Optical Coherence Tomography Angiography in Mice: Comparison with Confocal Scanning Laser Microscopy and Fluorescein Angiography

Abstract

Purpose: Optical coherence tomography angiography (OCT-A) allows noninvasive visualization of retinal vessels in vivo. OCT-A was used to characterize the vascular network of the mouse retina and was compared with fluorescein angiography (FA) and histology.

Methods: In the present study, OCT-A based on a Heidelberg Engineering Spectralis system was used to investigate the vascular network in mice. Data was compared with FA and confocal microscopy of flat-mount histology stained with isolectin IB4. For quantitative analysis the National Cancer Institute's AngioTool software was used. Vessel density, the number of vessel junctions, and endpoints were measured and compared between the imaging modalities.

Results: The configuration of the superficial capillary network was comparable with OCT-A and flat-mount histology in BALBc mice. However, vessel density and the number of vessel junctions per region of interest (P = 0.0161 and P = 0.0015, respectively) in the deep vascular network of BALBc mice measured by OCT-A was significantly higher than with flat-mount histology. In C3A.Cg-Pde6b+Prph2Rd2/J mice, where the deep capillary plexus is absent, analysis of the superficial network provided similar results for all three imaging modalities.

Conclusion: OCT-A is a helpful imaging tool for noninvasive, in vivo imaging of the vascular plexus in mice. It may offer advantages over FA and confocal microscopy especially for imaging the deep vascular plexus.

Translational relevance: The present study shows that OCT-A can be employed for small animal imaging to assess the vascular network and offers advantages over flat-mount histology and FA.

Keywords: confocal microscopy; confocal scanning microscopy; fluorescein angiography; optical coherence angiography; retinal vasculature.

Figure 1

Location of the superficial and deep plexus in the mouse retina. (A) Infrared fundus picture of the posterior pole of the mouse eye imaged with the OCT-A device. Yellow arrows indicate the selected area of the B-scan presented in (B). (B) Automatic segmentation of retinal layers using the Heidelberg Eye Explorer Software. The distance between the ILM and the IPL and between the IPL and the OPL was manually measured. (C) Based on the thickness measurements in (B) manual retinal layer segmentation was possible for the identification of the SVP (upper panel) and DVP (lower panel) located between the ILM and the outer boundary of the IPL and between the IPL and the outer boundary of the OPL, respectively. (D) Superficial (Sp) and deep (D) vessels are also visible in histology in the same retinal layers as described above. Arrows indicate vertical cuts of vessels in each layer. Scale bar, 100 μm.

Figure 2

Comparison of the retinal vasculature using different imaging modalities. SVP (A) and DVP (B) of the mouse retina obtained with OCT-A (A, B) FA (C), or confocal microscopy ([D] Scale bar, 100 μm). Red square delineates the corresponding areas of the superficial plexus that could be visualized in all three imaging modalities (n = 13).

Figure 3

Analysis of different features of the retinal vasculature using the AngioTool software. (A) Vessels of the superficial (Sp, left panel) and the deep (D, right panel) vasculature were automatically outlined using the AngioTool software, indicated by the yellow lines surrounding the vessels. Both OCT-A (upper panel) and flat-mount (bottom panel) pictures were included. In FA images (FAG) both the SVP and DVP were included in the quantification. (B) After the delineation of retinal vessels, the AngioTool software automatically calculated vessel density (red lines), number of end points (blue dots indicated with dark arrows) and number of junctions (blue dots indicated with white arrows). Scale bar, 100 μm. (C) Bar graphs were generated with GraphPad Prism and analyzed using unpaired t-tests. No statistically significant difference was observed comparing the superficial plexus calculated from OCT-A or flat-mounts, concerning vessels density, end points, and number of junctions. However, comparing the same values in the deep plexus we observed significant differences between OCT-A and flat-mounts (number of junctions: P = 0.0015; number of endpoints: P = 0.0071), although the vessel density showed less discrepancy (P = 0.0166). FA was not included in the quantification: the bar graph shows the absolute values of both the Sp and D plexus as the layers were not separately analyzed.

Figure 4

OCT-A imaging of C3A.Cg-Pde6b+Prph2Rd2/J mice. (A, B) Representative images illustrate retinal thickness in C3A.Cg-Pde6b+Prph2Rd2/J (A) mice and wild-type mice (B), respectively. (C) Vessel area (%) in SVP of wild-type mice and C3A.Cg-Pde6b+Prph2Rd2/J mice. No statistically significant difference in vessel density was observed comparing FA with OCT-A or flat-mount histology (unpaired t-test, ns, not significant).