# Label-free metabolic imaging of non-alcoholic-fatty-liver-disease (NAFLD)
liver by volumetric dynamic optical coherence tomography

Pradipta Mukherjee  1 Shinichi Fukuda  2,3 Donny Lukmanto  3 Toshiharu
Yamashita  4 Kosuke Okada  5 Shuichi Makita  1 Ibrahim Abd El-Sadek  1, 6
Arata Miyazawa  7 Lida Zhu  1  Rion Morishita  1 Antonia Lichtenegger  8, 1
Tetsuro Oshika  2 and Yoshiaki Yasuno1  1Computational Optics Group,
University of Tsukuba, Tsukuba, Ibaraki, Japan
2Department of Ophthalmology, Faculty of Medicine, University of Tsukuba,
Tsukuba, Ibaraki, Japan
3Department of Advanced Vision Science, Faculty of Medicine, University of
Tsukuba, Tsukuba, Ibaraki, Japan
4Laboratory of Regenerative Medicine and Stem Cell Biology, Graduate School of
Comprehensive Human Sciences, University of Tsukuba, Tsukuba, Ibaraki, Japan
5Division of Medical Sciences, Faculty of Medicine, University of Tsukuba,
Tsukuba, Ibaraki, Japan
6Department of Physics, Faculty of Science, Damietta University, 34517 New
Damietta City, Damietta, Egypt
7Sky Technology Inc., Tsukuba, Ibaraki, Japan
8Center for Medical Physics and Biomedical Engineering, Medical University of
Vienna, Austria  *Corresponding author<EMAIL_ADDRESS>
###### Abstract

Label-free metabolic imaging of non-alcoholic fatty liver disease (NAFLD)
mouse liver is demonstrated ex vivo by dynamic optical coherence tomography
(OCT). The NAFLD mouse is a methionine choline-deficient (MCD)-diet model, and
two mice fed MCD diet for 1 and 2 weeks are involved in addition to a normal-
diet mouse. The dynamic OCT is based on repeating raster scan and logarithmic
intensity variance (LIV) analysis which enables volumetric metabolic imaging
with a standard-speed (50,000 A-lines/s) OCT system. Metabolic domains
associated with lipid droplet accumulation and inflammation are clearly
visualized three-dimensionally. Particularly, the normal-diet liver exhibits
highly metabolic vessel-like structures of peri-vascular hepatic zones. The
1-week MCD-diet liver shows ring-shaped highly metabolic structures formed
with lipid droplets. The 2-week MCD-diet liver exhibits fragmented vessel-like
structures associated with inflammation. These results imply that volumetric
LIV imaging is useful for visualizing and assessing NAFLD abnormalities.

††journal: boe

This manuscript has been published in Biomedical Optics Express 13, 4071-4086
(2022). https://doi.org/10.1364/BOE.461433.

http://optics.bk.tsukuba.ac.jp/COG/

##  1 Introduction

Non-alcoholic fatty liver disease (NAFLD), the most common chronic liver
disease, is found in populations with obesity or type 2 diabetes [1, 2]. It
also frequently occurs in healthy people without any obvious risk factors [3].
NAFLD is a wide spectrum of metabolic diseases [4]. The initial stage of NAFLD
is hepatic steatosis [5] and then, it can progress to non-alcoholic
steatohepatitis (NASH) [6], hepatic fibrosis [7], cirrhosis [8], and
ultimately hepatocellular carcinoma (HCC) [9, 10]. After the transition from
NASH to a more advanced stage of hepatic fibrosis, the disease becomes
irreversible because of the collapse of the hepatic lobule and its
substitution with fibrotic tissue[11, 12].

Numerous studies have been carried out to understand the NAFLD pathogenesis
and to develop novel therapeutic strategies. However, NAFLD pathogenesis is
not yet clearly understood, and no drug is available. The only treatment is
lifestyle modification by diet and exercise [13]. The use of small animal
models has proven effective for the study of this disease not only for
studying the pathogenesis but also for examining the therapeutic effects of
various drugs [14, 15]. Such animal models can correctly reflect both the
histopathology and the pathophysiology of human NAFLD [15].

According to animal-model-based studies, significant amount of lipid droplets
(LDs) accumulate in the hepatocytes of hepatic steatosis [16, 17]. LDs provide
the lipid source for energy metabolism. NASH is an inflammatory disease
characterized by liver inflammation and ballooning of the hepatocytes [18].
Several inflammatory cytokines are produced by inflammatory cells during NASH.
Such inflammatory cytokines play a key role in the progression from hepatic
steatosis to NASH [19, 20]. Therefore, during an early stage of NAFLD, LDs and
inflammatory cells are accumulated and they are associated with high
metabolism.

To understand NAFLD pathogenesis, it is important to access the liver tissue
metabolism including those associated with LD and inflammation. In addition,
since the LD accumulation and inflammation can occur not only at the tissue
surface but also in deep tissue, it is important to visualize the metabolism
of deep tissues without slicing the sample.

Assessment of NAFLD progression can be performed via conventional histological
imaging of liver biopsy specimens. However, this method is invasive,
destructive, time-consuming, and labor-intensive [21]. Traditional non-
invasive imaging modalities, including ultrasonography [22], computed
tomography [23], and magnetic resonance imaging [24] can detect the presence
of fat content in the liver. However, such methods possess several
limitations, including no sensitivity to tissue metabolism and low spatial
resolution in the order of millimeters. Fluorescence microscopy can also
visualize the LDs and inflammatory cells [25, 26, 27, 28], but it has two
major limitations. First, it requires the use of an exogenous contrast agent.
Introducing such an agent may perturb the physiological condition of the
tissue. Second, it has limited image penetration of a few hundred micrometers;
hence, it is not a perfect modality for three-dimensional (3D) metabolism
investigation.

Optical coherence tomography (OCT) is a label-free, non-destructive imaging
modality with micron-scale spatial resolution and millimeter-scale depth
penetration, and it provides 3D assessment of tissue with a few seconds of
imaging time [29]. However, conventional OCT can only visualize tissue
morphology, and hence, it is not sufficient to investigate the metabolic
changes associated with LD accumulation and inflammation during NAFLD
pathogenesis. For metabolism investigation, it is necessary to extend the
contrast of OCT.

Among several contrast extensions of OCT, dynamic OCT is a recently developed
technique used to visualize metabolic activity at the cellular or subcellular
scale without any contrast agents [30, 31, 32, 33, 34, 35]. Rapid time-
sequential OCT acquisition and subsequent time-spectroscopic and/or
statistical analysis contrasts the intracellular motion which is closely
associated with tissue metabolism. These methods has been successfully
implemented in time-domain full-field [30] and Fourier-domain OCT systems [31,
32, 33, 34, 35]. We recently demonstrated a custom-made 3D scanning protocol
that uses a standard-speed swept-source OCT system to visualize volumetric
tomography of tissue dynamics within a few seconds [36].

In this paper, we demonstrate 3D metabolic imaging of NAFLD mouse model liver
using dynamic OCT. We employ our recently demonstrated 3D dynamic OCT
method[36], which enables volumetric dynamics imaging without exogenous
labeling using a standard-speed (50,000 A-lines/s) swept-source OCT system.
The mouse model is a high-fat methionine and choline-deficient (MCD) diet
model. Two mice were fed the MCD diet for either 1 or 2 weeks to induce
hepatic steatosis and NASH, the early stages of NAFLD. Dynamic OCT reveals
several unseen 3D structures not observed in conventional OCT. The results and
extensive discussion show that these structures correspond to the metabolism
associated with LD accumulation and inflammation.

##  2 Methods

###  2.1 Non-alcoholic fatty liver disease (NAFLD) model and study design

Three mice (wild type C57BL/6) were used in this study. For the investigation
of NAFLD, two mice were fed a methionine and choline-deficient (MCD) diet
(Oriental Yeast Co., Ltd., Japan) to induce non-alcoholic steatosis and
steatohepatitis [14, 15]. One of the two mice was fed the MCD diet for 7 days
(1-week model), and the other for 14 days (2-week model). One other mouse was
fed a normal diet as a control. All were almost 1-year old at the commencement
of the diets and were housed in normal conditions and kept on a 12-hour
light/dark cycle during the above-mentioned feeding period.

OCT and histological imaging were performed ex vivo. The mice were sacrificed
by cervical dislocation and their livers were dissected. The livers were
immersed in Dulbecco’s modified eagle medium (DMEM, Sigma-Aldrich, MA)
supplemented with 20% fetal bovine serum (Sigma-Aldrich, MA) and
penicillin/streptomycin (Gibco, MA) immediately after the dissection to avoid
quick death of the tissues. The OCT measurement was performed 1 hour after the
dissection.

All animal experiments were performed in accordance with the animal study
guidelines of the University of Tsukuba. All experimental protocols involving
mice were approved by the Institutional Animal Care and Use Committee of the
University of Tsukuba.

###  2.2 Dynamic OCT imaging by logarithmic intensity variance (LIV)

####  2.2.1 OCT system

The OCT measurement was performed with a custom-made swept-source Jones-matrix
OCT (JM-OCT) device. The JM-OCT uses a MEMS-based wavelength sweeping laser at
the 1.3 $\muup$m wavelength band (AXP50124-8, Axun Technologies, MA) with a
sweeping rate of 50 kHz. The objective (LSM03, Thorlabs Inc., NJ) has an
effective focal length of 36 mm and the effective NA of the probe optics is
0.048. It leads to a lateral resolution of 18.1 $\muup$m. The depth resolution
is 14 $\muup$m in tissue. The probe power on the sample was 12 mW. More
detailed descriptions are available elsewhere[37, 38].

Although the system is polarization-sensitive, only polarization-insensitive
scattering OCT intensity was used in this study. The polarization-insensitive
OCT is the average of absolute-squared intensities of the four OCT signals
corresponding to the system’s four polarization channels.

####  2.2.2 Scan protocol

Volumetric dynamic OCT was obtained by combining a repeating raster scan
protocol and a signal processing so-called logarithmic intensity variance
(LIV) [36]. This method enables three-dimensional dynamics imaging within a
few tens of seconds with the standard speed (50 kHz) OCT device.

For the repeating raster scan, the whole volumetric region (covering 3 mm
$\times$ 3 mm, 5 mm $\times$ 5 mm, or 6 mm $\times$ 6 mm lateral region) is
divided into four blocks where each block covers 3 mm $\times$ 0.75 mm, 5 mm
$\times$ 1.25 mm, or 6 mm $\times$ 1.5 mm. Each block is scanned by a raster
scan protocol consisting of 32 frames along the fast-scan direction times 512
A-lines/frame. This raster scan is repeated 16 times, so each position on the
sample is scanned 16 times, with an inter-frame-interval of 409.6 ms. At each
location, the time separation between the first and last frames is 6.55 s.
This long frame separation results in high-contrast dynamic OCT, even with a
small number of frames (Section 5.3 of Ref. [32]).

Each block is then sequentially measured using this protocol to obtain a
dynamic OCT volume. This volume consists of 2,048 frames in total and the
lateral volume size is 512 A-lines/frame times 128 frame locations. The total
acquisition time for a volume is 26.2 s.

####  2.2.3 Signal processing for dynamics imaging

The tissue dynamics is revealed by computing the LIV at each point in the
volume. Here, we model linear OCT intensity as
$I(x,z,t_{i})=I_{D}(x,z,t_{i})I_{S}(x,z)$ where $I_{D}(x,z,t_{i})$ and
$I_{S}(x,z)$ are a dynamic and static components of the OCT signal, $x$ and
$z$ are the lateral and axial positions, and $t_{i}$ is the acquisition time
point of the $i$-th frame. The dB-scaled OCT signal becomes
$I_{dB}(x,z,t_{i})=10\log I_{D}(x,z,t_{i})+10\log I_{S}(x,z)$. The LIV is
defined as the time variance of the logarithmic (dB)-scale OCT intensity
signal [32] as

$\displaystyle\begin{split}\mathrm{LIV}(x,z)&=\frac{1}{N}\sum_{i=0}^{N-1}\left[I_{dB}(x,z,t_{i})-\left<{I_{dB}(x,z)}\right>\right]^{2},\\\
&=\frac{1}{N}\sum_{i=0}^{N-1}\left[10\log I_{D}(x,z,t_{i})-\left<{10\log
I_{D}(x,z,t_{i})}\right>\right]^{2},\end{split}$ (1)

where $N$ is the number of frames, 16 in the present case.
$\left<{\quad}\right>$ represents averaging over the $N$ frames. Equation (1)
suggests that LIV is sensitive to the fluctuation magnitude of the dynamic
component, but is insensitive to the static component.

In later sections, the LIV images are shown as pseudo-color composite image as
described in Section 2.3 of [32]. In this composite image, the hue is the LIV,
the pixel brightness is the averaged dB-scale OCT intensity over $N$ frames,
and the saturation is set to 1 (full saturation) for all pixels. The phase of
the hue was selected to show regions of high and low dynamics in green and
red, respectively.

####  2.2.4 Slab projection image

To generate an en face slab projection image, the tissue surface was first
identified from the dB-scale OCT intensity image by applying the surface
segmentation algorithm as described in Ref. [38]. Here the OCT image is the
average of 16 frames. The region from the tissue surface to a depth of
100-pixel (724 $\muup$m) was extracted. This process was performed frame-by-
frame for the entire 3D dataset to form a slab. Slab projection images of OCT
and LIV were obtained by averaging the OCT intensity and LIV.

###  2.3 Birefringence and degree-of-polarization-uniformity computation

The depth-resolved birefringence was computed using a local Jones matrix
analysis method [39] and maximum a-posteriori (MAP) estimator [40]. The local
Jones matrix analysis was applied with 8-pixel depth separation (57.9
$\muup$m) and the MAP estimation was performed with a spatial kernel of 2
pixels (23.4 $\muup$m, lateral) $\times$ 2 pixels (14.5 $\muup$m, depth).

Degree of polarization uniformity (DOPU) was computed over a spatial kernel of
3 $\times$ 3 pixels with Makita’s noise correction [41].

###  2.4 Histology

Hematoxylin and eosin (H&E) staining histologies were obtained after the OCT
measurement. To perform H&E staining, liver samples were fixed using 4%
paraformaldehyde (PFA) (Nacalai Tesque, Kyoto, Japan). The samples were then
embedded in paraffin, and tissue sections of 12 $\muup$m thickness were
prepared. The sections were imaged under a microscope (BZ-X710, Keyence Corp.,
Osaka, Japan) with an 10$\times$ objective (NA = 0.3).

##  3 Results

###  3.1 Normal-diet induced mouse liver

Figure 1: Dynamics imaging of a normal-diet induced mouse liver. Cross-
sections of (a) scattering OCT and (e) LIV images. (b, f) En face slices of
the OCT and LIV images at the depth location indicated by the horizontal line
in (a). (c, g) Slab projection images of OCT and LIV. (d) H&E staining
histological micrograph. (h) Magnified image of the LIV projection image at
the region marked by the rectangular box in (g). All scale bars indicate 1 mm.

Figure 1 summarizes the 3D imaging results of the normal-diet-induced mouse
liver. The depth location of the en face slice is indicated by the horizontal
line in the cross-sectional OCT image [Fig. 1(a)]. The thin layer at the top
in Fig. 1(a) is the surface of the cultured medium.

In the OCT intensity cross-section and the en face slice [Fig. 1(a) and (b)],
the tissue appears homogeneous except for some small hypo-scattering spots
(yellow arrows). Similar appearance was found in our previous study [42] and
are known to be blood vessels. A tessellated texture appears in the slab
projection of OCT [Fig. 1(c)].

Unlike the homogeneous appearance of the OCT image, the LIV shows several
high-contrast structures. In the deep tissue region of the cross-sectional
image [Fig. 1(e)], several vertical stripes with high LIV (green) are
observed. Corresponding high-LIV structures are also found in the en face
image [Fig. 1(f), around the bottom]. Since high LIV indicates high temporal
fluctuation of the OCT signal, high intracellular motion would be occurring in
these high LIV structures.

The slab projection of the LIV [Fig. 1(g)] reveals that these high LIV regions
form vessel-like structures. In the magnified region of the slab projection
[Fig. 1(h)], several small dots can also be seen around the vessel-like
structures (white arrowheads). The vessel-like structure and the small dots
are expected to be perivascular tissue and lipid droplets, as we discuss later
in Sections 4.1 and 4.2. It is noteworthy that the patterns appeared in the
OCT intensity and LIV are totally uncorrelated.

Figure 1(d) shows representative H&E histology of the same sample. Normal
lobular patterns can be observed. The void structures are central and portal
veins (yellow arrowheads).

###  3.2 1-week MCD-diet-induced NAFLD model

Figure 2: Imaging results of a 1-week MCD diet induced NAFLD model. Cross-
sections of (a) scattering OCT and (e) LIV images. (b, f) En face slices of
the OCT and LIV images at the depth location indicated by the horizontal line
in (a), where the transversal field of view (FOV) is 6 mm $\times$ 6 mm. (c,
g) OCT and LIV slab projections and (i) volume rendering of the LIV
tomography. (d, h) OCT and LIV slab projections and (j) LIV volume rendering
of narrow FOV (3 mm $\times$ 3 mm) measurement. All scale bars indicate 1 mm.

Figure 2 shows the tomographies of a 1-week MCD-diet-induced NAFLD model.
Cross-sectional and en face slices of the OCT intensity [Fig. 2(a) and (b)]
reveal that the surface is wavy (yellow arrowheads). Such unevenness was not
observed in the normal-diet liver [Fig. 1]. The tissue is mostly homogeneous
except at the surface [Fig. 2(a) and (b)]. In the cross-section, the thin,
hyper-reflective layer above the tissue surface is the surface of the cultured
medium, and the dots in the medium are floaters. Although the slab projections
[Fig. 2(c) and (d)] show a clear domain structure, it is an artifact. Namely,
in one domain, the tissue was totally soaked in the cultured medium [yellow
arrowheads, Fig. 2(a)], while in the other domain, the tissue surface
contacted the surface of the medium [red arrowheads, Fig. 2(a)]. Hence, the
segmented surfaces of two domains are not the same types of interface. This
inconsistency of the segmentation created the pseudo-domain border [red
arrowheads, Fig. 2(c) and (d)].

The cross-sectional LIV image [arrowheads, Fig. 2(e)] reveals that the tissue
surface region exhibits high temporal fluctuation (green). Several vertical
stripes of high LIV (green) are also observed. Since its appearance is similar
to the projection artifact of OCT angiography [43], they are expected to be
the projection artifacts of LIV.

In the en face slice of the LIV image [Fig. 2(f)], the regions of high-LIV
signals form macroscopic, ring-shaped structure where the high LIV region
shows a granular appearance. Here the field of view (FOV) is 6 mm $\times$ 6
mm. The slab projections of the LIV [Fig. 2(g)] shows a similar pattern with
the en face slice. However, it also reveals some structural flows (dashed
circle). Similar appearances are also found in the slab projection of a
smaller-FOV measurement [Fig. 2(h)] (3 mm $\times$ 3 mm). The volume
renderings of the LIV [Fig. 2(i) and (j)] show the 3D morphology of the ring-
shaped, high-LIV structures. For visualizing the volume rendering images, a 3D
visualization software Amira (Thermo Fisher Scientific, MA, USA) was used. As
we will discuss later in Section 4.2, these high-LIV signals including the
small particles are caused by the motion of lipid droplets.

Note that, histology was not successfully obtained from this sample because
the liver was too oily and it adhered to the blades during the cryosection
procedure.

###  3.3 2-week MCD-diet-induced NAFLD model

Figure 3: The results of 2-week MCD diet induced NAFLD model liver. The first
to the third columns show the cross-sectional images (a, e), the en face
slices (b, f), and the slab projections (c, g) of OCT intensity (first row)
and LIV (second row) respectively. The fourth column (d, h) shows the volume
renderings of LIV with two different FOVs. The last row (i, j) shows the H&E
stained histological micrographs. The scale bars of OCT intensity and LIV
represent 1 mm and that in histology indicate 200 $\muup$m.

Figure 3 shows OCT, LIV, and histological images of the 2-week NAFLD mouse
liver. The cross-section and en face slice of the OCT intensity [Fig. 3(a) and
(b)] appear similar to those of the normal-diet mouse [Fig. 1(a) and (b)]. The
horizontal black line in the cross-section indicates the depth location of the
en face slice. The slab OCT projection [Fig. 3(c)] shows a granular hyper-
scattering pattern, which is not identical to the tessellated pattern of the
normal-diet liver [Fig. 1(c)].

The LIV cross-section [Fig. 3(e)] exhibits a high-LIV (green) layer near the
surface (white arrow), but unlike the 1-week MCD model [Fig. 2(e)], this high-
LIV layer is slightly below the surface. The cross-sectional image also
exhibits vertical high-LIV (green) stripes (yellow arrowheads), but there are
fewer stripes than in 1-week model, and they appear in a deeper region. In the
en face slice [Fig. 3(f)], this high-LIV signal shows fragmented, vessel-like
structures (yellow arrowheads). The same structures are more evident in the
slab projection [Fig. 3(g)] and the volume rendering [Fig. 3(h)]. The volume
rendering of a narrower-FOV measurement [3 mm $\times$ 3 mm, Fig. 3(d)] also
shows the same structure. It is noteworthy that this structure is not revealed
in the OCT intensity images.

The H&E histology micrographs of the same sample [Fig. 3(i) and (j)] exhibit
deep purple regions around the large vessels (yellow arrowheads), which may
suggest inflammatory cell infiltration as we will discuss in Section 4.3. The
normal lobular pattern seen in the normal-diet liver cannot be found in the
present liver. It may indicate hepatocellular ballooning as we will discuss in
Section 4.2.

##  4 Discussion

###  4.1 Vessel-like structures in the normal-diet liver: periportal and
perivenous zones

In the normal-diet liver, LIV allowed us to visualize 3D vessel-like
structures [Fig. 1]. Although vascular structure was not clear in the OCT
intensity in this study [Fig. 1(b) and (c)], our previous study of normal
mouse liver suggests that the high-LIV locates just beneath the vascular lumen
[42]. Accordingly, the high-LIV vessel-like structures in this study also
colocate with the tissue beneath or around vessels.

Here, we suspect that the high-LIV vessel-like structures correspond to high
metabolic activity in the periportal/perivenous zone. The liver is an organ
that is responsible for whole-body homeostasis [44], and hence, it is highly
metabolically active in general. Liver tissue has multiple functional zones
along the hepatic sinusoid, from the central vein to the portal vein[45, 46].
In the periportal zone, hepatic lobules are involved in oxidative energy
metabolism, gluconeogenesis, and urea synthesis [47, 48], and hence it is
metabolically active. In the perivenous zone (around the central vein),
hepatocytes are activated by the metabolic pathways of glycolysis and
liposynthesis [47, 48], and hence it is also metabolically active. Namely, the
tissues around the portal and central veins are metabolically active. Appelian
et al.showed that the contrast source of their dynamic OCT method, which is
based on time-frequency analysis of OCT, is ATP-consuming motion [30]. Hence,
the ATP-consuming motion in the metabolically-active periportal and perivenous
tissues might be the source of the high-LIV signals of the vessel-like
structures.

It is noteworthy that these vessel-like structures are hard to observe with
other modalities. As we have mentioned, the OCT intensity image in the current
study does not clearly visualize them. Although it was visible in our previous
OCT intensity imaging [42], the same study also showed that the OCT intensity
does not always visualize the vessel lumens. In H&E histological micrographs
[Fig. 1(d)], the cross-sections of the vessels were observed. However, the
micrographs did not clearly distinguish between the periportal and perivenous
zones. Fluorescence microscopy also cannot visualize these metabolically
active vessel-like structures with millimeter thickness in 3D, because it can
only penetrate a few hundred micrometers from the sample surface.

###  4.2 Lipid droplets in LIV images

LDs are lipid-storing cellular organelles comprising of a lipid core
surrounded by a phospholipid monolayer and associated proteins. They are key
regulators of cellular metabolism [16, 17]. A high-fat diet, such as the MCD
diet, results in the accumulation of excessive fat in the liver. It leads to
hepatic steatosis, an initial stage of NAFLD [5]. Hence, hepatic steatosis
exhibits the accumulation of LD [49].

LDs are the lipid source for energy metabolism, and their movement is
associated with nutrient transfer from one point to another [50]. Hence, LDs
exhibit highly dynamic 3D motion to regulate intracellular lipid storage [51,
50]. It may suggest that LDs exhibit high LIV signal.

With the continuous feeding of the high-fat MCD diet, the LD diameter can be
gradually increased [52]. Moon et al.observed the formation of a small number
of hepatic LDs also in a normal-diet mouse, but with smaller diameter of
around 1 $\muup$m [52]. Namely, LDs can be observed both in normal-diet and
MCD-diet mice, but its diameter and the number are larger for the latter mice.

In the normal-diet mouse liver [Fig. 1], small dots of high LIV were found
around the vessel-like structures, which are suspected to be the periportal
and perivenous tissues discussed in Section 4.1. Because this mouse was fat
even though it was fed a normal diet, some LDs may have formed in its
hepatocytes. Moon et al.showed fluorescent image of LDs in a normal-diet mouse
liver [52], and our LIV appearance resembles their fluorescent image. Hence,
the small high-LIV dots might be LDs.

In the 1-week MCD model (Fig. 2), the LIV signal forms a macroscopic ring
shape. Because this high-LIV ring is a collection of small high-LIV dots, it
might also correspond to a collection of LDs. It is also noteworthy that these
high-LIV dots exhibit tail artifacts [arrowheads in Fig. 2(e)], which may
indicate that these LDs are highly motile.

In the 2-week MCD model, a thin layer of high LIV was observed near the tissue
surface [Fig. 3(e)]. Because the suspected LDs of the 1-week MCD model are
also located around the surface, we suspect that this high-LIV layer in the
2-week model also represents LDs. The histologies [Fig. 3(i) and (j)] showed
enlarged and ballooned hepatocytes. Several previous studies of 2-week MCD
models showed similar histology and accumulation of LDs [53, 54, 55], which
also also supports our interpretation of this high-LIV appearance.

One difference between the 2-week and 1-week MCD models is that the latter
exhibits tail artifacts in the LIV image [arrowheads in Fig. 2(e)], while the
former does not. Moon et al.[52] showed that the size of LDs increases with
continuous feeding with the MCD diet. So, the LDs in the 2-week model might be
larger than those in the 1-week model. This difference in size can be
associated with the different appearances of the LIV and its tail artifacts,
but further investigation is required.

As we have discussed, LDs play a key role in liver metabolism. Hence, LIV can
contribute to liver metabolism studies.

###  4.3 High-LIV structure as an indicator of inflammation of NASH

Several fragmented vessel-like structures are visible in the LIV projection
[Fig. 3(g)] and volume rendering [Fig. 3(d) and (h)] of the 2-week MCD-diet
mouse liver. Based on their location and size, the vessel-like structures may
correspond to the purple clusters seen in the histology around relatively
large vessels [Fig. 3(i) and (j)]. Previous histological investigations of
2-week MCD-diet mouse livers suggest that these are inflammatory cell regions
[56, 57].

Inflammatory immune cells infiltrate in NASH liver [58] including Kupffer
cells, neutrophils, natural killer cells, and dendritic cells [59]. They
produce various pro-inflammatory cytokines, such as tumor necrosis factor
(TNF)-$\alpha$ and interleukin-6 (IL-6) [19, 20], which activate tissue
metabolism. For example, TNF-$\alpha$, which stimulates liver steatosis,
increases serum triglyceride levels, hence supplying energy to the tissue
[60]. Similarly, IL-6 is an important mediator of fatty acid metabolism[61].

This link between inflammatory cells and metabolism may explain the high-LIV
appearance of the fragmented vessel-like structures, which would allow LIV to
be used as an imaging biomarker of NASH.

###  4.4 Birefringence contrast investigation during NAFLD pathogenesis

The inflammatory immune cells that infiltrate during NASH development activate
the activation chain of pro-inflammatory cytokines, hepatic stellate cells,
and myofibroblasts [47]. Myofibroblasts produce excessive amounts of
extracellular matrix components and lead to hepatic fibrosis, followed by
cirrhosis, which is the almost end-stage of NAFLD. Currently, histology is the
standard technique for accurately identifying liver fibrosis, but it has
several limitations including invasiveness, sampling error, labor-intensive
staining, and long imaging time.

As mentioned in Section 2.2.1, we employed a polarization-sensitive OCT device
(JM-OCT) in this study, but we did not use its polarization-sensitive imaging
function. Among the polarization-sensitive JM-OCT images, birefringence is
known to be sensitive to fibrotic tissue [62]. Therefore, future studies with
the polarization-sensitive function of JM-OCT might be helpful for
understanding the fibrosis process and pathogenesis of NAFLD.

###  4.5 Comparison of the polarization contrasts among NAFLD stages

Figure 4: Cross-sectional birefringence (a, c, e) and DOPU (b, d, f) images of
the normal liver (first column), the 1-week NAFLD model (second column), and
the 2-week NAFLD model (third column), respectively. The scale bars represent
1 mm.

Since the OCT device used in this study is a JM-OCT [37, 38], polarization
sensitive images, i.e., birefringence and DOPU images, can be obtained in
addition to the polarization-insensitive OCT image. However, the polarization-
sensitive images were hard to interpret. Hence, we omitted these images from
the main part of this study. For the readers’ interest, here we discuss about
the polarization-sensitive images, although it is inconclusive.

Figure 4 are the examples of polarization-sensitive images of normal, 1-week
and 2-week NAFLD model livers. Figure 4(a) and (b) are cross-sectional
birefringence and DOPU of normal liver, respectively. The birefringence
changes along the depth from relatively low (blue) to moderately high (green).
Similarly, DOPU decreases gradually (red to green) along the depth. In the
1-week NAFLD model [Fig. 4(c) and (d)], the birefringence appears to be
moderately high (green), while the DOPU is low (green). In the 2-week NAFLD
model [Fig. 4(e) and (f)], similar appearances of birefringence and DOPU were
observed to those of the normal liver. Namely, the birefringence is low and
DOPU is high at the region close to the surface, and they become moderately
high (birefringence) and low (DOPU) in the deep region.

The interpretation of the high birefringence at low DOPU regions is an open
issue. In our JM-OCT, both birefringence and DOPU are computed with a spatial
kernel; 2 $\times$ 2 pixels, i.e., 14.5 $\muup$m (depth) $\times$ 23.4
$\muup$m (lateral) for birefringence, and 3 $\times$ 3 pixels, i.e., 21.7
$\muup$m (depth) $\times$ 35.2 $\muup$m (lateral) for DOPU [40, 41]. For the
birefringence computation, the tissue is assumed to be homogeneous within the
kernel. However, low DOPU appearance suggests that the tissue is not
homogeneous in reality, and hence, the estimated birefringence is not reliable
at this region. Conversely, if the true birefringence of the sample is high,
Stokes vectors, which are used for DOPU computation, alter along the depth,
and DOPU becomes low. Hence, low DOPU at a high birefringence region does not
necessarily mean random polarization, which is the main interest of DOPU
measurement.

###  4.6 Comparison of dynamics imaging methods

The dynamic-OCT methods visualize tissue dynamics by analyzing the temporal
characteristics of time-sequential OCT data. Several variations of dynamic-
methods have been developed. These methods can be categorized into two types.
Type-1 methods are methods which are sensitive to the magnitude of the signal
fluctuation, while type-2 methods are sensitive to the speed of the
fluctuation. Examples of type-1 methods include standard deviation of time-
sequential signals [63, 64, 30], cumulative-sum method [65], and LIV [32]. The
type-2 methods are further subdivided into two subtypes. One (type 2-1) is
correlation decay analysis including dynamic scattering OCT [66], inverse-
power-law analysis [64], exponential fitting of decorrelation curve [34], and
OCT-correlation-decay-speed analysis [32, 36]. The other (type 2-2) is power
spectrum analysis [67, 33, 31].

Type-1 methods, including presently used LIV, provide a high-contrast image of
dynamic structures. However, its ability for quantitative assessment is
limited. In contrast, type-2 methods provide more quantitative fingerprints of
tissue dynamics. For example, Lee et al.used complex correlation analysis of
time-sequential OCT signals to extract the velocity and diffusion coefficient
of moving particles [66]. Through power-spectrum analysis of time-sequential
OCT signals, Scholler et al.visualized several specific activities in retinal
organoids [67].

In the present study, we used only a type-1 method (LIV) to investigate the
mouse livers. In order to obtain the 3D distribution of LIV with a standard-
speed OCT, we acquired only 16 frames at a single location. It prevents us
from performing the correlation decay analysis that requires around 30 frames
as mentioned in Section 5.3.2 of [32]. For some less dynamic samples such as
in vitro spheroids than the mouse liver, we can obtain 32 frames at a single
location, and it enables correlation speed analysis [36]. In the future, new
scanning protocol and signal processing methods [68] might enable type-2
methods with a smaller number of time-sequential OCT frames.

###  4.7 Projection artifact of LIV

Figure 5: Scatter plot between dB-scale OCT intensity and LIV (b) at the
region delineated by red line in (a). No evident correlation was found between
the dB-scale OCT intensity and LIV. (c) The OCT intensity of Fig. 1(a) is
displayed at different intensity ranges. The vertical stripe structure shown
in the LIV image [Fig. 1(e)] is not visible in the intensity images.

The cross-sectional LIV images of normal [Fig. 1(e)] and 1-week model [Fig.
2(e)] exhibited several vertical high-LIV signals. We hypothesize that this
vertical LIV appearance is not associated with a real structure which could
appear in the OCT intensity images or an artifact due to the static intensity
OCT pattern, but a projection artifacts caused by high dynamics of superficial
tissues similar to that of OCTA. This hypothesis can be supported by the
following discussion.

According to the mathematical model (Section 2.2.3), LIV can be insensitive to
the static component of the OCT intensity but sensitive mainly to the
fluctuation magnitude of the dynamic component. To further validate this
point, we did additional analysis on the data of Fig. 1(a). Figure 5(b) shows
a scatter plot between the dB-scale OCT intensity and the LIV, where each
point represents a single pixel, and 500 pixels randomly selected in a tissue
region [delineated by red line in Fig. 5(a)] were plotted. As evident in the
plot, the correlation between the OCT intensity and LIV was low (r = 0.15).
Furthermore, the similar vertical structure to that of LIV cannot be seen in
the OCT intensity image [Fig. 5(c)], where the OCT images are displayed with
several intensity ranges in order to highlight possible low-contrast
structures.

According to these results, we believe the vertical stripe of LIV is not an
artifact caused by spatial distribution of OCT intensity. It might be a future
task to prove this stripe structure is a projection artifact similar to OCTA.
This can be further investigated by using fluctuation speed analysis (type-2
methods discussed in Section 4.6).

It is noteworthy that the en face LIV images, such as Fig. 1(g) and (h) and
Fig. 2(g) and (h) provide more interpretable structures, which can be directly
related to metabolic regions, than the cross-sectional images. This may be due
to the fact that en face projection images are less disturbed by the
projection artifact.

##  5 Conclusion

The metabolic activity of normal-diet, MCD-diet-induced 1-week, and 2-week
NAFLD model mouse livers were investigated in 3D and label-free approach by
volumetric dynamic OCT. In the normal-diet liver, highly dynamic vessel-like
structures and several highly dynamic dots were observed. The former might
correspond to high metabolic activity in the periportal and perivenous zones,
while the latter indicate accumulation of LDs. In the 1-week NAFLD model,
several vertical stripes of high-LIV signal were observed, which may indicate
highly-motile LDs and their projection artifacts. In the en face and volume-
rendering observations, the high-LIV regions form macroscopic, ring-shaped
structure and reveal structural flows. In the 2-week model, highly dynamic
fragmented vessel-like structures were observed, which are believed to
correspond to inflammatory cells around large vessels.

In conclusion, LIV imaging can be used to visualize the tissue metabolism
associated with LD accumulation and inflammation without exogenous labels.
Hence, LIV can be used to detect these abnormalities of the NAFLD model. In
the future, LIV can be used for more precise quantification of tissue
abnormality in NAFLD models, and hence will contribute to understanding of
NAFLD pathogenesis.

## Funding

Core Research for Evolutional Science and Technology (JPMJCR2105); Japan
Science and Technology Agency (JPMJMI18G8); Austrian Science Fund (Schrödinger
grant, J4460); Japan Society for the Promotion of Science (18H01893,
21H01836).

## Acknowledgments

Although this research was funded solely by the agencies listed, the project
is related to a joint research project between Yokogawa Electric Corp. and the
University of Tsukuba. The authors greatly appreciate fruitful technical
discussions with Hiroyuki Sangu (Yokogawa), Atsushi Kubota and Renzo Ikeda
(Skytechnology), Akihiro Shitoh, and Yuichi Inoue (Optosigma), Masato Takaya
(Tatsuta), and Naoki Fukutake (Nikon).

## Disclosures

Mukherjee, El-Sadek, Zhu, Morishita, Lichtenegger: Yokogawa Electric Corp.
(F), Sky Technology (F), Nikon (F), Kao Corp. (F), Topcon (F). Makita, Yasuno:
Yokogawa Electric Corp. (F), Sky Technology (F), Nikon (F), Kao Corp. (F),
Topcon (F), Tomey Corp (P). Okada: None. Miyazawa: Sky Technology (E). Fukuda,
Lukmanto, Yamashita: None. Oshika: Topcon (F), Tomey Corp (F).

## Data Availability Statement

The data that support the findings of this study are available from the
corresponding author upon reasonable request.

## References

  * [1] E. Fabbrini, S. Sullivan, and S. Klein, “Obesity and Nonalcoholic Fatty Liver Disease: Biochemical, Metabolic and Clinical Implications,” Hepatology 51, 679–689 (2010).
  * [2] H. B. El-serag, T. Tran, and J. E. Everhart, “Diabetes increases the risk of chronic liver disease and hepatocellular carcinoma,” Gastroenterology 126, 460–468 (2004).
  * [3] S. L. Friedman, B. A. Neuschwander-Tetri, M. Rinella, and A. J. Sanyal, “Mechanisms of NAFLD development and therapeutic strategies,” Nature Medicine 24, 908–922 (2018).
  * [4] S. Petta, C. Muratore, and A. Craxì, “Non-alcoholic fatty liver disease pathogenesis: The present and the future,” Digestive and Liver Disease 41, 615–625 (2009).
  * [5] P. Angulo, “Nonalcoholic Fatty Liver Disease,” New England Journal of Medicine 346, 1221–1231 (2002).
  * [6] F. Marra, A. Gastaldelli, G. Svegliati Baroni, G. Tell, and C. Tiribelli, “Molecular basis and mechanisms of progression of non-alcoholic steatohepatitis,” Trends in Molecular Medicine 14, 72–81 (2008).
  * [7] S. Schuster, D. Cabrera, M. Arrese, and A. E. Feldstein, “Triggering and resolution of inflammation in NASH,” Nature Reviews Gastroenterology & Hepatology 15, 349–364 (2018).
  * [8] M. Kitade, H. Yoshiji, R. Noguchi, Y. Ikenaka, K. Kaji, Y. Shirai, M. Yamazaki, M. Uemura, J. Yamao, M. Fujimoto, A. Mitoro, M. Toyohara, M. Sawai, M. Yoshida, C. Morioka, T. Tsujimoto, H. Kawaratani, and H. Fukui, “Crosstalk between angiogenesis, cytokeratin-18, and insulin resistance in the progression of non-alcoholic steatohepatitis,” World Journal of Gastroenterology : WJG 15, 5193–5199 (2009).
  * [9] F. Kanwal, J. R. Kramer, S. Mapakshi, Y. Natarajan, M. Chayanupatkul, P. A. Richardson, L. Li, R. Desiderio, A. P. Thrift, S. M. Asch, J. Chu, and H. B. El-Serag, “Risk of Hepatocellular Cancer in Patients With Non-Alcoholic Fatty Liver Disease,” Gastroenterology 155, 1828–1837.e2 (2018).
  * [10] K. Yasui, E. Hashimoto, Y. Komorizono, K. Koike, S. Arii, Y. Imai, T. Shima, Y. Kanbara, T. Saibara, T. Mori, S. Kawata, H. Uto, S. Takami, Y. Sumida, T. Takamura, M. Kawanaka, and T. Okanoue, “Characteristics of Patients With Nonalcoholic Steatohepatitis Who Develop Hepatocellular Carcinoma,” Clinical Gastroenterology and Hepatology 9, 428–433 (2011).
  * [11] H. Popper and S. Udenfriend, “Hepatic fibrosis: Correlation of biochemical and morphologic investigations,” The American Journal of Medicine 49, 707–721 (1970).
  * [12] R. Bataller and D. A. Brenner, “Liver fibrosis,” The Journal of Clinical Investigation 115, 209–218 (2005).
  * [13] B. A. Banini and A. J. Sanyal, “Nonalcoholic Fatty Liver Disease: Epidemiology, Pathogenesis, Natural History, Diagnosis, and Current Treatment Options,” Clinical medicine insights. Therapeutics 8, 75–84 (2016).
  * [14] Q. M. Anstee and R. D. Goldin, “Mouse models in non-alcoholic fatty liver disease and steatohepatitis research,” International Journal of Experimental Pathology 87, 1–16 (2006).
  * [15] Y. Takahashi, Y. Soejima, and T. Fukusato, “Animal models of nonalcoholic fatty liver disease/nonalcoholic steatohepatitis,” World Journal of Gastroenterology : WJG 18, 2300–2308 (2012).
  * [16] S. Kumar Natarajan, K. Rasineni, M. Ganesan, D. Feng, B. L. McVicker, M. A. McNiven, N. A. Osna, J. L. Mott, C. A. Casey, and K. K. Kharbanda, “Structure, Function and Metabolism of Hepatic and Adipose Tissue Lipid Droplets: Implications in Alcoholic Liver Disease,” Current Molecular Pharmacology 10, 237–248 (2017).
  * [17] D. G. Mashek, “Hepatic lipid droplets: A balancing act between energy storage and metabolic dysfunction in NAFLD,” Molecular Metabolism 50, 101115 (2021).
  * [18] A. C. Sheka, O. Adeyi, J. Thompson, B. Hameed, P. A. Crawford, and S. Ikramuddin, “Nonalcoholic Steatohepatitis: A Review,” JAMA 323, 1175–1183 (2020).
  * [19] S. Silvana, N. C. Boa Sorte, C. Alves, P. Mendes, C. Brites, and L. Silva, “Inflammatory Cytokines and Non-Alcoholic Fatty Liver Disease (NAFLD) in Obese Children and Adolescents,” Nutricion Hospitalaria (2018).
  * [20] L. R. Lopetuso, G. Mocci, M. Marzo, F. D’Aversa, G. L. Rapaccini, L. Guidi, A. Armuzzi, A. Gasbarrini, and A. Papa, “Harmful Effects and Potential Benefits of Anti-Tumor Necrosis Factor (TNF)-$\alpha$ on the Liver,” International Journal of Molecular Sciences 19, 2199 (2018).
  * [21] D. E. Kleiner, E. M. Brunt, M. V. Natta, C. Behling, M. J. Contos, O. W. Cummings, L. D. Ferrell, Y.-C. Liu, M. S. Torbenson, A. Unalp-Arida, M. Yeh, A. J. McCullough, and A. J. Sanyal, “Design and validation of a histological scoring system for nonalcoholic fatty liver disease,” Hepatology 41, 1313–1321 (2005).
  * [22] S. H. Saverymuttu, A. E. Joseph, and J. D. Maxwell, “Ultrasound scanning in the detection of hepatic fibrosis and steatosis.” Br Med J (Clin Res Ed) 292, 13–15 (1986).
  * [23] C. Ricci, R. Longo, E. Gioulis, M. Bosco, P. Pollesello, F. Masutti, L. S. Crocè, S. Paoletti, B. d. Bernard, C. Tiribelli, and L. D. Palma, “Noninvasive in vivo quantitative assessment of fat content in human liver,” Journal of Hepatology 27, 108–113 (1997).
  * [24] L. S. Szczepaniak, E. E. Babcock, F. Schick, R. L. Dobbins, A. Garg, D. K. Burns, J. D. McGarry, and D. T. Stein, “Measurement of intracellular triglyceride stores by H spectroscopy: validation in vivo,” The American Journal of Physiology 276, E977–989 (1999).
  * [25] S. Fukumoto and T. Fujimoto, “Deformation of lipid droplets in fixed samples,” Histochemistry and Cell Biology 118, 423–428 (2002).
  * [26] L. L. Listenberger and D. A. Brown, “Fluorescent Detection of Lipid Droplets and Associated Proteins,” Current Protocols in Cell Biology 35, 24.2.1–24.2.11 (2007).
  * [27] H. Wang, E. Wei, A. D. Quiroga, X. Sun, N. Touret, and R. Lehner, “Altered Lipid Droplet Dynamics in Hepatocytes Lacking Triacylglycerol Hydrolase Expression,” Molecular Biology of the Cell 21, 1991–2000 (2010). Publisher: American Society for Cell Biology (mboc).
  * [28] R. Somwar, C. T. Roberts, and O. Varlamov, “Live-cell imaging demonstrates rapid cargo exchange between lipid droplets in adipocytes,” FEBS Letters 585, 1946–1950 (2011).
  * [29] D. Huang, E. Swanson, C. Lin, J. Schuman, W. Stinson, W. Chang, M. Hee, T. Flotte, K. Gregory, C. Puliafito, and a. et, “Optical coherence tomography,” Science 254, 1178–1181 (1991).
  * [30] C. Apelian, F. Harms, O. Thouvenin, and A. C. Boccara, “Dynamic full field optical coherence tomography: subcellular metabolic contrast revealed in tissues by interferometric signals temporal analysis,” Biomedical Optics Express 7, 1511–1524 (2016).
  * [31] M. Münter, M. v. Endt, M. Pieper, M. Casper, M. Ahrens, T. Kohlfaerber, R. Rahmanzadeh, P. König, G. Hüttmann, and H. Schulz-Hildebrandt, “Dynamic contrast in scanning microscopic OCT,” Optics Letters 45, 4766–4769 (2020).
  * [32] I. A. El-Sadek, A. Miyazawa, L. T.-W. Shen, S. Makita, S. Fukuda, T. Yamashita, Y. Oka, P. Mukherjee, S. Matsusaka, T. Oshika, H. Kano, and Y. Yasuno, “Optical coherence tomography-based tissue dynamics imaging for longitudinal and drug response evaluation of tumor spheroids,” Biomedical Optics Express 11, 6231–6248 (2020).
  * [33] H. M. Leung, M. L. Wang, H. Osman, E. Abouei, C. MacAulay, M. Follen, J. A. Gardecki, and G. J. Tearney, “Imaging intracellular motion with dynamic micro-optical coherence tomography,” Biomedical Optics Express 11, 2768–2778 (2020).
  * [34] K. Kurokawa, J. A. Crowell, F. Zhang, and D. T. Miller, “Suite of methods for assessing inner retinal temporal dynamics across spatial and temporal scales in the living human eye,” Neurophotonics 7, 015013 (2020).
  * [35] M. Münter, M. Pieper, T. Kohlfaerber, E. Bodenstorfer, M. Ahrens, C. Winter, R. Huber, P. König, G. Hüttmann, and H. Schulz-Hildebrandt, “Microscopic optical coherence tomography (mOCT) at 600 kHz for 4D volumetric imaging and dynamic contrast,” Biomedical Optics Express 12, 6024–6039 (2021).
  * [36] I. A. El-Sadek, A. Miyazawa, L. T.-W. Shen, S. Makita, P. Mukherjee, A. Lichtenegger, S. Matsusaka, and Y. Yasuno, “Three-dimensional dynamics optical coherence tomography for tumor spheroid evaluation,” Biomedical Optics Express 12, 6844–6863 (2021).
  * [37] E. Li, S. Makita, Y.-J. Hong, D. Kasaragod, and Y. Yasuno, “Three-dimensional multi-contrast imaging of in vivo human skin by Jones matrix optical coherence tomography,” Biomedical Optics Express 8, 1290–1305 (2017).
  * [38] A. Miyazawa, S. Makita, E. Li, K. Yamazaki, M. Kobayashi, S. Sakai, and Y. Yasuno, “Polarization-sensitive optical coherence elastography,” Biomedical Optics Express 10, 5162–5181 (2019).
  * [39] S. Makita, M. Yamanari, and Y. Yasuno, “Generalized Jones matrix optical coherence tomography: performance and local birefringence imaging,” Optics Express 18, 854–876 (2010).
  * [40] D. Kasaragod, S. Makita, Y.-J. Hong, and Y. Yasuno, “Noise stochastic corrected maximum a posteriori estimator for birefringence imaging using polarization-sensitive optical coherence tomography,” Biomedical Optics Express 8, 653–669 (2017).
  * [41] S. Makita, Y.-J. Hong, M. Miura, and Y. Yasuno, “Degree of polarization uniformity with high noise immunity using polarization-sensitive optical coherence tomography,” Optics Letters 39, 6783 (2014).
  * [42] P. Mukherjee, A. Miyazawa, S. Fukuda, T. Yamashita, D. Lukmanto, K. Okada, I. A. El-Sadek, L. Zhu, S. Makita, T. Oshika, and Y. Yasuno, “Label-free functional and structural imaging of liver microvascular complex in mice by Jones matrix optical coherence tomography,” Scientific Reports 11, 20054 (2021).
  * [43] R. F. Spaide, J. G. Fujimoto, and N. K. Waheed, “Image Artifacts in Optical Coherence Angiography,” Retina 35, 2163–2180 (2015).
  * [44] M. W. Robinson, C. Harmon, and C. O’Farrelly, “Liver immunology and its role in inflammation and homeostasis,” Cellular & Molecular Immunology 13, 267–276 (2016).
  * [45] W. Birchmeier, “Orchestrating Wnt signalling for metabolic liver zonation,” Nature Cell Biology 18, 463–465 (2016).
  * [46] C. M. Kusminski and P. E. Scherer, “New zoning laws enforced by glucagon,” Proceedings of the National Academy of Sciences 115, 4308–4310 (2018).
  * [47] T. Kietzmann, “Metabolic zonation of the liver: The oxygen gradient revisited,” Redox Biology 11, 622–630 (2017).
  * [48] Y. B. A. Kang, J. Eo, S. Mert, M. L. Yarmush, and O. B. Usta, “Metabolic Patterning on a Chip: Towards in vitro Liver Zonation of Primary Rat and Human Hepatocytes,” Scientific Reports 8, 8951 (2018).
  * [49] M. Digel, R. Ehehalt, and J. Füllekrug, “Lipid droplets lighting up: Insights from live microscopy,” FEBS Letters 584, 2168–2175 (2010).
  * [50] M. Welte, “Fat on the move: intracellular motion of lipid droplets,” Biochemical Society Transactions 37, 991–996 (2009).
  * [51] S. Martin and R. G. Parton, “Lipid droplets: a unified view of a dynamic organelle,” Nature Reviews Molecular Cell Biology 7, 373–378 (2006).
  * [52] J. Moon, E. Kong, J. Lee, J. Jung, E. Kim, S. B. Park, and P. Kim, “Intravital longitudinal imaging of hepatic lipid droplet accumulation in a murine model for nonalcoholic fatty liver disease,” Biomedical Optics Express 11, 5132–5146 (2020).
  * [53] D. Overi, G. Carpino, A. Franchitto, P. Onori, and E. Gaudio, “Hepatocyte Injury and Hepatic Stem Cell Niche in the Progression of Non-Alcoholic Steatohepatitis,” Cells 9, 590 (2020).
  * [54] D. E. Kleiner and H. R. Makhlouf, “Histology of NAFLD and NASH in Adults and Children,” Clinics in liver disease 20, 293–312 (2016).
  * [55] Z. Hall, N. J. Bond, T. Ashmore, F. Sanders, Z. Ament, X. Wang, A. J. Murray, E. Bellafante, S. Virtue, A. Vidal-Puig, M. Allison, S. E. Davies, A. Koulman, M. Vacca, and J. L. Griffin, “Lipid zonation and phospholipid remodeling in nonalcoholic fatty liver disease,” Hepatology 65, 1165–1180 (2017).
  * [56] Y. Takahashi and T. Fukusato, “Histopathology of nonalcoholic fatty liver disease/nonalcoholic steatohepatitis,” World Journal of Gastroenterology : WJG 20, 15539–15548 (2014).
  * [57] X. Zhang, J. Han, K. Man, X. Li, J. Du, E. S. H. Chu, M. Y. Y. Go, J. J. Y. Sung, and J. Yu, “CXC chemokine receptor 3 promotes steatohepatitis in mice through mediating inflammatory cytokines, macrophages and autophagy,” Journal of Hepatology 64, 160–170 (2016).
  * [58] B. Gao and H. Tsukamoto, “Inflammation in Alcoholic and Nonalcoholic Fatty Liver Disease: Friend or Foe?” Gastroenterology 150, 1704–1709 (2016).
  * [59] M. Ganz and G. Szabo, “Immune and inflammatory pathways in NASH,” Hepatology International 7, 771–781 (2013).
  * [60] R. Yang, M.-J. Guan, N. Zhao, M.-J. Li, and T. Zeng, “Roles of extrahepatic lipolysis and the disturbance of hepatic fatty acid metabolism in TNF-$\alpha$ -induced hepatic steatosis,” Toxicology 411, 172–180 (2019).
  * [61] M. Vida, A. L. Gavito, F. J. Pavón, D. Bautista, A. Serrano, J. Suarez, S. Arrabal, J. Decara, M. Romero-Cuevas, F. Rodríguez de Fonseca, and E. Baixeras, “Chronic administration of recombinant IL-6 upregulates lipogenic enzyme expression and aggravates high-fat-diet-induced steatosis in IL-6-deficient mice,” Disease Models & Mechanisms 8, 721–731 (2015).
  * [62] S. Fukuda, A. Fujita, D. Kasaragod, S. Beheregaray, Y. Ueno, Y. Yasuno, and T. Oshika, “Comparison of intensity, phase retardation, and local birefringence images for filtering blebs using polarization-sensitive optical coherence tomography,” Scientific Reports 8, 7519 (2018).
  * [63] A. L. Oldenburg, R. K. Chhetri, J. M. Cooper, W.-C. Wu, M. A. Troester, and J. B. Tracy, “Motility-, autocorrelation-, and polarization-sensitive optical coherence tomography discriminates cells and gold nanorods within 3D tissue cultures,” Optics Letters 38, 2923–2926 (2013).
  * [64] A. L. Oldenburg, X. Yu, T. Gilliss, O. Alabi, R. M. Taylor, and M. A. Troester, “Inverse-power-law behavior of cellular motility reveals stromal-epithelial cell interactions in 3D co-culture by OCT fluctuation spectroscopy,” Optica 2, 877–885 (2015).
  * [65] J. Scholler, “Motion artifact removal and signal enhancement to achieve in vivo dynamic full field OCT,” Optics Express 27, 19562–19572 (2019).
  * [66] J. Lee, W. Wu, J. Y. Jiang, B. Zhu, and D. A. Boas, “Dynamic light scattering optical coherence tomography,” Optics Express 20, 22262–22277 (2012).
  * [67] J. Scholler, K. Groux, O. Goureau, J.-A. Sahel, M. Fink, S. Reichman, C. Boccara, and K. Grieve, “Dynamic full-field optical coherence tomography: 3D live-imaging of retinal organoids,” Light: Science & Applications 9, 140 (2020).
  * [68] R. Morishita, I. A. El-Sadek, P. Mukherjee, A. Miyazawa, K. Tomita, S. Makita, and Y. Yasuno, “Sparse frame acquisition toward fast volumetric dynamic optical coherence tomography imaging,” Proc. SPIE 11948, 119480I (2022).