figure table

# Retinal analysis of a mouse model of Alzheimer’s disease with multi-contrast
optical coherence tomography

Danielle J. Harper  Center for Medical Physics and Biomedical Engineering,
Medical University of Vienna, Waehringer Guertel 18-20/4L, 1090 Vienna,
Austria  Marco Augustin  Center for Medical Physics and Biomedical
Engineering, Medical University of Vienna, Waehringer Guertel 18-20/4L, 1090
Vienna, Austria  Antonia Lichtenegger  Center for Medical Physics and
Biomedical Engineering, Medical University of Vienna, Waehringer Guertel
18-20/4L, 1090 Vienna, Austria  Johanna Gesperger  Center for Medical Physics
and Biomedical Engineering, Medical University of Vienna, Waehringer Guertel
18-20/4L, 1090 Vienna, Austria  Institute of Neurology, General Hospital and
Medical University of Vienna, Waehringer Guertel 18-20/4J, 1090 Vienna,
Austria  Tanja Himmel  Institute of Pathology, University of Veterinary
Medicine, Vienna, Veterinaerplatz 1, 1210 Vienna, Austria  Martina Muck
Center for Medical Physics and Biomedical Engineering, Medical University of
Vienna, Waehringer Guertel 18-20/4L, 1090 Vienna, Austria  Conrad W. Merkle
Center for Medical Physics and Biomedical Engineering, Medical University of
Vienna, Waehringer Guertel 18-20/4L, 1090 Vienna, Austria  Pablo Eugui  Center
for Medical Physics and Biomedical Engineering, Medical University of Vienna,
Waehringer Guertel 18-20/4L, 1090 Vienna, Austria  Stefan Kummer  Core
Facility for Research and Technology, University of Veterinary Medicine,
Vienna, Veterinaerplatz 1, 1210 Vienna, Austria  Adelheid Woehrer  Institute
of Neurology, General Hospital and Medical University of Vienna, Waehringer
Guertel 18-20/4J, 1090 Vienna, Austria  Martin Glösmann  Core Facility for
Research and Technology, University of Veterinary Medicine, Vienna,
Veterinaerplatz 1, 1210 Vienna, Austria  Bernhard Baumann  Center for Medical
Physics and Biomedical Engineering, Medical University of Vienna, Waehringer
Guertel 18-20/4L, 1090 Vienna, Austria

###### Abstract

Recent Alzheimer’s disease (AD) patient studies have focused on retinal
analysis, as the retina is the only part of the central nervous system which
can be imaged non-invasively by optical methods. However as this is a
relatively new approach, the occurrence and role of pathological features such
as retinal layer thinning, extracellular amyloid beta (A$\beta$) accumulation
and vascular changes is still debated. Animal models of AD are therefore often
used in attempts to understand the disease. In this work, both eyes of 24
APP/PS1 transgenic mice (age: 45-104 weeks) and 15 age-matched wildtype
littermates were imaged by a custom-built multi-contrast optical coherence
tomography (OCT) system. The system provided a combination of standard
reflectivity data, polarization-sensitive data and OCT angiograms. This tri-
fold contrast provided qualitative and quantitative information on retinal
layer thickness and structure, presence of hyper-reflective foci, phase
retardation abnormalities and retinal vasculature. While abnormal structural
properties and phase retardation signals were observed in the retinas, the
observations were very similar in transgenic and control mice. At the end of
the experiment, retinas and brains were harvested from a subset of the mice
(14 transgenic, 7 age-matched control) in order to compare the in vivo results
to histological analysis, and to quantify the cortical A$\beta$ plaque load.

*Danielle J. Harper<EMAIL_ADDRESS>
##  1 Introduction

It is hypothesized that cerebral changes precede Alzheimer’s disease (AD)
symptom presentation by over 20 years [1]. Since the beginning of the
millennium, the number of deaths from AD in the United States has increased by
145%. For comparison, the number of deaths caused by heart disease (the number
one cause of death in the United States) has decreased by 9% in the same time
period [2]. AD is a chronic and irreversible neurodegenerative disorder with
no current cure. The time delay between the start of the disease and the
presentation of symptoms means that the disease is already at an advanced
stage before it can be detected, and even when a patient presents with AD
symptoms, a definitive AD diagnosis still remains challenging.

Post mortem diagnosis of AD is realized by the positive histological
identification of both extracellular amyloid beta (A$\beta$) plaques and
intracellular neurofibrillary tau tangles, which are both found in the brains
of AD patients [3, 4]. A key step along the road to an early AD diagnosis
would be an in vivo identification of A$\beta$ plaques. However as the plaques
are small (ranging from 10-200 $\mathrm{\SIUnitSymbolMicro m}$ [5]) and
located within the brain, this presents some logistical difficulties.

One recent idea to circumvent these difficulties is the use of the eye “as a
window to the brain”. The retina and the brain are derived from the same
embryological origin; they both extend from the neural tube. The retina is
therefore the only part of the central nervous system which can be imaged non-
invasively by optical methods. However the question still remains whether the
retina can hold the key to early AD diagnosis as there are few studies which
directly correlate findings in the retina to those in the brain. Those studies
which have been done before have found a correlation between the amplitude of
retinal vascular pulsatility and neocortical A$\beta$ scores (measured using
florbetaben positron emission tomography) [6], and also between fluorescent
components (measured using fluorescence lifetime imaging ophthalmoscopy) and
both p‐tau181‐protein concentration in the cerebral spine fluid and the mini-
mental state examination score [7].

Recent studies have also focused on the identification of extracellular
A$\beta$ accumulations in the retina of AD patients, however there are
conflicting reports on this topic [8]. Some reports have identified
extracellular A$\beta$ in the retina [9, 10, 11, 12], however other groups
have demonstrated that there was no A$\beta$ to be found [13, 14, 15, 16].
Another topic of current debate is whether AD could also be considered a
vascular disorder[17], and therefore the retinal vasculature of AD patients
has also recently been studied. Patients with AD may exhibit more tortuous
retinal vessels [18]. Narrowing of retinal blood vessels and reduced venous
blood flow rates have also both been found in AD patients [19, 20], and an
overall more sparse retinal microvascular network has been observed [21, 22].
A recent OCT angiography (OCTA) study has shown a reduced vessel density
specifically in the superficial capillary plexus [23]. It has also been
suggested that blood flow changes may precede neurodegeneration [20].

While the aforementioned studies have all demonstrated a reduction in blood
circulation in the retina in late stage AD, recent studies have indicated that
the retinal vessel density [24] and vessel diameter [25] both seem to be
increased in individuals suffering from preclinical AD. Such results are
consistent with the theory that an inflammatory response occurs in the retina
in the early stages of AD - a theory which has also been proposed for
neurovasculature [26, 27].

Retinal layer thinning, particularly in the retinal nerve fiber layer (RNFL),
is also present in the retina of AD patients [28, 29]. However looking forward
to a marker for diagnosis, RNFL thinning is not specific to AD and it is not
only associated with other diseases such as glaucoma [30] and Parkinson’s
disease [31], but also more generally with increasing age [32].

With many contradictory observations, it is clear that there is still a great
deal of research to be performed in order to fully understand the effects of
AD on the retina. Some attempts to do this have focused on the use of mouse
models of the disease. When performing studies on animal models, it is
important to know where the similarities and differences to the human disease
lie. While there are many mouse models of AD [33], this work focuses on a
doubly transgenic model which expresses a chimeric mouse/human amyloid
precursor protein (APP) and a mutant human presenilin 1 (PS1) [34, 35, 36].
The following paragraphs describe the retinal changes observed in this
transgenic APP/PS1 mouse model so far.

Much like in the case of the human, while well documented in the brain, [37,
38] the appearance of extracellular deposits of A$\beta$ in the retina is
disputed. Several studies have reported no extracellular deposits of A$\beta$
in the retina, despite plaques being present in the brain and an increased
expression of APP in the retina, similar to what has been found in humans
[15]. This has been reported in mice of several ages: 9 months old [39], 7-12
months old [40] and 13 months old [41]. It has been suggested that the
nonamyloidogenic pathway may endogenously limit A$\beta$ formation in the
retina [41]. A further extensive histological analysis of the retina concluded
that no identifiable retinal pathology exists in these mice [42]. Conversely,
extracellular deposits of A$\beta$ were found at the age of 27 months old in
the choriocapillaris and the nerve fiber layer, but not in the other layers
[43]. In another study, plaques were found in the inner plexiform layer and in
the outer plexiform layer. These plaques ranged in size from 5-20
$\mathrm{\SIUnitSymbolMicro m}$ in mice of 12-13 months old, and larger with
increasing age [44]. This study also reported no observed changes in retinal
layer thickness. Deposits were also found distributed throughout the retina of
transgenic mice at the age of 9 months and 17 months, but were identifiable as
young as 2.5 months, even before the plaques appeared in the brain [9].

There is therefore a need for more studies linking the retina and the brain in
both AD patients and in animal models of AD [45], and more work needs to be
done to assess and quantify the presence of A$\beta$ in the retina [46].
Optical coherence tomography (OCT) [47] may be a useful tool to employ for
this purpose. As a non-contact, non-invasive imaging modality, OCT has become
part of clinical routine for in vivo retinal diagnostics. Functional
extensions of OCT have made it possible to not only visualize contrasts based
on backscattered intensity (reflectivity), but also motion (OCTA) [48, 49, 50]
and polarization properties (PS-OCT) such as birefringence [51, 52, 53, 54].
The birefringence of A$\beta$ plaques has been studied in detail using
polarimetry [55, 56, 57, 58], and also with PS-OCT [59, 60]. The plaques
appear as hyper-scattering structures in standard reflectivity OCT images [61,
62, 63], but the addition of polarization-sensitive detection provides an
additional tissue-specific contrast. Furthermore, the combination of PS-OCT
with OCTA allows a simultaneous analysis of this tissue-specific contrast and
changes in the retinal vasculature.

In this work, the appearance of the retina of an APP/PS1 mouse model of AD was
evaluated by multi-contrast spectral domain OCT. By observing retinal changes
over a range of ages (45-104 weeks) in intensity-, polarization- and motion-
based contrast modes, the observations in the retina were documented, mapped
directly to histology, and compared to the A$\beta$ plaque load in the brain.

##  2 Materials and Methods

###  2.1 Optical coherence tomography

A modified version of a PS-OCT system described elsewhere was used in this
study [64]. In brief, the system operated at a central wavelength of 840
$\mathrm{nm}$ with a full width at half maximum bandwidth of approximately 100
$\mathrm{nm}$, resulting in an axial resolution of around 3.8
$\mathrm{\SIUnitSymbolMicro m}$ in retinal tissue. Light incident upon the
mouse eye was of a known polarization state, and the polarization-sensitive
detection allowed for the differentiation between polarization-preserving
tissue and polarization-altering tissue.

An additional refocusing telescope was added to the system to correct for
aberrations induced by poor focus of the mouse eye itself [65]. A diagram of
the modified version of the system can be found in Fig. 1. Two additional
achromatic doublet pairs (2 $\times$ AC254-080-B, Thorlabs and 2 $\times$
AC254-050-B, Thorlabs) were mounted on a translational stage, allowing the
focus to be manually optimized for each individual mouse eye while reducing
the beam diameter incident on the pupil from 0.8 $\mathrm{mm}$ to 0.5
$\mathrm{mm}$. Each mouse eye was aligned with respect to the 2.85
$\mathrm{mW}$ measurement beam to ensure the optic nerve head (ONH) was at the
center of the 1 $\mathrm{mm}$ $\times$ 1 $\mathrm{mm}$ field of view. With an
A-scan rate of 83 $\mathrm{kHz}$, five repeated B-scans (consisting of 512
A-scans each) were acquired at 400 unique locations. Such a scan pattern
allowed for an increased signal-to-noise ratio (SNR) in the reflectivity and
PS-OCT images, and also the ability to produce OCTA images.

---
Figure 1:  A modified version of the PS-OCT system first described by Fialová
et al. [64]. A refocusing telescope was added to the system to allow focus
correction of each individual mouse eye.

###  2.2 Mice

A breeding pair of APP/PS1 mice (APPswe, PSEN1dE9 MMRRC stock number
34829-JAX) was purchased from The Jackson Laboratory (Bar Harbor, ME, USA)
[34, 35, 36], and a breeding colony was established at the Division of
Biomedical Research at the Medical University of Vienna. Hemizygous mice were
bred with wildtype siblings and subsequently kept under controlled lighting
conditions (12 hours light, 12 hours dark) with food and water ad libitum.
Both eyes of 24 mutant mice (17 female, 7 male) and 15 wild-type littermates
(9 female, 6 male) were imaged using the multi-contrast OCT system. At the
time of imaging, the mice ranged in age from 45 weeks to 104 weeks. During the
experiment, the animals were anesthetized using an inhalational
isoflurane/oxygen mixture (4% isoflurane for 4 minutes in an induction chamber
to induce anesthesia, 2% delivered via a nose cone thereafter). To facilitate
the OCT imaging, pupils were dilated using topically applied tropicamide and
phenylephrine. The cornea was kept moisturized using artificial tear eye
drops, and heating pads were placed underneath the mice to prevent a reduction
in body temperature. All experiments were performed in accordance with the
ARVO Statement for the Use of Animals in Ophthalmic and Vision Research and
Directive 2010/63/EU. Ethics protocols were approved by the ethics committee
of the Medical University of Vienna and the Austrian Federal Ministry of
Education, Science and Research (BMBWF/66.009/0272-V/3b/2019).

---
Figure 2:  Histogram representations of the number of eyes used for analysis.
A total of 44 eyes from 24 APP/PS1 transgenic mice (a) and 28 eyes from 15
wildtype littermates (b) were imaged within an age range of 45 weeks to 104
weeks.

###  2.3 OCT image analysis

The analysis performed in this work was based on a previously-published multi-
contrast image processing pipeline including standard intensity-based
reflectivity contrast, polarization-based contrast and motion-based
angiographic contrast [66]. Prior to analysis, the images were corrected for
axial motion and the retina was flattened with respect to the retinal pigment
epithelium (RPE)/choroidal complex as detected by the cross-polarized channel
[67], a technique made possible by the polarization sensitive detection. Any
dataset for which the retinal flattening failed (due to poor signal-to-noise
ratio) was excluded, resulting in a total of 72 datasets for evaluation (44
eyes from 24 transgenic mice and 28 eyes from 15 wildtype control mice). A
graphical visualization of the age of the mice at each measurement can be
found in Fig. 2. A flow chart of the overall post-processing pipeline can be
seen in Fig. 3, and a description of the analysis can be found in the
following sections.

---
Figure 3:  A flow chart of the multi-contrast optical coherence tomography
(OCT) post-processing pipeline which consists primarily of reflectivity, phase
retardation and angiography data. HRF Hyperreflective foci.

####  2.3.1 Retinal thickness analysis

To allow for a comprehensive retinal thickness analysis, layer segmentation of
the retina was first performed using a previously-described algorithm [67].
The distance between the inner limiting membrane (ILM) and the posterior
surface of the RPE was defined as the total retinal thickness. The posterior
surface of the outer plexiform layer (OPL) served as the boundary between the
inner and outer retina, as defined in this manuscript. An annulus around the
optic nerve head was created with an inner diameter of 500
$\mathrm{\SIUnitSymbolMicro m}$ and an outer diameter of 900
$\mathrm{\SIUnitSymbolMicro m}$, and the mean total, inner and outer retinal
thicknesses were evaluated within this annular 3D volume. The annulus was then
cut in half transversely and the mean thicknesses were again calculated in the
two resulting regions, corresponding to the superior and inferior retina. Due
to mouse eye alignment, it is estimated that this boundary line is accurate to
within $\pm$ 30 $\mathrm{\SIUnitSymbolDegree}$. Coordinates of the
segmentation lines and the ONH annuli were stored for later use in OCTA
analysis.

To test for statistical significance, the retinal thickness measurements were
plotted as a function of age for both the transgenic and the wildtype mice. A
linear regression pre-test was performed to determine if the thickness
measurements were dependent on age. If the results were deemed significant
($p<0.05$), an analysis of covariance (ANCOVA) was performed to test for a
difference in the trend between the two groups. In the case where the pre-test
was deemed statistically insignificant ($p\geq 0.05$), regular analysis of
variance (ANOVA) was performed to test for a difference in the group means.
The gradient of the slope of the regression lines were also documented for
both transgenic and wildtype mice, corresponding to a measurement of reduction
of retinal thickness in units of $\mathrm{\SIUnitSymbolMicro m}$ per week.

####  2.3.2 Hyperreflective foci analysis

All 72 reflectivity datasets (44 eyes from 24 transgenic mice and 28 eyes from
15 wildtype control mice) were manually screened for hyperreflective foci in
the posterior layers of the retina, spanning the region from the posterior OPL
border to the posterior RPE (i.e. the whole outer retina). The hyperreflective
foci were then manually segmented using ITK Snap [68]. Using the data from
this segmentation, the number and location of hyperreflective foci was
evaluated for each eye.

####  2.3.3 Polarization properties

The phase retardation images were calculated for every B-scan by

$\delta=\arctan\left(\dfrac{A_{V}}{A_{H}}\right)$ (1)

where $A_{V}$ and $A_{H}$ correspond to the signal amplitudes of the co- and
cross-polarized channels, respectively [51, 69]. In the healthy mouse retina,
high retardation values are only expected in the melanin-containing regions,
namely the RPE, the choroid, and the remnants of the hyaloid artery near the
ONH. Depolarization occurs due to the fact that the melanin granules scramble
the polarization state of the incoming light beam, resulting in random phase
retardation values. Polarization-preserving tissues, i.e., the rest of the
retina, do not retard the phase of the incident beam, and therefore the phase
retardation values are low. All retardation B-scans were manually inspected
for abnormally high retardation signals from outwith these retinal layers. The
number of these abnormally high retardation signals was evaluated, and the
sources were investigated with retinal histology.

####  2.3.4 OCT angiography

The B-scan repetition allowed for the computation of OCTA images, revealing
locations of motion contrast. After bulk motion compensation and removal of
frames with uncorrectable motion, OCTA images were computed by calculating the
averaged magnitude of the complex differences between consecutive repeated
B-scans. The time delay between the acquisition of repeated B-scans ($\approx$
7.7 $\mathrm{ms}$ from one B-scan to the next) which provides the angiographic
contrast in the first place also makes the method very susceptible to motion.
All datasets were therefore manually visually screened, and data which
included severe motion artefacts or regions of poor angiography signal were
excluded. The angiography analysis was therefore performed on 39 transgenic
eyes and 16 wildtype eyes.

An automated OCTA processing pipeline was created which consisted of several
steps. The superficial vascular plexus (SVP) and the deep capillary plexus
(DCP) were segmented from the retina using the layer segmentation coordinates
obtained from the reflectivity data (SVP corresponds to the RNFL; DCP
corresponds to the OPL). A maximum intensity en-face projection was then
calculated over the SVP and the DCP independently, and the histograms of each
image were equalized using contrast limited adaptive histogram equalization
[70] before image binarization. The binary images were then morphologically
opened and closed (disk-shaped structuring element with a radius of 1), and
skeletonized to remove speckle noise and enhance the vessel connections. A
square averaging filter with a 5-pixel side length was then applied to the
images to create the final binary vessel representations of “vessel” vs. “non-
vessel”. The annuli around the ONH which had been calculated using the
reflectivity data were then applied to these vessel maps. The vessel density
was then calculated as the percentage of pixels which were marked as “vessel”
in the whole annulus, as well as in the superior and inferior retinal regions.

After this image processing was performed, a modified version of the Weber
contrast [71] was calculated to test the relationship of the OCTA vessel
intensity to the background, using the binary image as a mask. The mean
intensity value of all pixels determined both “vessels” ($\overline{I_{v}}$),
and “non-vessels” ($\overline{I_{b}}$), was calculated, and the modified Weber
contrast, $C_{W}$ was calculated as

$C_{W}=\frac{\overline{I_{v}}-\overline{I_{b}}}{\overline{I_{b}}}$ (2)

and the results were plotted as box-and-whisker diagrams for both transgenic
and wildtype mice.

###  2.4 Histology and immunostaining

After OCT imaging, a sub-group of the mice (14 transgenic, age: 54-104 weeks,
7 wildtype control, age: 54-103 weeks) were euthanized by cervical
dislocation. Immediately after sacrifice, the brains were extracted and the
eyes were enucleated for histological analysis.

####  2.4.1 Brain

The mouse brains were sagittally cut into two hemispheres, and one hemisphere
was prepared for histopathological workup. The samples were fixed in 4%
formalin and processed through graded alcohols and xylene into paraffin.
Sagittal brain sections with a thickness of 2.5 $\mathrm{\SIUnitSymbolMicro
m}$ were cut on a microtome, deparaffinized, rehydrated, and stained
immunohistochemically using an anti-A$\beta$ antibody (clone 6F/3D, diluted
1:100, Dako). The sections were evaluated using a slide scanner (Hamamatsu
NanoZoomer 2.0 HT) and saved for digital pathology. The images were analyzed
using Fiji [72]. First, the cortex was manually selected and the “ColSeg” tool
[73] was utilized to segment the plaques by their brown color. The “analyze
particle” tool was then used to count the plaque number and calculate the
plaque load in plaques per ${\mathrm{mm}}^{2}$. The plaque load was then
plotted as a function of mouse age, and a linear regression analysis was
performed on the data.

####  2.4.2 Retina

To obtain vertical histological slices of the retina, six left eyes from
wildtype (n=3) and transgenic (n=3) mice diagnosed with and without abnormal
OPL banding (see Section 3.5) were immersed unopened in Davidson’s fixative
for 24 hours at $None$C and processed through graded alcohols and xylene into
paraffin. Three-micron-thick sections were then cut, mounted onto slides,
deparaffinized, rehydrated, and stained with hematoxylin and eosin (H&E).

For wholemount preparation and A$\beta$ immunostaining, eyes were immersed in
4% paraformaldehyde (PFA) in 0.1 M phosphate buffered saline (PBS), pH 7.4.
Some eyes were fixed unopened. In the others, cornea and lens were removed and
the eyecups fixed in PFA for at least 24 h at room temperature. After rinses
in PBS, the retina was dissected free from RPE, choroid and sclera,
cryoprotected in ascending sucrose concentrations (10%, 20%, 30%), and snap-
frozen and thawed three times to increase antibody penetration. From each
mouse, the left retina was treated with 70% formic acid for 10 minutes and
then rinsed repeatedly in PBS, while the right retina was left untreated.
Retinal wholemounts were processed free-floating in 24-well plates and all
incubations and rinses were done with gentle rotation on a rocker table at
$None$C. Blocking of non-specific binding was performed in 3% normal donkey
serum in 0.1 M PBS, 0.25% Triton X-100 and 0.05% sodium azide (medium),
followed by incubation with mouse anti-human A$\beta$ (Abcam, ab11132, clone
DE2B4, 1:400 in medium) for 72 h. After washes in PBS, retinas were incubated
in donkey anti-mouse Fab fragments conjugated with Alexa Fluor 488 (Jackson
ImmunoResearch Laboratories, 1:500 in medium) for 24 h, rinsed, and
coverslipped (retinas ganglion cell side up) in Aqua/Polymount (Polysciences).
To serve as positive and negative controls, respectively, brains from
transgenic APP/PS1 mice and their wildtype littermates were harvested after
enucleation and fixed in 4% PFA for 24 h at $None$C. After washes in PBS,
brains were cryoprotected in ascending sucrose concentrations (10%, 20%, 30%),
snap-frozen in liquid nitrogen-prechilled isopentane and cut into
100-$\mathrm{\SIUnitSymbolMicro m}$-thick sections using a cryotome. The
sections were collected in PBS/0.05% sodium azide and processed under the same
conditions applied to retinal wholemounts. H&E-stained sections were examined
with brightfield illumination on a Zeiss Axio Imager Z2. Immunofluorescence
analysis was performed with a Zeiss LSM880 laser scanning microscope (LSM). A
total of 17 retinas from 11 transgenic mice (age: 54-103 weeks) and 11 retinas
from 6 wildtype control mice (age: 54-103 weeks) were suitable for detailed
histological examination. A 1:1 correlation of the retinal wholemounts to the
OCT image data was then performed by mapping the vessel pattern of the SVP as
visualized by the LSM to the corresponding OCTA datasets.

##  3 Results

###  3.1 Hyperreflective foci

For each retina, the whole $1\times 1$ ${\mathrm{mm}}^{2}$ area surrounding
the optic nerve head was evaluated. Of the 24 mutant mice, 16 showed HRF in at
least one eye. In the wildtype littermate control group, HRF were identified
in 12 out of the 15 mice. Figure 4a displays pie charts which document this in
terms of eyes; there were an equal number of eyes with and without HRF in the
transgenic mice, and a difference of only one in the wildtype mice. Since it
is difficult to identify small HRF in the plexiform layers due to the
appearance of hyperreflective blood vessels, a normalized probability
distribution of all identified HRF in the outer retina alone was plotted (Fig.
4b). A similar HRF distribution in transgenic and wildtype retinas was
observed. The number of HRF per eye was also counted for the transgenic (Fig.
4c) and the wildtype (Fig. 4d) mice. With the exception of one outlier in each
group, all outer retinas contained less than 10 HRF within the investigated
field of view. Qualitatively, the types of HRF also looked very similar
between transgenic and wildtype mice, examples of which can be found in Fig.
4e-h. Figure 4e and Fig. 4f show examples of larger HRF located anterior to
the ELM in the transgenic and wildtype animals, respectively, while Fig. 4g
and Fig. 4h show smaller HRF in the middle of the ONL. Neither the number of
HRF nor the size correlated with the age of the mice, for either group.

---
Figure 4:  Results of hyperreflective foci (HRF) analysis. a) Pie charts
indicating the number of eyes with and without HRF for both transgenic and
wildtype mice. b) HRF probability distribution displayed with respect to outer
retinal layer position for transgenic and wildtype mice. The distributions are
very similar, with most HRF appearing near the external limiting membrane
(ELM). ONL outer nuclear layer. IS inner segments. OS outer segments. RPE
retinal pigment epithelium. c) Histogram of HRF occurrence in transgenic mice.
d) Histogram of HRF occurrence in wildtype mice. e-h) Some examples of the
appearance of HRF in OCT reflectivity images. Each image is a maximum
intensity projection over four consecutive B-scans, where each B-scan is
already averaged 5 times and plotted on a log scale. e-f) HRF located above
the ELM in both the transgenic mouse retina (e) and in the wildtype retina
(f). g-h) HRF located in the middle of the ONL in both the transgenic mouse
retina (g) and in the wildtype retina (h). Ages of mice in weeks (w) are
indicated on sub-figures (e-h).

###  3.2 Retinal thickness

The total, outer and inner retinal thickness was calculated in an annulus
around the ONH, and also for the superior and inferior ($180\pm 30$) degree
sectors, resulting in nine thickness comparisons between transgenic and
wildtype mice. The results of this analysis can be found in Fig. 5. When
plotted as a function of age, all nine datasets displayed a general trend of
decreasing retinal thickness with age for both the transgenic mice and the
wildtype controls. Statistical pre-tests revealed that the dependence upon age
was significant for all data except three wildtype datasets: the total outer
retinal thickness ($p=0.075$), the superior outer retinal thickness
($p=0.124$) and the inferior inner retinal thickness ($p=0.101$). For these
three datasets, the comparison between transgenic and wildtype data were
analyzed with ANOVA, while all other analysis was performed with ANCOVA. No
statistical significance was found between the retinal thickness changes in
the transgenic and control groups, for any of the retinal regions. The results
of the statistical analysis can be found in Table 1. Using the gradient of the
slope of the linear regression analysis, a measurement of the decrease of
retinal thickness was documented in units of $\mathrm{\SIUnitSymbolMicro m}$
per week, as shown in Table 2. All trends were negative, therefore the
gradient of each slope is the negative of the value in the table.

---
Figure 5:  Analysis of retinal thickness as a function of age for both transgenic and wildtype mice. a-c) Total retinal thickness measured around the whole annulus (a), then subdivided into a superior half (b) and an inferior half (c). d-f) Outer retinal thickness measured around the whole annulus (d), in the superior half (e) and in the inferior half (f). g-i) Inner retinal thickness measured around the whole annulus (g), in the superior half (h) and in the inferior half (i). The corresponding statistical evaluation can be found in Table 1, and the gradients of the slopes can be found in Table 2. Table 1: Statistical evaluation for the retinal thickness analysis. All values displayed are p-values, and significance is defined as $p<0.05$. $\blacksquare$ indicates pre-test p-values for retinal thickness as a function of age for the transgenic group. $\blacksquare$ indicates pre-test p-values for retinal thickness as a function of age for the wildtype group. Pre-test p-values were calculated using linear regression analysis. $\diagdown$ $\diagdown$ indicates p-values from ANCOVA comparing the trends of the transgenic retinal thickness vs. age to the wildtype retinal thickness vs. age. $\diamond$$\diamond$ indicates values where the initial pre-tests failed, and therefore one-way ANOVA was performed to test for significance between the means of the two groups. There were no statistically significant changes in retinal thickness between the transgenic and the wildtype groups. | Position
---|---
| Total | Superior | Inferior
|  |  $\blacksquare$ $6.146\times 10^{-5}$ |  |  $\blacksquare$ $1.598\times 10^{-5}$ |  |  $\blacksquare$ $2.858\times 10^{-4}$ |
|  Total |  $\blacksquare$ $0.033$ | $\diagdown$ $\diagdown$ $0.421$ |  $\blacksquare$ $0.037$ | $\diagdown$ $\diagdown$ $0.392$ |  $\blacksquare$ $0.034$ | $\diagdown$ $\diagdown$ $0.466$
|  |  $\blacksquare$ $9.010\times 10^{-4}$ |  |  $\blacksquare$ $2.159\times 10^{-4}$ |  |  $\blacksquare$ $0.004$ |
|  Outer |  $\blacksquare$ $0.075$ | $\diamond$$\diamond$ $0.727$ |  $\blacksquare$ $0.124$ | $\diamond$$\diamond$ $0.716$ |  $\blacksquare$ $0.045$ | $\diagdown$ $\diagdown$ $0.647$
|  |  $\blacksquare$ $3.998\times 10^{-4}$ |  |  $\blacksquare$ $5.437\times 10^{-4}$ |  |  $\blacksquare$ $7.544\times 10^{-4}$ |
Retinal Thickness |  Inner |  $\blacksquare$ $0.038$ | $\diagdown$ $\diagdown$ $0.640$ |  $\blacksquare$ $0.030$ | $\diagdown$ $\diagdown$ $0.936$ |  $\blacksquare$ $0.101$ | $\diamond$$\diamond$ $0.835$
Table 2: Decrease in retinal thickness in units of $\mathrm{\SIUnitSymbolMicro m}$ per week. $\blacksquare$ indicates the values for the transgenic mouse and $\blacksquare$ indicates the values for the wildtype mouse. |  | Position
---|---|---
|  | Total | Superior | Inferior
Retinal Thickness | Total |  $\blacksquare$ 0.37 |  $\blacksquare$ 0.40 |  $\blacksquare$ 0.35
$\blacksquare$ 0.26 |  $\blacksquare$ 0.27 |  $\blacksquare$ 0.24
Outer |  $\blacksquare$ 0.24 |  $\blacksquare$ 0.26 |  $\blacksquare$ 0.23
$\blacksquare$ 0.16 |  $\blacksquare$ 0.14 |  $\blacksquare$ 0.17
Inner |  $\blacksquare$ 0.13 |  $\blacksquare$ 0.14 |  $\blacksquare$ 0.12
$\blacksquare$ 0.10 |  $\blacksquare$ 0.13 |  $\blacksquare$ 0.07

###  3.3 OCT angiography

Following layer segmentation of the SVP and the DCP, the vessel density was
quantified in these layers in the total, superior and inferior retina for both
transgenic and wildtype mice. Figure 6a shows a typical OCT angiogram from the
SVP of a transgenic mouse. Combining its binarized form (Fig. 6b) with the
annulus which was defined from the reflectivity data (Fig. 6c), binary
representations of the retinal vasculature around the ONH were obtained (Fig.
6d). Figure 6e-h shows the same processing steps for a wildtype mouse. The
binarized annulus (Fig. 6d,h) was then used as a mask on the annular angiogram
(Fig. 6c,g) in order to calculate the Weber contrast (Fig. 6i). The contrast
is similar between transgenic and wildtype mice. Figure 6j-m and Fig. 6n-q
show examples of the same analysis pattern for the DCP in the transgenic and
wildtype mice, respectively. The Weber contrast in this case (Fig. 6r) is
lower for both groups, although the contrast values remain similar between
transgenic and wildtype mice.

The vessel density calculations for all retinas in the SVP and the DVP are
shown in Fig. 6s and Fig. 6t, respectively. No significant differences were
observed between transgenic and wildtype mice for any of the retinal regions.

---
Figure 6:  a-h) Example of optical coherence tomography angiography (OCTA)
analysis of the superficial vascular plexus (SVP) of a transgenic mouse (a-d)
and a wildtype control (e-h). a,e) En-face OCTA depth projection through the
SVP. b,f) Binary representation of the SVP with white pixels corresponding to
blood vessels. c,g) Annulus around the optic nerve head (ONH) as provided by
the intensity-based contrast data. d,h) Binarized annulus, where the yellow
dashed line corresponds to the boundary between the superior retina (above)
and the inferior retina (below). i) Weber contrast comparing the intensity of
the angiogram signal of the blood vessels to the intensity of the background
in the SVP. j-q) Example of OCTA analysis of the deep capillary plexus (DCP)
of a transgenic mouse (j-m) and a wildtype control (n-q). j,n) En-face OCTA
depth projection through the DCP. k,o) Binary representation of the DCP with
white pixels corresponding to blood vessels. l,p) Annulus around the ONH as
provided by the intensity-based contrast data. m,q) Binarized annulus, where
the yellow dashed line corresponds to the boundary between the superior retina
(above) and the inferior retina (below). r) Weber contrast comparing the
intensity of the angiogram signal of the blood vessels to the intensity of the
background in the DCP. s-t) Vessel density analysis. Total, superior and
inferior vessel density calculated for transgenic and wildtype mice in the SVP
(s) and the DCP (t). Age (a-d, j-m): 93 weeks, Age (e-h, n-q): 76 weeks.
Single points in (i) and (r-t) correspond to data outliers. All scale bars =
100 $\mathrm{\SIUnitSymbolMicro m}$.

###  3.4 Phase retardation abnormalities

All individual B-scans of all mouse retinas were screened for phase
retardation abnormalities, i.e., depolarizing deposits located outwith the
known melanin-containing RPE and ONH regions. Such deposits were found in at
least one eye of 22 out of 24 transgenic mice and 11 out of 15 controls, and
there were no apparent differences between those deposits observed in
transgenic and wildtype groups. Figure 7a-b shows the two most common forms of
phase retardation abnormalities, which appear as small, round depolarizing
deposits beside a vessel wall (Fig. 7a) or beneath the RNFL adjacent to the
optic nerve head (Fig. 7b). No A$\beta$ plaques were identified in any of the
retinas where the PS-OCT data was correlated to histology (more details in
Section 3.6). However in some cases, melanin migration was found to be the
source of the contrast, as demonstrated in Fig. 7c-f.

---
Figure 7:  Depolarizing deposits. a) Example of depolarization along a vessel
wall (indicated by orange circle). b) Example of depolarization near the optic
nerve head (indicated by orange circle). c) Identification of migrated
melanin. After wholemounting the retina, the OCT angiography data (in red) was
used to correlate the vessels measured in vivo to the overview of the retina
provided by the ex vivo preparation (greyscale). d) PS-OCT image showed
location of abnormally high phase retardation in the inner nuclear layer
(indicated by yellow arrows). e) A high resolution confocal microscopy scan
was acquired at the area of interest marked in (c), at the depth position
marked by the yellow arrows in (d). A cluster of melanin is revealed at this
location, as seen in (f). Scale bar in bottom right applies to (a), (b) and
(d).

###  3.5 Double-banded OPL

Abnormalities in the structure of the ONL/OPL were found in the reflectivity
OCT images in both eyes in a total of 3/24 transgenic mice (age: 54 weeks, 67
weeks and 81 weeks) and 3/15 wildtype control (age: 67 weeks, 81 weeks and 97
weeks). Examples of the appearance of the double-banded OPL can be found in
Fig. 8. Figure 8a shows an example of a “normal” appearance of a retina
observed in a transgenic mouse, where the OPL appears as a single
hyperreflective band. In contrast, the hyperreflective OPL appears to split
into two in Fig. 8b. Similar double bands of hyperreflective OPL signal were
observed in the 3 wildtype mice as shown in Fig. 8c. To evaluate potential
structural bases underlying the atypical retinal layer contrast, exemplary
mouse retinas depicting OPL double-banding in the OCT exam were embedded in
paraffin, sectioned, and stained with hematoxylin and eosin. Microscopical
examination revealed that the double-banding of the OPL precisely correlated
witha rearrangement of proximal ONL somata towards the outer border of the INL
(Fig. 8d).

---
Figure 8:  Demonstration of retinal layer abnormalities. The outer plexiform
layer (OPL) is indicated with arrows. a) A transgenic mouse retina with a
typical appearance - the outer plexiform layer appears as one single
hyperreflective band. b) A transgenic mouse retina with the OPL disrupted,
appearing as a double-banded hyperreflective layer. This effect was observed
in 3/24 transgenic mice. c) A similar double-banding effect was also observed
in 3/15 wildtype littermates. d) H&E-stained histological slice of the same
mouse retina as in (c). The structural correlate of the double-banded OCT
signal in the OPL region appears to be rearranged proximal outer nuclear layer
somata. RNFL Retinal nerve fiber layer. GCL Ganglion cell layer. IPL Inner
plexiform layer. INL Inner nuclear layer. OPL Outer plexiform layer. ONL Outer
nuclear layer. IS/OS Inner/outer segment junction. RPE Retinal pigment
epithelium. BM Bruch’s membrane. CH Choroid. SC Sclera. Age (a-d): 81 weeks.

###  3.6 Retinal histology

####  3.6.1 Typical observations

To confirm human A$\beta$ in the retinas of APP/PS1 transgenic mice, indirect
immunofluorescent staining of retinal wholemounts was performed with a mouse
monoclonal antibody directed against amino acids 1-17 of human A$\beta$ (clone
DE2B4). The marker identifies intracellular A$\beta^{83}$ and safely detects
extracellular A$\beta$ without cross-reacting with APP83. This is evidenced by
the clear labeling of A$\beta$ plaques only in brain sections from APP/PS1
transgenic animals used as a positive control.

Following the donkey anti-mouse secondary antibody staining protocol outlined
in Section 2.4.2, it was expected that in addition to A$\beta$, sources of
endogenous IgG present in the retina would bind to the secondary anti-mouse
antibody and be highlighted. Figure 9a shows an example of a retinal
wholemount of a transgenic mouse with the peripapillary blood vessels
distinctly labeled due to abundant endogenous mouse IgG present in the serum.
Figure 9b demonstrates the precise fit of the ex vivo vessel pattern with the
in vivo OCTA image. The wavy appearance of some of the vessels is a result of
motion artefacts caused by breathing during the measurement. From the positive
control of the cortex of the transgenic mouse (Fig. 9c), it can already be
observed that signal of a similar intensity to the roundish A$\beta$ plaques
also comes from the capillary network. Figure 9d-f shows the equivalent images
for an example of a wildtype control mouse. In the cortex of the wildtype mice
(used as a negative control, Fig. 9f), only the capillary network showed
fluorescent labeling.

Outwith the blood vessels and capillary network, other sources of fluorescent
signal were found in both transgenic and control mice. Examples of such
features are shown in (Fig. 9g-l). In Fig. 9a, a blood vessel (indicated by
the solid box) appears to be intensely fluorescing. However, by analysing a
series of confocal optical sections (z-stacks) throughout this region (Fig.
9g-h), it became clear that this signal derived from aggregates of secondary
antibody-fluorochrome conjugate artefactually adhering to the surface of the
retinal ganglion cell layer. The signal did not derive from either neuronal or
non-neuronal structures within the retina it as the overview at 10$\times$
magnification made it appear. Similar observations were made in the wildtype
retinas too (Fig. 9j-k). Structures signalling intensely from within the
retina included areas where branches of retinal capillaries appeared to get
close together, reminiscent of micro-aneurysms (Fig. 9i), and microglia (Fig.
9l), identifiable by the dendritic morphology of their processes. Any source
of fluorescent signal which did not fall under one of these categories was
then considered a candidate for A$\beta$.

---
Figure 9:  Representative depictions of the ex vivo retina following
fluorescent staining against amyloid beta (A$\beta$). a) Retinal wholemount of
a transgenic mouse and b) its correlation to in vivo OCT data. c) The positive
control (the cortex of the trangenic mouse) shows fluorescent labeling of
A$\beta$ plaques and capillaries. d) Retinal wholemount of a wildtype mouse
and e) its correlation to in vivo OCT data. f) In the cortex of the wildtype
mouse (negative control), only capillaries are labeled. g-l) Some typical
observations seen throughout transgenic and wildtype mouse retinas. g) When
zooming in to the surface of the retina in the location indicated in the
dashed box (h), some scattered bright spots appear. The orthogonal views
(positions indicated by the white cross-hairs), however, show that these lie
only on the surface of the retina. i) Fluorescent signal positioned at a
capillary junction. j-k) Similar to (g-h), single, larger accumulations of
fluorescent tracer find themselves at the interface of vitreous and retina,
but not within the retina. l) Microglia, indicated by ovals, are identifiable
by their dendritic processes and are also found throughout the retina. This
image was acquired within the ganglion cell layer.

####  3.6.2 Potential deposits of retinal amyloid beta

Of the 17 mice which underwent retinal histology, only one mouse (transgenic,
age: 104 weeks) displayed fluorescent signals in the retina which could be
attributed to extracellular A$\beta$. In this mouse, there was one such area
of interest in the left eye, and seven in the right eye. Images of all
A$\beta$ plaque candidates can be found in Fig. 10. Figure 10a shows an
overview of the left retina. While many bright spots were observed, only the
one indicated by the dashed box did not exhibit the characteristics of what
was shown in Fig. 9. A z-stack through the retina confirmed that this plaque
candidate sat approximately 15 $\mathrm{\SIUnitSymbolMicro m}$ below the
surface of the retina, extending into the anterior IPL. Examples of z-planes
can be found in Figure 10b-g. A similar analysis of the right retina provided
images of the further seven plaque candidates, which can be seen in Fig.
10h-n. The locations of three of the eight plaque candidates were also covered
in the field of view of the in vivo OCT measurements, however no abnormalities
were found in these locations in the OCT data, using any mode of contrast.

---
Figure 10:  Candidates for fibrillary amyloid beta (A$\beta$) detected in the
retina of one mouse, as identified by confocal microscopy. a) Overview of the
ex vivo retina (left eye) acquired with a $10\times$ magnification objective
lens. b-g) En-face planes at 5 $\mathrm{\SIUnitSymbolMicro m}$ intervals at
the position identified by the dashed box in (a), where the zero-position is
at the interface of vitreous and ganglion cell layer. The fluorescent
abnormality, i.e., the A$\beta$ candidate, is indicated by the arrow in (d)
and (e). Scale bar in (g) is valid for (b-g). Images were acquired with a
$40\times$ magnification objective lens. h-n) Seven further A$\beta$
candidates were identified in the retina of the right eye of the same mouse
(all acquired with a $40\times$ magnification objective lens). All structures
were detected less than 40 $\mathrm{\SIUnitSymbolMicro m}$ from the surface of
the retina, i.e., between the retinal nerve fiber layer and the inner
plexiform layer. Scale bar in (h) is valid for (h-n).

###  3.7 Cortical amyloid beta plaque load

In a subset of the mice (14 transgenic mice and 7 wildtype littermates),
histological slices of the brain were prepared and immunohistochemically
stained against A$\beta$. Figure 11a shows an example of the staining results
for a transgenic mouse, while Fig. 11b shows an example of a similar region in
the control brain. In the transgenic mice, brown plaques were identifiable
throughout the entire cortex. The mouse depicted in Fig. 11a was 103 weeks old
at the time of sacrifice, and A$\beta$ plaques were also visible in abundance
in the hippocampal formation and the cerebellum, as well as in other areas of
the brain. To the contrary, no plaques were observed in any of the brain
regions in the 7 examined wildtype mice (Fig. 11b).

For the 14 transgenic mice, the plaque load was plotted as a function of age.
This plot can be found in Fig. 11c. Linear regression analysis revealed an
$R^{2}$ value of 0.439 and a p-value of 0.0098. The gradient of the slope
indicated that the plaque load increases by 0.354 plaques per
${\mathrm{mm}}^{2}$ per week over the investigated age range. Such a result
demonstrates that A$\beta$ plaque load increases with age in the transgenic
mouse brain, and that the trend is statistically significant.

---
Figure 11:  Quantification of the plaques per ${\mathrm{mm}}^{2}$ in the
cortex. a) Histological slice of a transgenic mouse brain,
immunohistochemically stained against amyloid-beta (A$\beta$). A$\beta$
plaques appear as brown deposits. Age: 103 weeks. b) Following the same
staining protocol, the wildtype littermates do not show any A$\beta$ plaques
in the cortex. Age: 103 weeks. c) Count of plaques per ${\mathrm{mm}}^{2}$ for
a subset of 14 transgenic mice (the subset of 7 controls showed no plaques).
Linear regression analysis showed a statistically significant trend of an
increasing plaque load with age ($R^{2}=0.439$, $p=0.0098$).

##  4 Discussion

Since the role of the retina in Alzheimer’s disease is still widely disputed,
the aim of this work was to provide a comprehensive overview of what can be
observed in the retina of an APP/PS1 mouse model using multi-contrast OCT, and
to compare this to histological results. A combination of reflectivity images,
PS-OCT images and OCTA were used to investigate the structure and function of
the retina. From the in vivo data alone, several retinal abnormalities were
successfully identified in this model, however there were no statistically
significant differences between the transgenic and wildtype groups. This
suggests that the hyperreflective foci, retinal thickness changes, phase
retardation abnormalities and structural differences which were measured with
OCT are either strain-related or age-related, rather than being due to the
genetic mutation itself.

The total retinal thickness was found to significantly decrease with age in
the superior and inferior halves as well as in the whole annulus around the
optic nerve head. No difference was seen, however, between the transgenic and
wildtype groups. These results provide an in vivo validation for that which
was previously observed ex vivo by Perez et al. [44]. In future studies, the
inner and outer retina could be subdivided further to quantify individual
layer thickness. Since RNFL thinning occurs in AD patients [28, 29], it may
also be interesting to quantify the RNFL thickness alone in this mouse model.
However, quantifying the RNFL thickness in mice using OCT is difficult, as the
peripapillary thickness of the healthy RNFL is only approximately 20
$\mathrm{\SIUnitSymbolMicro m}$ [74], and is interrupted by blood vessels and
ganglion cells. Automatic segmentation of the mouse RNFL is therefore
challenging, as it can not always be distinguished from the IPL in the OCT
images. Owing to its fibrous structure, the RNFL is also birefringent and
since it is much thicker in the human retina, the effect of the birefringence
is stronger. PS-OCT has already been proposed as a tool for imaging the RNFL
in glaucoma [75, 76], so RNFL measurements in AD patients may be a promising
future application for PS-OCT.

The phase retardation analysis performed in this study mainly identified
depolarization deposits associated with vessel walls and melanin pigments. No
depolarizing deposits were identified in locations which corresponded to
candidates for retinal A$\beta$, although locations of melanin migration which
were observed in this study mirror results which were documented in the human
in age-related macular degeneration [77]. Future experiments on this topic
could also consider HRF in the intensity data, but this becomes challenging in
layers which contain vasculature, as the edges of vessels can also appear as
hyperscattering features. Previous PS-OCT studies identifying migration of
melanin pigments have typically used the metric of degree-of-polarization-
uniformity (DOPU) to quantify the polarization scrambling, or depolarization,
of the structure in question [78, 66, 77]. In order to calculate DOPU, a
sliding window must be applied within the images, sacrificing the overall
resolution. In this study, as the depolarising structures were small, it was
decided to use the phase retardation images alone, as they provided higher
image resolution.

The OCTA analysis revealed no significant differences in the vessel density
between transgenic and wildtype groups in either the SVP or the DCP. This is
contrary to what has been observed in human vessel microangiography studies
[22, 23]. However, in the previous human studies, the area of interest usually
targets the fovea. Since mice do not have a fovea, there is not an equivalent
measurement in the mouse retina. The modified Weber contrast was similar
between transgenic and control groups; the wider standard deviation for the
wildtype can be attributed to the smaller sample size. The Weber contrast in
the DCP was lower than in the SVP for both groups, which is to be expected as
the blood flow in the DCP is lower than in the SVP and therefore the angiogram
signal is weaker due to the more random orientation of the red blood cells.
Angiograms with poor signal had already been excluded from the analysis as the
imaging of older retinas using optical methods can prove challenging. Older
mice generally develop cataracts [79], which results in a lower transmission
of light through the lens and therefore a poorer image quality. The shorter
the wavelength which is used for OCT, the more attenuating a cataract becomes.
Therein lies a trade-off between the axial resolution of the OCT images (the
shorter the wavelength, the higher the resolution) and the maximum SNR which
can be achieved in the retinal images. Care must also be taken that eye drops
are rigorously applied throughout the experiment to ensure a cataract does not
form while the animal is under anesthesia [80].

The disorganisation of the OPL/ONL structure observed in three transgenic mice
and three wildtype mice was not expected from current literature regarding
this mouse model. Previous studies in both the mouse [81] and the human [82]
have attributed similar OPL/ONL splitting to mutations in the CACNA1F gene
encoding for the L-type calcium channel Ca${}_{\text{v}}$1.4 which is also
expressed in the outer nuclear layer of the mouse retina. Without the
Ca${}_{\text{v}}$1.4 calcium channel, photoreceptor synapses are lost, and the
dendritic sprouting which occurs in the photoreceptor layer (in the second-
order neurons) is abnormal [83]. Whether this gene is defect in this
particular APP/PS1 mouse lineage is a topic which must be explored further.

Regarding the analysis of the immunolabeled wholemounted retinas, strong
fluorescent signal appears to derive from a range of sources. Examples of
fluorescent signal caused by aggregates of the secondary antibody which adhere
non-specifically to sticky remnants of the vitreous on the surface of the the
ganglion cell layer of the retinal wholemounts can be seen in Fig. 9g-h and
Fig. 9j-k. With the employed immunostaining protocol, non-A$\beta$ specific
signal also derives from binding of the anti-mouse secondary antibody to
endogenous IgGs present within, e.g., serum and microglia. This makes it
difficult to unequivocally assign biochemical A$\beta$ specificity to
highlighted structures. Therefore, in order to better delineate potential
deposits of intra- or extracellular A$\beta$, the morphology of the
fluorescent signal was also considered. In this study, both retinas of only
one transgenic mouse were highlighted as containing candidates for retinal
A$\beta$ deposits, displaying intense fluorescence signal and also presenting
with a fibril-accumulation-like structure. Of note, A$\beta$ immunopositive
assemblies with similar and distinct fibrillary appearance and a size in the
few $\mathrm{\SIUnitSymbolMicro m}$ range have been described in human AD
retinas [12].

Previous studies have indicated that A$\beta$ accumulations in the brain are
visible with PS-OCT and regular intensity-based OCT [59, 63, 60], however this
study was not able to recreate these findings in the retina. This could be due
to the difference in size of the A$\beta$ plaques - the plaque candidates
which are proposed in Fig. 10 are much smaller than those in the brain
(example in Fig. 11a). It has also been previously concluded that not all
plaques are visible by either contrast modality [60]. Hence negative OCT
findings do not rule out the presence of retinal A$\beta$ plaques. Even if
plaques could be seen in the retina with OCT, it would be difficult to
distinguish them from the HRF and the depolarizing deposits which are already
present in these retinas as observed in Fig. 4 and Fig. 7. In our
immunofluorescence protocol, our “positive control” is the brain, and not the
retina, as no retinal “positive control” sample exists. Despite our best
efforts to mimic retinal wholemount conditions in the control samples, the
tissues are simply not the same and therefore it cannot be ruled out that the
immunostaining protocol is less optimal for the retina than it is for the
brain. However, the candidates for retinal A$\beta$ identified in Fig. 10
would provide an argument that the protocol is indeed suitable.

Given the lack of differences between transgenic and control groups in the in
vivo OCT data, and the fact that A$\beta$ could only be identified ex vivo in
1 out of 11 transgenic animals, the suitability of this APP/PS1 mouse as a
model of the human must be called into question where the retina is concerned.
As with any other mouse model of AD, it only models some aspects of the
disease and not others, and each mouse model will experience different age-
related and strain-related changes in addition to anything caused by gene
mutation. This study finds itself among the conflicting reports regarding the
presence of A$\beta$ in the retina [8]. Our results indicate that
extracellular A$\beta$ may be found in the retina of this mouse model,
although not in all, or even most, samples. A much larger study would need to
be conducted in order to statistically determine the likelihood of identifying
A$\beta$ plaques in the retina of this mouse model. A topic of future
exploration could be a comparison of retinal observations in different APP/PS1
mouse models, also adding a quantification of microglia to the retinal
analysis [84].

The cortical A$\beta$ plaque load, evaluated alongside the retinal data,
showed a statistically significant increase with age in the transgenic mice.
Despite not being able to correlate this to any retinal changes, this study
has documented the typical observations which could be expected to be found in
the retina of this mouse model, both in vivo and ex vivo. The 1:1 mapping of
the OCT data to the retinal histology was crucial for this experiment,
allowing a detailed quantitative and qualitative analysis of structural and
functional features of this APP/PS1 mouse model. Tri-fold OCT imaging contrast
coupled with retinal wholemounts is therefore a promising method for the
analysis of animal models of many retinal diseases.

##  5 Conclusion

While the cortical immunohistochemical staining revealed clear, marked
differences in A$\beta$ plaque load between APP/PS1 transgenic mice and their
wildtype littermates, a similar difference was not observed in the retina.
Candidates for retinal A$\beta$ were only identified in 1 out of 17 transgenic
mice. Multi-contrast OCT did, however, reveal retinal abnormalities in these
mice, including deposits of migrated melanin and a double-banding of the ONL.
Owing to the occurrences in both transgenic and control mice, it is likely
that these are strain-dependent and not due to the genetic mutation itself.
Nevertheless, the combination of multi-contrast OCT with 1:1 mapping of
retinal histology allowed for a thorough documentation of what one would
expect to see in this APP/PS1 mouse model of AD.

### Disclosures

The authors have no relevant financial interests in the manuscript and no
other potential conflicts of interest to disclose. Part of this work has been
presented at the Association for Research in Vision and Ophthalmology (ARVO)
Annual Meeting in Vancouver, Canada, and the published abstract by Baumann et
al. can be found in Ref. [85] .

###### Acknowledgements.

Financial support from the European Research Council (ERC StG 640396 OPTIMALZ)
and the Austrian Science Fund (FWF, P25823‐B24) is gratefully acknowledged.
The authors would like to thank the team at the Neuropathology department at
the Medical University of Vienna for providing assistance and advice regarding
histology. Sincere thanks are also extended to the staff at the Division of
Biomedical Research at the Medical University of Vienna for the animal care.
Finally, the authors would like to thank Christoph K. Hitzenberger for his
continued support throughout the duration of this project.

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