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Single-Cell Screening of Tamoxifen Abundance and Eﬀect Using
Mass Spectrometry and Raman-Spectroscopy
Ahmed Ali,†,‡ Yasmine Abouleila,†,‡ Yoshihiro Shimizu,† Eiso Hiyama,§ Tomonobu M. Watanabe,†Toshio Yanagida,† and Arno Germond*,††

Riken Biodynamics Research Center (BDR), 6-2-3 Furuedai, Suita, Osaka 565-0874, Japan
Research Center, Misr International University, Cairo 19648, Egypt§
Graduate School of Biomedical and Health Sciences, 1-2-3 Kasumi, Hiroshima, 734-0037, Japan
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‡

S Supporting Information*

ABSTRACT: Monitoring drug uptake, its metabolism, andresponse on the single-cell level is invaluable for sustaining
drug discovery eﬀorts. In this study, we show the possibility ofaccessing the information about the aforementioned processes
at the single-cell level by monitoring the anticancer drug
tamoxifen using live single-cell mass spectrometry (LSC−MS)
and Raman spectroscopy. First, we explored whether Raman
spectroscopy could be used as a label-free and nondestructive
screening technique to identify and predict the drug responseat the single-cell level. Then, a subset of the screened cells was
isolated and analyzed by LSC−MS to measure tamoxifen andits metabolite, 4-Hydroxytamoxifen (4-OHT) in a highly
selective, sensitive, and semiquantitative manner. Our results
show the Raman spectral signature changed in response to tamoxifen treatment which allowed us to identify and predict the
drug response. Tamoxifen and 4-OHT abundances quantiﬁed by LSC−MS suggested some heterogeneity among single-cells. A
similar phenomenon was observed in the ratio of metabolized to unmetabolized tamoxifen across single-cells. Moreover, acorrelation was found between tamoxifen and its metabolite, suggesting that the drug was up taken and metabolized by the cell.
Finally, we found some potential correlations between Raman spectral intensities and tamoxifen abundance, or its metabolism,
suggesting a possible relationship between the two signals. This study demonstrates for the ﬁrst time the potential of using
Raman spectroscopy and LSC−MS to investigate pharmacokinetics at the single-cell level.

Dnative environment, followed by electrospray ionization (ESI)and mass spectrometry (MS) analysis is one of the approaches
used for single-cell analysis.18,19 An additional separation step
such as ion mobility separation (IMS),20,21 and capillaryelectrophoresis (CE)22,23 can also be used prior to MS
measurements to improve molecular coverage. Recent
innovations, such as the single-probe24,25 and the T-probe,26
allowed for online analysis of individual cells, with thepossibility of performing repeated measurements on thesame cell. However, MS-based techniques are inherently
destructive (with a varying degree of invasiveness), which
limits the amount of information gained, especially when
considering the small volume of single-cells. Arguably, the best
approach to increase the information gained from single-cellmeasurements in a nondestructive manner is to couple
noninvasive methods such as microscopic27 or spectroscopic
imaging28,29 to MS platforms, where the multimodal data

rug discovery is a crucial aspect of medicine that has
great societal impact.1 Conventionally, the drug discovery process starts with high-throughput screening of potential
chemical entities on cell populations by utilizing ensembleaveraged measurements. While convenient and informative,
population-level measurements are unable to distinguishpotential subpopulations2−5 nor do they account for theheterogeneous behavior of individual cells and its eﬀects on
pharmacokinetics6−9 and drug discovery.10 To address this,several studies succeeded in monitoring drug interactions on
the single-cell level, speciﬁcally, drug uptake11 or cellularresponse.12,13 However, these studies did not quantify thedrug, at least in a semiquantitative manner, neither were they
able to monitor drug-uptake, metabolism, and drug-response
for the same cell. Therefore, an approach that provides
semiquantitative information about these processes at thesingle-cell level is required.14
Mass spectrometry-based approaches can be used tomeasure drug uptake on the single-cell level. They possess
the required sensitivity and selectivity while providing
comprehensive information about the analyte in question ina label-free manner.15−17 Direct sampling of the cell in its© 2019 American Chemical Society

Received: September 25, 2018
Accepted: January 21, 2019
Published: January 21, 2019
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Figure 1. Schematic of single-cell analysis using Raman spectroscopy and mass spectrometry. (a) Single-cell measurement by confocal lineillumination Raman spectroscopy. A 532 nm low powered laser is focused on single living cells to acquire Raman spectra of each cell (410 cells in
total). (b) After each microscopic measurement, single-cells are picked up using a tapered glass capillary attached to a micromanipulator (58 cells
were sampled for MS analysis from the 410 measured by Raman spectroscopy). In 5 cells, MS measurement was not successful due to broken
capillary tips, therefore, these cells were removed from subsequent data analysis. (c) The content of single-cells trapped in the capillary are analyzed
by mass spectrometry after adding the ionization solvent from the opposite end of the capillary.

gained provides more information, and allows for repeatedmeasurements of the cell (at the possible expense ofthroughput). However, for drug-discovery applications, higher
throughput is needed to scan for cells aﬀected by the candidate
drug. To this end, it is worth exploring whether nondestructive,
label-free spectroscopy can be used to perform highthroughput screening of cells aﬀected by a given drug, followedby ambient MS ionization and analysis of the same cells tomonitor drug-uptake, and metabolism on the single-cell level.
Raman spectroscopy is utilized in diﬀerent applications in
the drug discovery process.30−32Notably, it has been used forstudying the structure−activity relationship in molecularbindings of compounds,33 optimizing reaction conditions andparameters,34,35 and in pharmacokinetic studies.36 On the
single-cell level, it excels at in vivo measurement andidentiﬁcation of metabolic cell-states in a high throughput,
nondestructive manner37−39 which is diﬃcult to achieve by
MS. Contrarily, MS can be used in monitoring the drug and its
metabolism in cells of interest in a highly selective andsensitive manner.40 Therefore, by integrating the two
approaches, one could perform untargeted monitoring of thecellular drug response using Raman spectroscopy, with thepossibility of investigating the drug response of the same cells
in terms of drug uptake and metabolization using mass
spectrometry, all at the single cell level (Figure 1). Eventually,
one could also explore the possible correlation between bothsignals.
In this study, we monitored the intercellular abundance oftamoxifen and its pharmacologically active metabolite (4Hydroxytamoxifen, 4-OHT) in a selective and semiquantitative
manner using live single-cell mass spectrometry (LSC−MS).
Since MS is destructive, with limited throughput, we exploredthe use of Raman spectroscopy as a label-free screening
method to identify cells that are aﬀected by the drug. The
possibility of predicting the eﬀect of the drug on single-cells
was investigated, which could be invaluable for future drug
discovery applications. Furthermore, we explored the possible

correlations between relevant Raman peaks and tamoxifen or
4-OHT abundances in single-cells as measured by MS.
Hepatocellular carcinoma (HepG2) cells were selected tosimulate liver cells in vitro since they take-up tamoxifen,
metabolize it, and are simultaneously aﬀected by it.41 As aproof of concept, we used two conditions (presence or absenceof tamoxifen) to highlight the possibility of accessing
information about the drug and its abundance by LSC−MS,
as well as the drug response, as measured by Raman
spectroscopy, at the single-cell level.

■

EXPERIMENTAL SECTION
Cell Culture. In this study, HepG2 cells were used tosimulate liver cells in vitro. Cells were obtained from RIKEN
biological resource center (BRC) cell bank. Frozen stocks
stored in a liquid nitrogen tank were thawed and preheated for
1 min in 37 °C water bath (Thermo minder, Taitec Co.,
Saitama, Japan). Afterward, cells were grown in a culture media
containing Dulbecco’s Modiﬁed Eagle’s medium (DMEM,
Sigma-Aldrich, Minnesota, U.S.A.), supplemented with 10%
fetal bovine serum (FBS) obtained from Hyclone laboratories
in Utah, U.S.A. and 0.1% penicillin-streptomycin (Nacalai
Tesque, Kyoto, Japan). The cells were kept for 2 days in ahumidiﬁed incubator at 37 °C and 5% CO2 (MC0−19A1C,
Sanyo Electric Co., Osaka, Japan). Experiments were
performed at three diﬀerent days, therefore, growth phasevariations had to be minimized. To do so, a pilot study wasdone to determine how to synchronize the growth phase of thecultured cells. Accordingly, all cultured cells were synchronized
to have 50−60% conﬂuency before drug treatment. In eachexperiment, cells were subcultured into 35 mm glass bottom
grid dish (Matsunami, Osaka, Japan) precoated with rat-tailcollagen coating solution (Cell Applications Inc., San Diego,
U.S.A.), and then incubated for 24 h prior to drug treatment.
Drug Treatment. In each experiment, cells were
subdivided into drug-treated and untreated subgroups, where
both were washed with PBS buﬀer twice. Then, tamoxifen
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is a supervised machine learning technique that has been
widely used on spectral data to perform a predictive analysis of
known groups. To demonstrate the predictive power of themodel and the generalization of our method, the normalized
spectral data from two independent experiments were used as a
training model, and new data from a third independent
experiment was used as test data. For cross-validation of themodel, a Venetian blind cross-validation with 10 splits wasapplied. A model with 2 components (Latent Variables) waschosen as the best model. To identify which Raman spectral
peaks contributed in the discrimination of the cells, we
calculated the score of Variable Importance in Projection(VIP) for each Raman wavenumbers. The VIP score of avariable is calculated as a weighted sum of the squared
correlations between the PLS-DA components and the originalvariable.46 It is a statistical measure of a variable’s importancein the PLS-DA model which can used to select relevant
predictors.46 Analyses were performed using the eigenvector
software (eigenvector Research Inc., Wenatchee, U.S.A.).
Single Cell Sampling for Mass Spectrometry Analysis.
After Raman measurements of a given cell, it was immediately
picked up using glass micropipettes with 3 μm bore-size. Dueto the limited throughput of manual micromanipulation, only
36 treated cells and 22 untreated cells were sampled. The
sampling setup included a micromanipulator attached to aplatinum-coated glass capillary (CT-2, Cellomics, Hiroshima,
Japan). The capillary holder was connected to a syringe toapply negative pressure on the capillary during sampling. Afterobserving the cell of interest in the Raman microscope, thecapillary holder was lowered using the micromanipulator, andthe cell was sampled by applying negative pressure on thecapillary with constant visual feedback from the microscope.47
To reduce variations in the volume sampled, only cells withsimilar dimensions were chosen (measured by Metamorph
software, Molecular devices, California, U.S.A.).
To each capillary, 2 μL of the ionization solvent were addedfrom the wide end by using a pipet attached to eppendorf
GELoader tips (Eppendorf, Hamburg, Germany). The
ionization solvent used in the analysis was a mixture of 80%
methanol, 10% DMSO, 0.1% formic acid. All reagents used in
the ionization solvent were of LC-MS grade and were obtained
from Sigma-Aldrich, Missouri, U.S.A. Furthermore, prior tomeasurements, 2 ng/mL (5.31 nM) of d5-tamoxifen (Cambridge Isotope Laboratories, Inc., Massachusetts, U.S.A.) was
added to the ionization solvent as an internal standard. The
capillary containing the ionization solvent and the cell was thenattached to a nanoelectrospray adapter (nano-ESI) that isconnected to the Q-Exactive mass spectrometer used in theanalysis (Thermo Fisher Scientiﬁc, Massachusetts, U.S.A.).
Mass Spectrometry Measurements. MS analysis was
performed using a Q-Exactive orbitrap instrument that waspreviously calibrated using Pierce LTQ Velos ESI calibration
solution (Thermo Fisher Scientiﬁc, Massachusetts, U.S.A.).
The inlet capillary temperature, spray voltage, and automatic
gain control target (AGC) were adjusted to provide the highest
sensitivity for tamoxifen analysis. This was done by repeatedmeasurements of tamoxifen standard (Sigma-Aldrich, Missouri,
U.S.A.) while adjusting the previously mentioned parameters
one at a time. Tandem MS (MS/MS) spectra was obtained fortamoxifen and 4-OHT standards (Sigma-Aldrich, Missouri,
U.S.A.) with the optimized MS method for later comparison
with the sample peaks. Furthermore, an experiment was done
to ensure that the media surrounding the treated cells did not(Sigma-Aldrich, Minnesota, U.S.A.) dissolved in dimethyl
sulfoxide (DMSO, Nacalai Tesque, Kyoto, Japan) was mixed
with the culture media to a ﬁnal volume of 2 mL and tamoxifen
concentration of 10 μM for the drug-treated group. In theuntreated group, a corresponding volume of solvent (DMSO)
was mixed into the medium as a control for the eﬀect of
DMSO. Both groups were further incubated for 24 h.
Raman Spectral Imaging and Spectral Preprocessing.
Immediately before observation, cell cultures were taken out
from the incubator, washed twice, and their medium was
replaced with warmed FluoroBrite DMEM (Thermo Fisher
Scientiﬁc, Massachusetts, U.S.A.). We used FluoroBriteDMEM media because it provides the same backgroundlevel as PBS when measured by Raman spectroscopy, whileproviding better nutrition for the cells. Samples were placed
into a heated microchamber (ibidi, Munich, Germany) ﬁxed
onto a motorized microscope stage (BIOS-L101T-S, OptoSigma, Tokyo, Japan). The microchamber was supplied with
5% CO2 and kept at 37 °C during measurements. Raman
spectral measurements were performed using a custom-built
slit-scanning confocal scanning Raman microscope describedin a previous study.42 We used a line illumination system
where the laser light is shaped as a plane which allow us toobtain 400 spectra in a single exposure. A 532 nm diodepumped solid-state laser (Ventus, Laser Quantum, U.K.) was
focused to a few micrometers above the optical glass surface
through the objective lens (NA: 0.95, UPL40, Olympus,
Tokyo, Japan). Spectra were recorded using a cooled CCD
camera (PIXIS BR400, Princeton Instruments, New Jersey,
U.S.A.) mounted on a polychromator. The polychromatorused a 1200 g/mm grating to maximize the spectral resolutionof the ﬁngerprint region (from 600 to 1700 cm−1). The spatial
resolution of our system is approximately 300 nm and spectral
resolution is 1 cm−1.
We used glass-bottom grid culture dishes, which allowed us
to perform Raman measurements on a single-cell while
recording its location, then sample the cell for subsequent
MS analysis. Living cells were exposed for 10 s with a laser
intensity of 2.4 mW/μm2. Single line exposure was used, andthe data obtained from pixels corresponding to each single-cellwere averaged. This approach minimized the time between
Raman and MS measurements of the same cell. Hyper-spectral
images were processed using homemade algorithms to remove
cosmic-rays, perform background subtraction, baseline correction using an iterative polynomial ﬁtting known as the
ModPoly algorithm,43 and vector-normalization, as describedin our previous study.42 In total, 410 spectra of single cells
were obtained. Right after spectral measurements, single cells
were picked using a custom-built capillary system for
subsequent mass-spectroscopy measurements (Figure 1). To
verify the reproducibility of the measurements, experiments
were performed three times on diﬀerent days, and in eachexperiment, both cells cultured in presence (approximately 80
to 120 cells) or absence of drugs (approximately 35 to 40 cells)
were measured.
Multivariate Analyses of Spectral Data. To discriminate
the cells treated in the absence or presence of drug, aprojection on latent structure (PLS-DA) model was built using
as input the spectral ﬁngerprint region of Raman spectra (600
to 1710 cm−1). The PLS is a regression model which employs
the fundamental concept of principle component analysis(PCA) but further aims to maximize the covariance between
the components, named Latent Variables (LVs).44,45 PLS-DA
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surrounding cells treated with 10 μM tamoxifen was aspiratedusing a 1 μm bore-size capillary with constant visual feedback
from the microscope. The volume sampled was then measured
by MS, tamoxifen and 4-OHT peaks were not found in thesurrounding media (Figure S-1 of the Supporting Information,
SI).
During the experiments, both selective ion monitoring(SIM) and MS/MS were used to identify the analytes inquestion using their exact mass and fragmentation patterns.
More information can be found about the instrument
parameters and the MS analysis method in Table S-1. It is
worth noting that some tamoxifen metabolites shared similarfragmentation patterns (mainly the fragments at m/z 58.06, m/
z 72.08, and m/z 91.05) due to having closely related parent
ions.48 Therefore, while both exact mass and MS/MS wereused initially for veriﬁcation, relative quantitation was done bymonitoring the exact mass values of the analytes in question.
All detected peaks were measured with less than 3 ppm error
margin in their respective m/z values.
Mass Spectrometry Data Processing and Analysis.
After MS measurements, peak areas were exported from the
manufacturer proprietary raw format to text ﬁles using an inhouse script. Each ﬁle contained the extracted ion chromatogram (XIC) of the peaks of interest. The ﬁles were thenimported into R statistical software49 for processing andanalysis. For each peak, its XIC was divided by that of theinternal standard that was obtained in the same MS scan toaccount for the small variations between scans. The peak ratiosof tamoxifen or 4-OHT and d5-tamoxifen were then logtransformed to reduce skewness. Out of the 36 tamoxifentreated cells sampled, 5 could not be measured by MS due tobroken capillary tips and were removed from subsequent
analyses. Tamoxifen metabolism was calculated by dividing theabundance of metabolized tamoxifen (4-OHT) to unmetabolized tamoxifen in each cell. Correlation studies were doneby calculating the Pearson correlation coeﬃcient using a twotailed test. All plots were done using ggplot2 package50 in R
statistical software.
Safety Considerations. During Raman operation possible
eye injury could occur from the laser. Laser safety goggles
should be worn to mitigate this risk.
The glass capillaries used for sampling cells have sharp tips
and could cause injury with improper handling. Forceps should
be used to handle the capillaries, and gloves should be worn at
all times.
The ionization solvent should be prepared in a fume hoodsince methanol is toxic and ﬂammable, and formic acid iscorrosive and volatile.
Unlike traditional ESI interfaces, the nano-ESI is an open
interface. Therefore, care should be taken to not touch the ion
source during measurement to avoid possible injury.

■

Figure 2. Spectral analysis of the ﬁngerprint proﬁle of single-cells
measured by Raman spectroscopy. (a) Average Raman spectra of
HepG2 cells cultured in presence (red line, n = 295) or absence ofdrug (black line, n = 115). (b) Spectral intensities of cells cultured in
the presence of tamoxifen subtracted by the averaged spectrum ofcells cultured in the absence of drug. Peaks for which strong
diﬀerences were observed are highlighted and annotated with theirassociated molecular bonds or compounds identiﬁed from theliterature.42,51,52

intensities between the two culture conditions. To highlightthese diﬀerences, we calculated the diﬀerences between the
spectral intensities of cells cultured in the presence oftamoxifen and the average spectrum of the cells cultured in
the absence of drug (Figure 2b). Peak annotations were
performed following previous studies.42,51,52 The peak at ∼752
cm−1, associated with cytochrome, and the peaks at ∼818,∼851, ∼922, ∼1001, ∼1060, ∼1170, ∼1236, ∼1296, ∼1445,∼1545, ∼1601, and ∼1651 cm−1 showed signiﬁcant diﬀerences(ANOVA, Tukey HSD, p < 0.05) between cells cultured withor without tamoxifen. These diﬀerences demonstrate that the
drug exposure results in a signiﬁcant modiﬁcation of thespectral ﬁngerprint suggesting that the identiﬁed peaks may beconsidered as potential indicators of the drug eﬀect on themetabolism of the cells, although the current experimentcannot verify the speciﬁcity of the peaks.
The reproducibility of our results was checked by performing three independent experiments on diﬀerent days. Spectral
diﬀerences (Figure S-2) and principal component analysis(Figure S-3) showed that diﬀerence in culture conditions(presence of absence of tamoxifen) overcome technical and

RESULTS

Raman Spectroscopy Is Able to Detect and Predict
the Drug Eﬀects at Single Cell-Level. First, we exploredthe possibility to do a screening of the eﬀects of drugs using
nondestructive, label-free, confocal Raman microscope. We
analyzed the ﬁngerprint region (∼600 to ∼1750 cm−1) of thespectrum of living single cells. The average spectra of cells
cultured in the presence or in absence of tamoxifen is shown in
Figure 2a. A visual comparison of the normalized spectral
intensities showed various diﬀerences in the averaged spectral
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the training data exhibited 94.7% sensitivity when aiming to
identify cells cultured with tamoxifen. When tested against thetest data (Figure 3a), the predictive ability to classify the cells
cultured in the presence of tamoxifen reached 100% sensitivity
and 72.0% speciﬁcity (Table S-2). These results showed that
our experimental approach could accurately predict the
samples aﬀected by tamoxifen, even when considering new
data obtained from a separate experiment.
In an attempt to extract the spectral wavelengths (i.e.,
Raman shift) that contributed to the discrimination model, we
calculated the VIP scores which give information about which
Raman shift are the most informative to the classiﬁcation(Figure 3b). The VIP scores showed that the peaks atwavenumbers ∼752, ∼818, ∼900, ∼1001, ∼1170, ∼1445,∼1545, ∼1601, and ∼1651 cm−1 were particularly dominant intheir contribution to the classiﬁcation model (Figure 3b). A
strong similarity is observed with the various peaks highlighted
in Figure 2b, suggesting that most of the peaks for which
spectral intensities are strongly diﬀerent between the two
culture conditions are likely responsible for the discriminationof these conditions in the PLS-DA model.
In the above analysis, all available spectral data obtained
from single-cells were used to ensure a suﬃcient number ofsamples in our model (n = 410). From those cells, 53 cells
were sampled and analyzed by Mass spectrometry. To verify
that the eﬀects of tamoxifen can still be predicted on thissmaller subset, we performed PLS-DA analysis using thespectral proﬁles of these cells only. Despite the smaller subset,
we could successfully discriminate and predict cells that
underwent tamoxifen treatment with a cross validated
sensitivity of 87.5% (Figure S-4, Table S-3).
To check if tamoxifen can appear in the Raman spectra of
single-cells as a results of intracellular uptake of the drug, we
compared the spectral proﬁle of tamoxifen solution, with thatof cells aﬀected by the drug. Tamoxifen peaks were not foundin cells aﬀected by the drug (Figure S-5), for more details, see
the SI.
LSC−MS Can Monitor Tamoxifen and 4-OHT in
Single-Cells. We evaluated the capability of LSC−MS47 tomonitor drug abundance in single cells by analyzing cells
treated with tamoxifen and untreated cells previously measured
by Raman spectroscopy. In total, 31 tamoxifen-treated cells
and 22 untreated cells were individually analyzed by MS. Intact
tamoxifen and 4-OHT [M + H]+ ions could be detectedsuccessfully at m/z 372.23 and m/z 388.23, respectively(Figure S-6). Both tamoxifen and 4-OHT were conﬁrmed bytheir MS/MS fragmentation pattern (Figures S-7 and S-8).
Fragmentation patterns were compared to that of previously
obtained spectra of standards, and the MetFrag database for
conﬁrmation.53
Tamoxifen Abundance and Its Metabolism Exhibit
Strong Variation Across Single-Cells. To investigate theextent of variation in tamoxifen abundance and metabolism insingle-cells, the variation of tamoxifen and 4-OHT was studied
across cells treated with the drug and compared with thevariation in background peaks in untreated cells which
represent technical variations (Figure 4a). The percent relative
standard deviation (%RSD) of tamoxifen and 4-OHT intreated cells was found to be 151% and 238% RSD,
respectively, while their corresponding background peaks inuntreated cells exhibited 17% and 12% RSD, respectively. This
disparity showed both the relatively low instrumental variations

biological variations from diﬀerent experiments, highlighting
the reproducibility of the measurements (SI Results).
To demonstrate if the spectral signature of single-cells candiscriminate between cells treated with tamoxifen anduntreated cells, we used a predictive model (PLS-DA, Figure3b). To account for technical variations, we combined the data

Figure 3. Predictive model of drug eﬀect on cell metabolism andassociated spectral biomarkers. (a) PLS-DA model of HepG2 cells
cultured in presence (circle dots) or absence (square dots) of drug. In
this analysis, only the predicted data (test data, from an independent
experiment) are shown. Ellipses represent 95% conﬁdence interval foreach cluster. (b) VIP scores associated with the predictive model. The
VIP scores show that the peaks contributing the most to thediscrimination of the two culture conditions. Most of the peaks are
similar to those observed when calculating spectral diﬀerences (Figure2b).

across several experiments. The ability to predict the data of an
independent experiment when the model is trained from thedata of other independent experiments would demonstrate agood reproducibility of our experimental setup and analysis.
Thus, data sets of two experiments were combined to be used
as a training data set (n = 290), and the new spectra of cells of
the third experiment were used as a test data (n = 120), assuggested in other studies.42 The model aims to predict theclass information (treated and untreated cells) from thespectral information used as input. Details of the test results
are provided in (Table S-2). Sensitivity is the proportion ofsamples correctly identiﬁed as belonging to a given group,
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Possible Linear Relations Were Found between
Raman Data and LSC−MS Data. Raman spectral proﬁlesrepresent the global signature of the drug response, while theRaman peaks shown in Figures 2b and 3b discriminatedbetween cells aﬀected by the drug and control cells. We
explored whether those peaks could correlate with MS data
and therefore would be representative of variations intamoxifen abundance or its metabolism in a linear manner.
Few potential correlations were found. The lipid/protein/
unsaturated fatty acid peak at wavelength 1651 cm−1 positively
correlated with tamoxifen abundance (r = 0.35, p = 0.045).
However, 4-OHT’s abundance in single-cells correlated
positively with the phenylalanine/protein peak at 1001 cm−1
(r = 0.55, p = 0.001), and the nucleic acid/protein/lipid peakfound at 1236 cm−1 (r = 0.40, p = 0.020), whereas it was
negatively correlated with the protein peak at 1545 cm−1 (r =−0.38, p = 0.030). Data are summarized in Table S-4 andFigure S-10. Moreover, we also investigated how tamoxifen
metabolism (calculated by the ratio of 4-OHT to tamoxifen ineach cell) could correlate with the spectral intensities of Ramanpeaks identiﬁed above. Tamoxifen metabolism showed two 2
possible correlations with Raman peaks 851 and 900 cm−1 (p <0.05), with correlation coeﬃcients of 0.35 and 0.37,
respectively, and two possible negative correlations with
Raman peaks at 1545 and 1651 cm−1 (p < 0.05), both having
a correlation coeﬃcient of −0.39 (Table S-4). Finally, we
studied the relationship between tamoxifen metabolism andthe peak ratios of the Raman peaks identiﬁed in our model.
Peak ratios of 20 Raman peak pairs correlated with tamoxifen
metabolism in single-cells (p < 0.05), with correlationcoeﬃcients ranging from −0.86 to 0.87 (Table S-5). Despite
the weak correlations, these data support the existence of a few
linear relations between Raman spectral data and MS data.
High and Low Tamoxifen Metabolizers Show Raman
Spectral Diﬀerences. In order to investigate the possible
relationship between the metabolic activity of cells and Raman
spectra, tamoxifen treated cells measured by MS were divided
into two groups, high and low metabolizers of tamoxifen. Thiswas done by calculating the ratio between metabolized (4OHT) and unmetabolized tamoxifen for each cell. Then we
determined the ﬁrst and fourth quartiles from the distributionof the ratios and selected the corresponding Raman spectra ofcells. Cells in the ﬁrst quartile exhibit poor tamoxifen
metabolism due to the low metabolized to unmetabolized
ratio, while cells in the fourth quartile exhibit high tamoxifen
metabolism (high metabolized to unmetabolized ratio).
Spectral signatures of cells of the ﬁrst quartile (n = 8), andfourth quartile (n = 8) were averaged and compared tohighlight the spectral peaks that strongly varies between themetabolization rate deﬁned by MS data. Signiﬁcant diﬀerences
could be found (ANOVA, Tukey HSD p < 0.05) for thearomatic compounds peak at 1001 cm−1, the lipid/protein
peak at 1445 cm−1, and the cytochrome peak at 752 cm−1
(Figure S-11). These peaks were previously identiﬁed in
Figures 2 and 3, which conﬁrm that these peaks could beconsidered as spectral biomarkers of the tamoxifen eﬀect.

Figure 4. Distribution of tamoxifen and 4-OHT abundance in drugtreated cells and their corresponding noise peaks in untreated cells.
(a) Boxplot of tamoxifen and 4-OHT normalized abundance values intamoxifen treated cells and corresponding background peaks inuntreated cells. (b) Density plot showing the broad distribution oftamoxifen and 4-OHT in tamoxifen treated cells, in contrast withcorresponding background peaks in control cells that represent
instrumental variation.

caused by LSC−MS and the large heterogeneity in the
distributions of both tamoxifen and its metabolite in single
cells.
To further study the distribution of tamoxifen and 4-OHT
across single cells, a density plot of tamoxifen and 4-OHT’sabundance in tamoxifen-treated cells as well as thecorresponding background peaks in untreated cells was made(Figure 4b). Both tamoxifen and 4-OHT abundances show abroad distribution across single-cells, especially when contrasted against the distribution of background peaks inuntreated cells. The distribution of tamoxifen metabolism
across the 31 cells was studied by calculating the ratio of
metabolized tamoxifen (4-OHT) and unmetabolized tamoxifen for each cell, where it exhibited 222% RSD in its
distribution.
To verify that the 4-OHT signal resulted from themetabolization of tamoxifen, we studied the correlation
between the drug and its metabolite (Figure S-9). Tamoxifen
and 4-OHT abundances in treated cells were found to bepotentially positively correlated, with a correlation coeﬃcientof 0.54 and a p-value of 0.001 (n = 31).

■DISCUSSION
Our manuscript aimed to highlight the utility of Raman and
LSC−MS in single-cell drug discovery applications. We
demonstrated that Raman spectroscopy is able to accurately
identify the cells that underwent drug treatment according totheir spectral ﬁngerprint. We also showed that a predictive
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Article

Analytical Chemistryof variations in its expression level on the single-cell level could
be explored in future experiments.
In an attempt to explore the possible relationship between
Raman spectral data and mass spectrometry data, weinvestigated the possible correlations between the two data
sets. Although the Raman spectra is the contribution of many
molecular compounds in cells, we hypothesized that some ofthose peaks might be correlated to tamoxifen or 4-OHTabundance in single-cells, as well as tamoxifen metabolism.
Among those 13 peaks isolated by our model (Table S-4), 6
were possibly correlated to either tamoxifen, 4-OHT or theratio between them (p < 0.05), while the ratios between 20
Raman peak pairs were correlated with tamoxifen metabolism.
Those peaks are associated with fatty acid, lipids and proteins
which is interesting since tamoxifen and 4-OHT are known toaﬀect fatty acid oxidation, lipid metabolism and membrane
dynamics.61,62 Although the observed correlations are relatively
weak, with nonsigniﬁcant FDR values, in our opinion, they
imply a possible relation between the two data sets, and maybe
of use as biomarkers for drug-eﬀect screening in future studies.
Despite our emphasize on achieving high throughput andreproducibility, especially when it comes to Raman measurements, the throughput-mismatch between Raman and LSC−
MS measurements needs to be minimized in future studies. On
this regard, we are working on automating the most timeconsuming and labor-intensive step in LSC−MS analysis whichis cell sampling. Furthermore, it is unclear how the observed
variations in spectral intensities could be aﬀected by variations
in drug concentration or time of drug exposure. This could beachieved by incrementally increasing the drug concentration or
exposure time and observing the Raman spectral changes.
Moreover, further investigations and comparative analysis
using other drugs must be performed to assess the speciﬁcity ofthe measured Raman response to tamoxifen.

model could, from spectra of unknown cells, predict the cells
that are aﬀected by tamoxifen, in a label-free manner (Figure3a). When using only cells measured by MS in our predictivemodel (n = 53), we could still discriminate between tamoxifentreated cells (n = 31) and untreated cells (n = 22) with a highdegree of sensitivity, showing the utility of our method in
studies in which fewer number of cells are available, such as
circulating tumor cells (Figure S-4 and Table S-3). Moreover, anumber of important peaks for discriminating the two culture
conditions were highlighted (Figures 2b and 3b), suggestingthese variations might be linked to the presence of the drug
itself inside cells or to metabolic variations generated by the
drug. By doing a screening based on these possible spectral
biomarkers, one could perform a fast screening of cells which
could be beneﬁcial in single-cell drug-discovery applications.
Tamoxifen is metabolized by the enzyme cytochrome P450,
and interestingly we observed that the intensity of the peaks at
752 cm−1, assigned to cytochrome, 1001 cm−1, assigned toaromatic compounds, and protein peaks at 1651 cm−1 hadincreased normalized intensity in the spectra of cells cultured
with tamoxifen. These peaks have been described as reliableindicators of the metabolism of cytochrome and glycolysis
activity in living cells,54 suggesting the observed variations
could account for modiﬁcation of the cytochrome or glycolytic
activity in these cells. In single cells exposed to tamoxifen, we
also observed an increased intensity in several major peaks
associated with saturated lipids (at 1296 cm−1, 1445 cm−1).
This suggests that the action of tamoxifen modiﬁed the lipid
metabolism, which is consistent with previous literature.54,55
However, to verify the above hypotheses further experiments
are needed.
Tamoxifen’s distribution has been studied on the patientlevel,56 tumor level,57 but not across single cells. In the second
part of our manuscript, we demonstrated that for the ﬁrst time,
we can access the abundance of tamoxifen and 4-OHT in
single-cells in a semiquantitative manner. The abundance oftamoxifen and 4-OHT revealed a strong variation among
single-cells. Despite this, tamoxifen and 4-OHT were found to
be potentially correlated, demonstrating the ability of our
method to monitor the interrelation between the drug and its
metabolite (Figure S-9). We hypothesize that the aforementioned heterogeneity has a biological, rather than, a technicalorigin. This is evident by the high %RSD of tamoxifen and 4OHT in single-cells (151% and 238%, respectively, Figure 4a),as well as the high variation in tamoxifen metabolism acrossindividual cells (222% RSD). This strong variation was also
observed when HepG2 cells were treated with other drugs in aprevious study done by our group.58 Furthermore, comparative
analysis between cells with low and high tamoxifen metabolism
revealed spectral diﬀerences for peaks also found in our model(Figure 3b) which highlight the variation in tamoxifen
metabolism on the single-cell level (Figure.S-11). Alternatively,
the technical variation in our method was estimated in aprevious study to be within (5%−25%).47 In this study, it wascalculated to be 17% and 12% for tamoxifen and 4-OHT,
respectively. While we cannot pinpoint the exact hypothesizedbiological cause for the strong variations in tamoxifen
abundance in single-cells, other work has implicated the cell
membrane as a possible candidate.59 In addition, variations in
4-OHT’s abundance could be explained by variations in the
uptake of its parent molecule, as well as, possible variations in
its metabolism. The expression levels of CYP2D6 has beenshown to be related to tamoxifen metabolism,60 but the eﬀect■

CONCLUSIONSIn light of our results, the combination of Raman spectroscopy
and LSC−MS proved beneﬁcial in identifying the response of
single-cells to tamoxifen and measuring it and 4-OHTabundance in a semiquantitative manner. We envision thatthe development of comprehensive platforms capable ofmonitoring both the pharmacokinetics and pharmacodynamics
in single-cells will allow single-cell measurements to complement ensemble-averaged measurements in the drug discovery
process.

■

ASSOCIATED CONTENT

S Supporting Information*

The Supporting Information is available free of charge on the
ACS Publications website at DOI: 10.1021/acs.analchem.8b04393.
Raman results robustness; Tamoxifen detection by
Raman spectroscopy; MS parameters; PLS-DA analysis(all cells); PLS-DA analysis (MS-measured cells) ;Raman peaks & MS correlation; Raman peak pairs ratios& MS correlation; Mass spectrum of media surrounding
cells; Raman spectra of the three experiments; PCA of
all cells measured by Raman; PLS-DA plot of cells
measured by MS; Raman spectra of tamoxifen standard;
Mass spectrum of tamoxifen and 4-OHT; Tamoxifen
MS/MS spectrum; 4-OHT MS/MS spectrum; Tamoxifen and 4-OHT scatterplot; Raman peaks and MS peaks
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Article

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scatterplots; and Raman spectra of high and low
tamoxifen metabolizers (PDF)

AUTHOR INFORMATION

Corresponding Author*Phone: +81-(0)-677-109-067. E-mail: arno.germond@gmail.
com (A.G.).
ORCID

Ahmed Ali: 0000-0003-2157-4399
Author Contributions

A.A, Y.A., and A.G. wrote the manuscript. A.A, Y.A., and A.G.
designed and performed the experiments. A.A and A.G.
performed the statistical analyses. A.G., E.H, Y.S., and T.Y.
supervised the project.
Notes

The authors declare no competing ﬁnancial interest.

■

ACKNOWLEDGMENTS■

REFERENCES

This manuscript was funded by RIKEN’s junior scientist (JRA)program and the Ichiro Kanehara foundation for thepromotion of medical sciences and medical care (grant
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Masuda, Mohamed Ashraf, Prof. Walaa Zarad, and Prof. Sami
Emara for critical discussion during the preparation of thismanuscript. This manuscript is dedicated to the memory of thelate Tsutomu Masujima.

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S-1

Supporting Information
Single-cell screening of tamoxifen abundance and effect using mass spectrometry and
Raman-spectroscopy
1
Ahmed Ali , Yasmine Abouleila1, Yoshihiro Shimizu1, Eiso Hiyama2, Tomonobu M.
Watanabe1, Toshio Yanagida1, Arno Germond1*
1- Riken Biodynamics Research Center (BDR), 6-2-3 Furuedai, Suita, Osaka 565-0874, Japan
2- Graduate school of biomedical and health sciences, 1-2-3 Kasumi, Hiroshima, 734-0037,
Japan.
* Corresponding authors:
Arno Germond, Ph.D.
Laboratory for Comprehensive Bioimaging,
RIKEN Biodynamics Research Center (BDR),
6-2-3 Furuedai, Suita, Osaka 565-0874, Japan
Email: arno.germond@gmail.com
Table of contents:
Reproducibility of Raman spectral measurements.
Cellular tamoxifen cannot be directly detected by Raman spectroscopy.
Table S-1. SIM mode and tandem-MS scan parameters.
Table S-2. PLS-DA analysis, detailed results for all cells analyzed by Raman (n =410).
Table S-3. PLS-DA analysis, detailed results for cells analyzed by MS only (n = 53).
Table S-4. Correlations values for 13 peaks that were identified in Figure 2b and 3b with MS
data.
Table S-5. Correlations between Raman peak pairs ratios identified in Figure 2b and 3b andtamoxifen metabolism
Figure S-1. Mass spectra of tamoxifen treated single cell compared with their surrounding media.
Figure S-2. Average spectrum of three independent experiments of single cells cultured in thepresence or absence of tamoxifen.
Figure S-3. Principle component analysis of all single-cells measured across three experiments.
Figure S-4. PLS-DA analysis of 53 cells isolated by MS after Raman measurements.
Figure S-5. Raman spectra of tamoxifen standard.
Figure S-6. Tamoxifen and 4-OHT MS1 spectrum in single-cell treated with the drug.
Figure S-7. Tamoxifen MS/MS spectrum in single-cell treated with the drug.
Figure S-8. 4-OHT MS/MS spectrum in single-cell treated with the drug.
Figure S-9. Scatter plot of tamoxifen and 4-OHT concentrations in single cells treated withtamoxifen.
Figure S-10. Scatter plots of Raman spectral differences and abundances of tamoxifen or 4-OHT
in single-cells.
Figure S-11. Average Raman spectra of high and low tamoxifen metabolizers.

S-2

Supplementary Results:
Reproducibility of Raman spectral measurements
Spectral measurements were performed across three independent experiments on different days.
Small variations in the peak intensities were observed in the average spectra across theexperiments (Fig. S-2). However, a comparison of spectral intensities clearly shows that thedifferences in culture condition (presence or absence of tamoxifen) are more important (Fig. S2). For example, the normalized intensity of the peak at ~1601 cm-1 was consistently higher forcells cultured in presence tamoxifen than without (ANOVA, Tukey HSD, p < 0.05)(Fig. S-2).
To study the variations across experiments and compare it to variations across the two culture
conditions, we use Principal Component Analysis (PCA). When plotting the average spectrum of
individual cells in a PCA space, some cluster seemed to emerge in the first two dimensions,suggesting the differences between the presence or absence of drug could allow discrimination ofcells (Fig. S-3a). On the other hand, when coloring cells differently depending on theirexperiment in the same PCA space (Fig. S-3b), we noticed no particular cluster between thethree independent experiments, suggesting the differences observed across experiments are notdominant.
Cellular tamoxifen cannot be directly detected by Raman spectroscopy
For a comparative purpose, we measured the spectrum profile of a solution of tamoxifen, which
revealed its major peaks (Fig. S-5). However, when measuring cells affected by tamoxifen, themajor peaks of tamoxifen were not found in the spectra of living cells. In our opinion, it isunlikely that tamoxifen could appear in the Raman spectra of single cells as a result of theintracellular uptake of the drug, because for the concentrations used in cell culture, the drug or its
metabolite are expected in nM concentration in single cells, which is far below the sensitivity ofa spontaneous Raman confocal system. Therefore, we hypothesize that the variations monitored
in several Raman peaks resulted from a complex metabolic response (including the influence ofprotein, lipids, etc.) of HepG2 cells following the drug exposure.

S-3

Table S-1. SIM mode and tandem-MS scan parameters. All parameters were optimized byrepeated measurement of tamoxifen standard targeting the best sensitivity.
Instrumental variable

Value

Spray voltage

1500 v

Capillary temperature

400℃

S-lens RF level90%

SIM range

347-397 m/z

SIM AGC target

5.00E+06

SIM maximum injection time

200 msSIM resolution

140,000
FWHM

MS/MS range

50-400 m/z

MS/MS AGC target

2.00E+05

MS/MS maximum injection time

100 msMS/MS resolution

17,500 FWHM

MS/MS isolation window

1 m/z

MS/MS Normalized collisionenergy (NCE)

35

S-4

Table S-2. Detailed results of the PLS-DA analysis (n = 410). Data from two independent
experiments were combined to predict the presence or absence of tamoxifen effects on cells
measured in a third independent experiment. Abbreviations are Calibrated (Cal), CrossValidation (CV) and Predicted (Pred).
Model parameters

Cells cultured
in presence oftamoxifen

Cells cultured
in absence oftamoxifen

Sensitivity (Cal)

0.977

1.000

Specificity (Cal)

1.000

0.977

Sensitivity (CV)

0.964

0.947

Specificity (CV)

0.947

0.964

Sensitivity (Pred)

0.720

1.000

Specificity (Pred)

1.000

0.720

RMSE (Cal)0.343420.777447

RMSE (CV)0.37909

0.794571

RMSE (Pred)

0.356542

0.954356

S-5

Table S-3. Detailed results of the PLS-DA analysis of samples analyzed by mass spectrometry
after Raman measurements (n = 53). PLS-DA analysis was performed on cells that were measureby mass spectrometry only (n = 31 treated cells, n = 22 untreated cells). Test data were randomly
chosen to account for 22% of the dataset (tamoxifen, n = 7, control n = 5). Abbreviations are
Calibrated (Cal), Cross-Validation (CV) and Predicted (Pred).

Model parameters

Cells cultured
in presence oftamoxifen

Cells cultured
in absence oftamoxifen

Sensitivity (Cal)

1.000

0.706

Specificity (Cal)

0.706

1.000

Sensitivity (CV)

0.875

0.706

Specificity (CV)

0.706

0.875

Sensitivity (Pred)

0.857

0.600

Specificity (Pred)

0.600

0.857

S-6

Table S-4. Correlations values for 13 peaks that were identified in Figure 2b and 3b with MS
data. We explored the possibility for linear relations with the abundance of tamoxifen or its
metabolite or the metabolic activity (4-OHT : tamoxifen ratio) as measured by massspectrometry in the 31 treated cells.
Raman
peak

Correlation
coefficient(tamoxifen)

p-value(tamoxifen)

Correlation
coefficient(4-OHT)

p-value
Correlation(4-OHT) coefficient(Tamoxifen
metabolism)

P-value(Tamoxifen
metabolism)

752 cm-1

0.03

0.884

-0.13

0.459

0.04

0.824

818 cm-1

-0.18

0.301

-0.06

0.733

0.25

0.146

851 cm-1

-0.19

0.278

0.02

0.898

0.35

0.044

900 cm-1

-0.21

0.226

0.18

0.312

0.37

0.033

1001 cm-1 -0.33

0.060

0.55

0.001

0.10

0.566

1060 cm-1 -0.27

0.129

-0.21

0.217

-0.04

0.815

1170 cm-1 -0.06

0.744

-0.11

0.537

0.09

0.613

1236 cm-1 0.18

0.310

0.4

0.020

-0.18

0.315

1296 cm-1 0.03

0.846

-0.07

0.675

-0.05

0.796

1445 cm-1 0.07

0.715

0.03

0.854

-0.16

0.517

1545 cm-1 -0.04

0.822

-0.38

0.030

-0.39

0.022

1601 cm-1 0.03

0.850

-0.13

0.479

-0.14

0.418

1651 cm-1 0.35

0.045

-0.08

0.653

-0.39

0.020

S-7

Table S-5. Correlations between Raman peak pairs ratios identified in Figure 2b and 3b andtamoxifen metabolism. We explored the possibility for linear correlations between the 4-OHT :Tamoxifen ratio (Tamoxifen metabolism) measured by mass spectrometry and ratios of peaks
identified in Fig 2b and 3b. 31 cells where used for the correlation studies. 20 ratios of Raman
peak pairs where found to be correlating with Tamoxifen metabolism in cells.
Raman peak ratio

Correlation coefficientp-value

1170/1001-0.86< 0.0011060/1001-0.86< 0.001

1296/1001-0.86< 0.001

922/851-0.84< 0.001

1601/1001-0.83< 0.0011060/851-0.82< 0.001

1170/851-0.8< 0.0011445/1001-0.65< 0.001

1545/1001-0.64< 0.001

1236/1001-0.64< 0.001

1296/851

-0.53

0.001

1545/851

-0.47

0.005

1651/1445

-0.46

0.006

1651/1545

-0.43

0.011

1651/1601

-0.42

0.012

1601/851

-0.36

0.035

1545/1445

0.45

0.008

1545/1236

0.56

0.001

1651/851

0.76< 0.001

1651/1001

0.87< 0.001

S-8

Figure S-1. Mass spectra of tamoxifen treated single cell compared with its surrounding media.
Tamoxifen and 4-OHT peaks can be observed in cells treated with tamoxifen, while they cannot
be observed in the surrounding media of the same cells.

S-9

Figure S-2. Average spectrum of three independent experiments of single cells cultured in thepresence (“with drug”) or absence of tamoxifen (“no drug”). Differences are observed between
the two culture conditions were consistent for several peaks across the spectral range. The
indicated arrow highlights one example peak for which differences between the conditions are
the most extreme, and in a reproducible manner across the three independent experiments.

S-10

Figure S-3. Principal Component Analysis (PCA) of all single cells measured across threeexperiments. (a) Data points, all representing the average spectrum of a single cell, were shaped
and colored depending on the culture conditions (in presence or absence of tamoxifen). Circle
dots are cells cultured in the presence of tamoxifen, and triangles are cells cultured without. (b)Data points are colored differently depending on the experiment replicate. Circle dots are cells
cultured in the presence of tamoxifen, and triangles are control cells.

S-11

Figure S-4. PLS-DA analysis of 53 cells isolated by MS after Raman measurements. Each point
represents the spectral data of single cells plotted onto two dimensions. Ellipses shows that cells
under drug influence and control cells formed two distinct clusters. Square dots represent cells
used as a control (no drug treatment), while square show cells that underwent tamoxifen
treatment. Ellipses represent 95% confidence interval for each cluster.

S-12

Figure S-5. Raman spectrum of purified tamoxifen drug (10% concentration). a) spectral profile
of tamoxifen obtained with a 532 nm laser over the range of the fingerprint region. It shows astrong dominant peak at 677 cm-1 that we could not identify. b) Over a narrow range the CC
chain associated peak at 1601 cm-1 could be seen although very weak in by comparison to otherpeaks. A strong peak at 1418 cm-1 was found.

S-13

Figure S-6. Tamoxifen and 4-OHT peaks in full scan mode in tamoxifen-treated cell. An
untreated cell spectrum is shown as control.

S-14

Figure S-7. Tamoxifen fragmentation profile. Tamoxifen’s presence in single cell was verified
by its 9 unique fragments.

Figure S-8. 4-OHT fragmentation profile indicated three unique fragments that were used for
positive identification.

S-15

Figure S-9. Relation between tamoxifen and 4-OHT concentrations in single cells treated withtamoxifen. A significant positive correlation (r = 0.54, p = 0.0007) could be observed between
tamoxifen and its metabolite. The red line represents the linear regression. The ellipse represents
95% confidence.

S-16

Figure S-10. Correlation plots between Raman spectral differences and abundances of tamoxifen
or 4-OHT. Raman spectral differences between the two conditions (with and without tamoxifen)
were used for correlations. Four major peaks with significant correlations (p = 0.05) werechosen. a) 4-OHT abundance correlates positively with the peak at 1001 cm-1 associated toaromatic compounds and proteins. b) 4-OHT correlates with peak at 1236 cm-1 associated to the
nucleic acid, protein and lipids c) 4-OHT negatively correlates with 1545 cm-1 associated toproteins. d) Tamoxifen abundance in single-cells positively correlates with the peak at 1651 cm-1
associated to the Amide I of proteins. Ellipses represent 95% confidence. Redlines represent
linear regression.

S-17

Figure S-11. Average, background-subtracted Raman spectrum of high (n = 8) and low (n = 8)
metabolizers of tamoxifen. The high and low metabolizers were identified the ratio of
metabolized to unmetabolized tamoxifen in each cell. Significant differences could be found(ANOVA, Tukey HSD p < 0.05) for several peaks, such as for the aromatic compounds peak at
1001 cm-1, the lipid/protein peak at 1445 cm-1, and the cytochrome peak at 752 cm-1 which werealso identified in Figure 2 and 3.