Significance: Microglia, a type of macrophage, were discovered a little more than a century ago by Pío del Río-Hortega. Since that time, we have gained an immense amount of knowledge on their origin and multifaceted function with the aid of labeling techniques and animal models, among other tools. Only recently have macrophage cells been imaged in living humans. Here we characterize macrophage spatial distribution and temporal dynamics in live human eyes using a label-free adaptive optics imaging approach. This investigation lays a foundation to better understand the body’s immune response not only to ocular diseases like glaucoma, but also to a vast array of neurological diseases with ocular manifestations, including Alzheimer’s disease, Parkinson’s disease, and multiple sclerosis.

Adaptive optics (AO), when coupled to different imaging modalities, has enabled resolution of various cell types across the entire retinal depth in the living human eye. Extraction of information from retinal cells is optimal when their optical properties, structure, and physiology are matched to the unique capabilities of each imaging modality. Despite the earlier success of multimodal AO (mAO) approaches, the full capabilities of the individual imaging modalities were often diminished rather than enhanced when integrated into multimodal platforms. Furthermore, many mAO designs added unnecessary complexity, making clinical translation difficult. In this study, we present a novel mAO system that combines two complementary approaches, scanning laser ophthalmoscopy (SLO) and optical coherence tomography (OCT), in one instrument using a simplified optical design, flexible alternation of scanning modes, and independent focus control. The mAO system imaging performance was demonstrated by visualization of cells in their mosaic arrangement across the full depth of the retina in three human subjects, including microglia, nerve fiber bundles, retinal ganglion cells and axons, and capillaries in the inner retina and foveal cones, peripheral rods, and retinal pigment epithelial cells in the outer retina. Multimodal AO is a powerful tool to capture the most complete picture of retinal health.

In vivo imaging of human retinal pigment epithelial (RPE) cells has been demonstrated through multiple adaptive optics (AO)-based modalities. However, whether consistent and complete information regarding the cellular structure of the RPE mosaic is obtained across these modalities remains uncertain due to limited comparisons performed in the same eye. Here, an imaging platform combining multimodal AO-scanning light ophthalmoscopy (AO-SLO) with AO-optical coherence tomography (AO-OCT) is developed to make a side-by-side comparison of the same RPE cells imaged across four modalities: AO-darkfield, AO-enhanced indocyanine green (AO-ICG), AO-infrared autofluorescence (AO-IRAF), and AO-OCT. Co-registered images were acquired in five subjects, including one patient with choroideremia. Multimodal imaging provided multiple perspectives of the RPE mosaic that were used to explore variations in RPE cell contrast in a subject-, location-, and even cell-dependent manner. Estimated cell-to-cell spacing and density were found to be consistent both across modalities and with normative data. Multimodal images from a patient with choroideremia illustrate the benefit of using multiple modalities to infer the cellular structure of the RPE mosaic in an affected eye, in which disruptions to the RPE mosaic may locally alter the signal strength, visibility of individual RPE cells, or even source of contrast in unpredictable ways.

We describe the design and performance of a multimodal and multifunctional adaptive optics (AO) system that combines scanning laser ophthalmoscopy (SLO) and optical coherence tomography (OCT) for simultaneous retinal imaging at 13.4 Hz. The high-speed AO-OCT channel uses a 3.4 MHz Fourier-domain mode-locked (FDML) swept source. The system achieves exquisite resolution and sensitivity for pan-macular and transretinal visualization of retinal cells and structures while providing a functional assessment of the cone photoreceptors. The ultra-high speed also enables wide-field scans for clinical usability and angiography for vascular visualization. The FDA FDML-AO system is a powerful platform for studying various retinal and neurological diseases for vision science research, retina physiology investigation, and biomarker development.

Cell-level quantitative features of retinal ganglion cells (GCs) are potentially important biomarkers for improved diagnosis and treatment monitoring of neurodegenerative diseases such as glaucoma, Parkinson’s disease, and Alzheimer’s disease. Yet, due to limited resolution, individual GCs cannot be visualized by commonly used ophthalmic imaging systems, including optical coherence tomography (OCT), and assessment is limited to gross layer thickness analysis. Adaptive optics OCT (AO-OCT) enables in vivo imaging of individual retinal GCs. We present an automated segmentation of GC layer (GCL) somas from AO-OCT volumes based on weakly supervised deep learning (named WeakGCSeg), which effectively utilizes weak annotations in the training process. Experimental results show that WeakGCSeg is on par with or superior to human experts and is superior to other state-of-the-art networks. The automated quantitative features of individual GCLs show an increase in structure–function correlation in glaucoma subjects compared to using thickness measures from OCT images. Our results suggest that by automatic quantification of GC morphology, WeakGCSeg can potentially alleviate a major bottleneck in using AO-OCT for vision research.

Purpose: To characterize retinal ganglion cell morphological changes in patients with primary open-angle glaucoma associated with hemifield defect (HD) using adaptive optics–optical coherence tomography (AO-OCT). Methods: Six patients with early to moderate primary open-angle glaucoma with an average age of 58 years associated with HD and six age-matched healthy controls with an average age of 61 years were included. All participants underwent in vivo retinal ganglion cell (RGC) imaging at six primary locations across the macula with AO-OCT. Ganglion cell layer (GCL) somas were manually counted, and morphological parameters of GCL soma density, size, and symmetry were calculated. RGC cellular characteristics were correlated with functional visual field measurements. Results: GCL soma density was 12,799 ± 7747 cells/mm², 9370 ± 5572 cells/mm², and 2134 ± 1494 cells/mm² at 3°, 6°, and 12°, respectively, in glaucoma patients compared with 25,058 ± 4649 cells/mm², 15,551 ± 2301 cells/mm², and 3891 ± 1105 cells/mm² (P < 0.05 for all locations) at the corresponding retinal locations in healthy participants. Mean soma diameter was significantly larger in glaucoma patients (14.20 ± 2.30 µm) compared with the health controls (12.32 ± 1.94 µm, P < 0.05 for all locations); symmetry was 0.36 ± 0.32 and 0.86 ± 0.13 in glaucoma and control cohorts, respectively. Conclusions: Glaucoma patients had lower GCL soma density and symmetry, greater soma size, and increased variation of GCL soma reflectance compared with age-matched control subjects. The morphological changes corresponded with HD, and the cellular level structural loss correlated with visual function loss in glaucoma. AO-based morphological parameters could be potential sensitive biomarkers for glaucoma.