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. 2026 May 19;17(6):3059–3071. doi: 10.1364/BOE.590238

Histology correlated adaptive optics polarisation sensitive optical coherence tomography

Thomas J Smart 1,*, Bruno Charbit 1, Ziqi Zhou 2, Yuan Tian 1, Arman Athwal 2, Jun Song 3, Myeong Jin Ju 3,4, Colin J Chu 1,5, Marinko V Sarunic 1,2,6
PMCID: PMC13271213  PMID: 42311299

Abstract

Adaptive optics polarisation sensitive optical coherence tomography (AO-PS-OCT) provides high-resolution and tissue-specific contrast for the study of retinal pathology. We used our recently developed AO-PS-OCT system to acquire longitudinal in-vivo images of laser injury in the mouse retina over a ten-day period. Hyper-reflective foci (HRF) were observed in both reflectance and PS-OCT modalities. Spatial registration with immunohistochemistry showed co-localisation of HRF in our PS-OCT modality with IB4-positive myeloid cells and probable pigment in the confocal transmission channel, suggesting uptake of melanin-containing RPE debris as a source of PS contrast. These results demonstrate the potential of AO-PS-OCT for discrimination of HRF in-vivo.

1. Introduction

Optical coherence tomography (OCT) is a non-invasive volumetric imaging modality. Since its introduction some 35 years ago [1], OCT has become a widely employed tool in ophthalmology [2]. Hyper-reflective foci (HRF) in retinal OCT images, a term defined as hyperreflective dots or roundish lesions within the retinal layers [3], are a biomarker of progression, treatment response and prognosis in several retinal diseases, such as age-related macular degeneration (AMD) [4], diabetic retinopathy [5] and uveitis [6]. However, the molecular content of these features is not well understood. OCT cannot directly provide molecular information and the clinicopathological correlate of HRF remains uncertain, ranging from lipid material, migrating retinal pigment epithelium (RPE) cells, macrophages/microglia, and degenerated photoreceptor cells [3,7,8].

Image contrast in traditional OCT is based on the intensity of light backscattered from the sample. Additional mechanisms of contrast have been developed, including polarisation-sensitive OCT (PS-OCT) which takes advantage of changes in the polarisation state of light scattered by the sample to derive a tissue-specific contrast mechanism [9]. Most structures in the eye do not change the polarisation state of light upon interaction. However, certain ocular structures are birefringent, and others are depolarising. Birefringent materials cause a relative retardation of orthogonal polarisation states, whilst depolarising materials scramble the polarisation state of light. Depolarisation can be caused by multiple scattering or by scattering at non-spherical particles [10]. Broadly, it is fibrotic materials (e.g. the cornea, the retinal nerve fibre layer, the sclera) that cause birefringence [11] and pigmented materials (e.g. melanin in the retinal pigment epithelium) which cause depolarisation [12]. PS-OCT has the potential to provide novel information in the setting of retinal pathology. For example, the ability to detect depolarising structures may provide information about the molecular content of HRF that allows for in-vivo discrimination. Here, we demonstrate high-resolution PS-OCT imaging of HRF by combining PS-OCT with adaptive optics (AO).

AO detects and compensates for aberrations present in the eye of a subject that cause degradation of image quality. Aberrations are typically compensated by a deformable mirror placed in the optical path of the imaging system. AO has previously been combined with OCT to enhance image resolution [13] and several groups have investigated the combination of AO with PS-OCT. Cense et al. combined AO with PS-OCT for human imaging and found improved signal to noise, smaller speckle size and increased lateral resolution compared to a system without AO [14]. Recently, Kurokawa et al. described an AO-enabled polarisation-sensitive human imaging system designed to detect early signs of neurodegenerative diseases such as glaucoma [15]. Our groups have previously performed PS-OCT imaging in mice, with only low-order aberration correction [16,17].

Several groups have presented preclinical OCT studies that correlate HRF with cellular markers in rodent models. Fan et. al. monitored HRF in the mouse following optic nerve crush injury and verified HRF as inflammatory cells with immunohistochemistry (IHC) [18]. Nat Nor et. al. reported characteristics of HRF in a rat model of diabetic retinopathy and showed that HRF corresponded to clusters of activated microglia [19]. Miller et. al. used OCT and scanning laser ophthalmoscopy to quantify the distribution and dynamics of microglia following laser injury. Their results showed migration of microglia and Müller cells to the damage site, as well as increased scattering from photoreceptors following injury [20].

In this report, we demonstrate the integration of PS contrast with our multimodality AO small animal retinal imaging system [21]. We present in-vivo images of a laser injury lesion over a ten-day period with higher-order aberration correction using our recently developed AO-PS-OCT system. AO-PS-OCT enables high-resolution, tissue-specific visualisation of histologic features such as HRF. We correlate of our in-vivo images with ex-vivo IHC to elucidate the source of contrast in our polarisation-sensitive images.

2. Methods

2.1. Polarisation-sensitive optical coherence tomography

The instrument used here is based on a custom-built small animal retinal imaging system that has been described in depth elsewhere [21,22]. In brief, the system integrates image-guided sensor-less adaptive optics with a fibre-based near-infrared spectral-domain OCT scheme. The light source is a superluminescent diode (SLD) with a central wavelength of 840 nm and a full-width half-maximum of 90 nm. The sample arm contains a variable lens (VL) that allows the position of the beam waist to be scanned in depth across different layers of the retina. A 69-actuator deformable mirror (DM) is used for correction of ocular aberrations. A pair of galvanometer mirrors scan the beam laterally across the region of interest in the retina. The VL, DM and GS are optically conjugated by lens-based telescopes with the pupil plane. A-scans are acquired from the two spectrometers simultaneously at a rate of 100kHz, with 500 a-scans per B-scan, and 400 B-scans per volume. The field of view is approximately 500 µm x 500 µm. At each location of the scan, two B-scans are captured to permit OCTA calculation, allowing PS-OCT and OCTA to be acquired simultaneously over a 4 second period. Alignment of the two spectrometers was carried out numerically [23]. The sensor-less AO algorithm has been described elsewhere [21]. Here, we optimised 21 Zernike modes using an image metric calculated on an en-face maximum intensity projection of the deep capillary plexus.

Recently, we have modified our system to provide PS contrast (see Fig. 1). The PS-OCT interferometer is based on the system described in [24]. Light from the SLD is split into sample and reference arms by a non-polarising beam splitter. In the sample arm, a quarter wave plate (QWP) is oriented at 450 to provide circularly polarised light at the cornea. In the reference arm, a QWP is oriented at 22.50 to provide balanced power in the two spectrometers. Light from the two arms is recombined at the beam splitter and launched into a polarisation-maintaining fibre. The light is subsequently split into orthogonal polarisation states by a polarising beam splitter and analysed on two independent spectrometers.

Fig. 1.

Fig. 1.

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Schematic of optical system. Superluminescent diode, SLD; polarisation paddle, PP; beam splitter, BS; quarter wave plate, QWP; dispersion compensation block, DC; variable lens, VL; deformable mirror, DM; galvanometer scanners, GM; polarising beam splitter, PBS. Diagram not to scale.

2.2. OCT post-processing

Following performance of the usual OCT post-processing steps (DC-subtraction, re-mapping from λ to k space, dispersion compensation), OCT volumes were processed to provide reflectance and polarisation-sensitive contrast. The degree of polarisation uniformity (DOPU) [25] was calculated following the methods set out in [16] and [26]. Local spatial averaging was applied over a 3 × 4 pixels kernel (approximately 3 µm x 4 µm) in each B-scan. Low DOPU values imply the presence of depolarising material whereas high DOPU values imply a lack of depolarising material. OCT angiography (OCTA) was calculated on twice repeated B-scans using the speckle-variance method [27,28]. The time-course images shown below are the average of at least 30 volumes acquired at each time point. Volumes acquired at different time points were laterally registered based on the OCTA signal from the deep capillary plexus to ensure that the same lesion is compared at each time point. En-face OCT reflectance images were generated by taking a maximum intensity projection across several slices. DOPU en-face images were generated by taking minimum intensity projections.

2.3. Mouse husbandry and retinal photocoagulation model

Cx3cr1GFPCcr2RFP transgenic mice (Jackson Laboratory, strain no. 032127; RRID: IMSR_JAX:032127) were used to generate time-course images. The laser injuries in this study were performed on Cx3cr1GFPCcr2RFP transgenic mice, in which monocytes express red fluorescent protein and microglia express green fluorescent protein. Cx3cr1GFPCcr2RFP mice were chosen for our experiment as microglia are a likely component of HRF. Cx3cr1GFPCcr2RFP mice are derived from a C57BL/6 genetic background.C57BL/6J mice (Charles River, strain no. 027; RRID: IMSR_CRL:027) and albino (Charles River, strain number 022, Crl:CD1(ICR)) mice were used for validation. Mice were maintained in the Biological Research Unit at the Institute of Ophthalmology, University College London. All procedures were performed under a UK Home Office Project Licence (PP1506797), in accordance with the Animals (Scientific Procedures) Act 1986, and adhered to the ARVO Statement for the Use of Animals in Ophthalmic and Vision Research. Laser injury and time course imaging was performed on three mice. Two laser injuries were induced on each mouse retina using the Phoenix MICRON 5 in-vivo Image-Guided Laser system with a power of 200 mW and a duration of 0.07 s. Approximately 1-hour after in-vivo imaging on day 10, mice underwent cardiac perfusion [29] with 2% paraformaldehyde (PFA) before their eyes were removed and transferred in 4% PFA for 1-hour. Retinal tissues were then fixed overnight at room temperature (RT) in a 1:4 dilution of BD Cytofix (BD Biosciences).

2.4. Immunohistochemistry

Eyes were dissected to extract the retina, then blocked following standard IHC protocol. The samples were stained at room temperature for 72-hours under agitation, using the antibody mix of Hoechst 33342 (1:500; Cat. no. H3570, ThermoFisher), Collagen IV (1:100; Cat. no. ab34710, Abcam) and Isolectin B4 AF594 (1:200; Cat. no. I21413, ThermoFisher). Collagen IV was used as a marker for retinal vasculature. Isolectin B4 (IB4) stains endothelial cells, which line the interior of blood vessels. In inflammatory conditions, IB4 also stains activated microglia. Secondary antibody (1:500; Donkey anti-Rabbit IgG AF532, cat. no. A-11009, ThermoFisher) was added after a series of 3x 30 minutes washes with PBS and incubated for 48 hours at room temperature under agitation. Tissues were post-fixed with 1%-PFA for 15 minutes at RT then whole retinas were mounted on Corning frosted microscope slides (Scientific Laboratories Supplies) and cleared with Clearing-enhanced 3D (Ce3D, BioLegend) overnight at RT. Leica TCS SP8 HyVolution (Leica Microsystems) confocal microscope with 20x objective and 2X optical zoom was used to image the stained samples. AF488 channel was captured to visualize Cx3Cr1GFP-expressing cells and transmitted light imaging was used to identify pigmented structures.

2.5. Registration procedure

Correlation of fiducial markers from in-vivo OCT images and ex-vivo IHC images enables mapping of the cellular information provided by IHC onto the structural information provided by OCT. The retinal vasculature was used as a set of fiducial markers for correlation between modalities. Collagen IV-stained vasculature in the IHC images was registered to the OCTA signal from corresponding regions of the retina. Resident immune cells such as microglia that are visualised in IHC can then be indirectly registered with the OCT signal by applying the displacement field provided by registration of the vasculature, potentially leading to cellular phenotyping of OCT structures. Registration was performed in two steps: firstly, an affine transformation was applied to the collagen IV channel, calculated on the basis of cardinal points selected in both images. Secondly, a non-rigid deformation was automatically calculated and applied to the rigidly registered image. The deformation matrices calculated for the collagen confocal channel were subsequently applied to the other confocal channels.

3. Results

3.1. AO-PS-OCT system validation

The efficacy of the polarisation-based contrast mechanism was demonstrated by comparing DOPU images of an albino mouse, which lacks melanin pigment, with pigmented mice (C57BL/6 and Cx3cr1GFPCcr2RFP) - see Fig. 2. The DOPU-based contrast clearly highlights the RPE and choroid in the pigmented mice whereas these layers do not provide contrast in the albino mouse. The lack of pigment in the albino mouse allows for deeper penetration of light, revealing extra-orbital features in both the intensity and PS images, consistent with the findings of [30]. We compare PS-OCT images from a Cx3cr1GFPCcr2RFP mouse and C57BL/6 mouse in Fig. 2 and do not observe any marked differences in the DOPU-based contrast.

Fig. 2.

Fig. 2.

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Top row: log-scale reflectance and DOPU of albino mouse retina. Second row: log-scale reflectance and DOPU of C57BL/6 mouse retina. Third row: log-scale reflectance and DOPU of Cx3cr1GFPCcr2RFP mouse retina. Retinal layers as indicated on reflectance image in top row: retinal nerve fibre layer (RNFL); inner plexiform layer (IPL); inner nuclear layer (INL); outer plexiform layer (OPL); outer nuclear layer (ONL); external limiting membrane (ELM); photoreceptor outer segments (OS); photoreceptor inner segments (IS); retinal pigment epithelium (RPE); choroid.

3.2. Time course imaging of laser injury in mouse retina

Following validation, we used our AO-PS-OCT to study laser lesions in the mouse retina. Note that photocoagulation was performed only to form focal photoreceptor damage in the outer retina, rather than to induce choroidal neovascularisation. Laser injury was implemented on day 0 in three mice. In-vivo retinal imaging took place on days 3, 7 and 10. The captured OCT volumes were processed to provide reflectance and polarisation-sensitive contrast. Figure 3 shows cross-sectional and en-face images of a single lesion in a single mouse at each time point (we label this mouse ‘mouse 1’ in the following figures). En-face images are projections across several slices within the outer nuclear layer (ONL). The red box in the day 3 cross-sectional image indicates the slices over which the projections were taken, as well as the extent of the x,y plane. Many, but not all, of the hyperreflective features in the DOPU images can be matched to features in the OCT reflectance images.

Fig. 3.

Fig. 3.

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Cross-sectional and en-face time-course images of a representative laser lesion in the same mouse retina acquired 3 days, 7 days and 10 days after injury (mouse 1). Red box in cross-sectional image at day 3 represents the region over which projections were taken. Scale bar limits have been adjusted for en face DOPU images to highlight HRF.

Three days after injury, we observe HRF close to the RPE in the cross-sectional images in both contrast mechanisms. As time progresses, we observe a reduction in the number of HRF and a migration towards the inner retina. At day 3, we also observe dispersed hyperreflective signals between the outer plexiform layer and the photoreceptor layer in the OCT reflectance image. This feature contracts and becomes less reflective over time, which is consistent with the observations made in [31]. We also see increased thickness of the outer retina over time. This is particularly apparent in the DOPU images. The en-face images from the ONL likewise illustrate a reduction in the number of HRF over time as well. We also observe a coalescence of the HRF over time and an increase in size of individual HRF.

To better understand the origin of the DOPU contrast of HRF in these images, spatial correlation of in vivo and ex vivo imaging was performed and is described in the following section.

3.3. Correlation of in-vivo imaging with ex-vivo immunohistochemistry imaging

Following longitudinal in-vivo imaging, the mice were swiftly sacrificed and ex-vivo IHC imaging of the retinas was carried out as described in section 2.4. Collagen IV was used to label retinal vasculature basement membranes and isolectin B4 (IB4) was used to label endothelial cells and activated microglia.

Figure 4 summarises the process of correlating of in-vivo and ex-vivo images. SubFig. 4(A) shows in-vivo cross-sectional images of the lesion at day 10. Note that the images shown in Fig. 4 are from a different mouse to those shown in Fig. 3 (we label this mouse ‘mouse 2’ in the following figures). 4B shows the OCTA signal from the superficial vasculature above the lesion and a maximum intensity projection of the ex-vivo retinal tissue at the same location from IHC imaging. The collagen IV channel is shown in yellow, IB4 in red, and GFP in green. The distinctive branching structure of the superficial vasculature is apparent in both the in-vivo OCTA and the ex-vivo collagen IV. 4C shows en-face projections of the deep capillary plexus (DCP) in OCT reflectance, OCTA and DOPU contrast. The three modalities all show a hyperreflective feature situated in the lower half of region of interest. This feature may also be observed in the cross-sectional images as a hyperreflective feature in the DCP. We believe the appearance of this feature in OCTA to be an artefact, rather than indicating flow [32]. 4D shows an en-face projection of the DCP in the collagen IV channel, as well as the result of registration of the collagen IV signal to OCTA signal.

Fig. 4.

Fig. 4.

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A: cross-sectional images of laser lesion 10 days after injury in OCT reflectance and DOPU modalities in mouse 2. B: OCTA projection of superficial vasculature, above site of lesion; IHC projection the same region. C: OCT reflectance, OCTA and DOPU projections at the deep capillary plexus (DCP); projections taken over region indicated in OCT reflectance image in panel A. D: collagen IV IHC projection of DCP; collagen IV registered to OCTA.

Figure 5 shows the results of correlating the IHC images with the DOPU signal. The IB4 channel is shown in red and the DOPU signal is now displayed with a green colour map to allow for visualisation of co-localisation. The images are from the same location as shown in Fig. 4. The registered IB4 channel is overlayed on the DOPU image. We see co-localisation of a hyperreflective DOPU feature with an IB4-labelled cell - circled in blue. We presume the IB4-labelled cell to be a myeloid cell. A magnified image of the presumptive myeloid cell is shown on the right-hand side. Also shown is the signal from the confocal transmission channel at the same location.

Fig. 5.

Fig. 5.

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Correlation of imaging modalities in mouse 2. The top row shows DOPU B-scan and an en-face projection of the region outlined in the B-scan (using a green colour map, for purposes of overlay). The second row shows IB4 from deep capillary plexus at lesion site; overlay of IB4 on DOPU after registration; magnified images from IB4 and confocal transmission channels from within blue circle.

Dark regions in the transmission channel may correspond to pigmented material. This co-localisation of potential pigment and immune cells may suggest that pigment has been internalised by the cells. We see many other instances of co-localisation between IB4-labelled cells and pigmented features in the transmission channel, two examples of which are shown in Fig. 6. Given that the DOPU signal is also aligned with the cell, it is possible that internalised pigment is the source of DOPU contrast in this hyperreflective feature. The hyperreflective feature is also observed in the OCT reflectance image. Melanin is well-known to provide hyperreflectivity in OCT reflectance images [33]. Supplement 1 (1.8MB, pdf) Figure S1 provides an example of a HRF in the OCT reflectance channel with a low contrast counterpart in the DOPU contrast. The HRF is co-localised with an IB4-labelled cell, but there is no accompanying dark feature in the transmission channel.

Fig. 6.

Fig. 6.

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Examples of co-localisation of IB4-labelled cells and likely pigmented material in the confocal transmission channel

Other potential contributors to the DOPU signal include microglia, Müller cells, and damaged photoreceptor nuclei, all of which Miller et. al. showed to be present at the site of injury in period following injury [20], as well as collapsing material from the vasculature. The OCT reflectance image in Fig. 4(A) shows damage to the OPL, raising the question as to whether endothelial material from the vasculature could contribute to the IB4-labelled structures. Whilst it is possible for small amounts of ruptured endothelial antigen to be dispersed in the tissue, there would need to be extensive damage and yet Collagen IV shows deep plexus is largely intact. The morphology is also too focal and rounded for this to be the most likely observation, but we cannot dismiss this as a possibility.

4. Discussion and conclusion

We have developed an adaptive-optics-enabled polarisation-sensitive OCT system for small animal imaging. In this paper we have demonstrated the abilities of our AO-PS-OCT system by performing longitudinal imaging of laser-induced injuries in the mouse retina. We imaged three mice over a 10-day period following laser injury. Aberrations were corrected up to 21 Zernike modes. We were able to detect small highly reflective foci (HRF) in the inner retina both in traditional reflectance-based OCT contrast and polarisation-sensitive DOPU-based contrast.

We studied the dynamics of detected HRF over several days and observed a reduction in their number as well as migration towards the inner retina. Most but not all HRF visible in DOPU have counterparts in intensity-based OCT contrast. Detection of these features in the PS-OCT modality raised the question of what causes their polarisation-sensitive contrast. To shed light on this question, we performed ex-vivo IHC confocal imaging and spatially registered these images with our in-vivo images.

Following registration, we observed strong co-localisation of a HRF in the DOPU signal with an IB4-labelled myeloid cell and a pigmented structure detected in the confocal transmission. In the IHC imaging we found many instances of IB4-labelled cells co-localised with pigmented structures in the confocal transmission channel. Altogether, this may suggest that myeloid cells internalise pigmented RPE debris following laser injury to the outer retina, and that these pigment-containing myeloid cells provide a source of DOPU contrast for the observed HRF. As noted, other sources of DOPU contrast may include microglia, Müller cells, damaged photoreceptors and collapsing material from the vasculature. HRF in human OCT imaging are seen in a number of retinal diseases, but their cause is not well understood. In some cases, clinicopathology attributes HRF to migratory RPE cells. The ability of AO-PS-OCT to detect depolarising material may allow HRF to be discriminated in-vivo.

The primary limitation of this work is the small numbers of mice and HRF imaged. The time taken between in-vivo imaging and tissue fixation (on the order of one hour) is another limitation; immune cells may have moved between in-vivo and ex-vivo imaging, leading to misalignment following vessel-based registration. It is also the case that since mice were only culled after in-vivo imaging on day 10, the molecular content of the features observed in the in-vivo imaging on day 3 and day 7 could not be elucidated. A further limitation is the two-dimensional registration procedure implemented here. Fiducial points selected from within the deep capillary plexus (DCP), in both the in-vivo and ex-vivo images, form the basis of the registration. Since retinal tissue is prone to distortion when flat-mounted for confocal imaging, 2D deformation fields calculated at a particular retinal layer (here the DCP) are unlikely to be effective for alignment at different depths within the tissue. The effects of this can be seen in the slight misalignment of features in the upper region of the DOPU-IB4 overlay in Fig. 5. As opposed to the cell circled in blue, which sits within the DCP layer, these hyperreflective features are positioned slightly below the DCP. The result is that the deformation fields calculated at the DCP do not satisfactorily align the features in the IB4 channel and the DOPU signal.

Future work will involve further validation of our findings with the potential for eventual translation to clinical studies. A greater sample size is required to confirm our findings. Three-dimensional registration may alleviate misalignment caused by distortion of the retinal tissue when prepared for ex-vivo confocal imaging. Cardiac perfusion should be performed immediately after in-vivo imaging to ensure the cellular alignment is as close as possible between in-vivo and ex-vivo imaging.

Supplemental information

Supplement 1. Supplemental figure.
boe-17-6-3059-s001.pdf (1.8MB, pdf)

Acknowledgements

The authors acknowledge infrastructure support from the National Institute for Health and Care Research (NIHR) Biomedical Research Centre at Moorfields Eye Hospital NHS Foundation Trust and UCL Institute of Ophthalmology (IoO). The views expressed are those of the authors and not necessarily those of the NHS, the NIHR or the Department of Health and Social Care. We thank Rose Avient for assistance in preparation of mouse retinas for confocal imaging. We thank Dr Matteo Rizzi’s group at the UCL IoO for the provision of an albino mouse. We thank the UCL IoO Microscopy Unit.

Funding

Wellcome Trust https://ror.org/029chgv08 ( 224586/Z/21/Z); Moorfields Eye Charity https://ror.org/017hm5p20 ( GR001475, GR001424); Canadian Institutes of Health Research https://ror.org/01gavpb45; Canadian Cancer Society https://ror.org/017343w90; Odette Maymon Fund; European Commission https://ror.org/00k4n6c32 Teaming for Excellence programme ‘Translational Research and Innovation in Ophthalmology Vision – Centre of Excellence TRIO-VI CoE’ ( 101136570); EPSRC-funded UCL Centre for Doctoral Training in Intelligent, Integrated Imaging in Healthcare (i4health) ( EP/S021930/1).

Disclosures

The authors declare no conflicts of interest.

Data availability

Data underlying the results presented in this paper are not publicly available at this time.

Supplemental document

See Supplement 1 (1.8MB, pdf) for supporting content.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Supplement 1. Supplemental figure.
boe-17-6-3059-s001.pdf (1.8MB, pdf)

Data Availability Statement

Data underlying the results presented in this paper are not publicly available at this time.

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