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Application of Flow Cytometry forBiomarker-Based Cervical CancerCells DetectionJian Ling, Ph.D.,1* Urs Wiederkehr, B.S.,2 Spring Cabiness, B.S.,3
Kenneth R. Shroyer, Ph.D., M.D.,4 and J. Paul Robinson, Ph.D.5
The Pap test used for cervical cancer screening is subjective,labor-intensive, and has relatively low sensitivity and specificityfor the detection of underlying clinically significant lesions. Theobjective of this study is to develop a biomarker/flow cytometry-
based approach for cervical cancer screening. Immunofluores-cence technology to quantify cervical cell expression of two bio-markers p16INK4A and Mcm5 was developed and evaluated byboth microcopy and flow cytometry. The capability of using bio-marker/flow cytometry approach to detect rare-event dysplasticcells in a large background of benign epithelial and inflammatorycells was evaluated. The results indicate that flow cytometrycould detect 0.01% dysplastic cells in a background of normalcervical epithelial cells with the combination of the two bio-markers. Thirty-two clinical specimens were used to test the bio-marker/flow cytometry-based approach and the results were com- pared with the liquid-based cervical cytology. The experimentyielded 100% sensitivity and 93% specificity with reference to theliquid-based cervical cytology. This study indicates the promiseof using multi-color fluorescence flow cytometry for biomarker-
based cervical cancer screening. This molecular-based, poten-tially high-throughput and automated method is expected to pro-vide an alternative/auxiliary means of cervical cancer screening.Diagn. Cytopathol. 2008;36:7684. ' 2008 Wiley-Liss, Inc.
Key Words: cervical cancer, cancer screening; flow cytometry;cancer markers; rare-event detection
Cervical cancer is the second most common cancer in
women, with 500,000 new cases reported each year and
250,000 deaths worldwide. Eighty percent of the deaths
occur in developing countries1
due to the lack of wide-spread screening programs. In developed countries, the
death rate from cervical cancer has been reduced signifi-
cantly through the adoption of population-wide screening
programs. According to the American Cancer Society,2
the cervical cancer death rate in the U.S. declined 48%
between 1973 and 1993.
Current Screening Methods
The recognized leading tools used in cervical cancer
screening programs are the Pap smear, pioneered by Dr.
George Papanicolaou in the 1930s, and liquid-based cervi-
cal cytology, introduced in the mid 1990s. In both meth-ods, cell specimens are collected by gently scraping the
surface of the cervix with a sampling device, such as a
plastic spatula or cytobrush. In the Pap smear, cells from
the spatula or cytobrush are smeared directly on a slide
and then fixed and stained using the Papanicolaou stain.
In liquid-based cervical cytology, the sample is first
rinsed into a liquid fixation solution to preserve the cells
and, thin layer or monolayer preparations are prepared
using density gradient centrifugation or filter membrane
technology using automated systems. Both Pap smears
and liquid-based cytology slides are subsequently stained
using the Papanicolaou stain.
Cervical cytology slides are initially screened by micro-
scopic examination by either a cytotechnologist or pathol-
ogist. Federal regulations require that all potentially
abnormal specimens be reviewed and diagnosed by a
qualified pathologist. Slides that are screened as normal,
however, may be reported without requiring pathologist
review. Cytologic abnormalities that may reflect underly-
ing cervical dysplasia or squamous cell carcinoma are
categorized under the Bethesda 2001 system as atypical
squamous cells of undetermined significance (ASC-US),
1Medical System Department, Automation and Data System Division,Southwest Research Institute, San Antonio, Texas
2Cytolution, Inc., San Jose, California3Applied Physics Division, Southwest Research Institute, San Antonio,
Texas4
Department of Pathology, University of Colorado Health ScienceCenter, Denver, Colorado
5Cytometry Laboratories, Bindley Bioscience Center, Purdue
University, West Lafayette, IndianaContract grant sponsor: Cytolution Inc.; Contract grant sponsor: NIH
(National Cancer Institute); Contract grant number: 1R21CA125370-01.*Correspondence to: Jian Ling, Ph.D., Southwest Research Institute,
6220 Culebra Rd., San Antonio, TX 78238. E-mail: [email protected] 18 June 2007; Accepted 6 October 2007DOI 10.1002/dc.20763Published online in Wiley InterScience (www.interscience.wiley.com).
76 Diagnostic Cytopathology, Vol 36, No 2 ' 2008 WILEY-LISS, INC.
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atypical squamous cells suspicious but not diagnostic for
a high grade squamous intraepithelial lesion (ASC-H),
low-grade squamous intraepithelial lesion (LSIL), high-
grade squamous intraepithelial lesion (HSIL), and squa-
mous cell carcinoma (SCC).3 Figure 1 illustrates the
morphological changes that are characteristic of the devel-
opment of precursor lesions. A general feature of the
high-grade dysplastic cells is that they typically have high
nuclear-to-cytoplasmic volume ratios and this ratio
increases as the severity of the lesion increases (Fig. 1).
Current guidelines provided by the American Cancer
Society recommend screening for women 21 year of age
and older. The preferred screening frequency is annual
unless there are three consecutive normal, technically sat-
isfactory Pap tests but is often increased to every 36 mo
if the Pap test indicates an abnormality.4 About 50 million
Pap tests are performed each year in the United States
and about 110 million worldwide.5,6
Human papillomavirus (HPV) is the main cause of cer-
vical dysplasia and carcinoma. Although HPV vaccines
are likely to be highly effective in preventing infection by
HPV vaccine types, cervical cancer screening programs
will still play crucially important roles for the detection ofcytologic abnormalities in currently infected patients and in
the detection of disease associated with nonvaccine types.
Limitations of Current Screening Methods
The major challenge for cervical cytology is the need to
detect rare-events. A liquid-based cervical cytology speci-
men contains a minimum of 5,000 normal squamous
cells; most samples contain 50,000 or more normal cervi-
cal squamous epithelial cells, as well as benign endocervi-
cal cells, and inflammatory components. High-grade squa-
mous intraepithelial lesions (HSILs), on the other hand,
may often be based on the detection of only a very small
number of abnormal cells, frequently in the range of 10
100 dysplastic cells/slide.
Federal guidelines permit cytotechnologists to screen
up to 100 slides in a normal 8-hr workday.7 Assuming a
minimum number of 5,000 cells per slide, cytotechnolo-
gist would review at least 500,000 cells/day and be
required to detect as few as 1050 dysplastic cells in a
positive specimen. Since *90% of all cases in most diag-
nostic practices are negative for cytologic abnormalities,
most of the screeners time and energy is expended look-
ing at healthy cells.8 Fatigue and monotony can reduce
the acuity of the screener and increase the chance that
rare positive cells could be overlooked.7
Current methods of cervical cancer screening are not
only labor-intensive but are also highly subjective and
have relatively low sensitivity and specificity for the
detection of some high-grade clinically significant lesions.
With the liquid-based Pap test, the sensitivity of cervical
screening has increased to about 80% from the 65% in
conventional Pap smear,9 resulting in an improvement of
the overall clinical, economic, and patient outcomes.
However, the specificity of liquid-based Pap test dropped
from 95% with conventional Pap smear to about 75%.9
Recently, the FDA approved the use of high risk HPV
testing in combination with the liquid-based cervical cy-
tology for primary screening of women over age 30.
Biomarkers for Cancer Screening
With the significant advances in genomics and proteomicsover the last decade, hundreds of articles have been pub-
lished on the subject of understanding the molecular
pathogenesis of cervical cancer.10 Molecular changes
have been recognized to be the earliest indication of cell
abnormalities. The objective of the current study was to
develop a method that can achieve a true molecular mea-
surement using immunofluorescence technology and flow
cytometry technologies. Such a molecular-based cervical
cancer screening method is expected to have higher sensi-
tivity and specificity compared to the current cervical cy-
tology methods.
A large number of biomarkers have been identified that
are overexpressed in cervical cancer cells.11 Some of themarkers that appear to have potential for cervical cancer
screening include p16INK4A (a cyclin-dependent kinase in-
hibitor protein), Mcm (minichromosome maintenance) pro-
teins, Cdc (cell division cycle) proteins, topoisomerase 2
alpha, PCNA, Ki-67, Cyclin E, p-53, and Rb (retinoblas-
toma) proteins.10,1215 This report is focused on the analy-
sis of two of these markers, p16INK4A 1628 and Mcm5.29,30
p16INK4A
protein. The p16INK4A protein has been used as
an immunohistochemical and immunocytochemical marker
in several studies to detect cervical cancer. In cervical car-
cinomas, viral DNA integration into the host genome may
result in disruption of the E2 open reading frame, resulting
in unregulated overexpression of HPV oncogenes E6 and
E7, E7-mediated catabolism of pRb, and the reciprocal
overexpression of p16INK4A.26 Almost 100% of high-grade
cervical dysplasias and invasive cancers have been shown
to express very high levels of p16INK4A, whereas normal
cervical squamous cells do not test positive for
p16INK4A.31,32 Several studies16,17,2628 have demonstrated
the successful combination of p16INK4A immunocytochem-
ical assay with the liquid-based Pap test. In studies per-
formed by Bibbo et al.,16 very high levels of p16INK4A
Fig. 1. Morphological changes (the increase of nucleus-to-cytoplasm ra-tio) of precursor lesions of cervical carcinoma.
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were detected in almost 100% of high-grade cervical dys-
plasias and invasive cancers, whereas no p16INK4A-positive
stain was found in normal cervical epithelia using the same
antibodies. A study performed by Murphy et al.21 also
reported that p16INK4A identified dysplastic squamous and
glandular cells of the cervix with a sensitivity of 99.9%
and a specificity of 100%. A practical limitation to the use
of p16INK4A as a cytologic diagnostic adjunct, however, is
that sporadic expression of this marker is also sometimes
present in scattered benign endocervical glandular cells
and in tuboendometrial metaplasia of the cervical mucosa,
which could lead to false positive classification of test
results.31
Mcm5 protein. The MCM proteins form a hexameric
helicase that denatures DNA at the initiation of DNA rep-
lication.10 Mcm5 has been extensively studied as a marker
for cellular proliferation expressed during the normal cell
cycle and recent studies indicate that Mcm5 may be a
marker for the presence of cervical intraepithelial neopla-
sia and carcinoma but is also expressed in low grade dys-
plastic lesions and in some normal proliferating squamous
cells.21,29,30
Although Mcm5 may be expressed in a lower proportion
of high grade dysplastic cells than is typically observed for
p16INK4A the expression of Mcm5 has not been reported in
benign endocerrvial glandular cells. Thus, dual (multiplex)
staining of both Mcm5 and p16INK4A could theoretically
increase overall test performance because these two bio-
markers are complementary in nature.
Flow cytometry for cervical cancer detection. Flow
cytometry is an ideal format for the analysis of single-cell
suspensions, quantifying cell structural and molecular fea-tures, and for the detection of rare events. The potential for
the use of flow cytometry for cervical cancer screening
began in the 1970s and was widely reported during the
1980s and early 1990s.3340
Most of these studies focused
on methods that use fluorescent dyes to stain nucleic acids
and use flow cytometry to measure DNA content (aneu-
ploidy) as a prognostic indicator of solid tumors. However,
the use of DNA content as an independent prognostic indi-
cator is uncertain and remains controversial.
This study took a different approach in the use of flow
cytometry by including the evaluation of p16INK4A and
Mcm5 as sensitive and specific markers for the detection
of cervical dysplasia and carcinoma.
Materials and Methods
Sample Preparation
Control samples. The cervical cancer-derived HeLa cell
line, which has been shown to overexpress both p16INK4A
and Mcm5 proteins, was used as the positive control in
this study. HeLa cells were fixed and preserved with a
methanol-based fixative (PreservCyt1 solution, Cytyc
Corp., Marlborough, MA). A previous study41 has shown
that the PreservCyt1 solution will preserve both cell
morphology and cellular molecular markers for at least
30 days. PreservCyt1 solution is also known to permeab-
ilize cells so that fluorochromes-labeled antibodies can
penetrate cells.
Clinical cervical specimens. Residual cervical cytology
specimens from PreservCyt1 vials were obtained from
the cytopathology laboratories at the University of Texas
Health Science Center at San Antonio and the University
of Colorado Health Sciences Center at Denver, following
IRB application approval of the study protocol. These
specimens have been reviewed by experienced cytopatho-
logists and classified by Bethesda 2001 terminology as
negative, ASC-US, LSIL, or HSIL. The clinical
specimens were filtered with 70-lm nylon mesh filter to
remove cell clusters before flow cytometry measurement.
Fluorescence Labeling
Antibodies and conjugation with fluorochrome. Mousemonoclonal antibodies to p16INK4A (Clone ZJ11) and
Mcm5 (Clone CRCT5.1) from Labvison Inc. (Fremont,
CA) were selected in the study. These two antibodies
were directly conjugated with PE and APC fluorochromes
using commercially available labeling kits (ProZyme Inc.,
San Leandro, CA). The conjugates were denoted as
p16INK4A-PE and Mcm5-APC antibodies. Corresponding
mouse IgG1 and IgG2b isotypes were also obtained and
conjugated to PE and APC, respectively, as the isotype
control.
Immunofluorescence staining. Before staining, a sample
was washed twice with phosphate buffered saline (PBS)
to remove the fixation solution. The second wash used astaining buffer (PBS plus 1% bovine serum albumin
(BSA) and 0.01% sodium azide) to block the intracellu-
lar nonspecific binding sites. The sample was concen-
trated to 100 lL and then simultaneously stained with a
cocktail of p16INK4A-PE and Mcm5-APC antibodies. In
immunofluorescence imaging, 1 lg/mL concentration of
antibody was used to stain the samples. In flow cytome-
try, the optimal antibody concentration was about 0.1
0.25 lg/mL. Flow cytometry is more sensitive for detec-
tion of the fluorescence signal owing to the use of a
laser as the excitation source and a photomultiplier tube
(PMT) as the detector.
The staining tube was kept on ice or in a 48C dark re-frigerator for 30 min. Then the stained cells were washed
twice with the staining buffer to remove the unbound con-
jugates. The same procedure and same concentration were
followed for isotype staining.
Quantitative Microscopy
Before performing the flow cytometry experiment, micro-
scopic imaging was performed to (1) verify the effective-
ness of the fluorescence stain, and (2) verify whether the
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overexpression of biomarkers is correlated to the abnor-
mal morphology of dysplastic cervical cells. A Nikon
Eclipse TE2000E inverted microscope and computer sys-
tem was used for the fluorescence imaging. Three fluores-
cence filters in the FITC, PE, and APC bands (i.e., the
530-nm, 575-nm, and 660-nm emission bands) were used.
During imaging, the microscope was first set in differen-
tial-interference-contrast (DIC) video mode and visually
focused on an imaging area which contained multiple
nonoverlapping cells. Then, four images (three fluores-
cence images in the FITC, PE, APC bands, and a DIC
image) were obtained for each imaging area. The DIC
image illustrates the morphology of the cells. The PE and
APC images show the expression of p16INK4A and Mcm5
markers in the cells. The FITC image measures cell auto-
fluorescence in the FITC band, which is used to correct
the autofluorescence in the PE and APC bands on a cell-
by-cell basis (see Data Analysis below).
Flow Cytometry
A FACS Aria flow cytometer (Becton-Dickinson, San
Jose, CA) and an FC 500 flow cytometer (Beckman-
Coulter, Miami, FL) were used in the flow experiments.
Five parameters were measured: forward-scatter (FS), side
scatter (SS), and FITC, PE, and APC fluorescence bands.
The FS and SS measurements were used to gate out cell
debris. The cell autofluorescence measured in the FITC
band was used to correct the autofluorescence in the PE
and APC bands on a cell-by-cell basis using a postcompen-
sation method (see Discussion). The remaining fluores-
cence measured in the PE and APC bands reflects the
expression levels of biomarkers p16INK4A
and Mcm5,respectively, in each cell. Before each flow experiment,
the flow cytometer was calibrated using fluorescence beads
to minimize the day-to-day variation of optics. The calibra-
tion procedure ensured the measurement of different
samples under similar conditions.
Data Analysis
For imaging data, a MATLAB program was developed
to quantitatively compare cell-to-cell average stain inten-
sities in fluorescent images. The software automatically
segments the fluorescent images to locate individual
cells. The average fluorescence intensities or the fluores-
cence density in the FITC, PE, and APC bands were
determined for each individual cell. The fluorescence
density, calculated by normalizing the total staining in-
tensity by cell area, provided a fair comparison of the
biomarker expression among different types of cervical
cells, which usually have large variation in size (from 25
to 65 lm in diameter).
For flow cytometry data, FCS Express (De Novo Soft-
ware, Thornhill, Canada) was used to perform gating
and autofluorescence correction. Fluorescence pulse peak
instead of pulse integral was used to represent the fluores-
cence density or biomarker expression of each cell
because pulse peak is not significantly affected by cell
size, as is the pulse integral (see Discussion).
Results
Microscopy Imaging Experiment
Comparison of antibody stain and isotype stain. Fixed
HeLa cells were stained with a cocktail of p16INK4A-PE
and Mcm5-APC antibodies. Matched aliquots of fixed
HeLa cells were stained with a cocktail of PE and APClabeled isotypes. The dot plot in Figure 2 shows that the
antibody-stained cells (denoted by light quadrangular
symbol) have significantly higher stain intensities in both
the PE and APC bands than that of the isotype-stained
cells.
Comparison of normal and dysplastic cervical cells.
Eleven cervical cytology specimens, including five nega-
tive and six positive specimens (1 ASC-US, 2 LSIL, and
3 HSIL), were used in a pilot imaging study. Each speci-
men was divided into two parts. One part was unstained
and used to establish autofluorescence compensation coef-
ficients, and the other part was stained with the cocktail
of p16INK4A-PE and Mcm5-APC antibodies. About
70 cells, including cells with differing morphology, were
imaged for each specimen. The average fluorescence
intensities were computed for each cell in imaging areas.
Figure C-1 gives an example of the DIC and fluorescence
images of a normal and an abnormal HSIL cell from one
of the HSIL cervical specimens. The autofluorescence in-
tensity of the cells in the PE and APC band has been
eliminated based on their autofluorescence intensity in the
FITC band. Figure C-1 indicates that the average PE and
Fig. 2. PE vs. APC fluorescence intensities of the HeLa cells stained withcocktail of isotypes (dark triangular symbol) and stained with cocktail ofp16
INK4A-PE and Mcm5-APC antibodies (light quadrangular symbol).
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APC intensities of the HSIL cell are significantly higher
than those of the normal cells.
The experiment also shows that most cells in the eleven
specimens had low stain intensities in both the PE and
APC bands. Only a small number of cells in the ASC-US,
LSIL, and HSIL specimens had high stain intensities.
This suggests that the biomarker overexpressed cells are
rare-events, which is similar to the morphology-based
Fig. C-1C-3. Fig. C-1. DIC (upper left), FITC (upper right), PE (lower left) and APC (lower right) images of a normal cell (a), and a HSIL dysplas-tic cell (b), from a HSIL cervical specimen. The numbers under the cells are the average fluorescence intensities of the cells. Fig. C-2. The dot plot(left) illustrates HeLa cells (red dots) identified and separated from normal cervical cells (blue dots) after staining with p16
INK4A-PE and Mcm5-APC
antibodies. The scatter plot (right) indicates the linear relationship between the number of spiked and the number of p16INK4A and Mcm5 positiveHeLa cells. Fig. C-3. Dot plots of PE (P16INK4A) vs. APC (Mcm5) immunofluorescence intensities of the cells in a negative (left) and a positive HSILcervical specimen (right). Each plot contains about 75,000 cervical cells.
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detection. The experiment also suggested that the overex-
pression of both the p16INK4A and Mcm5 biomarkers is
closely related to the abnormality of cell morphology.
Flow Cytometry Experiment
Spiking experiment. This experiment was conducted toshow the feasibility of biomarker-based flow cytometry in
the detection of rare cervical cancer cells among a large
number of normal cells in a cervical specimen. Aliquots
of normal cells were taken from six Pap test negative
specimens and combined to create a normal cervical cell
pool. The normal cells were counted and divided among
seven tubes, with each tube containing about 77,000 be-
nign squamous cells. One tube was left unstained for ref-
erence. The other six tubes were spiked with 10, 20, 50,
100, 500, and 1,000 HeLa cells using the serial dilution
method. The spiked samples were stained with the cock-
tail of p16INK4A-PE and Mcm5-APC antibodies.
The dot plot in the left panel of Figure C-2 illustratesan example of the PE versus APC intensities in the 500-
HeLa-spiked sample. The HeLa cells highly stained with
p16INK4A and Mcm5 (red dots) are identified and sepa-
rated from the normal cervical cells (blue dots). The scat-
ter plot in the right panel of Figure C-2 illustrates the lin-
ear relationship between the number of spiked and the
number of detected HeLa cells. The discrepancy between
the detected cells and the number of spiked cells could be
due to the following reasons: (1) the number of HeLa
cells that were actually added into each sample varied;
(2) cells were lost during the poststaining washing steps;
(3) a small portion of the samples could not be measured
due to the dead space between pipette and test tube; and(4) some HeLa cells did not have a high staining of
p16INK4A and Mcm5.
This study indicates that it is possible to use flow
cytometry to detect as low as 0.01% cancer cells among a
large number of normal cervical cells. The outcome
exceeded the expectation of detecting less than 0.1%
abnormal cells among normal cells, which is considered
by pathologists as being an acceptable limit for a cervical
cancer screening method.
Clinical specimens experiment. Thirty-two residual cer-
vical specimens from routine cervical cancer screening
were involved in this study. They were categorized as 15
negative and 17 positive (2 ASC-US, 1 LSIL, and 14
HSIL) cases. Each specimen was split into two aliquots.
One aliquot was unstained and used to measure the cell
autofluorescence in the three fluorescence bands: FITC,
PE, and APC. The other aliquot, containing around 75,000
cells, was stained with a cocktail of p16INK4A-PE and
Mcm5-APC antibodies and then run on the flow cytometer.
Figure C-3 shows two dot plots of a negative and a
positive (HSIL) specimen generated from flow measure-
ment. These two plots clearly show that the HSIL speci-
men has significantly more cells with high intensities in
both PE and APC bands than the negative specimen. The
high intensity in the PE and APC bands indicates that
both biomarkers p16INK4A and Mcm5 are overexpressed.
The detection threshold was set arbitrarily in this
experiment to maximize the separation between negative
and positive (ASC-US) specimens. In Table I, the clas-
sification of the thirty-two specimens determined by multi-
parameter flow cytometry is compared with the classifica-
tion by liquid-based Pap test. Using the Pap test as the
reference, the sensitivity and specificity of the flow
cytometry method to classify cervical specimens into
negative and positive (ASC-US) was 100 and93%, respectively.
Discussion
Autofluorescence Compensation
One of the major challenges in the use of flow cytometry
to measure biomarker expression of cervical cancer cells
is the autofluorescence problem. Fixed cervical cells have
very strong autofluorescence. Figure 3a shows a flow
cytometry plot of FITC versus PE of an unstained sample
that was mixed with HeLa and normal cervical cells. The
autofluorescence is present not only in the green band but
also in the yellow and even in red frequency bands. Inaddition, the cell-to-cell autofluorescence intensities can
vary over a 1,000-fold range in a specimen. Without cor-
rection for such a large variance of autofluorescence, it is
impossible to detect biomarker signals in PE and APC
bands. This is illustrated in Figures 3b and c, the dot plot
of the same sample but stained with p16INK4A-PE and
Mcm5-APC antibodies. These two figures indicate that
the PE and APC staining intensities of HeLa cells are
much lower than some of the normal cells that have high
autofluorescence in the same bands. Although cell-to-cell
autofluorescence has large variation, the dot plots suggest
that the intensities between the autofluorescence in the
yellow or red band and the autofluorescence in the green
band are linearly correlated among different cells. Using
this feature, the cell autofluorescence measured in the
FITC band (from the samples not stained with FITC
dyes) can be used to correct the autofluorescence in the
PE and APC bands on a cell-by-cell basis using a post-
compensation method. The ratios between the autofluores-
cence in PE or APC bands versus that in FITC band can
be determined beforehand from the unstained specimen.
Figure 3d illustrates the data in Figure 3b and c after
Table I. Comparison of the Classification of 32 Specimens BetweenFlow Cytometry and Pap Test
Positive in Pap test Negative in Pap test
Positive in flow cytometry TP 17 FP 1Negative in flow cytometry FN 0 TN 14
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autofluorescence compensation. HeLa cells are clearly
separated from normal cervical cells based on PE and
APC staining intensities.
The Measurement of Cervical Cell FluorescenceIntensity in Flow Cytometry
Another major challenge in the study, which is usually
not involved in the hematological application of flow
cytometry, is how to correctly measure the biomarker
expression levels in flow cytometry when cells of large
different sizes are mixed together. Cervical epithelial cells
in a sample can vary from 25 to 65 lm in size. The fluo-
rescence pulse integral (or area), often used in flow
cytometry to measure fluorescence intensity, is not appro-
priate in this case to compare the biomarker expression
(indicated by fluorescence dyes per unit volume or fluo-
rescence density) among cells of different sizes. For
example, the pulse integral of a small-size cell with high
biomarker expression may be smaller than a large-size
cell but with low biomarker expression. In this study,
pulse peak was used to estimate the fluorescence density
instead of pulse integral. As the size (2565 lm) of cervi-
cal cells is larger than the height (9 lm) of the excitation
laser beam in the flow cytometer, the pulse integral is
more significantly affected by cell size than the pulse
peak. The flow cytometry applications to large-size epi-
thelial cells are different from the applications to blood
cells, which are usually smaller than or comparable to the
excitation laser beam. A better way of estimating fluores-
cence density is to normalize pulse integral by pulse
width (or time-of-flight).42 However, pulse width mea-
surement from most commercially available flow cytome-
ters has a large variance. A slit-scanning system with
small focal spot size43 might provide a reliable measure-
ment of pulse width. A system with Coulter volume mea-
Fig. 3. (a) Dot plots of FITC vs. PE for an unstained cervical sample that was mixed with HeLa and normal cervical cells. ( b, c) FITC vs. PE andFITC vs. APC of the same sample in (a) but stained with p16
INK4A-PE and Mcm5-APC antibodies. HeLa cells were separated from normal cells
because of the additional stain intensity onto the autofluorescence intensity. (d) PE vs. APC of the stained sample after the autofluorescence in PE andAPC bands were compensated. HeLa cells, with high stain intensity in both PE and APC bands, were clearly separated from normal cells.
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surement may also be used to obtain a better estimation
of fluorescence density. How to modify flow cytometer
for epithelial cells analysis is still a topic needs to be
investigated. As pointed out by Dong et al., it is a crucial
step to obtain an optimal flow cytometry setting suitable
for analysis of epithelial cells.44
Summary and Future Research
This study demonstrated the feasibility of (1) using multi-
plex detection of p16INK4A and Mcm5 to detect dysplastic
cervical cells by immunofluorescence, (2) using multipara-
meter flow cytometry to detect rare-event dysplastic cells
from large background of normal cells, and (3) using mul-
tiparameter flow cytometry to identify positive cervical
specimens. Although the results were based on a limited
number of clinical specimens, this experiment demon-
strated the promise of using multiparameter flow cytometry
for biomarker-based cervical cancer screening. This molec-
ular-based, potentially high-throughput and automated
method is expected to provide an alternative/auxiliary
means of cervical cancer screening. The method developed
for cervical cancer screening in this study can be extended
to the diagnosis of other nonhematological cancer.
Future studies will make this technology more robust.
First, the threshold or gating to detect dysplastic cells was
arbitrarily set in this preliminary study. A cell sorting and
validation experiment is needed to optimize the threshold
setting and to reduce the false negatives and false positives
in the detection of abnormal cells. Second, the fluorescence
contrast between the biomarker positive and negative cells
needs to be enhanced, especially for the p16INK4A
bio-
marker. Third, cervical cells tend to cluster together. Toprovide enough cells in single suspension for flow cytome-
try measurement, sample preparation must include a proce-
dure of cluster desegregation. The potential solutions to
these problems will be investigated in future studies.
Acknowledgment
We thank Elizabeth Branch for her assistance in the prep-
aration of this manuscript.
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