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BUCCAL CELL MORPHOLOGY AS BIOMARKER OF AGING
by
Hyunkeun Joo
A thesis submitted to Johns Hopkins University in conformity with the requirements for
the degree of Master of Science in Engineering
Baltimore, Maryland
May 2021
ii
Abstract
Aging, a time-dependent physiological functional decline, is an important risk factor for
various diseases. Therefore, a biomarker of aging will efficiently measure physiological age,
help measure risk factors for various age-related diseases, and help apply intervention.
According to American Federation for Aging Research (AFAR), aging biomarkers should
predict the rate of aging and could be repeatedly tested on humans in a minimally invasive way.
The buccal cell micronucleus assay matches the criteria as the buccal cell could be collected non-
invasively by swabs inside the cheek, and cell death related to aging could be studied. There are
multiple cell types in buccal mucosa: proliferating cell, healthy cell, DNA damaged cell, cell
death-related cell. The trend of decreasing healthy cell type count and increasing cell death-
related cell type count from young buccal samples to old buccal samples is shown in various
studies. However, limitation arises as the scored buccal cell images could not be re-accessed for
validation. Therefore, we developed an image-based high-throughput workflow to efficiently
annotate buccal cell types and digitally save the data allowing re-accessibility for cell count
validation. Using the developed workflow, six young (age <25) and seven old (age >65) buccal
samples from the hospital are imaged and counted to study the correlation of buccal cell count
and aging. From young to old samples, healthy buccal cell count decreased, and cell death-
related buccal cell count increased with statistical differences.
Name of readers/advisors
Dr. Denis Wirtz (advisor)
Dr. Pei-Hsun Wu (reader)
iii
Table of Contents
Abstract.......................................................................................................................................... ii
Table of Contents.......................................................................................................................... iii
List of Tables.................................................................................................................................. v
List of Figures................................................................................................................................vi
1 Introduction................................................................................................................................. 1
1.1 Biomarker of aging.................................................................................................. 1
1.2 The buccal mucosa micronucleus cytome................................................................ 1
1.3 Buccal cell types…………………………………….............................................. 3
2 Materials and methods............................................................................................................... 4
2.1 Materials.................................................................................................................. 4
2.2 Methods....................................................................................................................5
2.2.1 Buccal sample collection................................................................................ 5
2.2.2 Buccal sample processing/imaging ................................................................ 5
2.2.3 Image processing/annotation.......................................................................... 6
3 Results.......................................................................................................................................... 7
3.1 Development of imaging method.................................................................................. 7
3.2 Development of cell counting method........................................................................... 9
3.3 Limitation in cell count................................................................................................ 11
3.4 Round-to-round cell count consistency test result....................................................... 12
3.5 Well-to-well cell count consistency test result............................................................ 13
3.6 Batch-to-batch cell count consistency test result......................................................... 14
3.7 Cell count result of young and old patient samples..................................................... 15
iv
3.8 image feature analysis.................................................................................................. 17
4 Conclusion................................................................................................................................. 19
4.1 Correlation of buccal cell count with aging................................................................. 19
4.2 Future plan................................................................................................................... 19
Bibliography................................................................................................................................. 20
Curriculum Vitae......................................................................................................................... 23
v
List of Tables
Table 3.1 Buccal cell count result collected from young and old patients.................................... 16
Table 3.2 Buccal cell average nucleus size of young and old samples..........................................18
vi
List of Figures
Figure 1.1 Different buccal cell types stained with propidium iodide and cellmask imaged with
fluorescence microscope.................................................................................................................. 2
Figure 3.1 Examples of blurry buccal cell images........................................................................... 8
Figure 3.2 Example images obtained by two imaging methods...................................................... 9
Figure 3.3 Cell annotation MATLAB graphic user interface........................................................ 10
Figure 3.4 Morphological characteristics comparison of nucleus only single cell images........... 12
Figure 3.5 Round-to-round buccal cell count test result................................................................ 13
Figure 3.6 Well-to-well buccal cell count test result..................................................................... 14
Figure 3.7 Batch-to-batch buccal cell count test result.................................................................. 15
Figure 3.8 Data visualization of buccal cell count result collected from young and old patients. 17
Figure 3.9 Nucleus size and intensity comparison.........................................................................18
1
1 Introduction
1.1 Biomarker of aging
Aging is a physiological functional decline closely related to the possibility of non-
communicable diseases such as cancer, Alzheimer’s disease, Down syndrome, and diabetes, etc.,
and death over time1-5. A biomarker is defined as “a characteristic that is objectively measured
and evaluated as an indicator of normal biological processes, pathogenic processes, or
pharmacological responses to a therapeutic intervention5.” Therefore biomarker of aging could
effectively measure physiological age, help measure risk factors of diseases as a possible early
detector to assess the extent of healthy aging. However, currently there is no universally agreed
biomarker of aging1,5,6. According to the American Federation for Aging Research (AFAR),
biomarker of aging should follow four criteria: (1) it must predict the rate of aging; (2) it must
monitor a basic process that underlies the aging process, not the effects of disease; (3) it must be
able to be tested repeatedly without harming the person; and (4) it must be something that works
in humans and in laboratory animals.
1.2 The buccal mucosa micronucleus cytome
Cell death is closely related to the regenerative capacity of tissues and could be studied in
buccal mucosa using buccal mucosa micronucleus cytome (BMCyt) assay7,8. BMCyt assay is a
highly approachable and non-invasive method as buccal mucosa could be safely collected by few
swabs inside a human’s cheek9-18. There are nine buccal cell types in buccal mucosa: (a) basal (b)
differentiated cell (c) binucleated cell (d) pyknotic cell (e) cell with condensed chromatin (f)
karyolitic cell (g) karyorrhectic cell (h) cell with nuclear bud (i) cell with micronucleus19. Eight
2
of the cell types are originated from the basal cell type, which is the proliferating cell8. The
differentiated cells are the normal healthy cell type; binucleated cell indicates cytokinetic failure;
pyknotic cell, cell with condensed chromatin, karyolitic, and karyorrhectic cell are undergoing
apoptosis; cell with nuclear bud and cell with micronucleus are DNA damaged cell types7,8.
In conventional studies with BMCyt assay, buccal cell samples are filtered, stained, fixed
in slides, and nine cell types are scored manually by eyes through microscope7. However,
limitation arises regarding human errors in visual cell count. As the image data of scored buccal
cells are not saved, the accuracy of the buccal cell counts could not be tested. In order to
overcome the limitation, we took a different approach to develop a new image-based high-
throughput workflow to digitally save the scored buccal cell image data allowing re-access for
validation.
Figure 1.1 Different buccal cell types stained with propidium iodide and cellmask imaged
with fluorescence microscope; (a) basal cell; (b) differentiated cell; (c) binucleated cell; (d)
pyknotic cell; (e) cell with condensed chromatin; (f) karyorrhectic cell; (g) cell with nuclear bud;
(h) cell with micronucleus.
a c b d e
e f g h
3
1.3 Buccal cell types
It has been proposed that there are nine cell types in the buccal mucosa. Each cell type
except for basal cell contains different nuclear features caused by DNA damage, cytokinetic
failure, or cell death7. A detailed description including biological and morphological
characteristics of each cell type is described below.
Basal cell (basal) is the proliferating cell that contains a similar-sized uniformly stained nucleus
and one to two thirds smaller sized cytoplasm compared to differentiated cell. The cytoplasm
tends to be more elongated compared to differentiated cell7.
Differentiated cell (diff) is the normal/healthy cell with a normal genome that contains similar
sized uniformly stained nucleus and one to two thirds bigger sized cytoplasm compared to basal
cell. The cytoplasm tends to be more angular compared to basal cell7.
Binucleated cell (BN) is an indicator of cytokinetic defect that contains two uniformly stained
nuclei of identical size in a single cytoplasm7.
Pyknotic cell (PYK) contains one to two thirds smaller sized uniformly stained nucleus and
cytoplasm of similar size compared to differentiated cell. The contraction of nuclei are caused by
chromatin condensation during apoptosis20.
Cells with condensed chromatin (CC) contains a nucleus with a striated pattern where the
aggregated chromatin is intensively stained7. The condensation of chromatin is expressed during
apoptosis, a programmed cell death21. The shape and size of the cytoplasm are similar to a
differentiated cell.
4
Karyolitic cell (KYL) has a depleted nucleus. The chromatin is completely disappeared due to
nuclear dissolution in cell death7. Only the cytoplasm with a similar shape and size compared to
differentiated cells is observed.
Karyorrhectic cell (KHC) contains more extensively aggregated nucleus compared to CC which
is caused by condensation of chromatin during apoptosis21. Nuclear breakage is often observed,
and the shape and size of cytoplasm are similar to differentiated cell7.
Cells with nuclear bud (NB) contains a similar sized nucleus compared to differentiated cell and
additional elongated nuclear material, nuclear bud, caused by gene amplification that is quarter
to half size of the main nucleus7.
Cells with micronucleus (MN) contains two nuclei of different size in a single cytoplasm. The
smaller nucleus is either damaged chromosome fragmentation or a chromosome not absorbed in
the nucleus after nuclear division22. The shape and size of the cytoplasm are similar to a
differentiated cell.
2 Materials and methods
2.1 Materials
Equipment
• Sterile cotton, foam, or flocked swab
• 96 well glass bottom plate
• Oven
• Fluorescence confocal microscope
5
Reagents
• Propidium Iodide
• Cellmask (far-red)
• Saccomanno’s fixative (50% ethanol, 2% polyethylene glycol diluted in water)
• Cell spreading solution (Methanol: Acetic Acid (3:1) solution)
2.2 Method
2.2.1 Buccal sample collection
Before buccal sample collection, the subject should avoid eating, drinking, and smoking
30 minutes prior to collection. After rinsing the subject’s mouth with 100 ml of water, gently but
firmly rub and rotate the swab ten times against the inside of the right or left cheek wall in a
circular motion starting from the middle and gradually increasing in circumference to produce an
outward spiral effect using sterile cotton, foam, or flocked swab7. It is important for the swab tip
not to be touched with gloves or against any surface. Also, the entire swab-tip must be contacted
with the whole area of the cheek. After collecting buccal samples from both cheeks, place the
head of the swab-tip into a 15ml tube with 5ml Saccomanno’s solution and rotate so that the cells
are dislodged. Buccal cell fixed in Saccomanno’s solution should be stored at 4°C for at least 30
minutes before usage.
2.2.2 Buccal sample processing/imaging
For buccal sample processing, transfer 1ml of fixed buccal cell solution into a separate
15ml centrifuge tube. Centrifuge the cells for 10 minutes at 1800 rpm at room temperature.
6
Aspirate off the supernatant and add enough cell spreading solution so that the mixed cell
solution reaches a density of ~2000 cells/30ml mixed cell solution. Then use a 26G syringe to
resuspend the cell solution. After the cell solution is completely mixed, allocate 30ml of the cell
solution to each well in a 96 well glass bottom plate. The plate should be dried in the oven at
60°C for ~25 minutes. While the plate is being dried, dilute propidium iodide (400x) and
Cellmask (70,000x) in DI H2O and vortex for staining solution. After the plate is completely
dried and is at room temperature, add 50ml of the staining solution to each well. The plate should
be placed in dark covered with aluminum foil for at least 25 minutes for staining. After 25
minutes, remove the staining solution and wash with DI H2O once. After washing, add 50ml of
DI H2O into each of the wells.
Buccal cell samples are imaged with single plane imaging in 10x objective and z-stack
imaging in 20x objective. For both imaging methods, Nikon Ti2 wide-field fluorescence
microscope is used. Using imaging routines, the selected portion of each well where the buccal
cells are densely located is divided equally (16 sections for 10x objective and 64 sections for 20x
objective). After loading the prepared plate on the microscope, the images of each section are
focused using the Nikon Perfect Focus system so that the buccal cell located in the lowest z-
length is focused in the TRITC channel (nucleus channel). After focusing is finished for every
section of each well, the z-value is set to 1μm, and z-step size is set to 20 steps only for z-stack
imaging. Before running the microscope, two channels are selected: TRITC and CY5. TRITC
channel is for nucleus imaging and CY5 channel is for cytoplasm imaging.
2.2.3 Image processing/annotation
7
For z-stack buccal cell imaging, 21 images are generated with 1μm of z-value interval for
64 sections per well. In order to obtain the clearest image, all images generated with z-stack
imaging go through a maximum intensity projection algorithm. After running the maximum
intensity projection algorithm, all images are stitched and analyzed by a set of high-throughput
analysis system developed by our lab. Our system can detect nuclei, extract image features (e.g.
computing parameters such as nucleus size, average intensity and shape factor) and generate
visualized data automatically. Images generated with single plane imaging are stitched and
analyzed with the system without the maximum intensity projection algorithm.
Using the generated image data and nucleus location data, every single buccal cell image
is annotated with either the ImageJ’s cell count plugin or the MATLAB graphic user interface
that allows single cell image annotation and saves the annotated data digitally.
3 Results
3.1 Development of imaging method
Buccal cells were initially imaged with single plane imaging with 10x objective. For
imaging time, it took ~12 minutes per well. However, this method contained focal plane issues.
8
Figure 3.1 Examples of blurry buccal cell images.
About 30% of the imaged cells per buccal cell sample were unable to be annotated due to
blurriness. The blurriness was caused by buccal cells located in different focal planes. Therefore,
the cells were fixed to the bottom of the plate and imaged with 20x objective using z-stack
imaging (z-value: 1μm, z-step size: 20 steps). The running time of microscope increased from
~12 minutes per well to ~30minutes per well.
9
Figure 3.2 Example images obtained by two imaging methods: (a) single-plane imaging and
(b) z-projection imaging.
3.2 Development of cell counting method
Buccal cells were initially annotated using the cell counter plugin in ImageJ. With the
cell counter plugin in ImageJ, the user had to zoom in and out multiple times to annotate each
cell and could not compare each cell image side-by-side which could easily cause miss-
annotation. Therefore, we developed a cell annotation MATLAB graphic user interface (GUI)
for time efficiency and higher comparability to reduce human error. With the GUI, the user can
see up to 100 single cell images at a glance.
a b
10
Figure 3.3 Cell annotation MATLAB graphic user interface.
The user has to load intensity range, image data, and nucleus location data in the ‘Load
img & data’ section; set up array size and image size in the ‘ArrayImage setup’ section; set up
cell type for annotation in ‘Cell Types’ section; and by clicking ‘show/update image’ button,
single cell images will be shown in the axes in ‘Annotate Cells’ section. After loading all the
data and selecting the cell type, the user can save the annotated data by clicking on the single cell
images. The saved annotation data is shown in the top left corner of each image.
11
The time spent for annotating ~500 cells per image is reduced to ~30 minutes from ~80
minutes by using the GUI. Also, as the user can compare each single cell image side-by-side,
human error was significantly reduced.
3.3 Limitation in cell count
Among nine cell types, BN, PYK, KYL, NB, and MN cell types were not frequent
enough (<20 per 1000 cells) to be an indicator of aging when ~500 cells per image were counted.
For CC cell type, a striated pattern was obvious enough to be counted visually. Basal cell type
was described as the cell that contains similar sized nucleus with a higher nucleus-to-cytoplasm
ratio compared to Diff cell type. However, the standard of nucleus-to-cytoplasm was not clear
enough to be determined by eyes. Also, a more intensive aggregation of chromatin for KHC cell
type compared to CC cell type was hardly observed. Therefore, the cell types were divided into
two categories in this study: (a) Basal, Diff, and KHC cell types (b) CC cell type. Category (a)
was considered as a healthy cell type in this study as Basal cells are proliferating cells; Diff cells
are normal/healthy cells; and KHC cell count’s average percentage among Basal, Diff, and KHC
cell count was 3.8% for 60 buccal cell samples in nature protocol’s cell count result7.
12
Figure 3.4 Morphological characteristics comparison of nucleus only single cell images: (a)
Diff cell type, (b) CC cell type
3.4 Round-to-round cell count consistency test result
Even though the cell annotation GUI and 20x z-stack imaging method reduced human
errors, potential bias still exists as the annotation depends on visual aspects. Therefore, in order
to test the cell count repeatability, three patient samples were randomly chosen, and their buccal
cell images were annotated by the same scorer three different times.
a b
13
Figure 3.5 Round-to-round buccal cell count test result: three rounds of cell count result of
identical buccal cell image from randomly chosen patients’ buccal samples are listed in tables
and visualized in graphs: (a) Basal+Diff+KHC category cell count (b) CC category cell count.
The coefficient of variance percentages for three rounds of cell counts per patient sample
were less than 3% which means that the scorer could almost replicate the cell count with minimal
human error.
3.5 Well-to-well cell count consistency test
The cell count results might vary when plated in different wells even if the buccal cell
solution from the same buccal cell batch is used. Therefore, three patient buccal cell samples
used for batch-to-batch cell count consistency test were plated in three different wells and
counted for comparison.
a b
14
Figure 3.6 Well-to-well buccal cell count test result: cell count results of three different images
generated from each of the three randomly chosen patients’ buccal samples are listed in tables
and visualized in graphs: (a) Basal+Diff+KHC category cell count (b) CC category cell count.
The coefficient of variance percentages for buccal cell counts from three different wells
per patient sample were less than 0.5% for Basal, Diff, and KHC category cell count and less
than 7% for CC cell count. The coefficient of variance percentages for CC cell count were higher
than expected. However, the high percentage is assumed to be caused by the low total cell count
number, ~500. Counting ~2000 cells per image is needed to verify well-to-well buccal cell count
consistency.
3.6 Batch-to-batch cell count consistency test result
For buccal cell count to be used as an aging biomarker, the cell count results should be
consistent for different batches of buccal cells collected from the same person. Therefore, three
a b
15
batches of buccal cell samples were collected in fourteen days interval from three members (age
<27) from our lab and counted.
Figure 3.7 Batch-to-batch buccal cell count test result: cell count results of buccal cell
samples collected in fourteen days interval from same subjects are listed in tables and visualized
in graphs: (a) Basal+Diff+KHC category cell count (b) CC category cell count.
The coefficient of variance percentages for three cell count results from three different
batches were less than 0.3% for Basal, Diff, KHC, and PYK category cell count and less than 5%
for CC cell count. Further cell count results of samples in the age range between twenty-five and
sixty-five should be examined in order to figure out if the coefficient of variance percentages for
CC cell counts are meaningful.
3.7 Cell count result of young and old patient samples
a b
16
Six young and seven old buccal cell samples from the hospital were imaged and counted.
Table 3.1 includes minimum, maximum, median, mean value, standard deviation (s.d.) among
samples in the same category, and the coefficient variance percentage. From young samples to
old samples, the average cell count of healthy category, Basal, Diff, and KHC cell types
decreased from 966.7 to 938.5, and cell death-related category, CC cell type, increased about two
folds, from 31.5 to 60.1. The statistical differences between young and old samples’ cell counts
were measured with Wilcoxon rank-sum test. The p-value was 0.002 for both categories.
Table 3.1 Buccal cell count result collected from young and old patients.
Basal+Diff+KHC no. CC no.
(a) young controls n=6 (age < 25)
Minimum 958.8 22.8
Median 966.6 33.4
Maximum 977.2 34.7
Mean 966.7 31.5
s.d. 5.7 4.1
cv(%) 0.6 13.1
(b) Older controls n=7 (age >65)
Minimum 929.7 52.0
Median 937.3 60.3
Maximum 947.4 68.9
Mean 938.5 60.1
s.d. 5.5 5.3
cv(%) 0.6 8.8
17
# Number shown in figure is occurrence per thousand cells
Figure 3.8 Data visualization of buccal cell count result collected from young and old
patients: (a) Basal+Diff+KHC category cell count (b) CC category cell count.
3.8 Image feature analysis
Nucleus size and intensity data of all annotated buccal cell were generated with our lab’s
high-throughput analysis program described in the image processing method section. Table 3.2
includes minimum, maximum, median, mean nucleus size value, standard deviation (s.d.), and
coefficient variance percentage. The average nucleus size of old samples was 19.6 pixel higher
than young samples’. However, due to the high coefficient of variance percentage, it was
difficult to determine if the increase of nucleus size is directly related to aging.
a b
18
Table 3.2 Buccal cell average nucleus size of young and old samples
In order to figure out the reasoning of nucleus size fluctuation, we compared nucleus size
data with intensity data. According to figure 4.1, nucleus size was directly related to intensity.
Therefore, microscope light power control or stain effect control should be performed for
intensity normalization so that the correlation of nucleus size and aging could be studied further.
Figure 3.9 Nucleus size and intensity comparison. The figure shows comparison of average
nucleus size and average intensity of the annotated buccal cells in each image.
Avg nucleus
size(young)
Avg nucleus
size(old)
Minimum(px) 173.8 167.8
Median(px) 332.7 397.1
Maximum(px) 450.2 510.6
Mean(px) 335.7 355.3
s.d.(px) 94.7 127.2
cv(%) 28.2 35.8
19
4 Conclusion
4.1 Correlation of buccal cell count with aging
Understanding aging and measuring physiological age are important keys to assess the
extent of healthy aging. Buccal cell micronucleus assay, a minimally invasive method to study
aging using buccal cells’ morphological characteristics, is developed in a way that the generated
data could be digitally saved and studied as a biomarker of aging in this research. A high
possibility of using buccal cell counts as a biomarker of aging is suggested with buccal cell count
data of six young, and seven old buccal cell samples from the hospital as the cell death-related
cell type, CC, count decreased from young samples to old samples with statistical differences.
However, the sample size of the cohort should be increased at least three folds, and the limitation
of visual scoring method should be overcome so that all the cell types could be accurately
annotated and compared.
4.2 Future plan
More young (age <25) and old (age >65) buccal cell samples will be collected from the
hospital and counted with our image-based high-throughput workflow so that we could test the
sensitivity and accuracy of buccal cell counts. Also, buccal cell samples for middle-aged group
(25< age <65) should be collected to verify that buccal cell count could be used for age clock.
For the cell count method, we are currently developing an image classification algorithm that can
automatically count buccal cells using supervised deep learning for more accurate data and time
efficiency.
20
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Curriculum Vitae
Hyunkeun Joo
[email protected] 562-507-7198
EDUCATION
JOHNS HOPKINS UNIVERSITY, Balitmore, MD
MSE, Chemical and Bimolecular Engineering: May 2021 expected GPA: 3.94
UNIVERISTY OF CALIFORNIA at RIVERSIDE, Riverside, CA
B.S., Chemical Engineering: June 2019
RESEARCH EXPERIENCE
JOHNS HOPKINS INSTITUTE OF NANOBIOTECHNOLOGY, Baltimore, MD
Aging Research Conductor (Dr. Denis Wirtz, PI) August 2019 – Present
• Studied buccal cell morphological characteristics for potential biomarker of aging, frailty and disease.
• Developed buccal cell processing workflow to automatically examine physiological age. • Developed MATLAB graphic user interface enabling fast and accurate single cell image
annotation. JOHNS HOPKINS UNIVERSITY, Baltimore, MD
Product Designer, Product Design Team (Marc Donohue, PI) August 2019 – May 2020
• Proposed a design concept: “A Concentric Multi-Layer System for Controlled Release of Chemicals”
• Conducted various titration experiments to collect supportive data. • Documented Patent Drafts and Invention Disclosure regarding the product design.
UNIVERSITY OF CALIFORNIA at RIVERSIDE, Riverside, CA
Research Assistant (Dr. Boniface Fokwa, PI) 2018 May – 2019 June
• Studied correlation between boron chain condensation and Hydrogen Evolution
Reaction (HER).
• Used press and arc melting to examine metal borides.
• Conducted analysis of chemical structures using XRD. PUBLICATIONS
• Park, H., Lee, E., Lei, M., Joo, H., Coh, S., Fokwa, B. “Canonic-Like HER Activity of
Cr1-xMoxB2 Solid Solution Overpowering Pt/C at High Current Density” Advanced
Materials, vol. 32, no. 28, 2020
• Lee, E., Park, H., Joo, H., Fokwa, B. “Unexpected Correlation Between Boron Chain
Condensation and Hydrogen Evolution Reaction (HER) Activity in Highly Active
Vanadium Borides: Enabling Predictions” Angewandte, vol. 59, no. 29, 2020
24
LEADERSHIP
Sergeant / Squad leader REPUBLIC of KOREA ARMY October 2013 – July 2015
• Served as a Fire Direction Center and planned training/education in Capital Artillery
Brigade. TECHNICAL SKILLS
Laboratory: Mammalian Cell Culture, Confocal Imaging
Technical: MATLAB, Python, Aspen, Adobe Photoshop
