In vivo cellular-resolution imaging of retina: modality, cells, and clinical implications

The retina, a crucial component of the human eye for vision, is responsible for converting light signals into neural signals that the brain can interpret. It’s a complex tissue, rich in photoreceptors, and supported by various other cell types, including inner nuclear layer cells, ganglion cells, pigmented epithelial cells, immune cells, and vascular cells. Each of these cells plays a vital role in visual processing and understanding of their function and interactions are essential for assessing vision health and diagnosing diseases. Traditionally, studying the retinal cells has relied heavily on histological techniques, which, despite their utility, offer only static images and require invasive procedures that preclude the observation of dynamic biological processes. In this context, recent advancements of in vivo imaging technologies have marked a significant leap forward. Techniques such as ophthalmoscopy, optical coherence tomography (OCT), adaptive optics (AO), two-photon excitation microscopy (TPM), and light-sheet fluorescence microscopy (LSFM) now enable the direct observation of retinal cells in living organisms. This shift from invasive, static methods to dynamic, non-destructive imaging allows for a more nuanced understanding of retinal cell behavior under physiological conditions. It opens up new avenues for the study of the retina’s complex ecosystem in both health and disease, facilitating early diagnosis of retinal conditions and offering new strategies for treatment. By offering a window into the live retina, in vivo imaging stands as a cornerstone of contemporary ophthalmology, promising to enhance our understanding of eye health and to spur innovations in the diagnosis and treatment of ocular diseases.

1 Introduction

Visual processing begins with light passing through the cornea and the lens, projecting onto the retina; transporting neural transduction of these light signals via the optic nerve to the cerebral cortex (1–4). Among them, the retina is a thin layer of tissue and shares an embryonic origin with the central nervous system (1). In the retina, the visual circuit involves light sensing by photoreceptors (3, 5–8), initial processing by cells in inner nuclear layer (INL) such as bipolar cells (4, 9) and amacrine cells (10), and feature extraction by retinal ganglion cells (RGCs) (11). Additionally, these processes are supported by cells in retinal pigment epithelium (RPE) (12–14), immune system (15–18), and vasculature (19, 20). In recent years, there has been growing recognition that many retinal diseases begin with dysfunction or loss at the cellular level long before traditional clinical imaging can detect structural abnormalities. Photoreceptor stress (21), RPE metabolic changes (22, 23), early microvascular instability (24), and activation of microglia (25) and Müller glia (26, 27) often precede measurable retinal thinning or visual decline.

With micron- or submicron- resolution provided by confocal microscopy and super-resolution microscopy in histological tissue examinations from animal models or humans, retinal cells and their ultra-structures can be revealed to gain knowledge of cellular function (28, 29). Immunostaining techniques can highlight specific targets to further improve visualization (30) and delineate between the cells and organelles (31–33). Furthermore, electron microscopy (EM), along with its various iterations, can visualize retinal structures at the nanometer level (29, 34). However, these imaging modalities are subjected to drawbacks including static snapshot, destructive process, time-consuming sample preparation, artifacts due to processing, and ethical and practical constraints.

In vivo imaging techniques are superior by overcoming these challenges, allowing longitudinal monitoring of ocular disease states across weeks and months, providing information about natural disease course, aging, and treatment efficacy both in research and clinics (9, 19, 35, 36). The history of in vivo ophthalmic imaging dates to the invention of the first ophthalmoscopy by Hermann Von Helmholtz (37, 38). Since that time, generations of physicians and engineers have improved upon his design to advance in this field. Digital fundus photography captures the true color image of the retina for quantitative analysis (39–41). Later, scanning laser ophthalmoscopy (SLO) offers higher contrast than fundus photography due to its capability to reduce scattering effects and allows for evaluation of fundus autofluorescence (42). Additionally, indocyanine green angiography (ICGA) and fluorescein angiography (FA) reveals the retinal and choroidal vasculature, both of which have advanced ophthalmologists’ understanding of retinal diseases (43–48). Further, the invention of optical coherence tomography (OCT) in the 1990s revolutionized ophthalmic imaging and quickly became an indispensable tool for all retinal clinicians and researchers (49). Retinal laminar tissue, blood vessels, and various lesions such as thickness thinning, edema, leakage, and neovascularization can now be readily examined in patients to assist in the diagnosis and management of ocular diseases (50–54).

Conventional modalities provide invaluable macroscopic views but remain limited in their ability to resolve individual cells or to monitor subtle cellular events that drive disease progression. The challenges preventing the visualization of retinal cells in vivo include limited aperture and resolution, motion due to breath and heartbeat, light safety concerns, and aberrations generated by the eye and the optical system (55–57). Recent technological advances in hardware and post-processing offer advantages in overcoming these difficulties towards cellular resolution retinal imaging (36, 58–63). Emerging technologies now enable in vivo visualization of individual photoreceptors (59, 64), ganglion cells (65, 66), RPE cells (64, 67), immune cells (68, 69), and microvascular elements (70, 71), offering unprecedented insight into disease onset and dynamics. Cellular-resolution imaging has already begun to show clinical relevance: adaptive optics (AO) can quantify photoreceptor integrity in inherited retinal diseases (72); visible light OCT (vis-OCT) provides enhanced layer contrast and enables retinal oximetry (73); full field OCT (FF-OCT) (74) can detect early microstructural abnormalities; and dynamic contrast OCT (DyC-OCT) (75, 76) and two-photon excitation microscopy (TPM) (77) have revealed functional cellular responses previously accessible only through histology or animal models. These capabilities open new pathways for early diagnosis, monitoring treatment response, evaluating neuroprotective therapies, and understanding the mechanisms underlying disorders such as age-related macular degeneration (AMD), diabetic retinopathy, glaucoma, and optic neuropathies. Thus, the transition from tissue-level to cellular-level retinal imaging represents a crucial evolution in both research and clinical care, providing the opportunity to detect disease earlier, stratify risk more precisely, and evaluate therapeutic efficacy at the level where pathology originates. In this review, we will highlight recent developments in in vivo retinal imaging techniques. A general overview of emerging imaging modalities for in vivo cellular imaging of retina are summarized in Section 2. Following that, we introduce studies on individual retinal cell types in Section 3, including the photoreceptors, INL cell bodies, RGCs, as well as supporting cells such as RPE cells, immune cells, blood cells. Finally, we discuss the potential clinical impact of these emerging technologies for the early diagnosis of retinal diseases in Section 4. We hope this review will provide perspectives for this exciting field and facilitate advancements in widespread clinical adoption.

2 General overview of imaging modalities

2.1 Scanning laser ophthalmoscopy

Ophthalmoscopy is crucial in examining the retina and optic disc; it has undergone continuous advancement since its inception and is readily utilized in ophthalmology clinics. Fundus photography was first commercially produced by Carl Zeiss in 1926 (78). Hansell and Beeson first proposed a new compact xenon arc lamp (FA5) in the Zeiss-Nordenson retinal camera to provide the existing system with an improved light source (79). Nevertheless, the optical and mechanical complexity of early devices limited their clinical utilization. With the development of the hand-held and digital fundus camera, contemporary ophthalmologists can easily capture retinal images with high resolution to assist the diagnosis of eye diseases (80–83).

SLO was first demonstrated by Webb et al. in 1981 (Figure 1A) (84); however, its non-confocal design suffered from loss of contrast because it accepted all reflected light (85). In 1997, Webb et al. presented the principles of confocal SLO (cSLO) that provided a higher contrast view of the fundus (86). Confocal SLO uses a highly collimated narrow beam of light in a small region to sweep across the retina. The confocal aperture (pinhole) minimizes scattered reflected light by allowing only focused light to reach the photodetector. The applications of cSLO with its high-resolution include identifying early glaucoma cases (87), examining choroidal circulation (88), and detecting retinal ganglion cell damage (89).

Figure 1

Figure 1. Schematic of the principles of five imaging modalities: (A) Scanning Laser Ophthalmoscopy, SLO; (B) Optical Coherence Tomography, OCT; (C) Adaptive Optics, AO; (D) Two-photon Excitation Microscopy, TPM; (E) Light-sheet Fluorescence Microscopy, LSFM.

Although SLO enjoys great popularity in ophthalmology, it also faces challenges for in vivo imaging. Eye motion poses a significant problem, making correction of motion distortions necessary (90). To date, many eye tracking techniques have been reported (91–93). Compared with the fundus camera, another limitation of SLO is its monochrome images. Although methodologies combining three wavelengths (red, green and blue) have been used to generate color SLO images (94), image quality is reduced due to significant loss of light.

2.2 Optical coherence tomography

OCT is an efficient and non-invasive imaging technique which can provide high-resolution cross-sectional images of biological tissues (49). It is based on the principle of Michelson interferometry, utilizing interference between the light beam passing through the sample (in this case the retina) and a reference beam to generate images (Figure 1B). The advantages of OCT, namely its high-resolution sectioning and high contrast imaging abilities, enable its application in many clinical fields such as cardiology, dermatology, gastroenterology, and ophthalmology (95–100).

OCT methods can be classified into two types, time domain (TD)-OCT and Fourier domain (FD)-OCT (101). When OCT was first reported by Huang et al. in 1991, time-domain detection was used to image ex vivo retinas and coronary arteries (49). FD-OCT, including spectral domain (SD)-OCT and swept-source (SS)-OCT (102, 103), features a relatively higher scanning speed and eliminates the need for depth scanning (104). SD-OCT measures all wavelengths of light simultaneously using a spectrometer, allowing for a higher sensitivity than TD-OCT and faster scanning speeds (29,000 to 80,000 A-scans per second) (105). Compared to SD-OCT, SS-OCT features a source modulation scheme to achieve the wideband light detection with narrow-linewidth, frequency-swept laser (106). The Fourier domain mode locking (FDML) laser enables SS-OCT to achieve an incredible A-scan rate of up to 3.35 MHz at 1060nm, thus enabling the capture of transient activities at high volume rate (107).

To improve sensitivity and specificity of diagnosis, numerous endeavors have been made by researchers to achieve higher resolution in OCT. The broadband light source, like multiplexed super luminescent diodes (SLD), improves the axial resolution of OCT, according to the principle of OCT imaging (108–110). For instance, Drexler et al. presented ophthalmic OCT systems that could image retinal and corneal morphology with an axial resolution of 2-3 µm based on broadband Ti: Al₂O₃ laser (111) and achieved the theoretical axial resolution of 1 µm in biological tissue by using a Kerr-lens mode-locked Ti: sapphire laser in TD-OCT (112). Later, researchers developed high-speed ultrahigh-resolution OCT (UHR-OCT) systems by combining FD-OCT techniques and broadband lasers as FD-OCT significantly improves the sensitivity of OCT and imaging speed (105, 113). For example, Wojtkowski et al. demonstrated an UHR-FD-OCT with an axial resolution of 2.1 µm in tissue and 16,000 axial scans per second (105). In 2017, Werkmeister et al. developed an UHR-OCT system with the 1.2 µm × 20 µm (axial × transverse) resolution for human corneal imaging (114).

Different from axial resolution, it is difficult to increase the transverse resolution of OCT because there exist trade-offs between the transverse spot size and axial focal range, aberrations, and ranging depth (115). Hence, more work related to focus extension (116) and aberration correction (117) needs to be conducted to improve the imaging performance of OCT. Besides the AO to correct aberration, which will be introduced in next section, full-field OCT (FF-OCT) demonstrates an important solution in achieving high transverse resolution (118, 119). previously, it was primarily limited to ex vivo samples (120–122). Recently, with the improvement of cameras and other advancements, researchers were able to acquire the cellular retinal imaging in vivo in humans with the FF-OCT (123). Efforts are continuously made to push FF-OCT imaging reliable to be more suitable for clinical studies in patients in terms of real-time, high-sensitivity, and large field of view (74, 124–126).

Most OCT devices use near-infrared (NIR) light. However, OCT using visible light (vis-OCT) may offer extra benefits. Vis-OCT was first reported in 2002 (127). Compared with NIR-OCT, it has better axial resolution because the axial resolution has quadratic dependence on the center wavelength of the light source. For instance, Lichtenegger et al. achieved an axial resolution of 0.88 μm in brain tissue using a broad visible light spectrum (425–685 nm) (128). On top of that, the visible spectral range of vis-OCT makes it suitable for detecting biological tissues at shorter wavelengths. Nowadays, vis-OCT has been applied to image human eye. Yi et al. demonstrated the first human retinal imaging using vis-OCT, and the results show that vis-OCT has higher contrast for the photoreceptor inner and outer segment (IS/OS), the outer segment of photoreceptors (OS), and the retinal pigmented epithelium (RPE) imaging than NIR-OCT (129). Chen et al. proved the feasibility of vis-OCT oximetry in humans and increased its accuracy by using the statistical-fitting approach (130). Recently, Yi’s team achieved the first vis-OCT angiography for human retinal imaging, and they were capable of measuring sO₂ in vessels with diameter smaller than 100 µm (131).

Another new development capable of providing high-contrast imaging between cellular structures is dynamic contrast OCT (DyC-OCT). DyC-OCT is a label-free method that detects temporal variations in light signal intensity to illustrate changes in cellular activity and motion, producing functional maps of live cells and tissues across a wide range of timescales (132, 133). Uses of DyC-OCT include visualizing cell and tissue morphology by highlighting regions of high temporal signal variation (134), and assessment of cell viability by monitoring cellular responses to physical and chemical stimuli (135). However, most applications of DyC-OCT currently utilize ex vivo living tissues as the repetitive scanning needed for 3D DyC-OCT limits in vivo usage due to the presence of motion artifacts (133). The use of parallel OCT methods or machine-learning algorithms can be used to significantly reduce imaging time (133).

2.3 Adaptive optics

AO is a technology that was initially developed in astronomy to compensate for the blur-inducing aberrations caused by atmospheric turbulence (136). Since the first AO system was used in retinal imaging (137), AO has gained popularity in ophthalmology due to its ability to sharpen retinal images previously blurred by ocular aberrations (138). A conventional AO system for retinal imaging consists of three essential components: a wavefront sensor, a corrective element, and a control system (Figure 1C) (59). The wavefront sensor measures the ocular aberrations, which signals the AO control system to modify the corrective element to cancel out aberrations.

AO has been successfully combined with fundus cameras, scanning laser ophthalmoscopy, two-photon excitation microscopy, and optical coherence tomography (137, 139–141). The invention of Hartmann-Shack wavefront sensor and the deformable mirror (DM) made it possible for AO flood-illumination ophthalmology (AO-FIO) to perform single-cellular imaging in vivo of cone cells (137, 142). In 1997, Liang et al. reported the first AO fundus camera, which had the capability of imaging microscopic structures the size of single cells in the retina (137). Since that time, AO boost modality has been used to observe the microcystic changes in the inner retina of patients (143), visualize the vasculature in living human retina (144), and image foveal cones and rods (145). With the help of the wavefront sensor and the DM, Roorda et al. invented the first AO-SLO in 2002, which had a higher imaging quality than AO-FIO (140). AO-SLO has been widely used in the diagnosis of patients with eye diseases (146). Compared to OCT, the axial resolution of FIAO and AO-SLO is relatively low. Therefore, the combination of AO technology and OCT is essential, because it can achieve a higher axial resolution (below 3 µm) (139). In 2004, Hermann et al. combined AO and TD-OCT to improve the signal-to-noise (SNR) of the system up to 9 dB (147). Due to the high axial resolution of AO-OCT, it can provide detailed images of inner retinal layers (139, 147). Recently, AO has been combined with OCTA, generating significantly reduced shadowing artifacts of the inner retinal vasculature (148). Additionally, Zhang et al. have integrated OCT into an existing AO-SLO system for in vivo imaging of mice retina capable of providing a ~6 µm axial resolution for AO-OCT and ~1 µm lateral resolution for AO-SLO-OCT (149).

Recent developments in AO utilize machine learning to further clean up image. Zhou et al. created two semi-supervised models, RGC-CCT and RGC-CPS, capable of identifying ganglion cells from AO-OCT volumes with minimal manual annotation (150). Additionally, P-GAN, a separate model developed by Das et al., is capable of extracting RPE cell features muddied by speckle noise from a single AO-OCT scan, significantly reducing imaging time while preserving cellular imaging accuracy (151). Integrating AI in the post-processing pipeline provides promise for expanding AO imaging into clinical use by reducing scan acquisition time and mitigating operator dependency.

AO-based retinal imaging technology shows great potential in clinical utility (152–155). However, the combination of AO and ophthalmic modalities significantly increases cost and system complexity, which presently limits the commercialization of these systems.

2.4 Two-photon microscopy

Two-photon microscopy (TPM) provides large depth penetration and is very suitable for high-resolution deep imaging of living tissues (156). The idea of multiphoton excitation was first proposed by Maria Göppert-Mayer et al., and Franken et al. conducted related works in nonlinear optics (157, 158). The principle of TPM is that when two or more photons of a higher wavelength hit the fluorophore simultaneously, they are absorbed, resulting in fluorophore excitation and emission of light at half wavelength (Figure 1D). In 1963, Kaiser et al. reported the first two-photon excitation of CaF₂:Eu²⁺ fluorescence (159). Later, Denk et al. achieved two-photon fluorescence microscopy by a scanning microscope with ultrafast pulsed lasers (160).

TPM is an alternative to conventional single-photon confocal microscopy. Compared with single-photon confocal microscopy, TPM has three advantages. The first advantage is its ability of deep-tissue imaging (161, 162); TPM uses longer wavelengths which are less subject to absorption and scattering effects compared to the shorter wavelength light used in single-photon confocal imaging (162). TPM also exhibits increased efficiency compared to single photon confocal imaging by collecting all useful information about a single location to generate images (162, 163). This advantage also contributes to its high-resolution imaging in deep tissues, because higher fluorescence collection efficiency means greater signal intensity (higher photon flux) (164). Finally, photobleaching and photodamage are limited to narrow region around the focus by TPM (160, 165).

To date, TPM has been widely used in cellular and subcellular imaging in various organs of living animals (166, 167). TPM is a promising technique for retinal imaging given its ability to detect both structural and biochemical processes of the eye. In 2004, Imanishi et al. first applied TPM to the eye, revealing previously uncharacterized structures (retinosomes) distinct from other cellular organelles in the dissected mouse eye (168). Maeda et al. used TPM to characterize the early phase of retinal degeneration (169). Palczewska et al. developed an AO-TPM system to monitor early molecular changes in retinoid metabolism related to eye diseases (141).

The research on eye detection by TPM is incomplete and limited to animal models due to its safety concerns in human use with nonlinear optical effect (170). Nevertheless, it is still a promising tool for human disease detection, considering the absorption spectra of human eye tissues and the biochemical process revealed by TPM (171–174). Another key advantage of TPM is that it offers the possibility of imaging intrinsic fluorophores that are outside the normal transmission window of the eye. Palczewska et al. found that reducing the pulse repetition frequency and lowering the average laser power is a viable option to improve safety (141). Alternatively, the use of infrared lasers at the maximum permissible exposure ensures both corneal and retinal protection, all while maintaining imaging efficacy (175). However, work is needed to make this technology suitable for clinical utilization. Future studies may improve TPM acquisition techniques by exploring safety problems in animal models and balancing high-resolution imaging with the reduction of the laser power (176–178).

2.5 Light-sheet fluorescence microscopy

Light-sheet fluorescence microscopy (LSFM) is a fluorescence microscopy with good optical sectioning capability (Figure 1E). In 1903, Siedentopf and Zsigmondy described the first version of LSFM (ultramicroscopy) (179). They projected sunlight through a split aperture to directly observe gold nanoparticles. Many years later, researchers rediscovered the idea of using light-sheets to image, after which, many methods like orthogonal-plane fluorescence optical sectioning (OPFOS), selective plane illumination microscopy (SPIM), and digitally scanned laser light-sheet microscopy (DSLM) were reported (180–182). The milestone of LSFM was in 2004 when Huisken et al. developed SPIM to generate multidimensional images of live embryos with high resolution (181). This breakthrough accelerated the rapid development of LSFM (183). In a conventional LSFM, the sample is illuminated by a thin laser beam (a sheet of light). The fluorescence from the illuminated plane is collected in a perpendicular direction from the light-sheet axis. Therefore, the light can be captured by a camera and be generated into an image (184). Because the sample is only illuminated by a thin sheet of perpendicular light (2–6 µm), LSFM has the advantage of reduced photodamage/bleaching over other fluorescence microscopic techniques, such as confocal fluorescence microscopy and TPM (185, 186). Accordingly, LSFM can achieve high-speed and high-resolution imaging while using relatively less light energy. Additionally, new advancements have integrated AO with LSFM, addressing issues relating to image degradation caused by sample-induced optical aberrations (187, 188). More recently, LSFM has been applied in retinal imaging. Luo et al. demonstrated the applicability of LSFM for the assessment of optic nerve regeneration by imaging the optic nerve and brain of the mouse (189). Icha et al. used the multi-view fusion method to image the developing zebrafish eye (190). Prahst et al. illustrated the potential of quantitative 3D/4D LSFM imaging in understanding human eye especially pathological, neuro-vascular, and degenerative processes (191). Current LSFM techniques require sample preparation including hydrogel embedding and hooks (186); therefore, it still cannot be used in imaging live animal eyes and human eyes.

2.6 Limitations and clinical challenges of cellular-resolution retinal imaging

To facilitate comparison across modalities, Table 1 summarizes the major technical and practical characteristics of OCT, AO-based imaging, TPM, and LSFM from a clinical perspective. Each modality also faces important limitations that must be considered when interpreting results or envisioning future clinical translation. Although OCT is widely used clinically (199), achieving reliable cellular-resolution imaging remains challenging. Raster scanning introduces motion artifacts, especially during B-scan stacking for volumetric imaging. In vis-OCT, shorter wavelengths improve axial resolution but decrease penetration depth and impose stricter light safety limitations, potentially requiring reduced field of view or slower acquisition. AO enhances lateral resolution but adds substantial system complexity, including wavefront sensing, deformable mirrors, and demanding optical alignment (200). These systems remain sensitive to fixation instability, tear-film fluctuations, and small eye movements, all of which can degrade AO correction (201). TPM provides powerful functional and fluorescent imaging capabilities but is fundamentally constrained by light safety: nonlinear excitation requires high peak power that exceeds safe exposure limits for the human retina. Tissue scattering further limits penetration depth, confining TPM imaging largely to superficial structures in rodent eyes. Many TPM applications also require exogenous fluorescent labeling, which precludes human use. LSFM enables rapid, volumetric, high-contrast imaging of ex vivo tissue, but its geometry—requiring orthogonal illumination and sample mounting—precludes in vivo ocular application. It is also incompatible with natural eye motion, and thus currently serves exclusively as a tool for studying retinal organization, vascular architecture, and developmental processes in fixed or cleared specimens.

Table 1

Table 1. Specifications and features of in vivo cellular-resolution imaging modalities.

Despite differing optical architectures, several challenges are shared across all high-resolution retinal imaging techniques. Motion artifacts from microsaccades, respiration, and cardiac pulsation impair the stability needed for cellular visualization. Light safety constraints limit photon flux, especially in vis-OCT and nonlinear microscopy. Small fields of view prolong acquisition time and complicate clinical workflows. Furthermore, large data volumes require advanced registration, denoising, and segmentation algorithms to achieve clinically usable outputs. Partial histological validation for some modalities (e.g., vis-OCT, AO-OCT) means that certain cellular features remain interpretive rather than definitively confirmed. Additionally, cost, complexity, and lack of commercial systems continue to hinder translation of AO and ultrahigh-resolution OCT into everyday practice. Advances in ultrafast imaging (MHz OCT (202), line-scanning scheme (203), computational aberration correction, more efficient light sources, real-time motion tracking, and machine-learning-based denoising and segmentation are expected to reduce many of these barriers. Continued integration of in vivo imaging with histological validation and longitudinal functional studies will also expand clinical confidence and interpretation of cellular-scale biomarkers.

3 Applications in imaging retinal cells

The organization of this section follows the flow of visual information through the retina. We begin with photoreceptors, which transduce light into electrical signals, then proceed to bipolar and amacrine cells in the inner nuclear layer, and finally to retinal ganglion cells, the output neurons of the eye. After covering the neuronal visual pathway, we review the supporting cell types essential for retinal homeostasis and disease processes, including the retinal pigment epithelium (RPE), immune cells such as leukocytes, microglia, and Müller glia. This structure emphasizes the functional circuitry of vision rather than strict anatomical layering, while still covering all major retinal cell classes.

3.1 Photoreceptor cells

Photoreceptor cells are a primary interest of cellular resolution retinal imaging given their role in the visual processing circuit and various retinal diseases. Located in the outer retina (Figure 2A), these cells can be visualized and quantified in vivo by a variety of imaging modalities. The application of AO to SLO and OCT technologies has greatly facilitated the ability to achieve cellular resolution imaging of these cells in both animals and humans (Figure 2). (2, 63, 204–209) Several existing papers summarize the related studies on photoreceptors and limitations of AO-SLO (59, 63, 210–212) and AO-OCT (206, 207, 213) imaging. Here, we will focus on the emerging techniques and studies for visualizing and quantifying photoreceptor appearance and their functional response.

Figure 2

Figure 2. In vivo images of photoreceptors shown through different image modalities. (A) AO-OCT image of the human retina measured at 5 degrees temporal to the fovea; approximately 10 registered B-scans were compiled for this image. (Reproduced/adapted from Wells-Gray et al., 2018). (B) Color-coded map of the trichromatic cone mosaic in a human subject (small = blue; medium = green; large = red). (Reproduced/adapted from Zhang et al., 2019). (C-F) Offset-aperture images showing cones from monkeys through multiple imaging modalities in a 40 × 40 µm field of view. Cones are seen in each imaging modality while the visibility of rod photoreceptors varies. The white square in E depicts the location of zoomed-in views (C, D, F); C = Two-Photon Excitation Fluorescence; D = Confocal; F = multi-offset. (Adapted from Rossi et al., 2017). (G-J) AO-OCT en face images of the cone mosaic (G+I) and their zoomed in images (H+J) in a healthy human control (G+H) and a patient with Retinitis Pigmentosa (I+J). (Adapted from Lassoued et al., 2021). (K-M): Phase response of cones varies with cone type (S, M, L) and wavelength of the stimulus. Major tick marks on the x-axis represent time in seconds. The dashed gray line at 0 seconds represents the onset of the 5-ms stimulus flash. The phase response was referenced to the average of the prestimulus optical path length. Cone responses are colored red (L), green (M), or blue (S) based on the k-mean classification and expected spectral sensitivity of each cone type to the stimulus wavelength. Average responses of the grouped traces are shown in graphs (K-M). (Adapted from Zhang et al., 2019).

Cones can be confidently visualized using confocal AO-SLO and there are already couple of specific review papers (63). However, rods are difficult to image with AO-SLO techniques given their relatively smaller size and variable reflectance. Recently, Scoles et al. used non-confocal split-detection AO-SLO to view peripheral cone and rod inner segments in humans, the location of which can be correlated to the cones seen ex vivo in confocal images of donor eyes (214) (63). Accordingly, split detection, image averaging, as well as decreases in pinhole size and imaging wavelength have been proposed to improve rod visualization (63, 215). Significantly, Tan et al. noninvasively captured nanometer-scale, light-evoked deformations not only in cones but also in rods, the RPE, and the subretinal space (216). This method allows for detection of scotopic rod responses down to 0.01% bleach levels with functional mapping across a 12°field in a single flash, making it incredibly useful in detecting early rod disfunction and monitoring disease progression (216).

Notably, Rossi et al. explored the visualization of photoreceptor cells and other retinal cells using an non-confocal offset-aperture detection scheme of AO-SLO in monkeys and humans (Figures 2C-F) (2). Recently, vis-OCT (71) without AO has been explored to observe photoreceptor cell somas in the outer nuclear layer due to the high resolution and strong scattering in visible light band; yet, in comparison, current vis-OCT systems are less effective than the IR AO-OCT systems. In addition to these approaches, a recent multi-MHz phase-stable SS-OCT developed by Lee et al. demonstrated 3D, depth-invariant cellular-resolution imaging of the living human retina over a 3 × 3 mm field, presenting a significant improvement in field of view at the given resolution (217). Additionally, detection and quantification of photoreceptor functional response to light has also been of great interest in recent works, as the ability to do so with non-invasive methods holds great promise in both disease diagnosis (Figures 2G-J) and treatment (218–222). These efforts were focused on measuring the length of individual cone cells (19). Gofas-Salas et al. discussed using non-confocal split-detection AO-SLO to visualize inner photoreceptor segments, even if the outer photoreceptor segments have been damaged in pathological or methodological processes (223–225).

Optoretinograms (ORG) are an exciting tool in this field, given their ability to provide measurements of retinal structure and functional cellular response (218, 221, 226).While traditional methods of microperimetry and electroretinography (ERG) provide aggregate assessments of photoreceptor function, they cannot provide results at the individual cellular level (218). Again, AO holds great promise in this area, providing cellular resolution imaging of light-induced photoreceptor functional changes in the intact human eye (218). ORG has been categorized into intensity-based and phase-based subtypes (218). Given that previous studies combining AO-OCT-SLO technologies have been used to differentiate between rods and cones and visualize the processes within each cell type (19, 227), these modalities are particularly applicable to this area. Simultaneous AO-OCT and AO-SLO imaging systems have been measured photoreceptor responses to light stimuli (215). Accordingly, intensity-based protocols have demonstrated that different types of cones respond differently to light stimulus intensity and wavelength (Figures 2K-M) (208, 218, 219) Bernucci et al. recently utilized a supercontinuum laser-based, AO-OCT ORG to objectively measure cone sensitivities across the visible light spectrum (228). Such systems have separately been used to document the transient deformation that photoreceptor outer segments undergo as a result of light exposure and phototransduction (220, 229), which was later validated with histological examination and ERG (230). Given the physical deformations that occur during cell function, photoreceptor outer segments have been described by some as particularly suited for ORG technology (231). Phase resolved AO-OCT is able to capture images with sufficient speed and resolution to track changes in the optical path lengths of outer segments (219). Pandiayan et al. introduced a 16 kHz line scan AO-SD-OCT system with an anamorphic detection paradigm which improved roll-off and light capture efficiency. This system can also be tailored to the relevant clinical application, capturing areas from an individual photoreceptor to 100um wide (219). Phase-sensitive AO-OCT systems have been applied to the retinitis pigmentosa disease model, allowing for detailed cellular-level tracking of functional responses in cone cells (222).

3.2 INL cells: bipolar and amacrine cells

A variety of imaging modalities have been used to image this cell layer in animal models. Rare studies have demonstrated that AO technology is capable of providing high resolution images of bipolar cells in live human eyes (232). More recently, cells within the INL have also been observed indirectly. Liu et al. utilized AO-OCT combined with long acquisition times to observe moving organelles and, thus, infer RGC soma locations (233). The images obtained from this study demonstrated the RGC dendrites and synapses with bipolar and amacrine cells within the INL (233). RGC soma projections were then spatially mapped to infer the locations of underlying INL cells, photoreceptors and RPE cells with through focus-imaging (233). Given its ability to resolve cells at multiple retinal depths and define reflectance of cell somas in visible light range, vis-OCT may be uniquely positioned to observe INL cells. Pi et al. recently reported using vis-OCT volumetric registered and averaged images to view cells in the inner nuclear layer of rats (Figures 3A, B) (71). Previously, Zhang et al. utilized temporal speckle-averaging of infrared OCT images to visualize INL cells (234). Comparably, vis-OCT resulted in more detailed imaged of INL cell bodies, along with RNFL bundles and photoreceptors (71). Schroeter et al. used two-photon microscopy and time-lapse confocal microscopy to image retinal bipolar cell axon terminals in live zebrafish (235). Lu et al. previously used adeno-associated viruses to transduce specific promoters and enhancers in ON-type rod bipolar cells in both mice and marmoset monkeys (236). Similarly, Wang et al. utilized transpupillary two-photon fluorescent imaging to provide cellular resolution of amacrine cells in mice retinas in vivo (Figure 3C) (237). Recent advancements improved on two-photon fluorescent imaging with AO, enabling clear visualization of synaptic structures and dynamic retinal changes in disease models for mice (68). Using a similar method, functional calcium imaging of INL neurons was also achieved (197). These authors used VGAT-Cre transgenic mice injected with an adeno-associated virus vector to encode a fluorescence-based calcium sensor with yellow fluorescent proteins (237). By adapting a standard multiphoton microscope, the optical set up described by these authors provides a relatively simple approach to both short- and long-term experiment durations with minimal animal stress (237). In addition to structural imaging, ORG offers promise for future wide-field, layer specific structural and functional imaging of INL cells due to the method’s high sensitivity and axial resolution (216, 238).

Figure 3

Figure 3. INL cell types revealed in different imaging modalities. (A) Cross-sectional image of rat retina acquired in vivo using vis-OCT shows the cells in INL layers. (From Pi et al., 2020). (B) En face projection of INL slab in vis-OCT images delineates INL cell bodies. (From Pi et al., 2020). (C) Amacrine cells in mouse retina is labeled by injecting AAV-EF1α-FLEX-Twitch2b into VGat-Cre transgenic mice and imaged in vivo with two-photon microscopy (TPM). (From Wang et al., 2021). (D) Confocal image of a vertical section through a postmortem human donor retina (age 36 years) visualized INL cell bodies labeled with DAPI nuclear staining. (From Masri et al., 2021). (E) Confocal image revealed the OFF-midget (labeled with antibodies against recoverin, Green) and ON (labeled with antibodies against islet-1, Magenta) bipolar cells. (From Masri et al., 2021). (F) Confocal image of amacrine cells in the human retina labeled with antibodies against glycine transporter 1 (GlyT1, green) for glycinergic amacrine cells, or glutamic acid decarboxylase (GAD-6, magenta) for GABAergic amacrine cells. Displaced GABAergic amacrine cells are visible in the ganglion cells layer (arrowhead) but were not quantified. (From Masri et al., 2021). (G) Confocal image revealed the horizontal cells in the human retina processed with antibodies against parvalbumin (green) and calbindin (magenta) and shows that H1 horizontal cells expressing parvalbumin alone and H2 cells expressing both parvalbumin and calbindin (arrows). A cell body expressing calbindin alone can also be observed (arrowhead). (From Masri et al., 2021) (H) Bipolar cell axons were identified and traced with second harmonic generation (SHG) imaging in a transgenic green fluorescent protein (GFP) mouse. Note that terminal of three subtypes of bipolar cells (CBC4: type 4 cone bipolar cell, CBC7: type 7 cone bipolar cells, RBC: rod bipolar cells) are distinguishable and penetrate to different depth in IPL by the overlap with GFP at zero IPL (green square). (From Arafat Meah, et al., 2022].

However, reports of in vivo visualization of retinal bipolar cells are still limited with histology remaining as the standard approach to visualize the INL cells. Fluorescent techniques have been used to directly observe INL cells in postmortem human retinal samples (Figure 3D) (239); specifically, they have revealed antibody-labelled OFF- and ON-Bipolar cells (Figure 3E) (239), amacrine cells (Figure 3F) (239), and horizontal cells (Figure 3G) (240). Such techniques highlight the spatial orientation between INL cell types (239). Studies employing transgenic green fluorescent protein mice and second harmonic generation imaging revealed that the axons of different bipolar cell types extend to varying retinal depths (Figure 3H) (240).

3.3 Retinal ganglion cells

3.3.1 RGC somas

RGCs share many features with neurons in the CNS and possess similarities to the brain in terms of anatomy, function, as well as immunologic and insult response (1). They are transparent and thus less contrast for optical imaging. Applications of AO has perhaps been the most successful in improving contrast imaging of RGCs and visualizing individual ganglion cells in vivo (63, 233). Previous imaging of inner retinal cells with AO-SLO has required transgenic models, induced fluorescent protein expression, or administration of exogenous contrast (63). For instance, fluorescent confocal AO-SLO has been utilized to visualize the somas, dendrite processes, and axons of labeled ganglion cells in the mouse retina in vivo (205, 241). Two-photon fluorescence AO-SLO has also been used to visualize these structures, allowing for the identification of various cellular features depending on the depth of focus for the incident beam (242). These authors were able to perform 5-µm axial sectioning and resolve dendrites in different layers (242).

Rossi et al. was the first to utilize confocal AO-SLO to image individual neuron somas in the RGC layer of monkeys and humans without fluorescent labels or high light levels (2). Concurrently, Liu et al. described an AO-OCT technique with highly-processed singly-scattered light to image RGC somas in vivo in the human retina (Figures 4A-D) (233), through long acquisition times and 3D subcellular image registration and using moving organelles functioned as a contrast agent for the inference of soma locations (233).

Figure 4

Figure 4. Images of RGC somas. (A) Three-dimensional registered and averaged AO-OCT volume with green dashed line signifying the cross-sectional B-scan shown in the inlay. Yellow arrow within inlay indicates the same GCL soma seen in (C) Images shown in B-D were extracted at depths of 13, 22, and 46 μm below the inner limiting membrane (ILM). Scale bar in D also applies to B and (C, B) A complex network of nerve fiber bundles of varying sizes ranging from 30 μm (blue arrow) to 3 μm. GCL somas are seen near the image bottom (green arrow). (C) GCL somas of varying sizes, as indicated by the color arrows. The red arrow points to a large soma, while the blue and white arrows depict edges of vessel walls and the close proximity of GCL somas. (D) Dense connections between bipolar synapses and dendrites of ganglion and amacrine cells. (Images A-D and corresponding caption adapted permission from Liu et al., 2017). (E) Vglut2-Cre transgenic mice labelled by injecting AAV-EF1α-FLEX-Twitch2b were used to image RGCs with in vivo two-photon techniques. (Reproduced from Wang et al., 2021). (F) Full AO-TPM images depicting RGCs (green) and blood vessels (red). (From Qin et al., 2020). (G, H) Magnified images comparing in vivo vis-OCT fibergraphy (G) and ex vivo confocal microscopy (H) images of RGC axon bundles. Orange arrows 1–4 signify small RGC axon bundles visible in both imaging modalities. Red arrows 5–7 demonstrate a comparison of blood vessels. Scale bars: 50 µm. (Adapted with permission from Miller et al., 2020). (I) En face vis-OCT image demonstrating nerve fiber bundles.

Recent advancements have applied machine learning to further enhance RGC imaging using AO-OCT. Two models — RGC-CCT and RGC-CPS — leverage both labeled and unlabeled datasets to identify RGC somas in 3D AO-OCT volumes, outperforming traditional fully supervised methods (150). Further, Zhang et al. used temporal speckle averaging (TSA) of AO-OCT and OCTA images to increase image contrast and reduce signal to noise ratio; this method provided cellular-resolution images in mice eyes without extrinsic contrast agents, relying on temporal evaluation of speckle patterns to reduce noise via image stacking (234). Gofas-Salas et al. recently demonstrated a radial multi-offset detection AO-SLO can be used to image RGCs; in this system, a single wavelength source afforded increased power while decreasing optical aberrations (223). AO-SLO combined with calcium imaging has been performed in vivo in macaques to measure the RGC response to light (243). Additionally, calcium response measurements in RGCs have been used to visualize the efficacy of vision restoration techniques; Cheong and colleagues utilized a calcium indicator to confirm uptake and expression of a viral vector in RGCs from retinal degeneration mouse models (244).

Later, Laforest et al. described a transscleral illumination approach in humans in which RPE and choroid backscattered light is used to illuminate the inner retinal layers, which are then imaged through a non-dilated pupil to produce dark field images (245). When combined, this technique generates phase imaging with double the pupil’s numerical aperture (245). While the technique described by Liu and colleagues required relatively long capture durations (10 minutes), the technique of Laforest et al. demonstrated rapid retinal imaging which would be more conducive to clinical translation, particularly in subjects who have difficulty with target fixation (233, 245). Continual improvements in the applications OCT technology have also improved imaging of these cells. Pfäffle et al. utilized phase evaluation algorithms to full-field SS-OCT data to demonstrate GCL and IPL activation in human eyes in vivo (246). Notably, signals from these layers were only visualized after post-imaging processing suppressed artifacts induced by blood pulsations and motion (246). Visible-light OCT combined with volumetric registration and image averaging has been also used to observe RGC somas within the ganglion cell layer (71), while combination OCT and confocal SLO systems have been used to obtain longitudinal images of single RGCs (247).

Standalone TPM methods and those using AO have also been described. Wang et al. visualized RGC somas and axon fascicles in vivo using Vglut2-Cre transgenic mice injected with an adeno-associated virus vector to encode a fluorescence-based calcium sensor with cyan fluorescent proteins (Figure 4E) (237). Qin et al. used an AO-TPM system with a nonlinear fluorescent guide star to obtain subcellular (submicron) resolution for in vivo mice retinas (Figure 4F) (197). Importantly, these authors combined ocular aberration correction with a diffraction-limited point spread function to maximize efficiency of the photon excitation, revealed neuron soma and dendrite images (197).

3.3.2 Nerve fiber layer (RGC axons)

AO-SLO imaging, and in combination with other technologies, has been used to image RNFL and correlate these findings to biomarkers of human disease (248–250). Geng et al. utilized fluorescent confocal AO-SLO to directly visualize nerve bundles in transgenic mice in vivo (205).TPM imaging has also proved useful in RNFL imaging. Jayabalan et al. obtained real-time, concurrent two-photon ICGA and FA images using a single light source; in this system, two-photon FA was used to image the RNFL of rabbits and rats (251). These authors reported that efficiency of the two-photon technique can be optimized through the modification of pulse duration and excitation wavelength; this system can also be modified to image multiple animal models (251). Single light source systems have also been described in AO-OCT. Jian et al. described an AO-FD-OCT setup with a one light source for both wavefront detection and image capture (252). This system utilized a refraction cancelling lens to decrease corneal back reflection and lower-order visual aberrations, resulting in increased contrast and brightness of nerve fiber bundles in mice retinas in vivo (252). These authors later combined wavefront sensorless AO with FD-OCT, which allowed for precise depth selection and focus on specific retinal structures (61).

More recently, Miller at al. visualized individual RGC axon bundles of varying sizes in mice using visible-light OCT fibergraphy (Figures 4G, H) (253). These authors quantitatively compared the results obtained through vis-OCT imaging with the confocal microscopy of flat-mounted, antibody-labelled specimens, finding agreement between imaging results and demonstrating the potential for vis-OCT as a non-invasive method for visualizing these axons (253). Further, visible-light OCT combined with volumetric registration and image averaging has been used to image RNFL bundles in rat eyes (Figure 4I) (71). The resulting images were roughly equivalent to those AO-OCT when imaging for RNFL bundles; however, vis-OCT was able to capture a wider field of view (71). Notably, speckle noise remained an issue that affected image quality in vis-OCT images. Elsewhere, scan modulation has been used in vis-OCT systems to improve image quality by increasing the contrast-to-noise ratio in in vivo images by 2.35 dB (254). Future applications of vis-OCT technology with advanced image processing may decrease the number of images needed to average to obtain adequate resolution, while minimizing the transient photoreceptor bleaching that occurs following visible light exposure (71).

3.4 Retinal pigment epithelium cells

Although anatomically the RPE lies externally to photoreceptors, we present it after the neuronal pathway because of its role as a metabolic and structural support system for all photoreceptor-dependent visual processing. A variety of modalities have been employed to image RPE cells, including a variety of AO and fluorescence-based imaging (63, 255–260). In a recent review, Wynne et al. discussed the opportunities and challenges in imaging RPE cells owing to their low internal contrast and high light scattering properties (63). Fundus autofluorescence (FAF) has been frequently used to image RPE cells due to its intrinsic fluorophores, lipofuscin and melanin (63). A recent review by Schmitz-Valckenberg and colleagues describes the utility of FAF, as well as the variety of fluorophores that can used in both animal models and humans (261). Roorda et al. first used AO-SLO to image the human RPE layer in patients with cone-rod dystrophy, finding that the RPE was more easily visualized without the presence of photoreceptors (Figure 5A) (255). Morgan et al. used AO-SLO to the detect autofluorescence of the RPE cells in live monkeys with healthy photoreceptors (256). The concentration of cytoplasmic lipofuscin was thought to contribute to the autofluorescence pattern seen in Figure 5B (63, 256). Since this work, recent studies have evaluated melanosome and lipofuscin granule density in the RPE cells of transgenic mice in vivo using small wavelength autofluorescence (SWAF) with directional backscattering (262). The same study also utilized directional OCT and spectrometrically-integrated SLO to evaluate RPE melanolipofuscin and lipofuscin granule density (262). Transscleral optical phase imaging (TOPI) has also been used to obtain high resolution depictions of all retinal layers, including the RPE. By applying near-IR light transsclerally, the cone reflectivity that accompanies trans-pupil illumination is avoided, resulting in high contrast structural images of the RPE (63, 263).

Figure 5

Figure 5. Retinal pigment epithelial (RPE) cell mosaics shown through various imaging modalities. (A) Confocal AO-SLO images of RPE cells from a patient with cone-rod dystrophy (Reprinted from Roorda et al., 2007). (B) AO-SLO images augmented with short wave autofluorescence demonstrate RPE cells in a monkey (Reprinted from Morgan et al., 2009). (C) Dark-field AO-SLO images of RPE cells from a healthy human subject (Reprinted from Scoles et al., 2013). (D) AO-OCT images of human RPE cells (Reprinted from Liu et al., 2016). (E) AO-ICG visualization of human RPE cells (Reprinted from Tam et al., 2016). (F) Human RPE cells shown under AO infrared autofluorescence (Reprinted from Liu et al., 2017). Scale bars, 50 mm. Figure and caption adapted with permission from Wynne et al.,2021.

Lipofuscin visibility on dark-field AO-SLO has been used to image RPE cells at certain eccentricities with low light levels and lessened subject discomfort, but sacrificing contrast and image quality(Figure 5C) (63, 257). More recently, Jayabalan et al. described TPM imaging as an efficient way to detect minute changes in lipofuscin distribution, alongside functional and structural retinal information (251). These authors discussed TPM imaging in comparison to confocal FAF, which provides relatively lower resolution estimations of lipofuscin concentrations (251). Accordingly, confocal fundus autofluorescence could only detect RPE changes after significant decreases in lipofuscin concentrations (251), potentially making TPM a more sensitive methodology for future studies. Interestingly, AO-two photon techniques have been used in primates, but at light levels that would be phototoxic in humans (264).

Additional adaptations of AO technology have been used to visualize the RPE with subcellular resolution. Liu et al. utilized organelle motility as a contrast medium to view human RPE cells in vivo with AO-OCT (Figure 5D) (265). Through image averaging, these authors were able to increase contrast and produced three-dimensional reflectance profiles of the RPE mosaic. This group later combined AO-OCT with speckle field dynamics to quantify organelle dynamics, as an evaluation of cellular health and functionality (198). Recently, an AI model (P-GAN) has been developed to clean up speckle-obscured RPE cellular features from a single AO-OCT volume, eliminating the need to capture and average multiple scans, thus reducing imaging time (151). Using P-GAN, Das et al., were also successful in providing improved RPE cell contrast by 3.5-fold (151). In future studies, organelle motility may be an important biomarker in visualizing overall RPE health status (198, 265).

In addition to the studies using endogenous fluorophores, exogenous dye sources have also been explored. Indocyanine green (ICG) dye heterogeneously localizes to the RPE layer after systemic injection (260) and remains stable for up to 24 hours (63). Tam et al. confirmed that AO-ICG ophthalmoscopy is able to image individual RPE cells within the RPE mosaic in living human eyes (Figure 5E) (260). Liu et al. combined AO with infrared-autofluorescence to measure the density of the RPE mosaic in healthy human eyes (Figure 5F) (233). With this modality these authors were able to calculate the number of cone cells supported by each RPE cell. Grainger et al. used short-wavelength autofluorescence (SWAF) and infrared autofluorescence (IRAF) to characterize in vivo morphometry and multispectral autofluorescence of the retinal pigment epithelial (RPE) cell mosaic and its relationship to cone cell topography across the macula. Future applications of these techniques may improve the understanding of the many disease pathologies influenced by RPE cell dysfunction or destruction.

3.5 Immune cells

3.5.1 Leukocytes

Label-free recording of immune cell activity is particularly important, as it is unknown if exogenous labels alter biologic immune response in the tissue microenvironment. Joseph et al. imaged myeloid cell dynamics in live mouse retinas using label-free phase contrast AO-SLO and time-lapse videography (15). These authors induced uveitic conditions by injecting lipopolysaccharide into the eye and subsequently measured cell motility during acute inflammatory reactions from their onset to resolution. They were able to observe leukocyte tissue infiltration, including rolling, crawling, and trans-endothelial migration stages, as well as differentiate tissue resident retinal neutrophils (15). Notably, their methodology utilized near-infrared light (796nm) at lower power levels than required in multi-photon techniques, thereby minimizing the risk of phototoxicity (15). In a subsequent experimental stage, these authors used fluorescent antibody labeling to confirm neutrophil location; interestingly, they reported higher visibility using phase contrast than fluorescence labeling, highlighting the capability of phase contrast to image these cell types (15).

3.5.2 Microglia

Studies of microglia in animal models often rely upon labeling techniques for visualization (16, 60); however, such techniques can be challenging as these cells share common markers with other macrophages and peripheral myeloid cells (16). In previous works, Wahl and colleagues imaged enhanced green fluorescent protein (EGFP)-labelled microglia in mice in vivo using a hill-climbing algorithm in a wavefront sensorless AO system (266). Later, Wahl and colleagues discussed a multimodal sensorless AO imaging system that combines OCT, OCTA, confocal SLO, and fluorescence detection, demonstrating high resolution, in vivo time-lapse and volumetric imaging of fluorescent-labelled microglia in mice (Figure 6A) (36). They also observed microglia branching with sensorless AO-SLO (36). Zawadzki et al. combined AO-SLO and phase-variance OCT/widefield SLO to localize microglia in 3D in the live mouse retina (267). AO-SLO images were registered to precise axial planes provided by phase variance OCT and widefield SLO, allowing for direct localization of microglia in specific retinal layers (267).

Figure 6

Figure 6. Images of microglial cells in the retina. (A) In vivo AO-Confocal SLO fluorescence images of EGFP labeled microglia in mice. Microglia images were color-coded with time from 0 to 49 minutes shown in the scale bar. White arrows 1–4 note signify areas of growth and retraction. (Adapted with permission from Wahl et al., 2019). (B) Macrophage cells resolved above the ILM in cross-sectional views from averaged AO-OCT volumes in a healthy control subject. (Adapted with permission from Hammer et al., 2020). (C) Example of the spatial patterning of macrophage-like cells relative to the superficial retinal vasculature in a healthy subject. Shown is a montage of 3-μm en face OCT-R slabs located just above the ILM. (Reproduced from Castanos et al., 2020) (D) AO-SLO images revealing the three-dimensional migration of microglia at 6 and 24 hours after injury in a living mouse. (E) Depth-encoded AO-SLO image of microglia in a Cx3CR1+/GFP mouse, with color indicating axial position within the retinal layers. (F) Display of microglial cell density across the nerve fiber layer (NFL), inner plexiform layer (IPL), and outer plexiform layer (OPL), measured approximately 750 μm from the optic nerve head (ONH). Significant differences were observed between NFL and IPL (P = 7.45 × 10^−5) and NFL and OPL (P = 3.5 × 10^−5), with no difference between IPL and OPL (P = 0.77). *P < 0.0001. (Panels D-F reproduced with permission from Miller et al., 2019).

Label-free imaging techniques have also been explored. Gofas-Salas et al. recently described a radial multi-offset detection pattern combined with AO-SLO to image human retinal microglia (223). Hammer et al. utilized label-free AO-OCT to describe microglial spatial distribution in human eyes in vivo (60). Notably, these authors focused on the cells visible above the ILM, which may be more easily targeted without labelling techniques (Figure 6B) (60). Interestingly, Hammer et al. suggested that human microglia may have different dynamic characteristics compared to those seen in animal models (60). Human macrophages were also more likely to be seen in the periphery than in the central macula in healthy eyes (Figure 6C) (60, 268).

Further, much effort has focused on the imaging of microglial cellular dynamics. As a part of their immune function, microglial cells have highly dynamic processes which aid in continuous environmental surveillance (16). In a recent review, Eme-Scolan and Dando describe the imaging tools that can be used to observe live, dynamic behaviors of retinal microglial cells in vivo (16). AO-SLO has been used to describe the spatial distribution, dynamic behavior, and morphology of retinal microglia (16, 17). Confocal SLO has been used to quantify microglial dynamics following laser injury in mice retinas in vivo (269–272). Miller et al. recently used an AO-SLO and SLO-OCT system to demonstrate microglial volumetric distribution and dynamic behavior in the days and weeks following laser injury in mice retinas in vivo (Figures 6D-F) (17). In addition, Qin et al. developed an AO-two photon excitation fluorescence microscopy system which used a non-linear fluorescent guide star to visualize time-lapse dynamics of microglial behavior at subcellular resolution in the live mouse retina (197). They demonstrated that this imaging method can be used to characterized microglia dynamics and suggested that further application of this methodology may provide insight into cell interactions within the retinal microenvironment (197). Mezu-Ndubuisi et al. utilized fluorescein angiography, SD-OCT, and focal electroretinography to assess gliosis and microglia activation in mice models of oxygen-induced ischemic retinopathy (273). They noted retinal thinning and inner retinal dysfunction on imaging, while histological assessment demonstrated gliosis, tissue disorganization, and ectopic rod-bipolar cell synapses (273).

Imaging retinal microglia dynamics in human eyes has also been investigated. Kurokawa et al. observed retinal dynamics in human eyes in vivo across a variety of time intervals including seconds, minutes, and one year (274). Importantly, these authors developed novel post-capture processing methods involving temporal correlation; they corrected for motion artifacts in subcellular-resolution images through volumetric B-scan registration and time averaging AO-OCT volumes (274). Kurokawa et al. also reported temporal dynamics of hyalocytes, a macrophage-like cell just anterior to the ILM in the cortical vitreous, providing evidence that AO-OCT can be used to track in vivo function motion of these cells as they sample the retinal environment (274). Separately, Rui et al. designed a fiber-bundle with a central confocal fiber surrounded by six fibers to obtain multi-offset AO-SLO imaging of one focal plane (57). With this device, human microglial movement was quantified in both healthy and inflammatory disease states. Compared to previous iterations of this technology, the fiber bundle-AO-SLO allowed for dynamic visualization over short time intervals (57).

Some authors have suggested that glial cell mediated neurotoxicity plays a role in the pathogenesis of glaucoma, making imaging of these cells and their functional characteristics over time of particular interest to this disease process (60, 275). In retinal vascular diseases, gliosis occurs prior to vascular leakage and damage can be seen on fluorescent angiography (276). Glial activation can precede the onset of retinopathy and is thought to affect a variety of cell types including horizontal, amacrine, ganglion and photoreceptor cells (275, 276). Kumar and Zhuo utilized a transgenic mouse model of diabetic retinopathy to observe gliosis using confocal SLO in a live genetically engineered mouse model (276). These authors noted progressive astrocyte hyperplasia that preceded vascular structural changes (276). Similarly, Bosco et al. utilized live imaging confocal SLO to image the dynamics of fluorescent microglia in a murine model of glaucoma (275). Mice that exhibited increased and early microglial activation tended to have a higher severity of optic nerve damage (275). Overall, these authors noted that confocal SLO was able to track gliosis over time and establish this cell type as an indicator and predictor of prognostic outcomes (275). Interestingly, they documented variable spatial patterns of microglial activation and suggested that these cells may be responding to focal retinal changes during the earliest disease stages (275). In the future, high resolution, in vivo imaging may be useful clinically in detecting early neurodegenerative progression (275), which may inform treatment planning or alterations in treatment course.

3.5.3 Müller cells

Müller cells are a type of glial cell located in the inner retina and function in a variety of regulations in the retina (277). Müller cell dysfunction has been linked to the neuronal excitotoxicity and neurodegeneration seen in retinal diseases (277). Previously, Prasse et al. imaged fluorescently stained Müller cells with excitation at 633nm in whole mount monkey and human retinas, demonstrating that these cells are not light-reflecting but may also be light-guiding (278). Similar results have been found in Guinea pig retinas (279). However, in vivo studies of Müller cells are limited. Recently, AO-OCT has also been used to view foveal Müller cells in healthy human eyes with resolution of 3.4-µm and 3-µm in the axial and transverse dimensions, respectively (Figure 7) (280). Zhang et al. described an OCT-confocal SLO system capable of simultaneous cellular resolution images of in vivo Müller cells and microglia, even with expression of different fluorophores (247). In a recent pilot study, Arrigo et al. utilized structural OCT data to detect and quantify peripheral Müller cells in the human retina in vivo (281). Their results were similar to the histological findings in imaged patients requiring enucleation. These authors note that this technique is limited to a resolution of 8-µm, which may be more useful in identifying peripheral Müller cells compared to those in the foveal region (281).

Figure 7

Figure 7. AO-OCT images of Müller cells in the human fovea. The AO-OCT image (A) and a magnified subregion (yellow square, (B) depicting Müller cells spanning diagonally from the inner limiting membrane to external limiting membrane (ELM) (ELM = asterisk). (C) AO-OCT foveal image, illustrating distinctive structural features of Müller cells. Also depicted are highly reflective dots within the outer nuclear layer (white arrow in B, likely cone nuclei) and small pores in the ELM (yellow arrow in B, likely the openings between adjacent Müller cell–photoreceptor junctions). (Adapted and reproduced with permission from Kadomoto et al., 2021).

4 Discussion

In summary, in vivo imaging techniques have significantly progressed over the last several decades, affording the ability to view cellular and sub-cellular detail in living models. Such improved capabilities have greatly facilitated investigations in ophthalmology and vision research. For instance, Azimipour et al. measured light-evoked, functional responses of human rods and cones (215). Others have proposed that imaging the processes of the visual cycle may aid in the quantification of rod and cone function (19). Retinoids can be visualized with two-photon excitation, providing insight into the visual cycle and photoreceptor function (264). Further, healthy eyes have been shown to transiently increase the length of their outer segments in response to light exposure; however, this process is thought to decrease retinal diseases. Lassoued and colleagues combined AO and phase-sensitive OCT to measure changes in optical path length inside cones in models of retinitis pigmentosa (222). This technique obtained 3D reflectance profiles at single cone resolution and was able to changes in optical path length with a sensitivity of 5nm (222). The cellular resolution of this technique is of particular importance for diseases that cause degeneration in individual photoreceptor cells, as changes at the cellular level can be noted prior to gross changes in cell density (222).

In addition, in vivo cellular resolution has already shown positive impact for early diagnosis of retinal diseases. Bosco et al. imaged a mouse model of inherited glaucoma using confocal SLO in vivo (275). These authors determined that microgliosis at the optic nerve head may be an indicator for ganglion cell stress and damage (275). The investigation of microglia behavior in various disease states provides the opportunity to investigate their role in disease potentiation, as well as potential therapeutic markers (60, 276). The ability to effectively image the microenvironment of these cells provides the opportunity to monitor therapy at the cellular level and discover which drugs may successfully intervene at earlier timepoints in the pathogenic process. Recent improvements in AO have allowed fundus cameras, SLO and OCT to capture near cellular resolution images of photoreceptors, ganglion cells and microvasculature. Clinically, this facilitates diagnosis, management and monitoring of various ocular conditions like AMD, diabetic retinopathy and glaucoma, allowing physicians to detect microscopic structural changes and with more precise monitoring of disease progression and therapeutic response (282–284). For example, advancements in AO-2PFM have allowed researchers to observe the effect of lidocaine administration on suppressing RGC hyperactivity, demonstrating its potential in evaluating pharmacological therapies and tracking progression of retinal diseases (68). In combination with fluorescent SLO imaging, as a surrogate for amyloid-beta (Aβ) plaque quantification in the brain, Sidiqi et al. found that retinal Aβ levels correlated with those in the cortex and were elevated in older mice (285). Interestingly, Aβ was more likely to be found in inner retinal layers with some found inside RGCs (285).

Although in vivo cellular-resolution imaging is advancing rapidly, the interpretative certainty of many techniques remains limited by incomplete histological correlation. For some modalities, such as AO-SLO cone mosaics, OCT lamination, and TPM-based structural imaging, extensive validation in human donor tissue or in animal models provides strong confidence in cellular identity. However, for several emerging high-resolution approaches, including vis-OCT, AO-OCT, and dynamic-contrast OCT, the biological interpretation of observed structures is still partly inferential and based on optical contrast mechanisms rather than direct one-to-one histological confirmation. This gap highlights an important direction for future work: integrating in vivo imaging with post-mortem tissue analysis or genetically targeted labeling to rigorously validate cellular and subcellular features. Addressing this limitation will be essential for translating these technologies into clinical biomarkers.

The future directions of in vivo cellular imaging of the retina are poised to revolutionize our understanding of ocular health and diseases. Advancements in this field are expected to focus on enhancing resolution, improving functional imaging, and integrating artificial intelligence (AI) for better diagnosis and management of retinal diseases. For instance, Lee et al. developed an advanced SS-OCT with computational aberration correction capable of attaining wide-field, 3D, cellular resolution imaging of photoreceptors, nerve fiber layers, and capillaries, which aids in the identification of early biomarkers in retinal disease (217). Additionally, phase-sensitive OCT images have shown capable of observing 10nm changes in tissue movement, providing insights into the functional responses of retinal cells, particularly photoreceptors (222, 231). As mentioned above, ORG is an emerging, non-invasive imaging modality capable of capturing retinal neuronal function (221, 231), and has recently been shown to differentiate cone classes and generate density maps with greater speed and accuracy compared to previous methods (208, 231). By combining different imaging modalities, such as OCT, fundus photography, and retinal fluorescein angiography, a more comprehensive view of the retina can be achieved. This approach can provide valuable information on both structural and functional aspects of retinal health. By analyzing vast datasets from retinal images, AI algorithms can assist in identifying subtle patterns that may indicate early stages of disease, which might be missed by the human eye. This could lead to automated, highly accurate diagnostic systems that can predict and prevent severe ocular conditions. For instance, AI analysis of fundus photos, OCT and OCT angiography is capable of screening and providing early diagnoses and management of various ocular conditions such as diabetic retinopathy and AMD (286–288). Telemedicine and remote imaging technologies will also become more prevalent, making retinal imaging more accessible, especially in underserved areas. Portable, user-friendly imaging devices, coupled with cloud-based AI diagnostics, could democratize eye care, allowing for frequent, non-invasive monitoring of retinal health. All of these advancements are heading towards more sophisticated, non-invasive, and comprehensive diagnostic methods to not only enhance our understanding of retinal diseases but also pave the way for more personalized and effective eye care.

Author contributions

SP: Conceptualization, Methodology, Validation, Funding acquisition, Resources, Supervision, Writing – original draft, Writing – review & editing. RB: Methodology, Writing – original draft. SY: Writing – review & editing. LW: Writing – review & editing, Writing – original draft.

Funding

The author(s) declared that financial support was received for this work and/or its publication. We appreciate the funding support from Alcon Research Institute, Eye and Ear Foundation, and University of Pittsburgh to Dr. Shaohua Pi. We also acknowledge support from NIH CORE Grant P30 EY08098, an unrestricted grant from Research to Prevent Blindness to the Department of Ophthalmology.

Conflict of interest

The authors declared that this work was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

The author SP declared that they were an editorial board member of Frontiers, at the time of submission. This had no impact on the peer review process and the final decision.

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