# Label-free drug response evaluation of human derived tumor spheroids using
three-dimensional dynamic optical coherence tomography

Ibrahim Abd El-Sadek  1,2 Larina Tzu-Wei Shen  3 Tomoko Mori  3 Shuichi Makita
1 Pradipta Mukherjee  1 Antonia Lichtenegger  1,4 Satoshi Matsusaka  3 and
Yoshiaki Yasuno  1  1Computational Optics Group, University of Tsukuba,
Tsukuba, Ibaraki 305-8573, Japan.
2Department of Physics, Faculty of Science, Damietta University, New Damietta
City 34517, Damietta, Egypt.
3Clinical Research and Regional Innovation, Faculty of Medicine, University of
Tsukuba, Ibaraki 305-8575, Japan.
4 Center for Medical Physics and Biomedical Engineering, Medical University of
Vienna, Währinger Gürtel 18-20, 4L, 1090, Vienna, Austria.
<EMAIL_ADDRESS>
###### Abstract

We demonstrate label-free drug response evaluations of human breast (MCF-7)
and colon (HT-29) cancer spheroids via dynamic optical coherence tomography
(OCT). The MCF-7 and HT-29 spheroids were treated with paclitaxel (PTX, or
Taxol) and the active metabolite of irinotecan (SN-38), respectively. The
drugs were applied using 0 (control), 0.1, 1, and 10 $\muup$M concentrations
with treatment times of 1, 3, and 6 days. The samples were scanned using a
repeated raster scan protocol and two dynamic OCT algorithms, logarithmic
intensity variance (LIV) and late OCT correlation decay speed (OCDSl)
analyses, were applied to visualize the tissue and cellular dynamics.
Different drug response patterns of the two spheroid types were visualized
clearly and analyzed quantitatively by LIV and OCDSl imaging. For both
spheroid types, structural corruptions and reduction of LIV and OCDSl were
observed. These results may indicate different mechanisms of the drug action.
The results suggest that dynamic OCT can be used to highlight drug response
patterns and perform anti-cancer drug testing.

††journal: boe††articletype: Research Article

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

##  1 Introduction

Cancers are ones of the deadliest human diseases [1, 2]. The burden of cancer
and the related mortality rate could be reduced through early detection and
provision of appropriate treatment in a patient-specific manner. Hopefully,
cancer patients may have a high chance of recovery if they are diagnosed early
and treated appropriately using suitable anti-cancer drugs. However, there are
several types of cancer based on which cell type has been mutated. The type of
cancer varies from patient to patient and thus the suitable drug will also
vary patient by patient.

One of the most effective approaches to understand the cancer’s biology and
select anti-cancer drugs appropriately is to cultivate patient-derived tumor
cells as a three dimensional (3D) cluster, so-called multi-cellular tumor
spheroid (MCTS). An MCTS closely mimic the main features of in vivo solid
tumors, including their structural organization and the gradients of their
oxygen, pH, and nutrients [3, 4, 5, 6]. In addition, the MCTS is similar to
the in vivo solid tumor in several ways, including growth kinetics, cell
interactions, metabolic rates, and its response or resistance to the applied
chemotherapy and radiotherapy [7, 8]. MCTSs have therefore been used widely in
the field of anti-cancer drug investigations. An anti-cancer drug’s efficacy
can be evaluated based on its impact on the shape and volume alteration of the
MCTS, the dissociation of the MCTS cell aggregations, and the MCTS cells
viability degradation [9, 10, 11].

MCTS evaluations can be performed using several conventional methods, e.g.,
staining histology [12, 13], fluorescence microscopy [14, 15, 16], and bright
field microscopy [17, 18]. Although these methods are the gold standards, they
have several limitations. First, most of these methods use chemical labeling
and/or tissue slicing and are thus invasive. Second, most of them are 2D
imaging modalities. Third, the image penetration depth of these methods is
limited to a few hundreds of microns, which prevents entire-depth imaging of
thick tissues such as the MCTS. Finally, the quantitative capabilities of
these methods are low.

Some of the limitations mentioned above, e.g., invasiveness and limited
penetration depth, can be overcome using optical coherence tomography (OCT)
[19] and its microscopic version so-called optical coherence microscopy (OCM)
[20]. OCT is a low-coherence interferometry-based biomedical imaging modality
that provides high-resolution, label-free, non-invasive, 3D imaging over a few
millimeters penetration depth.

Several hardware and software extensions have been integrated with
conventional OCT to provide additional contrasts to the OCT. For example,
attenuation coefficient (AC) imaging[21, 22] is sensitive to tissue density,
and was found to be sensitive to both the apoptotic and necrotic processes of
human fibroblasts [23]. The tissue polarization properties can be measured
using polarization-sensitive OCT [24], which provides collagen-sensitive [25,
26] and melanin-sensitive [27, 28, 29] contrasts. Tissue stiffness, which is
an important parameter in tumor investigations, can also be evaluated via
optical coherence elastography [30, 31, 32, 33].

One interesting OCT extension is dynamic OCT. As an emerging technique,
dynamic OCT provides tissue activity/sub-cellular motion contrasts. Dynamic
OCT represents a combination of high-speed OCT systems with the signal
processing of sequentially acquired OCT frames. Time-frequency analysis-based
en face dynamic full field OCT (DFF-OCT) has been demonstrated previously and
was found to be sensitive to the adenosine triphosphate (ATP) consumption-
related tissue metabolism [34, 35, 36]. High-speed scanning OCT and time
spectroscopic analysis have been used to perform for cross-sectional dynamic
imaging[37, 38]. Both of these methods are able to provide cross-sectional
tissue activity/sub-cellular motion contrast. However, the methods may not be
suitable for volumetric imaging applications.

Recently, 3D dynamic OCT methods have been demonstrated [39, 40, 41, 42].
However, acquisition of the volumetric dynamics requires long measurement
times [39, 40] or use of ultra-fast OCT systems [41, 40, 42].

Recently, we demonstrated 3D dynamic OCT, which combines a standard-speed
swept-source OCT device with a custom-designed 3D scanning protocol and was
shown to acquire volumetric tissue dynamics in 52.4 s [43, 44]. The 3D tissue
dynamics were visualized and quantified using our previously proposed dynamic
OCT signal analysis algorithms[38].

This 3D dynamic OCT technique visualized the viable and necrotic cell layers
of MCTSs successfully. In addition, similarity between the fluorescence
microscopy and en face dynamic OCT contrasts was demonstrated [44]. In the
same work, a preliminary study of the tumor spheroid’s response to anti-cancer
drug was presented. However, that study involved only one type of tumor (MCF-7
cell line, human breast cancer), and only one anti-cancer drug, paclitaxel
(PTX, or Taxol), was used.

In general, treatment of different tumor cell types with different anti-cancer
drugs leads to different spatial patterns of the anti-cancer drug responses.
The spatial pattern differences among the different tumor spheroids and
various drug types are worth to be investigated to aid in the selection of
appropriate anti-cancer drugs, and these differences can be highlighted using
our proposed dynamic OCT method.

In this paper, more generalized drug response studies based on the 3D dynamic
OCT are presented. Breast and colon cancers and the combination of these
cancers are among the most prevalent causes of cancer-related death
worldwide[45, 46, 47, 48]. Therefore, we selected two tumor cell types for
this study, including the human breast cancer (MCF-7) and human colon cancer
(HT-29) cell-lines. The breast and colon cancer spheroids are treated using an
anti-cancer drug (PTX) and an active metabolite of Irinotecan (SN-38),
respectively. Image-based 3D observation of the time-course drug response
shows different spatial patterns of the drug effects among the different tumor
and drug types. In addition, quantification of the dynamic OCT and the
spheroid morphology also reveals different time courses among the cell and
drug types.

##  2 Methods

###  2.1 Protocols and samples of drug response studies

Figure 1: Time protocol for spheroid cultivation and measurement. The same
time protocols were used for both MCF-7 and HT-29. 60 spheroids were cultured
for both cell types. Each spheroid was seeded with 1,000 cells on day-1. The
drugs, i.e., PTX for MCF-7 and SN-38 for HT-29, were applied on day-5. On
days-6, 8, and 11, fluorescence (FLUO) and then OCT imaging were performed.
The fluorescence contrast agents were applied 3 hours before FLUO imaging.

The tumor spheroid drug response evaluation study involves use of two
different human-derived tumor cell-lines, including human breast
adenocarcinoma (MCF-7) and human colon cancer (HT-29) cell-lines. Both cell-
lines were cultured using the same protocol in line with the study protocol
depicted in Fig. 1. On day-1, 1,000 tumor cells were seeded in each well of a
ultra-low attachment 96-well-plate and 60 wells were prepared. The cells were
cultured at a temperature of 37 ∘C and the cell-culture chamber was supported
with 5 % CO2. The culture medium contained a 1:1 mixture of Eagle’s minimal
essential medium (EMEM; for MCF-7) or Dulbecco’s modified eagle medium (DMEM;
for HT-29) and F12 (Invitrogen, Waltham, MA) with a 2 % B-27 supplement
(Invitrogen), 2 ng/mL of basic fibroblast growth factor (bFGF; Wako, Osaka,
Japan), 2 ng/mL of epidermal growth factor (EGF; Sigma-Aldrich, St. Louis,
MO), 100 U/mL of penicillin G, and 0.1 mg/mL of streptomycin sulfate (Wako,
Osaka, Japan). The cells were aggregated with each other and formed one
spheroid in each well on day-5. On the same day, each spheroid was then
treated using 0.1 $\muup$M, 1 $\muup$M, or 10 $\muup$M of the relevant anti-
cancer drugs. The MCF-7 spheroids were treated using paclitaxel (PTX, Sigma-
Aldrich; also known as Taxol), while the HT-29 spheroids were treated using an
active metabolite of Irinotecan (SN-38, Chemscene LLC, NJ). In addition,
untreated (0 $\muup$M) spheroids were retained as control samples. After drug
application the spheroids were kept within the same culture environment.

The spheroids were then extracted from the cultivation and measured using the
fluorescence (FLUO) and dynamic OCT microscopes at three time-points of
days-6, 8, and 11, i.e., 1-, 3-, and 6-days after drug application,
respectively. Here, we also denote the days 6, 8, and 11 as the 1st, 3rd, and
6th days of treatment (TD-1, -3, and -6). At each time-point, five spheroids
were measured for each of the four drug concentrations, including control (0
$\muup$M), meaning that 20 spheroids were measured in total. The measurements
were performed in a room temperature of 25 ∘C.

###  2.2 Fluorescence microscopic imaging

Live/dead fluorescence assays were performed using the THUNDER imaging system
(Leica Microsystems, Wetzlar, Germany) with a microscopic objective with a
numerical aperture (NA) of 0.12. Two contrast agents were applied for three
hours before the FLUO imaging. The first agent is calcein acetoxymethyl
(Calcein-AM; Dojindo, Kumamoto, Japan), which stains the living (viable) cells
and emits a green fluorescence signal. The second agent is propidium iodide
(PI; Dojindo), which contrasts the dead cells by emitting red fluorescence
signal.

###  2.3 3D dynamic OCT imaging

####  2.3.1 OCT device

A polarization-sensitive Jones matrix swept-source OCT (JM-OCT) was used to
perform the three-dimensional (3D) tissue dynamics imaging. A light source
with a central wavelength of 1.3 $\muup$m and a scanning speed of 50,000
A-lines/s was used. The axial (in tissue) and lateral resolutions were 14
$\muup$m and 18.1 $\muup$m, respectively. The complete specification for the
JM-OCT has been published elsewhere [26]. Although this device is
polarization-sensitive, only polarization-insensitive OCT images, which are
the average of four OCT intensity images acquired through four polarization
channels, were used for the tissue dynamics imaging.

####  2.3.2 OCT data acquisition protocol

The spheroid samples were transferred to our 3D JM-OCT system after FLUO
imaging. For the 3D dynamics imaging, the lateral imaging field was divided
into eight regions. Each region consists of 16 B-scan locations and was
scanned repeatedly using a quick raster scanning protocol for 32 times in 6.55
s. Therefore, 32 sequential frames were obtained at each location in the
sample with a frame repetition time of 204.8 ms. The OCT volume consists of a
total of 128 locations and was captured in 52.4 s. The scanning area was 1
$\times$ 1 mm2. Further details about the 3D dynamics scanning protocol are
available elsewhere in the literature [44].

####  2.3.3 Dynamic OCT algorithm: Logarithmic intensity variance (LIV)

Figure 2: Schematic depictions of two dynamic OCT algorithms:(a) LIV and (b)
OCDSl. LIV is defined as the temporal fluctuation (variance) of the dB-scaled
OCT intensity. As a result, LIV is expected to be sensitive to the magnitude
of the tissue dynamics. On the other hand, OCDSl represents the speed of the
dynamics and is defined as the decorrelation rate, i.e., as the slope of the
auto-correlation curve [red curve in (b)] of the dB-scaled OCT intensity. The
slope is computed here by performing a linear regression of the auto-
correlation curve over a specified delay-time range [204.8, 1,228.8 ms].

The time-sequential OCT signals were processed using logarithmic-intensity-
variance (LIV) algorithm[38]. The LIV is a measure of the magnitude of the
temporal fluctuations of the dB-scaled OCT signals at each location in the
tissue as illustrated in Fig. 2(a), and it is defined as:

$\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>_{t}\right]^{2}\\\
$ (1)

where $I_{dB}(x,z,t_{i})$ is the dB-scaled (logarithmic) OCT intensity,
$t_{i}$ is the sampling time for the $i$-th frame, where $i$ = 0, 1, 2,….,
$N$-1, and $N$ is the total number of frames, which is 32 in our case, and
$\left<{}\right>_{t}$ represents averaging over time.

Because the LIV is a measure of the magnitude of the signal fluctuations, it
is believed to be sensitive to the magnitude of the intracellular motility.

An LIV image is presented as a pseudo-color image in which the hue and the
value of the HSV (hue, saturation, value) color representation are set as the
LIV and the averaged OCT intensity image, respectively.

####  2.3.4 Dynamic OCT algorithm: Late OCT correlation decay speed (OCDSl)

The time-sequential OCT signals are also processed using yet another dynamic
OCT algorithm: “late OCT correlation decay seed (OCDSl).” OCDSl is defined as
the speed (rate) of auto-correlation decay of the sequentially captured dB-
scaled OCT intensity and it is expected to be sensitive to the slow tissue
dynamics [38].

To obtain the OCDSl, the temporal auto-correlation $\rho_{A}(\tau_{i};x,z)$
was first computed as:

$\rho_{A}(\tau_{i};x,z)=\frac{\mathrm{Cov}\left[I_{dB}(x,z,t_{i}),\,I_{dB}(x,z,t_{i}+\tau_{i})\right]}{\mathrm{Var}\left[I_{dB}(x,z,t_{i})\right]\mathrm{Var}\left[I_{dB}(x,z,t_{i}+\tau_{i})\right]},$
(2)

where the numerator represents the covariance between $I_{dB}(x,z,t_{i})$ and
$I_{dB}(x,z,t_{i}+\tau_{i})$, and $\mathrm{Var}[\,\,]$ represents the
variance. $\tau_{i}$ represents the delay time, which is defined as $i\Delta
t$ where $i$ is an integer variable. $\Delta t$ is the B-scan repetition time,
which is 204.8 ms in this case.

The OCDSl is then defined as the correlation decay rate, i.e., as the slope of
$\rho_{A}(\tau_{i};x,z)$ over a specific range of $\tau$ = [204.8, 1228.8 ms]
as illustrated in Fig. 2(b).

An OCDSl image is presented as a pseudo-color image, which is generated in the
same manner as the LIV image, but in this case the hue is the OCDSl signal.
Further details about the LIV and OCDSl algorithms are published elsewhere
[44].

####  2.3.5 OCT-based volumetric quantification

To quantify the volumetric alterations of the morphology and viability of the
spheroid cells, the spheroid volume was computed via B-scan by B-scan
segmentation. The segmentation was performed using the find-connected-region
plugin of the Fiji ImageJ software with a manually defined OCT intensity
threshold. The initial segmentation contained the bottom surface of the well-
plate because it shows high OCT intensity. This well-plate surface was removed
manually.

In addition to the spheroid volume, the mean values of the LIV and OCDSl over
the entire spheroid volume were computed using the segmentation results.
Furthermore, by applying empirically defined cut-offs of 3 dB2 and 2 $\times
10^{-4}$ ms-1 for the LIV and OCDSl, respectively, the necrotic cells volume
(i.e., the volume of the LIV and OCDSl signals that are lower than the cut-
offs in each case) is computed [44]. The necrotic cell ratio (= necrotic
volume / entire spheroid volume) is then computed. These computed quantities
are plotted as a function of the treatment time for each drug concentration,
as shown in Section 3.

##  3 Results

###  3.1 MCF-7 spheroid response to PTX

Figure 3: LIV, OCDSl, and FLUO images of MCF-7 spheroid treated with PTX. The
images are en face slices at around the center of the spheroids. The B-scan
cross-sections and the intensity OCT images are available in the Supplementary
materials (as Fig. S2 and Fig. S1, respectively.) Although the difference
between the control sample and the PTX-treated samples is not obvious with the
1-day treatment, the 3- and 6-day-treated spheroids showed significantly
different appearances to the control sample. The appearances of the FLUO
images are almost consistent with those of the LIV and OCDSl images. The scale
bars represent 200 $\muup$m in each case.

Figure 3 summarizes the en face LIV, OCDSl, and FLUO images of the MCF-7
spheroids treated with PTX. In the control case (the first column), the low
LIV (red) and low OCDSl (red) regions increase over time. These low-dynamics
regions at the center of the spheroid may correspond to the dead cells (red)
shown in the FLUO images. It is also found that the boundaries between the
high and low signal regions are clearer in the OCDSl images than the LIV
images.

On the first day of treatment (TD-1), the PTX-treated spheroids show similar
appearances to the control sample. However, as the treatment time increases,
the spheroids show shape corruption, volume reduction, and degradation of the
LIV and OCDSl signals. The FLUO images show increase of dead cells over time
in addition to the shape corruption.

Figure 4: Magnified LIV, OCDSl, and FLUO images of representative spheroids
from Fig. 3. The upper row shows one treated with 0.1-$\muup$M PTX, while the
lower row shows one treated with 1-$\muup$M PTX. The treatment time in both
cases is 3 days. The appearance of the FLUO images is more consistent with the
OCDSl images than LIV images.

Notably, the LIV and OCDSl images of the 0.1-$\muup$M and 1-$\muup$M cases on
day-8 (TD-3) show completely different patterns (as magnified in Fig. 4). In
the 0.1-$\muup$M case, tessellated low dynamics (red) patterns appear in the
OCDSl image (black dotted shapes), while the LIV images shows low dynamics
(red) overall. The FLUO image also shows some domains composed of dead cells
(red, yellow dashed lines). In the 1-$\muup$M case, the LIV image shows two
domains including a central low-dynamics (red) region and a peripheral region
that is a mixture of low (red) and high (green) dynamics. In contrast, the
OCDSl image shows three concentric domains of low, high, and low dynamics. The
FLUO image shows dead cells (red) at the periphery (indicated by yellow
arrowheads) and the center regions,with live cells (green) located in between.
In both cases, the appearance of the FLUO images are more consistent with
OCDSl than LIV images.

For the reference of the readers, OCT intensity images corresponding to the
images shown in Fig. 3 are presented in the supplementary material (Fig. S1).
The fast-scan cross-sections of LIV and OCDSl extracted at the locations
indicated by the white arrow heads in Fig. 3 are also presented in a
supplementary figure (Fig. S2).

Images of additional spheroids measured at under each treatment condition and
at each time point are presented in a supplementary figure (Fig. S3) and are
consistent with the images in Fig. 3.

Figure 5: Quantitative analysis of the MCF-7 spheroids treated with several
concentrations of Taxol. (a) Spheroid volume, (b) mean LIV, (c) mean OCDSl,
and (d) and (e) necrotic cell ratios computed based on LIV and OCDSl,
respectively. The plotted values represent the averages of five samples in
each case, while the error bars represent the $\pm$ standard-deviation range.

In Fig. 5, the spheroid volume (a), the mean LIV (b), the mean OCDSl (c), the
LIV-based necrotic cell ratio (d), and the OCDSl-based necrotic cell ratio (e)
are plotted as functions of the treatment time. The spheroid volumes of the
control case increases over time may be due to the growth of the spheroids. In
contrast, the drug-treated spheroids show decreasing volume trends.

The mean LIV and mean OCDSl over the entire spheroid [Fig. 5(b) and (c)] show
decreasing trends over time for all PTX concentrations, including the control
case. A significant reduction in the mean LIV was found for all PTX
concentrations, except for 10$\muup$M from TD-1 to TD-3 (P = 0.047, 0.018, and
0.006 for the control, 0.1, and 1.0 $\muup$M) and from TD-3 to TD-6 (P =
0.006, 0.006, and 0.018 for the control, 0.1, and 1.0 $\muup$M). For the
10-$\muup$M samples, the TD-1-to-TD-3 and TD-3-to-TD-6 reductions were
insignificant, but TD-1-to-TD-6 reduction was significant (P = 0.006).
Significant reductions in the mean OCDSl were also found for the three PTX
concentrations from TD-1 to TD-3 (P = 0.030, 0.006, 0.018 for the control,
0.1, and 1 $\muup$M) and from TD-3 to TD-6 (P = 0.006, 0.006, 0.030 for
control, 0.1, and 1 $\muup$M). For the 10-$\muup$M case, the reduction from
TD-1 to TD-3 (p = 0.006) was significant, although it was not significant from
TD-3 to TD-6 (p = 0.148). This may occur because the mean OCDSl has already
become very low on TD-3. All the p-values of these tests are summarized in
Table S1 (supplementary).

The necrotic cell ratios obtained with both LIV and OCDSl [Fig. 5(d) and (e),
respectively] clearly increase over the treatment times for all PTX
concentrations. Significant increases in the LIV-based necrotic cell ratios
were found from TD-1 to TD-3 (P = 0.030, 0.006, and 0.047 for 0.1 $\muup$M, 1
$\muup$M and 10 $\muup$M, respectively), and from TD-3 to TD-6 (P = 0.006,
0.047, and 0.030 for 0.1 $\muup$M, 1 $\muup$M, and 10 $\muup$M, respectively)
for all PTX concentrations except for the control sample. For the control
sample, the observed increase was significant from TD-3 to TD-6 (P = 0.006). A
significant increase in the necrotic cell ratio was also found in the OCDSl-
based necrotic cell ratio from TD-1 to TD-3 for all PTX concentrations,
including the control (P = 0.030, 0.018, 0.010, and 0.006 for the control, 0.1
$\muup$M, 1 $\muup$M and 10 $\muup$M). Similar increases were also found from
TD-3 to TD-6 for all concentrations, except for the 10-$\muup$M case (P =
0.006, 0.006, and 0.030 for the control, 0.1 $\muup$M, and 1 $\muup$M). All
statistics are summarized in Table S1 (supplementary).

The degradation in the mean LIV and mean OCDSl and the increasing necrotic
cell ratios for the PTX-treated spheroids may be related to inhibition of the
intracellular transport through the microtubules. This point will be discussed
in detail later in Section 4.1.

###  3.2 HT-29 spheroid response to SN-38

Figure 6: En face LIV, OCDSl, and FLUO images of an HT-29 spheroid treated
with SN-38. The LIV and OCDSl patterns are very different to those for MCF-7
when treated with PTX (Fig. 3). Specifically, the LIV images of the TD-1
spheroids show high LIV (green) at the center surrounded by low LIV (red).
After longer treatment times, the LIV images show a granular appearance. The
OCDSl images show concentric domain structures. In addition, the OCDSl and
FLUO images are found to be highly correlated. The scale bars represent 200
$\muup$m in each case.

Figure 6 summarizes the en face LIV, OCDSl, and FLUO images of the HT-29
spheroids after treatment with SN-38. For the TD-1 spheroids at all SN-39
concentrations, including the 0 $\muup$M (control) case, the LIV appearance is
contrary to that of the MCF-7 spheroids with PTX. Specifically, the center
region shows high LIV (green), and this area is surrounded by low LIV (red)
regions. For the 10-$\muup$M case in particular, the low LIV region is further
surrounded by a moderately high LIV periphery. In contrast, the appearance of
the OCDSl images is similar to that in the MCF-7 case. Namely, the center
shows low OCDSl (red), and this area is surrounded by a high OCDSl periphery
(green).

For the longer treatment times (TD-3 and TD-6), the LIV images show granular
patterns of high and low LIV, and clear domain structures are not observed.
This appearance is contrary to that of the MCF-7, and is cell-type dependent.
In contrast, most of the OCDSl images show double or triple concentric domain
structures. These structures may be the result of two factors, which include
drug effect from the peripheral side and hypoxia or lack of nutrients starting
from the center region. The appearances of the OCDSl images are quite
consistent with the FLUO images. The dynamic OCT and FLUO images show clear
volume reductions and/or structural corruption under the SN-38 treatment.

Figure 7: Magnified images of HT-29 spheroids with sever volume reduction and
shape corruption. These images were extracted from Fig. 6. High levels of
pattern correlation between the OCDSl and FLUO patterns are observed, while
the LIV patterns are not correlated well with the other images.

Figure 7 shows magnified images of representative spheroids with both volume
reduction and structural corruption. These magnified images demonstrate the
high pattern correlation between the OCDSl and FLUO images, as indicated by
the arrowheads and dashed lines. In contrast, the LIV patterns are not similar
to the OCDSl and FLUO images.

Figure 8: Morphological and dynamics analysis of the HT-29 spheroid when
treated with SN-38. The plots are presented in the same order as those in Fig.
5. The plotted values represent the average values of five samples (N=5) under
each treatment condition and the error bars represent the $\pm$ standard-
deviation range.  Figure 9: LIV images of the five individual spheroids when
treated with 10 $\muup$M of SN-38 for 6 days. The first spheroid showed
exceptionally low LIV for all spheroid regions and was thus excluded from the
statistical analysis as an outlier.

Figure 8 shows the morphological and mean dynamic OCT signal alterations for
the HT-29 spheroid treated with SN-38. It was observed that the data in the
TD-6 10-$\muup$M case had much standard deviations (i.e., error bars in the
figure) than the other cases for the mean LIV [Fig. 8(b)], LIV-based, and
OCDSl-based necrotic cell ratios [Fig. 8 (d) and (e), respectively]. By
observing the LIV images for all five spheroids in this case [Fig. 9], it was
found that one spheroid case (spheroid 1) exhibited very low LIV values over
the entire spheroid region. We suspect that this spheroid may be fragmented
because of some cultivation issues or because of spheroid handling during the
measurement. We therefore regard this case as an outlier and excluded it from
the subsequent statistical analysis. The plots that include this outlier are
presented with red spots and red lines, while those that exclude it are shown
with orange color in Fig. 8.

The control spheroid volume [Fig. 8(a)] increased from TD-1 to TD-3, and then
decreased slightly from TD-3 to TD-6. This volume alteration of the control
spheroid may indicate the growth and spontaneous corruption of the HT-29
spheroid over the cultivation time. In contrast, the spheroids that were
treated with SN-38 showed monotonic reductions in their spheroid volumes over
time for all drug concentrations.

The mean LIVs [Fig. 8(b)] increased from TD-1 to TD-3 for all drug
concentrations, and then decreased from TD-3 to TD-6 for the control sample (P
= 0.006, Mann-Whitney test; supplementary Table S2).

The mean OCDSl in the 1-$\muup$M and 10-$\muup$M SN-38 concentration cases
demonstrated significant reductions over time, as illustrated in [Fig. 8(c)].
The significant reduction in the 1-$\muup$M case was found from TD-3 to TD-6
(P = 0.006, Mann-Whitney test), while that in the 10-$\muup$M was found from
TD-1 to TD-3 (P = 0.018). For all other SN-38 concentrations, the treatment-
time dependency was not observed. All statistical test results are presented
in supplementary Table S2.

The LIV cut-off-based necrotic cell ratio demonstrated a reduction in the
necrotic cell ratio from TD-1 to TD-3 for all SN-38 concentrations, and the
ratio then became stable from TD-3 to TD-6. The reduction in LIV-based
necrotic cell ratio from TD-1 to TD-3 was significant in the 1-$\muup$M and
10-$\muup$M cases of SN-38 (P= 0.047 and 0.010, respectively; Mann-Whitney
test).

On the other hand, the necrotic cell ratios based on the OCDSl cut-off remain
stable over time for low concentrations (i.e., the control and 0.1 $\muup$M
cases), while that of the 10-$\muup$M case increases from TD-1 to TD-3 (P =
0.006). The ratio of the 1-$\muup$m shows a significant increase from TD-3 to
TD-6 (P = 0.006). The results of this test are presented in Table S2
(supplementary).

The morphological and dynamic-OCT signal alterations of the HT-29 spheroid
treated with SN-38 may be related to the drug mechanism of SN-38, as will be
discussed later in Section 4.2. The OCT intensity, cross-sectional LIV and
OCDSl results for the same data presented in Fig. 6 are presented in Figs. S4
and S5 in the supplementary material. In addition, the responses of one
additional HT-29 spheroid under each treatment condition are presented as
supplementary Fig. S6.

##  4 Discussion

###  4.1 Drug effects of PTX appearing on OCT and dynamic OCT

Dynamic OCT imaging of the control MCF-7 spheroid [Fig. 3 (first column)]
showed circular low dynamic signals (LIV and OCDSl) surrounded by a high-
dynamics shell. Similar image appearances were observed in our previous
studies [38, 44]. The low and high dynamics observed at the spheroid core and
the periphery are believed to highlight the well-known necrotic core (caused
by the lack of both oxygen and nutrients supply) and the viable rim of the
tumor spheroids, respectively [49, 3]. In addition, the increase of low
dynamics area over the cultivation time [Fig. 3 (first column)] may indicate
an increase in the necrotic area over the long cultivation time.

Similarity between the dynamics patterns of the MCF-7 spheroids treated with
PTX for one day and that of the control spheroid was observed in Fig. 3 (first
to third rows). This may indicate either the low efficacy of PTX or the
resistance of the tumor cells when the treatment time is only one day.

In addition to the findings mentioned above, the general tendency of the
structural and dynamic OCT observed in this study can be explained as follows
using the drug mechanism of PTX. Microtubules (MTs) are polymers that are
formed by polymerization of $\alpha$ and $\beta$ tubulin dimers [50]. This MTs
have three important functions in the eukaryotic cells. First, MTs are the
main constituents of the cytoskeleton, which maintains the cell structure and
supports the cell with the mechanical resistance to deformation [51]. Second,
the MTs are involved in mitosis as the main constituents of the mitotic
spindles [50, 52]. Third, the MTs act as platforms for intracellular transport
of the vesicles and organelles [53, 50].

For the functions described above, the MTs exhibit highly dynamic behavior.
They exert rapid and stochastic phase transitions from extension (by
polymerization) to shrinkage (by de-polymerization) [50, 54]. This dynamic
instability allows the MTs tips to search for and bind to intracellular
components, e.g., capturing chromosomes during mitosis. [50, 54].

PTX is an MT-stabilizing anti-cancer drug. This drug prevents MT
depolymerization and suppresses the dynamic behavior of the MTs [55, 56].
Therefore, it leads to cell cycle and mitotic arrest, which ultimately leads
to cell death [55, 56].

The structural OCT findings for the MCF-7 spheroid after treatment with PTX
showed spheroid shape corruption and volume reduction over the treatment time.
This spheroid shape corruption may indicate malfunctioning of the first
function of the MTs (i.e., maintaining the cell structure and the resistance
to cell deformation) under the application of PTX. On the other hand,
reduction of the spheroid volume may indicate inhibition of mitotic cell
divisions (the second function of the MTs) under the effect of PTX.

On the other hand, the spheroids that were treated with PTX showed reductions
in their LIV and OCDSl signals over the treatment time. The LIV and OCDSl
reductions may indicate a reduction of the intracellular motility/transport
through the MTs (the third function of MTs) and may also indicate PTX-induced
cell death.

###  4.2 Drug effects of SN-38 appeared on OCT and dynamics OCT

The dynamics patterns of the TD-3 HT-29 spheroid under all the SN-38
concentrations, except for the control case [Figs. 6 and 7] showed a clear low
OCDSl layer at the periphery and this layer corresponded well with the
peripheral dead cells (red fluorescence) observed in the FLUO images. Among
the three concentrations, the 10-$\muup$M case showed evident changes in the
mean LIV and the mean OCDSl from TD-1 to TD-3 [Fig. 8(b) and (c)], and also
showed the smallest spheroid volume among all concentrations, even at TD-1
[Fig. 8(a)]. These results indicate that OCT and dynamic OCT imaging can
demonstrate higher drug effects of 10-$\muup$M SN-38 than the other lower
concentrations.

On TD-6, all the SN-38-treated spheroids at all concentrations (0.1, 1.0 and
10 $\muup$M) showed severe structural corruption. In comparison, on TD-3,
these shape corruptions were not evident for all concentrations. However, the
OCDSl images showed clear low OCDSl layers at the periphery that were not
found in the control case. In addition, spheroid volume analysis [Fig. 8(a)]
showed evidently smaller spheroid volumes, even at TD-3. These findings
suggest that dynamic OCT can assess the more moderate drug effect of SN-38
than that can be visualized using non-dynamic OCT.

The alterations of OCT and dynamic OCT signals and the images of the HT-29
spheroids [Figs. 8 and 6, respectively] can be understood more by taking the
mechanism of SN-38 into account. Irinotecan is a DNA topoisomerase I (Topo-I)
inhibitor that is used to treat the advanced stages of colorectal cancer and
it is also approved for second-line treatment in metastatic colorectal cancer
[57, 58]. SN-38 is an active metabolite of Irinotecan, which is 100- to
1000-fold more active when compared with the Irinotecan itself [57, 59].
Because Topo-I is an enzyme involved in DNA transcription and replication, the
inhibition of Topo-I caused by SN-38 induces DNA damage and a transient
S-phase (the cell cycle in which DNA is replicated) arrest. SN-38 application
then leads to irreversible breaks in the single-strand-DNA that are
subsequently transformed into double-strand-DNA breaks. As a result of these
double-strand-DNA breaks, a series of different apoptotic-related signaling
pathways are activated. This then results in apoptotic cell death [57, 60].
The damage of DNA and subsequent DNA replication may suppress cell division,
and this can account for the smaller spheroid size at TD-3. This DNA damage
then progresses into apoptosis, and this can lead to the structural corruption
observed at TD-6. The thin low-OCDSl (red) peripheral layers observed in all
SN-38 treated spheroids on TD-3 may indicate the onset of apoptosis at the
spheroid-drug interface. We suspect that the low-OCDSl may indicate the cell
death process because the necrosis at the control spheroid center, which is
another type of cell death, also exhibits low OCDSl.

###  4.3 Combined interpretation of LIV and OCDSl

####  4.3.1 LIV and OCDSl highlight different aspects of dynamics

LIV is a measure of the magnitude of the temporal fluctuations in the dB-
scaled OCT intensity and it may be sensitive to the magnitude of the
intracellular motility. On the other hand, the OCDSl quantifies the speed of
the OCT intensity fluctuations and it may be sensitive to the speed of the
intracellular motility, as discussed elsewhere [38, 44]. Because of this
fundamental difference between them, the LIV and OCDSl may highlight different
cellular processes. And this may explain why different image patterns appeared
in LIV and OCDSl. For example, in the control MCF-7 spheroids [Fig. 3], OCDSl
shows clearer boundaries between the central and peripheral regions than LIV.
In addition, in some cases, OCDSl shows tessellated domain structures such as
those shown in Fig. 4 that are not visible in the corresponding LIV images.

Although the identification of the cellular process that accounts for these
LIV and OCDSl findings remains an open issue, we conclude that LIV and OCDSl
provide different information and thus are complementary methods.

####  4.3.2 Combined interpretation of LIV and OCDSl of tumor cell necrosis

Figure 10:  Detailed analysis of apparent pattern differences between the
control MCF-7 (right) and HT-29 (left) spheroids. (a), (f) Cross-sections of
the OCT intensity, LIV, and OCDSl. (b), (g) Time course of the OCT intensity
in the core areas of the spheroids (as indicated by the yellow boxes). MCF-7
shows very low fluctuations, while HT-29 shows high levels of fluctuation.
(c), (h) Auto-correlation curves for both spheroids, showing rapid decay as
the auto-correlation became very low at the first sampling point of the delay
time (204.8 ms). (d), (i) OCDSe cross-sections of the same spheroids, showing
the fast decay (green)at the center parts of the spheroid. These OCDSe images
were computed from a cross-sectional frame sequence of 350 frames acquired at
a single location. (e), (j) Bright-field microscopy images of the spheroids,
which show the necrotic cores as dark central region.

By comparing the dynamics OCT images of the TD-1 control spheroids of MCF-7
and HT-29 [Fig. 10], it can be found that OCDSl exhibit similar appearances
for both cell types. Namely, the center region shows low OCDSl, while the
periphery exhibits high OCDSl. In contrast, the LIV patterns of these
spheroids are opposite to each other. Namely, the MCF-7 spheroid shows low and
high LIV at its central and peripheral regions, respectively, while the HT-29
spheroid showed high and low LIV, respectively, in the corresponding regions
[Fig. 10 (a) and (f)].

To further investigate this difference, the time sequence of the OCT intensity
[Fig. 10(b) and (g)] and the autocorrelation curves [Fig. 10(c) and (h)] are
presented; these figures show the averaged intensity and the autocorrelation
curves in the yellow boxed regions indicated in the OCT and dynamic OCT
images. Both autocorrelation curves show that the correlation is strongly
decayed even at the first computing point (204.8 ms), and thus the signal
fluctuations in both cases are very fast. In comparison, the time sequence OCT
intensities show that the signal fluctuation magnitude is small for MCF-7 but
it is large for the HT-29 spheroid.

To provide further validation of this fast autocorrelation decay, a single
B-scan location in the same spheroids was scanned 350 times in 4.48 s, i.e.,
it was scanned with much higher temporal density. This is the protocol that we
presented for our original 2D dynamic imaging, and it enables yet another
dynamic OCT contrast, the fast/early correlation decay speed (OCDSe) [38].
This contrast is similar to OCDSl, but the slope of the correlation decay is
defined at an earlier time range [12.8, 64 ms] than OCDSl, and thus it is more
directly sensitive to the fast signal fluctuations. The OCDSe images of the
spheroids are presented in Fig. 10 (d) and (i). As shown in these images, the
OCDSe is high at the center regions of both spheroids. This represents further
proof that the dynamics at the center regions are fast.

It is known that the center part of the spheroid becomes necrotic because of
the lack of oxygen and other nutrients [61, 49, 62, 6]. In addition, it has
been reported previously that cell necrosis shows a fast correlation decay
[63]. As a result, we suspect that the center regions of both spheroids are in
the necrotic process.

It is also known that the necrotic process takes a long time after the cell
death starts [64]. The difference in the magnitude of the signal fluctuations
may be accounted for by the time after the onset of cell death. Namely, we can
assume that the onset of cell death for this particular MCF-7 spheroid was
early, and hence the necrotic process has been progressed, this results in the
low fluctuation magnitude. One the other hand, the cell death onset of the
HT-29 spheroid may be late, which means that the signal fluctuation magnitude
is still large.

To further investigate these differences in the spheroid core activities,
bright field microscopic images acquired using (IX71, Olympus) microscope with
an objective with 4x magnification and an NA of 0.13 from the same samples
that were presented in Fig. 10 are shown in Fig. 10.

The bright field image of the MCF-7 spheroid [Fig. 10 (e)] shows a dark
appearance at its center, which is a so-called necrotic core, with very clear
boundaries surrounded by a bright appearance (viable cells). In contrast, the
HT-29 spheroid [Fig. 10 (j)] shows a diffusive border. This may indicate that
the necrotic core has not completely formed yet. These bright field images
thus support our hypothesis.

In addition, our previous study involving time lapse imaging of the MCF-7
spheroid demonstrated that the LIV at the central region was high at the early
time points and it gradually degraded along hours [38]. These results may
provide further support for our hypothesis.

###  4.4 Other dynamic OCT methods

Including our LIV and OCDSl, there are several dynamic OCT methods. These
methods can be classified into two categories: magnitude evaluations and speed
evaluations of the OCT signal fluctuations. The latter category can be
categorized further into two subcategories that include autocorrelation
analysis and time spectrum analysis.

The magnitude analysis may include following research works. The cumulative
sum (cumsum) method demonstrated by Scholler quantifies the signal fluctuation
magnitude using a Brownian bridge model, and it has high tolerance to bulk
sample motion [65]. The motility amplitude method demonstrated by Oldenberg is
also a metric to quantify the signal fluctuation magnitude [66]. These
modalities are expected to visualize the fluctuation magnitudes of tissue and
cellular dynamics.

The correlation analyses can quantify the speeds of the tissue dynamics.
Leroux et al.computed the autocorrelation decay of an OCT signal and fitted it
with a biexponential function, so that cellular dynamics with multiple speed
were quantified [67].

Spectrum analysis is another method that can be used to quantify the temporal
characteristics of the OCT signal fluctuations and tissue dynamics. The
spectroscopic analysis demonstrated by Oldenburg et al.is one of the earliest
demonstrations of this approach [66]. Apelian et al.combined a signal
frequency analysis with a high-resolution full-field OCT imaging, and enabled
high-resolution and functional imaging of ex vivo animal tissues [34].
Scholler et al. visualized the activity of retinal organoids through power-
spectrum analysis of time-sequential OCT signals [36]. Recently, Münter et
al.used a time-frequency analysis to enable three-dimensional visualization of
tissue dynamics, and visualized the cellular scale dynamics of ex vivo animal
tissues [40, 42]. Both the correlation and spectral analysis methods may
contrast the speed of the tissue dynamics.

In the present study, we used two methods: LIV and OCDSl. LIV is a method to
quantify the magnitude of the signal fluctuation, quantifies the speed of the
fluctuation. As discussed in Section 4.3, the combination of these two methods
provides a comprehensive interpretation of the dynamic processes occurring in
the tissue.

##  5 Conclusion

The spatial patterns of the anti-cancer drug responses of two types of human
tumor spheroid, including those derived from breast (MCF-7) and colon (HT-29)
cancer cell-lines have been visualized using two types of dynamic OCT
algorithms. The response patterns observed in dynamic OCT images were
consistent with the corresponding fluorescence microscopy patterns. In
addition, the spheroid volume and mean dynamic OCT signal values were
computed. The time-course changes in these values revealed different trends
among the two types of spheroid and the anti-cancer drugs.

In conclusion, dynamic OCT can be used to highlight the difference in drug
response patterns among different tumor spheroids and different drug types.
Therefore, dynamic OCT can be a useful tool for anti-cancer drug testing and
for the optimal selection of anti-cancer drugs.

## Acknowledgments

The authors greatly appreciate the fruitful technical discussions that were
held with Hiroyuki Sangu (Yokogawa), Arata Miyazawa, Atsushi Kubota and Renzo
Ikeda (Sky technology), Akihiro Shitoh and Yuichi Inoue (Optosigma), Masato
Takaya (Tatsuta), and Naoki Fukutake (Nikon).

## Funding

Core Research for Evolutional Science and Technology (JPMJCR2105); Japan
Society for the Promotion of Science (18H01893, 21H01836, 22K04962); Austrian
Science Fund (Schrödinger grand J4460); Japan Science and Technology Agency
(JPMJMI18G8).

## Disclosures

Abd El-Sadek, Makita, Mukherjee, Yasuno: Yokogawa Electric Corp. (F), Sky
Technology (F), Nikon (F), Kao Corp. (F), Topcon (F). Shen, Mori,
Lichtenegger, Matsusaka: None.

## Data availability

Data underlying the results presented in this paper are not publicly available
at this time but may be obtained from the authors upon reasonable request.

## Supplemental document

Six supplementary figures (Figs. S1 to S6) and two supplementary tables
(Tables S1 and S2) are available in Supplement 1.

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## Supplementary materials

Here we present the OCT intensity and cross sectional images of the spheroids
pretested in the full-length manuscript. In addition, the measurement results
of additional MCF-7 and HT-29 spheroids are presented and they are consistent
with those presented in the full length manuscript. The tables elaborating the
statistical significance analysis are also presented.

## Supplementary Figures

Figure 11: Cross-sectional and en face OCT intensity images of the MCF-7
spheroid presented in Fig. 3 in the full-length manuscript. The intensity
images show the morphological alteration of spheroid, while no tissue activity
contrast can be observed. Scale bars represent 200 $\muup$m.

Figure 12: Cross-sectional LIV and OCDSl images of the MCF-7 spheroid treated
with PTX. The images were extracted from the locations indicated by white
arrow head on the en face images in Fig. 3 in the full length manuscript, and
they show similar tendency to the en face images. Scale bar represent 200
$\muup$m.

Figure 13: En face LIV and OCDSl images of additional MCF-7 spheroids treated
with PTX. The measurement and treatment were performed with the same protocol
to that of Fig. 3 in the full-length manuscript. The images are presented in
the same order as Fig. 3 in the full-length manuscript. Scale bar is
applicable for all the LIV and OCDSl images and it represents 200 $\muup$m.

Figure 14: Cross-sectional and en face OCT intensity images of the HT-29
spheroids presented in Fig. 6 in the full-length manuscript. The intensity
images show the morphological alteration of the HT-29 tumor spheroid. Scale
bars represent 200 $\muup$m.

Figure 15: The cross-sectional LIV and OCDSl images of HT-29 spheroids
extracted from the locations indicated by white arrow heads in Fig. 6 in the
full length manuscript. The cross-section LIV and OCDSl images show similar
appearances to those of the en face images pretested in Fig. 6. Scale bar
represent 200 $\muup$m.

Figure 16: The en face LIV and OCDSl images of additional HT-29 spheroids
treated with SN-38. The images are presented in the same manner of Fig. 6 in
the full-length manuscript. The LIV and OCDSl images show similar patterns to
those presented in Fig. 6 in the manuscript. Scale bar represents 200
$\muup$m.

## Supplementary Tables

Table 1: Mann-Whitney significance test results of MCF-7 spheroid response to PTX based on the mean LIV, mean OCDSl, LIV cut-off based necrotic cells ratio, and OCDSl cut-off based necrotic cells ratio. The test is performed between each pair of treatment time points. PTX concentration | Control | 0.1-$\muup$M | 1-$\muup$M | 10-$\muup$M
---|---|---|---|---
1-day and 3-day Mean LIV-based [U-statistics, P-value] | [4.0,0.047]* | [2.0,0.018]* | [0.0,0.006]* | [5.0,0.071]
3-day and 6-day Mean LIV-based [U-statistics, P-value] | [0.0,0.006]* | [0.0,0.006]* | [2.0,0.018]* | [5.0,0.071]
1-day and 6-day Mean LIV-based [U-statistics, P-value] | [0.0,0.006]* | [0.0,0.006]* | [0.0,0.006]* | [0.0,0.006]*
1-day and 3-day Mean OCDSl-based [U-statistics, P-value] | [3.0,0.030]* | [0.0,0.006]* | [2.0,0.018]* | [0.0,0.006]*
3-day and 6-day Mean OCDSl-based [U-statistics, P-value] | [0.0,0.006]* | [0.0,0.006]* | [3.0,0.030]* | [7.0,0.148]
1-day and 6-day Mean OCDSl-based [U-statistics, P-value] | [0.0,0.006]* | [0.0,0.006]* | [0.0,0.006]* | [0.0,0.006]*
1-day and 3-day For LIV-based necrotic cells ratio [U-statistics, P-value] | [5.0,0.071] | [3,0.030]* | [0.0,0.006]* | [4.0,0.047]*
3-day and 6-day For LIV-based necrotic cells ratio [U-statistics, P-value] | [0.0,0.006]* | [0.0,0.006]* | [4.0,0.047]* | [3,0.030]*
1-day and 6-day For LIV-based necrotic cells ratio [U-statistics, P-value] | [0.0,0.006]* | [0.0,0.006]* | [0.0,0.006]* | [0.0,0.006]*
1-day and 3-day For OCDSl-based necrotic cells ratio [U-statistics, P-value] | [3.0,0.030]* | [2.0,0.018]* | [1.0, 0.010]* | [0.0,0.006]*
3-day and 6-day For OCDSl-based necrotic cells ratio [U-statistics, P-value] | [0.0,0.006]* | [0.0,0.006]* | [3.0,0.030]* | [5.0,0.071]
1-day and 6-day For OCDSl-based necrotic cells ratio [U-statistics, P-value] | [0.0,0.006]* | [0.0,0.006]* | [0.0,0.006]* | [0.0,0.006]*

Table 2: Mann-Whitney significance test results of HT-29 spheroid response to SN-38 based on the mean LIV, mean OCDSl, LIV cut-off based necrotic cells ratio, and OCDSl cut-off based necrotic cells ratio. The test was done between each pair of treatment time points. SN-38 concentration | Control | 0.1-$\muup$M | 1-$\muup$M | 10-$\muup$M
---|---|---|---|---
1-day and 3-day Mean LIV-based [U-statistics, P-value] | [5.0,0.071] | [7,0.148] | [5.0, 0.071] | [1.0, 0.010]*
3-day and 6-day Mean LIV-based [U-statistics, P-value] | [0.0, 0.006]* | [9,0.265] | [8,0.201] | [5.0, 0.135]
1-day and 6-day Mean LIV-based [U-statistics, P-value] | [11,0.417] | [11, 0.417] | [6.0,0.105] | [3,0.055]
1-day and 3-day Mean OCDSl-based [U-statistics, P-value] | [9,0.265] | [6,0.105] | [8,0.201] | [2,0.018]*
3-day and 6-day Mean OCDSl-based [U-statistics, P-value] | [6,0.105] | [7,0.148] | [0.0,0.006]* | [3,0.055]
1-day and 6-day Mean OCDSl-based [U-statistics, P-value] | [10,0.338] | [10, 0.338] | [0.0,0.006]* | [0.0,0.009]*
1-day and 3-day For LIV-based necrotic cells ratio [U-statistics, P-value] | [5.0,0.071] | [4.0,0.047]* | [5.0, 0.071] | [1.0,0.010]*
3-day and 6-day For LIV-based necrotic cells ratio [U-statistics, P-value] | [7.0, 0.148] | [10.0,0.338] | [5.0,0.0718] | [8.0,0.356]
1-day and 6-day For LIV-based necrotic cells ratio [U-statistics, P-value] | [5.0, 0.0718] | [6.0,0.105] | [4.0,0.047]* | [1.0,0.018]*
1-day and 3-day For OCDSl-based necrotic cells ratio [U-statistics, P-value] | [9.0,0.265] | [10,0.338] | [9,0.265] | [0.0,0.006]*
3-day and 6-day For OCDSl-based necrotic cells ratio [U-statistics, P-value] | [6.0,0.105] | [7.0,0.148] | [0.0,0.006]* | [4.0,0.088]
1-day and 6-day For OCDSl-based necrotic cells ratio [U-statistics, P-value] | [8.0,0.201] | [10.0,0.338] | [0.0,0.006]* | [0.0,0.009]*