due to a decrease in density. In contrast, the mean (indicated by further separation of the blue and orange dashed lines) and spread of both ICD and FND increase substantially with cell loss. Reprinted from Cooper RF, Wilk MA, Tarima S, Carroll J. Evaluating descriptive metrics of the human cone mosaic. Invest Ophthalmol Vis Sci. 2016;57:2992–3001. Licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Figure 6

Visualizing enlargement of photoreceptor inner segments with split-detector AOSLO. In a normal retina, cones in the fovea typically are not visible using split-detector AOSLO. Foveal images from a subject with closed-globe blunt ocular trauma (A) show heterogeneous cone diameters surrounding the central lesion, while images from a subject with GUCY2D-associated cone-rod dystrophy (B) show a small island of contiguously-packed enlarged cones. Normal cones at 1036 μm (∼3°) from the fovea (C), compared to enlarged cones within the transition zone from subjects with retinitis pigmentosa (D) and choroideremia (E) at the same eccentricity as (C). Scale bars: 100 μm (A, B) and 10 μm (C–E). (A) and (B) are reprinted from Scoles D, Flatter JA, Cooper RF, et al. Assessing photoreceptor structure associated with ellipsoid zone disruptions visualized with optical coherence tomography. Retina. 2016;36:91-103. © 2016 by Ophthalmic Communications Society, Inc. (C) and (D) reprinted from Sun LW, Johnson RD, Langlo CS, et al. Assessing photoreceptor structure in retinitis pigmentosa and Usher syndrome. Invest Ophthalmol Vis Sci. 2016;57:2428-2442. Licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License. (E) Reprinted from Sun LW, Johnson RD, Williams V, et al. Multimodal imaging of photoreceptor structure in choroideremia. PLoS One. 2016;11:e0167526. Licensed under a Creative Commons Attribution License.