Quantitative Assessment of Experimental Ocular Inflammatory Disease

Abstract

Ocular inflammation imposes a high medical burden on patients and substantial costs on the health-care systems that mange these often chronic and debilitating diseases. Many clinical phenotypes are recognized and classifying the severity of inflammation in an eye with uveitis is an ongoing challenge. With the widespread application of optical coherence tomography in the clinic has come the impetus for more robust methods to compare disease between different patients and different treatment centers. Models can recapitulate many of the features seen in the clinic, but until recently the quality of imaging available has lagged that applied in humans. In the model experimental autoimmune uveitis (EAU), we highlight three linked clinical states that produce retinal vulnerability to inflammation, all different from healthy tissue, but distinct from each other. Deploying longitudinal, multimodal imaging approaches can be coupled to analysis in the tissue of changes in architecture, cell content and function. This can enrich our understanding of pathology, increase the sensitivity with which the impacts of therapeutic interventions are assessed and address questions of tissue regeneration and repair. Modern image processing, including the application of artificial intelligence, in the context of such models of disease can lay a foundation for new approaches to monitoring tissue health.

Keywords: EAU; OCT; automated analysis; image processing; uveitis.

Figure 1

Tissue states in ocular inflammation. Healthy ocular tissue is ‘immune-privileged’ and under low-level immunosurveillance. Specific (ocular antigen driven) and non-specific (extra-ocular inflammation) stimuli disturb this homeostasis and increase interactions across the blood retinal barrier making the tissue more vulnerable to the development of disease. In uveitis following active immunization, this starts with the prodrome (8), which can resolve back to the healthy state. When the prodrome progresses to clinical EAU in immunocompetent animals, there is an influx of cells to a maximum (peak) followed by a reduction in immune cell content, which does not return to base line. The post-peak (in EAU described as secondary regulation) is distinguished from the pre-peak by changes in the relative proportion of different lymphocyte populations (CD4 T regulatory cells, CD8 T resident memory cells). There is currently no evidence that disease proceeds directly from pre-peak to post-peak, nor that eyes that have reached peak disease ever return to the normal healthy state.

Figure 2

Clinical score can be insensitive to underlying pathology. Mouse eyes imaged using Micron IV with OCT (Phoenix technology group, CA). Two mouse eyes (A, C) and (B, D) imaged using Micron IV (Phoenix technology group, CA) and assessed by fundal photography (A, B) and OCT (C, D). Retinal photographs scored in a set of images by an observer blinded to the treatment groups, both received the same summary clinical score. Scale bar 100 µm.

Figure 3

OCT of the normal mouse retina delineates layers and allows retinal dimensions to be quantified. Scale bars are 100 microns and illustrate differences in axial and lateral resolution. GCL, ganglion cell layer; IPL, inner plexiform layer; INL, inner nuclear layer; OPL, outer plexiform layer; ONL, outer nuclear layer; ELM, external limiting membrane; IS/OS, inner and outer segments; RPE, retinal pigment epithelium (68).

Figure 4

Multimodal analysis of EAU. Mouse eyes were imaged at day 0 and day 13 after the induction of EAU and one representative image of the same eye is shown (A–C). Clinical disease can be assessed by photography (A), measurements of retinal thickness and optic nerve diameter at three points from the temporal, nasal and optic nerve regions of the OCT B-scans (B), 3D-reconstuction of retinal infiltrate (C) and summary data of retinal scores from all groups (D). Summary scores are assembled from unsupervised quantitative assessment of vitreal involvement, manual segmentation and measurement of inner and outer layer thickness and optic nerve diameter transformed and represented as Z-scores.

Figure 5

Changes in retinal thickness in mouse eyes following intra-vitreal paraquat instillation were measured on day 10. Images were visualized by OCT, manually segmented, and measured at three points in the temporal, nasal, and optic nerve regions. Measurements are expressed as positive and negative Z-scores relative to a PBS injected control group. Changes in the inner and outer layers are decoupled.