Spectral-domain optical coherence tomography of the rodent eye: highlighting layers of the outer retina using signal averaging and comparison with histology

Abstract

Spectral-Domain Optical Coherence Tomography (SD-OCT) is a widely used method to observe retinal layers and follow pathological events in human. Recently, this technique has been adapted for animal imaging. This non-invasive technology brings a cross-sectional visualization of the retina, which permits to observe precisely each layer. There is a clear expansion of the use of this imaging modality in rodents, thus, a precise characterization of the different outer retinal layers observed by SD-OCT is now necessary to make the most of this technology. The identification of the inner strata until the outer nuclear layer has already been clearly established, while the attribution of the layers observed by SD-OCT to the structures corresponding to photoreceptors segments and retinal pigment epithelium is much more questionable. To progress in the understanding of experimental SD-OCT imaging, we developed a method for averaging SD-OCT data to generate a mean image allowing to better delineate layers in the retina of pigmented and albino strains of mice and rats. It allowed us to locate precisely the interface between photoreceptors and retinal pigment epithelium and to identify unambiguously four layers corresponding to the inner and outer parts of photoreceptors segments. We show that the thickness of the various layers can be measured as accurately in vivo on SD-OCT images, than post-mortem by a morphometric analysis of histological sections. We applied SD-OCT to different models and demonstrated that it allows analysis of focal or diffuse retinal pathological processes such as mutation-dependent damages or light-driven modification of photoreceptors. Moreover, we report a new method of combined use of SD-OCT and integration to quantify laser-induced choroidal neovascularization. In conclusion, we clearly demonstrated that SD-OCT represents a valuable tool for imaging the rodent retina that is at least as accurate as histology, non-invasive and allows longitudinal follow-up of the same animal.

Figure 1. Retinal layer thickness measures in C57BL/6JRj wild-type mice by SD-OCT and histology.

Retinal thickness in nasal and temporal sides in SD-OCT image (A) and in corresponding histological section (B). (C) Measures of retinal layers thickness by SD-OCT and histology in C57BL/6JRj mice, n = 11, Mann Whitney test. (D) Retinal thickness evaluated by SD-OCT and histology in C57BL/6JRj mice. Each pair of point represents the whole retina thickness of the same eye measured with SD-OCT (blue dots) and histology (orange dots). IPL: inner plexiform layer, INL: inner nuclear layer, OPL: outer plexiform layer, ONL: outer nuclear layer, OLM: outer limiting membrane, RPE: retinal pigmented epithelium. SD: Standard Deviation. Scale bars: 50 µm.

Figure 2. Characterization of pigmented and albino retina layers by SD-OCT.

Histological sections of C57BL/6JRj pigmented mouse retina (A) and BALB/cJ albino mouse retina (C). SD-OCT images of pigmented mouse retina (B) and albino mouse retina (D). Retinal detachment induced by subretinal oil injection in C57BL/6JRj pigmented mouse (E) and BALB/cJ albino mouse (F). Schematic representations of pigmented mouse outer retina (G) and albino mouse outer retina (I). Zoom on pigmented mouse outer retina (H) and albino mouse outer retina (J). GCL: ganglion cell layer, IPL: inner plexiform layer, INL: inner nuclear layer, OPL: outer plexiform layer, ONL: outer nuclear layer, OLM: outer limiting membrane, IS: inner segments, OS: outer segments, RPE: retinal pigmented epithelium, BM: Bruch's membrane. Scale bar: 50 µm.

Figure 3. Characterization of a retinal degeneration mouse model by SD-OCT.

SD-OCT images of control mice retina (A) and rho−/− mice retina (B) from post-natal day 21 (P21) to 180 (P180). Magnification (X2.4) of P21 and P180 control mice outer retina (C) and rho−/− mice (D). (E) Measures of INL thickness obtained from SD-OCT data in control and rho−/− mice (P21: p = 0.0123; P180: p = 0.7125). (F) Measures of ONL thickness obtained from SD-OCT data in control and rho−/− mice (P21 and P180: p<0.0001). (G) Measures of ONL thickness obtained from morphometric measurements on cryostat sections in control and rho−/− mice (P15 and P180: p = 0.0022). Statistical significance of the difference between groups was analyzed at the initial time-point (P15 or P21) and the latest time-point (P180) studied by Student's T-test for E and F (n = 23 per group) and by Mann Whitney test for G (n = 6 per group). IPL: inner plexiform layer, INL: inner nuclear layer, ONL: outer nuclear layer, OLM: outer limiting membrane, RPE: retinal pigmented epithelium. SD: Standard Deviation. Scale bars: 50 µm.

Figure 4. SD-OCT imaging in other pathological models: rd8 mutation and light-challenge.

(A) Typical ocular lesions of rd8 mutation in crb1 gene (C57BL/6NRj mice in which presence of the rd8 mutation was confirmed by genotyping). (B–C) SD-OCT follow-up of the outer retina during a light-challenge in C57BL/6JRj mice. Control unexposed three month-old mouse has a normal appearance with 4 bands of different reflectance corresponding to the PR segments (B). Mice were then exposed to light during 4 days as described in the “methods” section and the retina was imaged by SD-OCT at day 3 (D3), 7, 14 and 21 after starting the illumination (C). The light-challenge leads to a temporary abolition of the distinction between the two bands forming the outer segment, with a peak at D7 (right panels: enlargement of the area enclosed by a white box on the left view). INL: Inner Nuclear Layer, ONL: Outer Nuclear Layer, IS: Inner Segments, OS: Outer Segments. Scale bars: 50 µm.

Figure 5. Quantification of laser-induced choroidal neovascularization (CNV) in C57BL6/JRj.

(A) Laser-induced CNV (Yag 532 Eyelite parameters: 100 µm, 50 ms, 400 mW) was visualized immediately after laser impact using SD-OCT imaging as described in the “materials and methods” section. Based on this image, a CNV volume is extrapolated using the following formula (4/3π*a*b²)/2, in which a is the polar radius and corresponds to the measure along the vertical axis and b is the equator radius and corresponds to the horizontal axis. (B) Linear regression showing that data obtained from extrapolation or Imaris 3D reconstruction (described step by step hereafter) are statistically equivalent (r² = 0,94, n = 8). (C) Imaris software allows a 3D rendering of SD-OCT imaging. Data shown here arise from the same SD-OCT sequence than shown in panel A. (D) The neovascularization volume, just above the RPE cell layer, was delimitating manually (representative white dotted line in one slice) in about 20 slices (over 100) along z-axis to create a 3D mask. Based on this manual delimitation the Imaris software computed a 3D mask shown in yellow (E). The final visualization, that allowed CNV volume quantification, was obtained after automated mask thresholding (F). OPL: Outer Plexiform Layer, RPE: Retinal Pigmented Epithelium, CHO: Choroid. Scale bar: 50 µm.