May 2024
Volume 13, Issue 5
Open Access
Uveitis  |   May 2024
Vitreoretinal Interface Cells Correlate In Vivo With Uveitis Activity and Decrease With Anti-Inflammatory Treatment
Author Affiliations & Notes
  • Francesco Pichi
    Eye Institute, Cleveland Clinic Abu Dhabi, Abu Dhabi, United Arab Emirates
    Cleveland Clinic Lerner College of Medicine, Case Western Reserve University, Cleveland, OH, USA
  • Piergiorgio Neri
    Eye Institute, Cleveland Clinic Abu Dhabi, Abu Dhabi, United Arab Emirates
    Cleveland Clinic Lerner College of Medicine, Case Western Reserve University, Cleveland, OH, USA
  • Shaikha Aljneibi
    Eye Institute, Cleveland Clinic Abu Dhabi, Abu Dhabi, United Arab Emirates
  • Steven Hay
    Eye Institute, Cleveland Clinic Abu Dhabi, Abu Dhabi, United Arab Emirates
  • Hannah Chaudhry
    Eye Institute, Cleveland Clinic Abu Dhabi, Abu Dhabi, United Arab Emirates
  • Ester Carreño
    University Hospital Fundación Jiménez Díaz, Madrid, Spain
    University Hospital Rey Juan Carlos, Madrid, Spain
    Instituto de Investigación Sanitaria Fundación Jiménez Díaz, Madrid, Spain
  • Correspondence: Francesco Pichi, Eye Institute, Cleveland Clinic Abu Dhabi, Al Maryah Island, PO Box 112412, Abu Dhabi, UAE. e-mail: ilmiticopicchio@gmail.com 
Translational Vision Science & Technology May 2024, Vol.13, 15. doi:https://doi.org/10.1167/tvst.13.5.15
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      Francesco Pichi, Piergiorgio Neri, Shaikha Aljneibi, Steven Hay, Hannah Chaudhry, Ester Carreño; Vitreoretinal Interface Cells Correlate In Vivo With Uveitis Activity and Decrease With Anti-Inflammatory Treatment. Trans. Vis. Sci. Tech. 2024;13(5):15. https://doi.org/10.1167/tvst.13.5.15.

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      © ARVO (1962-2015); The Authors (2016-present)

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Abstract

Purpose: To highlight the utility of en face swept-source optical coherence tomography angiography (SS-OCTA) in assessing vitreoretinal interface cells (VRICs) of patients with active uveitis and their dynamics.

Methods: In this prospective, single-center study, 20 eyes from patients with active uveitis were analyzed using six 6 × 6-mm macular scans at three time points: active inflammation (baseline), clinically improving (T1), and resolved inflammation (T2). VRICs were visualized using 3-µm en face OCT slabs on the inner limiting membrane. The variation of VRIC number, density, and size over time was assessed, and VRIC measurements were compared with clinical grading.

Results: At baseline, the VRIC count was significantly higher (552.5 VRICs) than that of the healthy controls (478.2 VRICs), with a density of 15.3 cells/mm2. VRIC number decreased significantly to 394.8 (P = 0.007) at T1, with a density of 10.9 cells/mm2 (P = 0.007). VRIC size reduced from 6.8 µm to 6.3 µm at T1 (P = 0.009) and remained stable at T2 (P = 0.3). Correlation coefficients between inflammatory parameters (anterior chamber cells and National Eye Institute vitreous haze), and VRIC count indicated a positive correlation at baseline (r = 0.53), weakening at T1 (r = 0.36), and becoming negative at T2 (r = −0.24).

Conclusions: En face SS-OCTA revealed increased VRIC number and size in active uveitis, likely due to monocyte recruitment. Post-inflammation control, VRIC number, size, and density significantly decreased, returning to normal despite residual anterior chamber cells or vitreous haze.

Translational Relevance: Visualization of VRICs by in vivo OCT opens up new opportunities for therapeutic targets.

Introduction
The retina, an integral component of the central nervous system, exhibits a unique vulnerability to pathological conditions, necessitating precise immune surveillance and maintenance mechanisms. Within this context, retinal microglia are emerging as pivotal immune sentinels derived from the myeloid lineage akin to macrophages.1,2 These cells play a fundamental role in the retinal immune defense, executing functions from antigen presentation to phagocytosis, thus ensuring the structural and functional integrity of the retina.3 The importance of retinal microglia extends beyond mere maintenance, as they are crucial in identifying and responding to pathogenic assaults, highlighting their indispensable role in retinal health and disease.2,4 
In addition to microglia, the retinal microenvironment is home to what the literature has defined as macrophage-like cells (MLCs), including resident macrophages and hyalocytes.3 These cells participate in a spectrum of functions, from antigen presentation to the phagocytosis of apoptotic cells and debris. Hyalocytes, in particular, play a distinctive role in maintaining vitreous clarity and regulating angiogenesis within the eye.5,6 The synergy between these cellular entities underlines the complexity of immune responses in the retina, emphasizing the need for a thorough understanding of their interactions and functions. 
Advancements in imaging technologies, such as optical coherence tomography (OCT), have revolutionized our ability to visualize and study these cells within the retina, offering unprecedented insights into their dynamics and roles in retinal diseases.7 This is particularly pertinent in the study of uveitis, an inflammatory condition where the early involvement of monocytes and macrophages is critical to the pathogenesis of the disease.8 The application of en face OCT has facilitated a novel understanding of the dynamics of these vitreoretinal interface cells (VRICs) in uveitis, revealing the potential of this technology as a biomarker for disease activity and response to therapy.9,10 
This study aims to leverage en face OCT to quantitatively assess VRICs in the context of uveitis, investigating their role in the pathophysiology of the disease and the potential implications for therapeutic intervention. By elucidating the contributions of microglia and other VRICs to uveitis, we aspire to enhance the diagnostic and therapeutic landscape for this and related retinal conditions. 
Methods
This prospective study included consecutive patients who presented at the Eye Institute, Cleveland Clinic Abu Dhabi (CCAD) between August 1, 2022, and February 1, 2023, and were diagnosed with posterior uveitis or panuveitis. Ethical approval was obtained from the CCAD ethical committee (#A-2022-042), and the study adhered to the principles of the Health Insurance Portability and Accountability Act of 1996 as well as the tenets of the Declaration of Helsinki. Written informed consent was acquired from all enrolled subjects. 
Participants were systematically approached during their visits to the uveitis clinic, provided they met the following inclusion criteria: (1) age ≥ 18 years and (2) a confirmed diagnosis of active non-infectious posterior uveitis or panuveitis (either new-presentation patients or flare-up of a previously diagnosed condition). Uveitis was defined as “posterior” when the primary site of inflammation was the retina or the choroid, and as “panuveitis” when inflammation affected the anterior chamber, the vitreous, and the retina or choroid. If the uveitis was bilateral and both eyes were active at the time of inclusion, both eyes were included in the study. Exclusion criteria were (1) cornea, lens, or vitreous opacities hindering the quality of the retina scans (swept-source OCT achieving a signal strength of <7), or (2) a previous history of pars plana vitrectomy or epiretinal membrane or inner limiting membrane (ILM) peeling. 
Patients meeting these criteria were assessed at three distinct time points. The three sequential assessments were characterized as follows: 
  • Active uveitis (designated as “baseline”) was characterized by the presence of posterior segment inflammation, which included one or more of the following indicators: vitritis, active vasculitis, leakage at the optic nerve head detected through fundus fluorescein angiography, or active retinal/choroidal lesions in at least one eye.
  • Resolving uveitis (termed as T1, 1–2 months from baseline) indicated a clinical reduction in the manifestations of inflammation compared to T0, achieved through treatment intervention.
  • Quiet eye (referred to as T2, 4-6 months from baseline) signified the absence of clinical evidence of intraocular inflammation in at least one eye. This time point was delayed until treatment-induced remission was achieved in the case of a uveitis flare-up during the steroid tapering regimen.
The times at which T1 and T2 were achieved were different for each patient. 
At the initial time point (baseline), we collected patient demographic information encompassing age, gender, and the underlying etiology of uveitis. We systematically recorded clinical parameters throughout subsequent visits, including assessing best-corrected visual acuity (BCVA) measured in Early Treatment Diabetic Retinopathy Study (ETDRS) letter scores, Standardization of Uveitis Nomenclature (SUN) anterior chamber grading for anterior chamber inflammation evaluation,11 and the National Eye Institute (NEI) vitreous haze score for grading vitritis.12 
On the same day as the clinical assessments, we followed a standard imaging protocol that consisted of spectral-domain optical coherence tomography (SD-OCT) utilizing the SPECTRALIS HRA+OCT system developed by Heidelberg Engineering (Heidelberg, Germany). Additionally, ultra-widefield (UWF) color photography and UWF fluorescein angiography (UWF-FA) were conducted concurrently using the Optos California system (Optos, Dunfermline, UK). After proper laboratory evaluations were obtained and infectious conditions were excluded, all patients received systemic steroids starting at a dose of prednisone of 1 mg/kg/day, which was then slowly tapered. 
Image Acquisition
The investigation employed the PLEX Elite 9000 swept-source optical coherence tomography angiography (SS-OCTA) device, with a frequency of 100 kHz (Carl Zeiss Meditec, Dublin, CA). This instrument operates at a central wavelength of 1060 nm with a bandwidth spanning 100 nm. It achieves a scan speed of 100,000 A-scans per second and provides an A-scan depth of 3.0 mm within the tissue. The machine has a full-width at half-maximal axial resolution of approximately 5 µm in tissue and offers a lateral resolution of approximately 12 µm at the retinal surface. Three-dimensional OCTA scans, covering regions of 6 × 6 mm centered on the fovea, were captured for each eye on the same day. Each scan was comprised of 500 A-scans per B-scan, repeated twice at each of the 500 B-scan positions. 
A skilled ophthalmic photographer (S.H.) was instructed to conduct scans for both eyes until six OCTA scans met predefined acceptance criteria. These criteria included ensuring clear and sharp focus, minimizing artifacts and saccades, eliminating significant horizontal banding, maintaining good centration and regular illumination, and achieving a signal strength of 7 or higher (to exclude cases with media opacity that could impact the image analysis). 
Following image acquisition, the built-in software of the machine generated OCTA and corresponding en face OCT images. The software automatically segmented the layers of the retina. OCTA scans of the superficial capillary plexus, situated between the ILM and the inner plexiform layer, were exported for each set of six scans. The vitreoretinal interface (VRI) slab was adjusted to a 3-µm slab from the ILM specifically for the PLEX Elite 9000 SS-OCTA device, corresponding to –7 µm to –10 µm in the VRI segmentation. The corresponding en face OCT was utilized to exclude the strong hyperreflectivity of the retinal nerve fiber layer (RNFL) underneath, facilitating the visualization of macrophage-like cells on the retinal surface. 
Image Analysis
To perform elastic registration of consecutive superficial capillary plexus OCTA images, we utilized the “register virtual stack slices” plugin available in ImageJ Fiji (National Institutes of Health, Bethesda, MD) on a primary dataset consisting of six 6 × 6 OCTA scans, as described by Thevenaz et al. in 1998.13 Subsequently, the registered OCTA images were subjected to averaging, employing the “AvgNoiseRmvr” plugin (accessible at https://iplab.dmi.unict.it). This step aimed to enhance the signal-to-noise ratio, thereby improving the visual representation of capillary networks. 
Furthermore, we applied the same transformation matrix to the corresponding 3-µm en face OCT slabs using the “transform virtual stack slices” plugin. Afterward, the registered en face OCT images underwent averaging, once again employing the “AvgNoiseRmvr” plugin for noise reduction and enhancement of structural contrast. To further refine the registered en face OCT images, we coded an ImageJ macro to automatically obtain the VRIC image. Briefly, the code employed a background removal technique based on the fast Fourier transform (FFT) and its inverse (as illustrated in Fig. 1). 
Figure 1.
 
Imaging processing applied to a set of OCTA images. (A) OCTA at the level of the superficial capillary plexus of a single image. (B) Averaging of the six registration single images. (C) Single image of the en face OCT at 3 µm of the ILM. (D) Averaging of six images after using the transform of the OCTA images for registration. (E) FFT of image (D). (F) The FFT was edited to delete the frequency components that correspond to the background and linear artifacts. (G) Inverse FFT of image (F). (H) Particles after automatic thresholding of image (G). (I) Final macrophage-like cells (VRICs) after removing the particles smaller and bigger than expected VRIC size and circularity. (J) Density heat map after assigning a color according to cell density for each region of interest (blue, no particles; green, one particle; yellow, two particles; red, three particles; white, four or more than four particles).
Figure 1.
 
Imaging processing applied to a set of OCTA images. (A) OCTA at the level of the superficial capillary plexus of a single image. (B) Averaging of the six registration single images. (C) Single image of the en face OCT at 3 µm of the ILM. (D) Averaging of six images after using the transform of the OCTA images for registration. (E) FFT of image (D). (F) The FFT was edited to delete the frequency components that correspond to the background and linear artifacts. (G) Inverse FFT of image (F). (H) Particles after automatic thresholding of image (G). (I) Final macrophage-like cells (VRICs) after removing the particles smaller and bigger than expected VRIC size and circularity. (J) Density heat map after assigning a color according to cell density for each region of interest (blue, no particles; green, one particle; yellow, two particles; red, three particles; white, four or more than four particles).
To characterize VRICs effectively, we applied a threshold to filter out particles, and subsequently we employed the “analyze particles” filter. This filter excluded elements with an area smaller than 576 µm2 or larger than 4896 µm2, ensuring that only particles with a circularity above 0.5 (range, 0–1), consistent with the reported area and morphology of retinal microglia,14 were considered. The software automatically conducted the counting and extraction of the size of elements that met these specified criteria when they had been established. Furthermore, to improve results, visualization the macro divided the 500 × 500-pixel image into 15 × 15-pixel squared regions of interest (ROIs) and calculate the number of VRICs per ROI to automatically assign a color according to the number of VRICs in each particular ROI (as illustrated in Fig. 1). The assigned colors were as follows: blue, no VRICs; green, one VRIC; yellow, two VRICs; red, three VRICs; and white, four or more than four VRICs. VRIC size was expressed in square micrometers (µm2) and their density in cells/mm2
Normative VRIC Data
Normative VRIC data for comparison were obtained from our previous cross-sectional study in which we scanned 22 healthy eyes recruited from 11 patients (mean age, 30.1 ± 9.31 years; 63.6% females) attending the general ophthalmology clinic at Cleveland Clinic Abu Dhabi.10 
Dynamic Assessment of VRICs
To confirm the mobility of these hyperreflective dots, consistent with their cellular identity, we conducted (on one of the authors) eight consecutive high-definition 6 × 6-mm macular scans centered at the fovea with the PLEX Elite 9000 SS-OCTA device every 1 hour for 8 hours. The resulting sequential images (Fig. 2) were saved as video and presented 3 times in a loop to prove the movement of the VRICs (Supplementary Movie S1). 
Figure 2.
 
Zoomed image of an en face OCTA with the detected VRICs superimposed and highlighted in red. Of note, the VRICs present pseudopodia and are more concentrated in the perivascular space.
Figure 2.
 
Zoomed image of an en face OCTA with the detected VRICs superimposed and highlighted in red. Of note, the VRICs present pseudopodia and are more concentrated in the perivascular space.
Statistical Analysis
Descriptive statistics for continuous variables include the mean and standard deviation (SD) where appropriate; non-normally distributed data are reported as medians. Differences in BCVA, clinically graded anterior chamber (AC) cells and vitreous haze, and VRIC count, size, and density among the three time points were tested using linear mixed models with the patient as the random effect to account for the presence of two eyes from the same subject. Variations in the number, size, and density of VRICs over time were assessed using linear mixed models considering both the eye and the patient as random effects in order to account for repeated measurements within the same eye and for nesting of two eyes within the same subject. 
The influence of clinical variables including visit, type of uveitis (posterior uveitis or panuveitis), SUN AC cell grade, and NEI vitreous haze score on VRIC parameters (number, size, and density) was tested with a linear mixed model, with the patient as the random effect to account for the presence of two eyes from the same subject. To quantify the strength and direction of linear associations between VRIC parameters and subjective inflammatory markers, we employed the Pearson correlation coefficient. Specifically, we calculated Pearson correlation coefficients separately for each combination of VRIC parameters (number, size, and density) with inflammation measurements (AC cells and vitreous haze) at each of the three time points. The resulting coefficients provided insight into the degree of correlation, with positive values indicating positive associations and negative values indicating negative associations. Additionally, the magnitude of the correlation coefficient indicated the strength of the observed relationships. The statistical analyses were run on RStudio 1.1.383 (R Project for Statistical Computing, Vienna, Austria). P < 0.05 was considered statistically significant. The Bonferroni correction was applied when multiple comparisons were performed. 
Results
The study included 20 patients with active uveitis. Four patients (20%) had bilateral active uveitis at enrollment in the study; of these, three patients had one eye excluded because the SS-OCT image quality was lower than 7 (two eyes had dense vitiris, and one eye had a posterior subcapsular cataract), and one patient had previous pars plana vitrectomy in one eye for a vitreous hemorrhage secondary to a vasoproliferative tumor. Overall, 20 eyes of 20 patients were enrolled for analysis and follow-up. Among the patients, three were female (15%), and the mean age was 41 ± 6.7 years. Four patients (20%) had a diagnosis of multiple sclerosis, four patients (20%) of Vogt–Koyanagi–Harada syndrome, two patients (10%) of Beçhet's disease, and two patients (10%) of tubulo-interstitial nephritis and uveitis syndrome. Eight patients (40%) were idiopathic (Table 1). Overall, 16 eyes (80%) had panuveitis, and four eyes (20%) had posterior involvement (10% bacillary layer detachments and subretinal fluid, 10% superficial infiltrates). SD-OCT showed cystoid macular edema in two eyes with active uveitis at baseline (10%), and subretinal fluid in two eyes (10%). Furthermore, 16 eyes (80%) of seven patients had active vasculitis with vascular leakage on UWF-FA. 
Table 1.
 
Demographic Characteristics of the Uveitis Cohort
Table 1.
 
Demographic Characteristics of the Uveitis Cohort
BCVA and Clinically Graded AC Cells and Vitreous Haze
Average BCVA significantly improved from 64.3 ETDRS letters at baseline to 76.2 ETDRS letters at T2 (P < 0.0001). Average AC cells decreased from 3.1 to 0.02 (P = 0.0001), and the average vitreous haze significantly reduced from 1.8 at baseline to 0 at T2 (P < 0.0001) (Fig. 3). In the linear mixed model, BCVA was positively influenced by time (P = 0.03 from baseline to T1 and P = 0.0001 from baseline to T2). The SUN AC cell grading and NEI vitreous haze clinical scores showed a negative influence but did not reach statistical significance. By contrast, BCVA was not influenced by any SUN AC cell grading or NEI vitreous haze score. 
Figure 3.
 
In our cohort of 20 eyes with panuveitis and posterior uveitis, treated with prednisolone 1 mg/kg/day, average AC cells (light blue) decreased from 3.1 at baseline to 0.02 at T2 (P = 0.0001), and the average vitreous haze (dark blue) significantly reduced from 1.8 at baseline to 0 at T2 (P < 0.0001). At baseline, the mean number of VRICs detected was 552.5 ± 251.9. This number was significantly reduced to 394.8 ± 155.3 (P = 0.007) at T1. The VRIC number was stable at T2, where a further reduction was not significant (P = 0.5).
Figure 3.
 
In our cohort of 20 eyes with panuveitis and posterior uveitis, treated with prednisolone 1 mg/kg/day, average AC cells (light blue) decreased from 3.1 at baseline to 0.02 at T2 (P = 0.0001), and the average vitreous haze (dark blue) significantly reduced from 1.8 at baseline to 0 at T2 (P < 0.0001). At baseline, the mean number of VRICs detected was 552.5 ± 251.9. This number was significantly reduced to 394.8 ± 155.3 (P = 0.007) at T1. The VRIC number was stable at T2, where a further reduction was not significant (P = 0.5).
VRIC Number and Size
At baseline the mean number of VRICs detected was 552.5 ± 251.9 with a density of 15.3 ± 6.9 cells/mm2. This number was significantly reduced to 394.8 ± 155.3 (P = 0.007) at T1, with a density of 10.9 ± 4.3 cells/mm2 (P = 0.007) (Figs. 45). At baseline, the mean size of VRICs detected was 6.8 ± 1 µm. This figure was significantly reduced to 6.3 ± 0.5 µm (P = 0.009) at T1. All of these en face SS-OCTA–based measurements were stable at T2, where further reductions in VRIC number, density, or size were not significant (P = 0.5, P = 0.5, and P = 0.3, respectively) (Figs. 45). 
Figure 4.
 
A 43-year-old female with panuveitis secondary to multiple sclerosis was scanned at three time points while inflammation was being treated with oral prednisolone. (A) VRICs are highlighted in red, showing their progressive decrease in number at the various time points. (B) Color map of VRIC density; it is clear that their numbers from baseline to T1 decreased, whereas from T1 to T2 the density remained stable.
Figure 4.
 
A 43-year-old female with panuveitis secondary to multiple sclerosis was scanned at three time points while inflammation was being treated with oral prednisolone. (A) VRICs are highlighted in red, showing their progressive decrease in number at the various time points. (B) Color map of VRIC density; it is clear that their numbers from baseline to T1 decreased, whereas from T1 to T2 the density remained stable.
Figure 5.
 
A 29-year-old male with idiopathic panuveitis was scanned at three time points while inflammation was being treated with oral prednisolone. (A) VRICs are highlighted in red, showing their progressive decrease in number at the various time points. (B) Color map of VRIC density; it is clear that their numbers from baseline to T1 decreased, whereas from T1 to T2 the density remained stable.
Figure 5.
 
A 29-year-old male with idiopathic panuveitis was scanned at three time points while inflammation was being treated with oral prednisolone. (A) VRICs are highlighted in red, showing their progressive decrease in number at the various time points. (B) Color map of VRIC density; it is clear that their numbers from baseline to T1 decreased, whereas from T1 to T2 the density remained stable.
The correlation coefficients between AC cells and VRIC count at the different time points suggested varying degrees of association. At baseline, there was a moderate positive correlation (r = 0.53), indicating that, as AC cell counts increased, VRIC counts tended to increase, as well. At T1, the correlation remained positive but weakened (r = 0.36), suggesting a somewhat weaker relationship between these variables. Interestingly, at T2, we observed a negative correlation (r = –0.24), indicating that as AC cell counts decreased, VRIC counts tended to remain unchanged at this particular time point (Table 1). 
When examining the potential correlation between NEI vitreous haze and VRIC count at the three distinct time points, the Pearson correlation coefficients obtained were r = 0.26 at baseline, r = 0.06 at T1, and r = –0.31 at T2. The VRIC density and size had a weak correlation with both AC cells at baseline and T1 and a negative correlation at T2; density and size of the VRIC did not seem to correlate with vitreous haze score at any time point (Table 2). 
Table 2.
 
Correlations Between VRIC Parameters and Subjective Inflammatory Grading
Table 2.
 
Correlations Between VRIC Parameters and Subjective Inflammatory Grading
Dynamic Video Result
The resulting video showed the VRIC movement over a span of 8 hours in the healthy eye of one of the authors (see Supplementary Movie S1). The dendrite-like cellular protrusion and the interrelations of the cells can be visualized with clinical OCTA (Fig. 2). 
Discussion
In the present study, we have successfully repeated visualization of hyperreflective VRICs, previously described in the literature as MLCs, of patients with active uveitis using averaged en face SS-OCTA images, and we have demonstrated how their number, size, and density decrease when the inflammation gets under control. Since van Furth and colleagues15 introduced the “mononuclear phagocytic system” concept to classify highly phagocytic mononuclear cells and their precursors in 1972, knowledge regarding their role in the human body progressed exponentially. Specifically in the eye, the retina contains resident microglia, a type of myeloid-derived cell primarily consisting of macrophages. These microglia are strategically positioned within the glial limitans surrounding the inner retinal vessels and are distributed throughout the retinal tissue.16 Microglia express several markers associated with macrophages, including cluster of differentiation 14 (CD14), CD11b, and epidermal growth factor (EGF)-like module-containing mucin-like hormone receptor-like 1 (EMR1), also known as F4/80 in the animal model.16 Monocyte-derived macrophages infiltrating the retina can differentiate into microglia-like cells but are distinct from resident microglia.17,18 These monocyte-derived macrophages can remain in the retina for extended periods, adopting a microglia-like morphology and gene expression profile, although they exhibit differences in gene expression, such as class II major histocompatibility complex (MHCII) levels.17,18 The infiltration of monocyte-derived macrophages into the retina is part of the immune response to degeneration, and their presence can be crucial in disease states affecting the retina.19 
Over the last 2 years, there has been a growing interest in utilizing OCTA and SS-OCT for the visualization of retinal microglia, which appear as hyperreflective bodies that the existing literature identifies as macrophage-like cells.2024 However, the term “macrophage” implies cells of myeloid origin. Many tissues, including retina and brain, have immune cells of both yolk sac and myeloid origin. Given the marked context-specific development of both types, we decided not to refer to these hyperreflective bodies by their origin (MLC) because that cannot be determined. We thus adopted the term VRICs to refer to their location. Hyperreflective VRICs are increased in numbers in inflammatory retinal conditions, including both proliferative and non-proliferative diabetic retinopathy, as well as retinal vein and artery occlusions. Within our research group, we have adopted a methodology involving concurrent OCTA and en face OCT imaging of the same retinal region (as pioneered by Castanos et al.7) and have incorporated sequential scans of the identical area using tracking mechanisms to enhance image clarity and to cross-sectionally visualize VRICs in uveitis and make comparisons with healthy controls.9,10 
Furthermore, it is important to note that the term “macrophage-like cells” remains a speculative designation. Thus far, only studies conducted ex vivo or in animal models have provided insights into the dynamic behavior over time of microglia, monocyte-derived macrophages, and hyalocytes under various physiological conditions, elucidating alterations in cell morphology and motility in response to injury.1,2,25 All investigations applying readily available retinal imaging modalities for in vivo visualization of these hyperreflective bodies have presented static snapshots taken at a single time point. Thus, we cannot affirm with certainty that the hyperreflective bodies are cells (in particular, microglial cells) that are increasing and moving around the retina surface after an insult. As such, we believe that referring to these hyperreflective bodies by their location (VRICs), instead of assuming their nature (MLCs), is a more scientifically sound option. 
To illustrate the mobility of hyperreflective bodies observed in en face SS-OCTA images at the retinal surface, we conducted seven 6 × 6 OCTA scans within 1 hour on a healthy eye belonging to one of the authors. Subsequently, we processed the seven en face averaged images using ImageJ to generate a video stack, enabling us to track the positions of these hyperreflective bodies over time. The accompanying Supplementary Movie S1 demonstrates that these hyperreflective bodies (which we term VRICs and the literature suggests may be MLCs), exhibit movement over the course of 7 hours, characterized by pseudopodic extensions. This dynamic behavior of hyperreflective bodies on the surface of a healthy retina strongly supports their classification as microglial cells. These resident macrophage-like or dendritic-like cells continually survey their surrounding environment, poised to respond promptly to any retinal insults. 
Indeed, during three-dimensional migration, macrophages can adopt either an ameboid or mesenchymal migratory movement. When they engage in ameboid migration, these cells assume a rounded or polarized shape as they move through the extracellular matrix. In our study, VRICs showed an ameboid movement pattern on the retinal surface and along blood vessels.26 This is aligned with their critical immune patrolling and response functions, as well as chasing chemotactic signals and revealing an increase in shape plasticity, which confirmed the outcome of previous studies performed in vitro.27 
After establishing the provisional nature of hyperreflective corpuscles as VRICs, our prospective study demonstrated a significant increase in their number (552.5 cells) and density (15.3 cells/mm2) in patients with active uveitis when compared to the corresponding measurements in healthy controls (as reported by Pichi et al.10). These findings in inflamed eyes align with our group's prior results (average cell count of 546.1 cells and an average density of 15.17 cells/mm2, in patients with active posterior uveitis). This phenomenon may be attributed to the infiltration of perivascular macrophages originating from circulating monocytes, which enter the retina following an insult.28 As reported by London et al.,29 it has been demonstrated that these cells differ from resident microglia, which appear to play no role as effector cells in human posterior uveitis.8 
The VRIC increase in size in active uveitis might be an indirect feature of the proinflammatory phenotype of these hyperreflective bodies.30 Although our initial measurements of these VRICs align with previous findings regarding their detection in active uveitis,9,10,22 our current study focused on monitoring their dynamics over two distinct time intervals, coinciding with the administration of appropriate anti-inflammatory therapy (prednisolone, 1 mg/kg). Our research demonstrated a significant reduction in their quantity, dimension, and concentration at T1 (1–2 months into treatment compared to baseline) (P = 0.007, P = 0.009, and P = 0.007, respectively). Furthermore, no significant further changes were observed at T2 (3–4 months from baseline) (P = 0.5, P = 0.3, and P = 0.5, respectively), indicating a return to their baseline state. These findings correspond with a positive correlation initially observed between VRIC size and density and inflammatory markers (particularly strong for AC cells and moderately so for vitreous haze) at the baseline, which weakens at T1 and disappears at T2. Collectively, these data suggest that VRICs promptly respond to ocular inflammation by presenting antigens and recruiting circulating monocytes, thereby contributing to the mounting of an immune response characterized by AC cells and vitreous haze. When an appropriate anti-inflammatory treatment is initiated, VRICs revert to a steady state, diminishing in number, size, and concentration, even in cases where inflammation has not been fully resolved and AC and vitreous cells and proteins continue to decrease without complete resolution. 
This absence of any significant change in VRICs after T1 could be explained by their measurements returning to the levels of healthy controls.10 These data give rise to various hypotheses on potential discrepancies of the immune response between the two immune compartments of the eye. The different levels of correlation might be interpreted first as an unmatched immune response between the anterior chamber driven by the anterior chamber–associated immune deviation and the immune compartment of the posterior pole.31 On the other hand, this might be simplistically a slower clearance of the AC cells in the attempt to restore the immune homeostasis, albeit poor scientific literature is available regarding the aqueous humor dynamic in uveitis. Further analysis and exploration are undoubtedly necessary for a better understanding of cell interplay in the context of our research.32 
The present study has several limitations. First, the sample size of the patients studied was small, and there was heterogeneity in terms of inflammatory pathologies, which may impact the VRIC measurements. A second limitation is the heterogeneity in the timing between T1 and T2 visits; however, even if the timings were not standardized, we ensured that at T1 the uveitic subjective parameters had decreased and that at T2 the uveitis was completely quiet. Furthermore, we have not scaled the images per axial length to the correct lateral scale of the OCTA image; however, given that we were imaging the same subject at different time points, the axial length was a constant for the same subject and that limitation was partially overcome. 
In addition, although our methodology allowed us to appreciate the VRIC interplay with the retinal surface immune compartment, this represents only a bidimensional representation of the potential migratory activity of VRICs. The x- and y-planes are the only ones explored in our manuscript, as we currently have no information on the z-plane that might identify VRICs through the neuroretinal layers.26 Although it might be perceived as marginal, this biodynamic aspect may significantly contribute to defining the macrophage migration dynamic, clarifying whether they adopt a prevalent ameboid or mesenchymal migratory pattern or behave differently depending on the plane that they interact with. In addition, the potential migration toward the retinal pigment epithelium might lead to further hypotheses on the interplay of VRICs with the choroidal immune compartment. Furthermore, it would be interesting to break down the different uveitis entities to segregate VRIC behavior in different uveitis subsets and identify whether the primary signal might come from the retina or the choroid, which still remains poorly explored. 
Despite these limitations, the strength of our study lies in highlighting the dynamic nature of VRICs in uveitic patients. Traditionally, the retina was considered an immune-privileged site, presumed to have limited immune cell involvement. On the other hand, recent advances in research have unveiled the active and multifaceted roles of these immune cells in maintaining retinal homeostasis and responding to various challenges. In retinal diseases, although macrophages can play a role in phagocytosing damaged retinal cells and modulating the inflammatory response, dendritic cells excel in antigen presentation as essential bridge builders between the innate and adaptive immune systems. They capture and process antigens and present them to T cells, initiating the immune response. Their presence in the retina during uveitic conditions underscores their participation in the pathogenesis of these diseases. 
Considering this evolving understanding of the intricate interactions between these immune cells and retinal tissues, researchers are exploring innovative therapeutic approaches. Among these approaches, dendritic cell–based vaccines have emerged. These vaccines aim to modulate the immune response by targeting specific antigens related to retinal diseases, allowing for precise regulation of the immune response. By harnessing dendritic cells and their associated CD markers, these vaccines can potentially ameliorate intraocular inflammation and represent a highly specific and technologically advanced avenue for managing and treating retinal diseases. These advances open new horizons for personalized and effective treatment strategies for retinal diseases, and an effective in vivo visualization of those cells could play an effective role in future uveitis treatments. 
Acknowledgments
Disclosure: F. Pichi, None; P. Neri, None; S. Aljneibi, None; S. Hay, None; H. Chaudhry, None; E. Carreño, Active Biotech (C), Allimera (F), Eyevensis (C), Novartis (F), GlaxoSmithKline (C) 
References
Santos AM, Calvente R, Tassi M, et al. Embryonic and postnatal development of microglial cells in the mouse retina. J Comp Neurol. 2008; 506: 224–239. [CrossRef] [PubMed]
Roubeix C, Dominguez E, Raoul W, et al. Mo-derived perivascular macrophage recruitment protects against endothelial cell death in retinal vein occlusion. J Neuroinflammation. 2019; 16: 157. [CrossRef] [PubMed]
O'Koren EG, Yu C, Klingeborn M, et al. Microglial function is distinct in different anatomical locations during retinal homeostasis and degeneration. Immunity. 2019; 50: 723–737.e7. [CrossRef] [PubMed]
Nayak D, Roth TL, McGavern DB. Microglia development and function. Annu Rev Immunol. 2014; 32: 367–402. [CrossRef] [PubMed]
Wieghofer P, Engelbert M, Chui TY, Rosen RB, Sakamoto T, Sebag J. Hyalocyte origin, structure, and imaging. Expert Rev Ophthalmol. 2022; 17: 233–248. [CrossRef] [PubMed]
Vagaja NN, Chinnery HR, Binz N, Kezic JM, Rakoczy EP, McMenamin PG. Changes in murine hyalocytes are valuable early indicators of ocular disease. Invest Ophthalmol Vis Sci. 2012; 53: 1445–1451. [CrossRef] [PubMed]
Castanos MV, Zhou DB, Linderman RE, et al. Imaging of macrophage-like cells in living human retina using clinical OCT. Invest Ophthalmol Vis Sci. 2020; 61: 48. [CrossRef] [PubMed]
Forrester JV, Huitinga I, Lumsden L, Dijkstra CD. Marrow-derived activated macrophages are required during the effector phase of experimental autoimmune uveoretinitis in rats. Curr Eye Res. 1998; 17: 426–437. [CrossRef] [PubMed]
Carreno E, Hernanz I, Collado B, Pichi F. Description of macrophage-like cells in active ocular toxoplasmosis [published online ahead of print October 3, 2023]. Ocul Immunol Inflamm. 2023.
Pichi F, Neri P, Aljeneibi S, et al. In vivo visualization of macrophage-like cells in patients with uveitis by use of en face swept source optical coherence tomography [published online ahead of print September 18, 2023]. Ocul Immunol Inflamm. 2023.
Jabs DA, Nussenblatt RB, Rosenbaum JT, Standardization of Uveitis Nomenclature Working Group. Standardization of uveitis nomenclature for reporting clinical data. Results of the First International Workshop. Am J Ophthalmol. 2005; 140: 509–516. [PubMed]
Nussenblatt RB, Palestine AG, Chan CC, Roberge F. Standardization of vitreal inflammatory activity in intermediate and posterior uveitis. Ophthalmology. 1985; 92: 467–471. [CrossRef] [PubMed]
Thevenaz P, Ruttimann UE, Unser M. A pyramid approach to subpixel registration based on intensity. IEEE Trans Image Process. 1998; 7: 27–41. [CrossRef] [PubMed]
Davis BM, Salinas-Navarro M, Cordeiro MF, Moons L, De Groef L. Characterizing microglia activation: a spatial statistics approach to maximize information extraction. Sci Rep. 2017; 7: 1576. [CrossRef] [PubMed]
van Furth R, Cohn ZA, Hirsch JG, Humphrey JH, Spector WG, Langevoort HL. The mononuclear phagocyte system: a new classification of macrophages, monocytes, and their precursor cells. Bull World Health Organ. 1972; 46: 845–852. [PubMed]
Zhang C, Tso MO. Characterization of activated retinal microglia following optic axotomy. J Neurosci Res. 2003; 73: 840–845. [CrossRef] [PubMed]
Ronning KE, Karlen SJ, Burns ME. Structural and functional distinctions of co-resident microglia and monocyte-derived macrophages after retinal degeneration. J Neuroinflammation. 2022; 19: 299. [CrossRef] [PubMed]
Karlen SJ, Miller EB, Wang X, Levine ES, Zawadzki RJ, Burns ME. Monocyte infiltration rather than microglia proliferation dominates the early immune response to rapid photoreceptor degeneration. J Neuroinflammation. 2018; 15: 344. [CrossRef] [PubMed]
Yu C, Roubeix C, Sennlaub F, Saban DR. Microglia versus monocytes: distinct roles in degenerative diseases of the retina. Trends Neurosci. 2020; 43: 433–449. [CrossRef] [PubMed]
Zeng Y, Wen F, Mi L, Ji Y, Zhang X. Changes in macrophage-like cells characterized by en face optical coherence tomography after retinal stroke. Front Immunol. 2022; 13: 987836. [CrossRef] [PubMed]
Zeng Y, Zhang X, Mi L, et al. Characterization of macrophage-like cells in retinal vein occlusion using en face optical coherence tomography. Front Immunol. 2022; 13: 855466. [CrossRef] [PubMed]
Zeng Y, Zhang X, Mi L, et al. Macrophage-like cells characterized by en face optical coherence tomography were associated with fluorescein vascular leakage in Behcet's uveitis. Ocul Immunol Inflamm. 2023; 31: 999–1005. [CrossRef] [PubMed]
Zhang NT, Nesper PL, Ong JX, Wang JM, Fawzi AA, Lavine JA. Macrophage-like cells are increased in patients with vision-threatening diabetic retinopathy and correlate with macular edema. Diagnostics (Basel). 2022; 12: 2793. [CrossRef] [PubMed]
Wang W, Sun G, Xu A, Chen C. Proliferative diabetic retinopathy and diabetic macular edema are two factors that increase macrophage-like cell density characterized by en face optical coherence tomography. BMC Ophthalmol. 2023; 23: 46. [CrossRef] [PubMed]
Ong JX, Nesper PL, Fawzi AA, Wang JM, Lavine JA. Macrophage-like cell density is increased in proliferative diabetic retinopathy characterized by optical coherence tomography angiography. Invest Ophthalmol Vis Sci. 2021; 62: 2. [CrossRef] [PubMed]
Travnickova J, Nhim S, Abdellaoui N, et al. Macrophage morphological plasticity and migration is Rac signalling and MMP9 dependent. Sci Rep. 2021; 11: 10123. [CrossRef] [PubMed]
Cougoule C, Van Goethem E, Le Cabec V, et al. Blood leukocytes and macrophages of various phenotypes have distinct abilities to form podosomes and to migrate in 3D environments. Eur J Cell Biol. 2012; 91: 938–949. [CrossRef] [PubMed]
Ebneter A, Kokona D, Schneider N, Zinkernagel MS. Microglia activation and recruitment of circulating macrophages during ischemic experimental branch retinal vein occlusion. Invest Ophthalmol Vis Sci. 2017; 58: 944–953. [CrossRef] [PubMed]
London A, Benhar I, Mattapallil MJ, Mack M, Caspi RR, Schwartz M. Functional macrophage heterogeneity in a mouse model of autoimmune central nervous system pathology. J Immunol. 2013; 190: 3570–3578. [CrossRef] [PubMed]
Lendeckel U, Venz S, Wolke C. Macrophages: shapes and functions. ChemTexts. 2022; 8: 12. [CrossRef] [PubMed]
Ikezu T, Gendelman HE. Neuroimmune Pharmacology. Cham: Springer International; 2017.
Alaghband P, Baneke AJ, Galvis E, et al. Aqueous humor dynamics in uveitic eyes. Am J Ophthalmol. 2019; 208: 347–355. [CrossRef] [PubMed]
Figure 1.
 
Imaging processing applied to a set of OCTA images. (A) OCTA at the level of the superficial capillary plexus of a single image. (B) Averaging of the six registration single images. (C) Single image of the en face OCT at 3 µm of the ILM. (D) Averaging of six images after using the transform of the OCTA images for registration. (E) FFT of image (D). (F) The FFT was edited to delete the frequency components that correspond to the background and linear artifacts. (G) Inverse FFT of image (F). (H) Particles after automatic thresholding of image (G). (I) Final macrophage-like cells (VRICs) after removing the particles smaller and bigger than expected VRIC size and circularity. (J) Density heat map after assigning a color according to cell density for each region of interest (blue, no particles; green, one particle; yellow, two particles; red, three particles; white, four or more than four particles).
Figure 1.
 
Imaging processing applied to a set of OCTA images. (A) OCTA at the level of the superficial capillary plexus of a single image. (B) Averaging of the six registration single images. (C) Single image of the en face OCT at 3 µm of the ILM. (D) Averaging of six images after using the transform of the OCTA images for registration. (E) FFT of image (D). (F) The FFT was edited to delete the frequency components that correspond to the background and linear artifacts. (G) Inverse FFT of image (F). (H) Particles after automatic thresholding of image (G). (I) Final macrophage-like cells (VRICs) after removing the particles smaller and bigger than expected VRIC size and circularity. (J) Density heat map after assigning a color according to cell density for each region of interest (blue, no particles; green, one particle; yellow, two particles; red, three particles; white, four or more than four particles).
Figure 2.
 
Zoomed image of an en face OCTA with the detected VRICs superimposed and highlighted in red. Of note, the VRICs present pseudopodia and are more concentrated in the perivascular space.
Figure 2.
 
Zoomed image of an en face OCTA with the detected VRICs superimposed and highlighted in red. Of note, the VRICs present pseudopodia and are more concentrated in the perivascular space.
Figure 3.
 
In our cohort of 20 eyes with panuveitis and posterior uveitis, treated with prednisolone 1 mg/kg/day, average AC cells (light blue) decreased from 3.1 at baseline to 0.02 at T2 (P = 0.0001), and the average vitreous haze (dark blue) significantly reduced from 1.8 at baseline to 0 at T2 (P < 0.0001). At baseline, the mean number of VRICs detected was 552.5 ± 251.9. This number was significantly reduced to 394.8 ± 155.3 (P = 0.007) at T1. The VRIC number was stable at T2, where a further reduction was not significant (P = 0.5).
Figure 3.
 
In our cohort of 20 eyes with panuveitis and posterior uveitis, treated with prednisolone 1 mg/kg/day, average AC cells (light blue) decreased from 3.1 at baseline to 0.02 at T2 (P = 0.0001), and the average vitreous haze (dark blue) significantly reduced from 1.8 at baseline to 0 at T2 (P < 0.0001). At baseline, the mean number of VRICs detected was 552.5 ± 251.9. This number was significantly reduced to 394.8 ± 155.3 (P = 0.007) at T1. The VRIC number was stable at T2, where a further reduction was not significant (P = 0.5).
Figure 4.
 
A 43-year-old female with panuveitis secondary to multiple sclerosis was scanned at three time points while inflammation was being treated with oral prednisolone. (A) VRICs are highlighted in red, showing their progressive decrease in number at the various time points. (B) Color map of VRIC density; it is clear that their numbers from baseline to T1 decreased, whereas from T1 to T2 the density remained stable.
Figure 4.
 
A 43-year-old female with panuveitis secondary to multiple sclerosis was scanned at three time points while inflammation was being treated with oral prednisolone. (A) VRICs are highlighted in red, showing their progressive decrease in number at the various time points. (B) Color map of VRIC density; it is clear that their numbers from baseline to T1 decreased, whereas from T1 to T2 the density remained stable.
Figure 5.
 
A 29-year-old male with idiopathic panuveitis was scanned at three time points while inflammation was being treated with oral prednisolone. (A) VRICs are highlighted in red, showing their progressive decrease in number at the various time points. (B) Color map of VRIC density; it is clear that their numbers from baseline to T1 decreased, whereas from T1 to T2 the density remained stable.
Figure 5.
 
A 29-year-old male with idiopathic panuveitis was scanned at three time points while inflammation was being treated with oral prednisolone. (A) VRICs are highlighted in red, showing their progressive decrease in number at the various time points. (B) Color map of VRIC density; it is clear that their numbers from baseline to T1 decreased, whereas from T1 to T2 the density remained stable.
Table 1.
 
Demographic Characteristics of the Uveitis Cohort
Table 1.
 
Demographic Characteristics of the Uveitis Cohort
Table 2.
 
Correlations Between VRIC Parameters and Subjective Inflammatory Grading
Table 2.
 
Correlations Between VRIC Parameters and Subjective Inflammatory Grading
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