The authors thank Toshio Yanagida for his support and RIKEN internal collaborative funds attributed to Dr. Arno Germond.

1. Cell culture
<ol>
	<li>Culture cells of interest in an appropriate culture media. Penicillin-streptomycin may be added to avoid contamination. In the case of HepG2 cells, culture cells in a culture media containing Dulbecco&#39;s modified Eagle&#39;s medium (DMEM) supplemented with 10% fetal bovine serum (FBS) and 0.1% penicillin-streptomycin. To faciltate Raman spectroscopy measurements, cells can be grown on a 0.1% gelatin-coated glass-bottom dish or quartz slides.</li>
	<li>Incubate cells for 2 days at 37 &#176;C and 5% CO2 in a humidified incubator.</li>
	<li>Synchronize cell cultures to reach 70% confluency.</li>
	<li>Subculture cells into a 35 mm glass-bottom grid dish or quartz slides using the same medium at a seeding density of 0.7 x 106, then incubate at 37 &#176;C for 24 h. 
	NOTE: Culture dishes or slides can be pre-coated with collagen or gelatin coating solution with a culture surface ratio of 5 ug/cm2 to allow them to fix, ensuring their survival during measurement.</li>
</ol>
2. Drug treatment
<ol>
	<li>Remove cell cultures from the incubator and wash 2x with prewarmed PBS buffer (37 &#176;C). 
	NOTE: It is optimal to remove cells for drug treatment at a confluency of 50%-60%.</li>
	<li>Divide cells into drug-treated and untreated subgroups in 35 mm culture dishes.</li>
	<li>Mix the drug of choice with the culture media. For example, dissolve tamoxifen in dimethyl sulfoxide (DMSO) and mix with the culture media to obtain a final volume of 2 mL and tamoxifen concentration of 10 &#181;M. This will be the drug-treated group.</li>
	<li>Mix a corresponding volume of solvent (DMSO) into the medium as a control to study the effects of DMSO. This will be the control group.</li>
	<li>Incubate both groups in 2 mL of the spiked media prepared in steps 2.3-2.4 for 24 h. The expected confluency after incubation should be 70%-80%.</li>
</ol>
3. Raman spectral imaging and spectral processing
NOTE: Although Raman spectroscopy systems are commercially available, the Raman spectroscopy system used here is a home-built line-scanning confocal microscope previously described7,8. Briefly, this system is equipped with a 532 nm diode pumped solid-state laser. The laser light is shaped into a plane using a cylindrical lens, which allows measurement of 400 spectra in a single exposure. Raman spectra were recorded using a cooled CCD camera mounted on a polychromator that uses a 1,200 grooves/mm grating to maximize the spectral resolution of the fingerprint region (from 500-1,800 cm-1). This spectral area contains a high density of frequencies specific to molecules that generates Raman scattering. A water-immersionobjective lens (NA = 0.95) is also used. The spatial resolution of this system is ~300 nm and the spectral resolution is 1 cm-1. To ensure cell survival during the experiment, a microchamber fixed onto a motorized microscope stage is used.
<ol>
	<li>Prior to spectral measurements, verify the alignment of the optics. A 50 &#181;m pinhole can be used to verify that the pinhole and laser position match exactly. Enter the spectrophotometer slit when narrowed as much as possible.</li>
	<li>Use ethanol to calibrate the spectrophotometer prior to each experiment. To do so, place EtOH in a glass-bottom dish, measure the spectrum at a given laser intensity (measured at sample) for 1 s, and associate the peak to known wavelengths7.</li>
	<li>Minimize the laser intensity at the sample to ~2.4 mW/&#181;m2 so that cells survive laser exposure.</li>
	<li>Set up the microchamber at 5% CO2 and 37 &#176;C.</li>
	<li>Once the microscope system is ready, remove cells from the incubator and immediately rinse cells 2x with warmed PBS (37 &#176;C) buffer, then add 2 mL of warmed PBS (37 &#176;C) or DMEM to resuspend the cells. 
	NOTES: Both PBS and FluoroBrite DMEM-based media are sufficient options for Raman spectroscopy measurements because they produce a minimal background signal.</li>
	<li>Add 10 &#181;L of water onto the water-immersion objective lens and delicately place the glass-bottom cell dish onto the microscope stage.</li>
	<li>Measure each cell by focusing the laser line. A 15 s exposure time per cell is sufficient here to obtain a cross-section of a cell with a clear Raman signal. A galvano mirror allows scanning of one cell or a group of cells within several dozen minutes. 
	NOTES: Higher resolution of full spectral imaging of cells requires more time and can lead to photodamage. Here, cells were measured using a single-line exposure to obtain a cross-section of each cell. This approach is a good trade-off to increase throughput and obtain sufficient information to discriminate cells, while also ensuring cell viability by limiting photodamage.</li>
</ol>
4. Preprocessing of spectral data and multivariate analyses
NOTE: Preprocessing is a necessary step prior to additional analysis in order to remove unwanted technical variations within the spectral data. Due to diversity of the methods and software, an exhaustive list cannot be provided, and there are many helpful reviews found in the literature7,8. In this section, we briefly describe the approach used to analyze and interpret spectral Raman data obtained from living single cells.
<ol>
	<li>Extract and preprocess Raman images to remove possible cosmic ray interference. 
	NOTE: Spectral axis of spectra obtained during different days/weeks/months may include some variations due to small technical variations during calibration using ethanol. This will strongly impact subsequent multivariate analyses and statistical comparisons. In the case that experiments are performed during different weeks/months, small optical variations are expected. In this case, data must be interpolated to correct for eventual spectral shifts of the data across experiments. Interpolation using cubic spline is used here. After this step, all spectra axis should be aligned. A range of 500-1,800 cm-1 is considered for subsequent analysis.</li>
	<li>Extract spectral data of cells and background (absence of cells) from each image using a homemade algorithm. Subtract the background signal from the cell&#39;s signal. Then, average the spectra of the remaining pixels, which should correspond to a single cell. The following steps are performed using the 2D spectra of cells.</li>
	<li>Perform a baseline correction using the ModPoly9 or any other algorithm that it estimated to fit sufficiently. Trim the spectral range to 600-1,700 cm-1 to select the fingerprint region and ensure that there is no unwanted edge effects on the spectra due to bad polynomial fitting.</li>
	<li>Perform a normalization step such as vector normalization (intensity at each wavenumber is divided by the global l2 norm or maximum singular value of a spectrum) to normalize the spectral intensity10, although other normalizations can be considered.</li>
	<li>Prepare a dataset with the appropriate label for each class/condition. 
	NOTE: Comparative spectral analyses can be perfored to explore the nature of possible differences among cell class/conditions (e.g., by subtracting the average spectrum of the control group to other groups to identify regions of interest [such as potential biomarkers]). ANOVA and Fisher scores calculations can also be performed10.</li>
	<li>To identify treated and untreated cells based on spectral features, multivariate analyses can be applied. A normalized spectral data should be used as a training dataset, and an unknown dataset (without label) from a replicate experiment should be used as test data, if possible. 
	NOTE: Discriminant analysis performed on some vector of a principal component analysis (PCA-DA10), projection on latent scores followed by discriminant analysis (PLS-DA), and support vector machines (SVM)11 are models often used in the field, and each presents different statistical considerations. Preprocessing of data should be performed consequently.</li>
	<li>Use machine learning that fits the experimental objectives. Here, a projection on latent structure (PLS) model is built using the spectral fingerprint region of Raman spectra (600-1,710 cm-1)11,12. Mean-center the data as necessary. For cross-validation of the model, different techniques can be applied. 
	NOTE: Here, a Venetian blind cross-validation with 10 splits is applied. The model complexity (number of components or latent variable) should be tested so that the best model minimizes the root mean square error (RMSE) value. It was found that three latent vectors (LVs) provided the best discrimination with our dataset.</li>
	<li>Identify which Raman spectral peaks contribute to discrimination of the cells (e.g., by plotting the score of variable importance in projection [VIP] for each Raman wavenumber or the magnitude of the regression coefficient). 
	NOTES: The VIP score of a variable is calculated as a weighted sum of the squared correlations between the PLS-DA components and original variable. Details regarding PLS and VIP scores algorithm can be found in the literature11,12.</li>
</ol>
5. Single-cell sampling set-up and procedures
<ol>
	<li>Fix the cell sampling system onto the Raman microscope as shown in Figure 1. Connect the 3D micromanipulator to the glass capillary holder that is attached to an empty syringe for sample sucking by applying negative pressure (Figure 1).</li>
	<li>Set the microscope to a high magnification field (40x) to observe the tip of the glass capillary and make sure it is not broken. Control the position of the glass capillary using the micromanipulator (x-, y-, z-axes). Ensure that the capillary tip is centered in the field of view, then move the capillary up on the z-axis to give clearance for the culture dish later. 
	NOTE: Microsampling of the cells is performed by the glass capillary for cells with diameters between 10-15 &#181;m. A capillary with a bore size of ~5 &#181;m is recommended. If the bore size is too small, the capillary tip will be plugged by the cell, and if it is too large, the sensitivity of later MS measurements may be compromised.</li>
	<li>Place the sample plate/dish on the stage of the microscope, adjust the magnification and focus, select the target cell on the grid dish, and move it into the center of view. Then, carefully lower down the glass capillary using micromanipulator (z-axis) until the tip comes into focus. 
	NOTE: Be sure not to move the capillary in the x- and y-axes until the capillary is in focus.</li>
	<li>Under microscopic observation, touch the target single cell with the capillary tip, then start applying negative pressure using the syringe to trap the cell inside the capillary tip. Record this procedure by taking a photo or video to check the timing and sucked location of the cell precisely, if necessary.</li>
	<li>Move the capillary up on the z-axis. Then, detach the capillary from the capillary holder using forceps in preparation for MS analysis.</li>
</ol>
6. Mass spectrometry measurements
<ol>
	<li>Calibrate the mass accuracy of the MS instrument according to the manufacturer&#39;s recommendations. After calibration, make sure that the mass error is no greater than 3 ppm.</li>
	<li>Optimize the MS instrument to parameters that are best suited for the analyte of interest. 
	NOTE: In the case of tamoxifen and 4-OHT analysis, the instrument parameters are set to the following: inlet capillary temperature: 400 &#176;C, spray voltage: 1500 V, automatic gain control target (AGC): 5.00E+06, S-lens RF level: 90%, SIM range: 347-397 m/z, SIM maximum injection time: 200 ms, SIM resolution: 140,000 FWHM, MS/MS range: 50-400 m/z, MS/MS AGC target: 2.00E+05, MS/MS maximum injection time: 100 ms, MS/MS resolution: 17,500 FWHM, MS/MS isolation window: 1 m/z, MS/MS normalized collision energy (NCE): 35.</li>
	<li>Set up an automatic acquisition method with a duration of 5 min for SIM mode to achieve relative quantitation, and another MS/MS method for positive identification of the drug and its metabolite. The parameters of the acquisition method should be set to the optimized values mentioned in step 6.2.</li>
	<li>Prepare the ionization solvent under a fume hood. The solvent composition depends on the analyte of interest. Here, the organic solvent used consists of 80% MeOH, 10% DMSO, and 0.1% formic acid.</li>
	<li>Mix an appropriate internal standard with the organic solvent prior to measurements. In this experiment, 5.31 nM of d5-tamoxifen is used as an internal standard.</li>
	<li>To avoid false positives, aspirate the media surrounding the cells treated with the drug using a 1 &#181;m bore-size capillary with constant microscopic observation to avoid sampling any cellular parts.</li>
	<li>Add 2 &#181;L of the ionization solvent to the wide end of the capillary containing the media using a pipette attached to loader tips. Then, analyze the sampled media by MS, check for the presence of the analyte of interest (normally, it should not be detectable).</li>
	<li>Add 2 &#181;L the ionization solvent to the capillary containing the cell, fix the capillary to a nanoelectrospray adapter (nano-ESI) connected to a suitable mass spectrometer, and start the automatic acquisition method.</li>
</ol>
7. Mass spectrometry data processing and analysis
NOTE: Any suitable software can be used to perform data analysis. However, if researchers wish to perform data analysis using a software that is not provided by the MS vendor, then the raw data should be converted from the proprietary vendor format to an open format or as a text file first (which was done here).
<ol>
	<li>Normalize the data by dividing the peak area of the analyte of interest(s) by that of the internal standard from the same MS scan. Then, log transform the peak ratios to reduce skewness.</li>
	<li>Plot the normalized intensity of the drug or its metabolite as a boxplot or density curve to visualize distribution across single cells. Here, R statistical software is used, along with the ggplot2 package.</li>
	<li>Calculate the metabolized drug to unmetabolized drug ratio by dividing the abundance of the drug metabolite by that of the unmetabolized parent molecule in each cell (i.e., 4-OHT and tamoxifen, respectively. 
	NOTE: The correlation between variations of specific Raman peaks of interest and the variations in MS peaks of the drug or its metabolites can be studied. This is an addition to the possible correlation between the drug itself and its metabolite in single cells. This can be done by calculating the Pearson correlation coefficient using a two-tailed test. More advanced integrative approaches should also be considered.</li>
</ol>

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The authors declare no conflicts of interest.

In this manuscript, a simple case was chosen in which HepG2 cells were exposed (or not) to tamoxifen. The ability of a Raman spectroscopy and mass spectrometry system is demonstrated to monitor the effects of tamoxifen on cells. Raman spectroscopy allowed identification of potential biomarkers that reflected a general response of single cells to drug exposure. Some heterogeneity between single cells was observed, suggesting that some cells did not respond to drug exposure. On the other hand, LSC-MS was capable of performing a targeted analysis of the drug and its metabolite at the single-cell level, in which a high degree of heterogeneity was observed in the drug and its metabolite abundance. This heterogeneity helps explain why some cells are affected by the drug while others are seemingly not, despite the cells originating from a supposedly uniform population12.
Among particular aspects of this technique that require attention, it is important to evaluate the quality of the microscope set-up and signal processing to ensure reproducibility of the data. If preprocessing of the spectra is done carefully, the signal variations should be maximized at the local maximum of each peak. By contrast, the baseline and edge of the spectra should overlap between the tested cell conditions. Another important aspect is the multivariate model used to investigate differences between treatments. One must carefully evaluate the models and model parameters to ensure a precise and accurate analysis. One advantage of the PLS model, unlike neural networks, is that it allows access to the weights associated with each wavelength (Raman shifts) that best distinguish the conditions tested by the model.
Despite Raman spectroscopy successfully discriminating the drug response, it should be stressed that this technique is limited in its use to provide biological interpretation. This is mainly due to the complexity of the spectral signal, which encompasses a mixture of thousands of molecules. Therefore, further investigation is required to evaluate systematic variations between Raman spectral intensities and variations in drug concentrations. Also, similar studies of other cell lines are required to evaluate the generalization of spectral biomarkers associated with tamoxifen.
Furthermore, it may be of interest to perform living tissues measurements to assess pharmacodynamics and study how drugs penetrate and flow within each cell. Furthermore, it should be noted that the sampling step in LSC-MS is highly dependent on the skill of the operator. Parameters such as spatial resolution, cell position inside the capillary after sampling, and throughput strength are wholly operator dependent, which limits large scale adoption of LSC-MS. Although, automated sampling systems may alleviate this issue. Furthermore, while LSC-MS excels at sampling adherent or floating cells in their native states, it performs more poorly in sampling cells embedded in tissue sections. This is due to the sampling capillary tip&#39;s tendency to break if the sample density is high. Therefore, another approach such as the single-probe may be more suitable in such cases14,15.
Since the cells used here are sampled in ambient conditions with minimal sample preparation, LSC-MS can be easily integrated with other technologies, as shown by its integration with Raman in this protocol. Another similar integration with 3D holography has allowed for achieving absolute quantitation of cellular metabolites on the subcellular level16. Additionally, integration with flow cytometry has allowed for the uncovering of metabolic biomarkers in single circulating tumor cells of neuroblastoma cancer patients17,18.
In the future, due to recent increasing interest in combining datasets from imaging modalities19, it may also be of interest to study the systematic variations between Raman signals and mass spectrometry results (as well as other omics methods) by using integrative computational approaches. Interestingly, we have already found several weak but significant linear correlations between the intensities of Raman peaks identified by VIP scores and the abundance of tamoxifen or its metabolite at the single-cell level as identified by MS4. This data may suggest a metabolic relationship between MS profiles and Raman spectra and the possibility to predict these values.

Cells respond differently to changes in their microenvironment at the single-cell level, a phenomenon termed cellular heterogeneity1. Despite this, current drug discovery studies are based on average measurements of cell populations, which obfuscate information about potential subpopulations as well as single-cell variations2. This missing information may explain why some cells are more susceptible to drugs while others are resistant. Interestingly, the lack of single-cell information about drug response is a possible reason for the failure of phase II clinical trials of drugs3. Therefore, to address this issue, cellular interactions with the drug (i.e., uptake, metabolism, and response) must be measured at the single-cell level.
To achieve this, we have designed a unique system in which living single cells are screened using label-free Raman spectroscopy then further characterized using mass spectrometry4. Raman spectroscopy provides a molecular fingerprint of the cellular state, a complex spectrum resulting from the contributions of many molecules inside the cell. Despite this complexity, it can be considered that Raman fingerprints reflect a whole cell&#39;s structure and metabolism5,6. Raman spectroscopy excels at measuring cellular states in a noninvasive and relatively high throughput manner, which makes it useful for screening and assessing drug response at the single-cell level.
In contrast, MS provides the required sensitivity and selectivity for measuring drug uptake at the single-cell level. Since MS is destructive (the sample [cell] is typically consumed during analysis), integrating it with nondestructive, label-free Raman spectroscopy can provide a high throughput and sensitive system. This combined platform is capable of providing more information about drug uptake, metabolism, and effects at the single-cell level.
This manuscript elucidates a protocol used to study cellular interactions with drugs at the single-cell level using in vitro cultures by using an integrated Raman-MS platform. To do so, hepatocellular carcinoma cells (HepG2) and tamoxifen are used as a model. HepG2 cells were chosen because they take up tamoxifen and metabolize the drug, and they are simultaneously affected due to its hepatotoxic effects. Two states are used in this manuscript: drug-treated cells vs. non-treated cells (control).

<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>0.1% penicillin-streptomycin</td>
			<td>Nacalai Tesque</td>
			<td>09367-34</td>
			<td></td>
		</tr>
		<tr>
			<td>35mm glass bottom grid dish</td>
			<td>Matsunami</td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>4-Hydroxy Tamoxifen standard</td>
			<td>Sigma-Aldrich</td>
			<td>94873</td>
			<td></td>
		</tr>
		<tr>
			<td>532 nm diode pumped solid-state laser</td>
			<td>Ventus, Laser Quantum</td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>BIOS-L101T-S motorized microscope stage</td>
			<td>OptoSigma</td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>CT-2 cellomics coated sampling capillaries</td>
			<td>HUMANIX</td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>d5-Tamoxifen standard</td>
			<td>Cambridge Isotope Laboratories</td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>Dimethyl sulfoxide LC-MS grade</td>
			<td>Nacalai Tesque</td>
			<td>D8418</td>
			<td></td>
		</tr>
		<tr>
			<td>Dulbecco&#39;s Modified Eagle&#39;s medium</td>
			<td>Sigma-Aldrich</td>
			<td>D5796</td>
			<td></td>
		</tr>
		<tr>
			<td>Eppendorf GELoader tips</td>
			<td>Eppendorf</td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>fetal bovine serum</td>
			<td>Hyclone laboratories</td>
			<td>SH3006603</td>
			<td></td>
		</tr>
		<tr>
			<td>FluoroBrite DMEM</td>
			<td>Thermo Fisher Scientific</td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>Formic acid LC-MS grade</td>
			<td>Sigma-Aldrich</td>
			<td>33015</td>
			<td></td>
		</tr>
		<tr>
			<td>HepG2 cell line (RCB1886)</td>
			<td>RIKEN cell bank center</td>
			<td>RCB1886</td>
			<td></td>
		</tr>
		<tr>
			<td>MC0-19A1C Incubator</td>
			<td>Sanyo Electric Co.</td>
			<td>MC0-19A1C</td>
			<td></td>
		</tr>
		<tr>
			<td>Methanol LC-MS grade</td>
			<td>Sigma-Aldrich</td>
			<td>1060352500</td>
			<td></td>
		</tr>
		<tr>
			<td>MMO-203 3-D Micromanipulator</td>
			<td>Narshige</td>
			<td>MMO-203</td>
			<td></td>
		</tr>
		<tr>
			<td>NA:0.95, UPL40 water-immersion Olympus objective lens</td>
			<td>Olympus</td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>Nanoflex nano-ESI adaptor</td>
			<td>Thermo Fisher Scientific</td>
			<td>ES071</td>
			<td></td>
		</tr>
		<tr>
			<td>On-stage incubator</td>
			<td>ibidi</td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>Pierce LTQ Velos ESI calibration solution</td>
			<td>Thermo Fisher Scientific</td>
			<td>88323</td>
			<td></td>
		</tr>
		<tr>
			<td>PIXIS BR400 cooled CCD camera</td>
			<td>Princeton Instruments</td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>Q-Exactive Orbitrap</td>
			<td>Thermo Fisher Scientific</td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>Rat-tail collagen coating solution</td>
			<td>Cell Applications Inc.</td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>Tamoxifen standard</td>
			<td>Sigma-Aldrich</td>
			<td>85256</td>
			<td></td>
		</tr>
	</tbody>
</table>

an integrated raman spectroscopy and mass spectrometry platform to study single-cell drug uptake, metabolism, and effects

Cells are known to be inherently heterogeneous in their responses to drugs. Therefore, it is essential that single-cell heterogeneity is accounted for in drug discovery studies. This can be achieved by accurately measuring the plethora of cellular interactions between a cell and drug at the single-cell level (i.e., drug uptake, metabolism, and effect). This paper describes a single-cell Raman spectroscopy and mass spectrometry (MS) platform to monitor metabolic changes of cells in response to drugs. Using this platform, metabolic changes in response to the drug can be measured by Raman spectroscopy, while the drug and its metabolite can be quantified using mass spectrometry in the same cell. The results suggest that it is possible to access information about drug uptake, metabolism, and response at a single-cell level.

Single-cell analysis of drug interactions (uptake, metabolism, and effects) is essential in uncovering any hidden or drug-resistant subpopulation as well as understanding the effects of cellular heterogeneity. In this protocol, two complementary techniques were used to measure the aforementioned interactions in single cells: Raman spectroscopy and MS. Raman spectrometry rapidly identifies cells affected by drugs based on spectral biomarkers of the drug response. MS is used to monitor the uptake and metabolism of the drug in a selective and semi-quantitative manner. Cells were first screened by Raman spectroscopy then individually sampled for analysis by MS.
A comparative analysis of the average spectrum of each condition (with and without drug treatment) is shown in Figure 2. The averaged spectrum of the two conditions clearly differ at various peaks, which were previously identified and assigned to molecular compounds2. In particular, the peaks at 1000 cm- (assigned to aromatic compounds such as phenylalanine and tyrosine) show strong differences. The significance of the statistical difference should be assessed by further multivariate analyses.
The data set was then used to train a PLS model (steps 4.5-4.8) aimed to distinguish the two cell treatments (with drug: n = 290, without drug: n = 115). The predictive ability to classify the cells cultured in the presence of tamoxifen reached 100% sensitivity and 72% specificity in the test data (unknown from the cross-validated trained model). Sensitivity is a measure of the true positives that are correctly identified by the model, while specificity is a measure of the actual negatives that are identified by the model. Alternative models such as SVMs, LDAs, and neural networks may provide similar or better results, although a comprehensive comparison has not been performed in this study.
Based on the PLS model, the VIP scores were calculated, which represent the importance of wavelengths (Raman shifts) in discriminating the experimental conditions (Figure 3). Importantly, the highest peaks of the VIP profiles corresponded to Raman peaks for which strong differences were seen between the two treatments. This confirmed the specific molecular differences between treated and untreated cells. Consequently, researchers can identify possible spectral biomarkers that reflect the response of single cells to drug treatment. These biomarkers can be tested further to verify their biological relevance and generalization across various conditions and cell lines.
A live single-cell mass spectrometry (LSC-MS) system was able to detect both the drug and its metabolites in single, drug-treated HepG2 cells that were previously measured by Raman spectroscopy. In addition, tandem MS may be used to confirm the structure of both molecules. After positive identification, the relative abundance of the drug and its metabolites were measured in each cell and compared to background peaks in untreated cells. Strong variation was observed in tamoxifen abundance, and this phenomenon was even more pronounced in the case of its metabolite, 4-OHT (Figure 4). The relationship between tamoxifen abundance and its metabolites was also studied, in which a significant positive correlation was found between the two (r = 0.54, p = 0.0001, n = 31).
<img alt="Figure 1" class="xfigimg" src="/files/ftp_upload/60449/60449fig1.jpg" /> 
Figure 1: Cell picking system mounted on a microscope stage. <a href="https://www.jove.com/files/ftp_upload/60449/60449fig1large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 2" class="xfigimg" src="/files/ftp_upload/60449/60449fig2.jpg" /> 
Figure 2: Averaged spectrum of the drug-treated cells (with tamoxifen: n = 295) and untreated cells (without tamoxifen: n = 115). Raman peaks can be identified from the literature. Most of the strong spectral differences are statistically significant (ANOVA, p &#8804; 0.5) as described previously4. This figure has been modified from a previous publication4. <a href="https://www.jove.com/files/ftp_upload/60449/60449fig2large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 3" class="xfigimg" src="/files/ftp_upload/60449/60449fig3.jpg" /> 
Figure 3: VIP scores extracted from the predictive PLS model. VIP scores reflect the wavelengths that contribute to distinguishing between the two classes in the model. Most of the peaks correspond to specific molecules that are observed as spectral biomarkers of drug effects on drug-treated cells. This figure has been modified from a previous publication4. <a href="https://www.jove.com/files/ftp_upload/60449/60449fig3large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 4" class="xfigimg" src="/files/ftp_upload/60449/60449fig4.jpg" /> 
Figure 4: Distribution of tamoxifen abundance and its metabolite. Distribution of tamoxifen abundance and its metabolite, 4-OHT (measured at the single-cell level) compared to endogenous peaks in the untreated cells (control). This figure has been modified from a previous publication4. <a href="https://www.jove.com/files/ftp_upload/60449/60449fig4large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>

Watch this Scientific Journal Video about An Integrated Raman Spectroscopy and Mass Spectrometry Platform to Study Single-Cell Drug Uptake, Metabolism, and Effects at JoVE.com

An Integrated Raman Spectroscopy and Mass Spectrometry Platform to Study Single-Cell Drug Uptake, Metabolism, and Effects

This protocol presents an integrated Raman spectroscopy-mass spectrometry (MS) platform that is capable of achieving single-cell resolution. Raman spectroscopy can be used to study cellular response to drugs, while MS can be used for targeted and quantitative analysis of drug uptake and metabolism.

Cells are known to be inherently heterogeneous in their responses to drugs. Therefore, it is essential that single-cell heterogeneity is accounted for in ...

an-integrated-raman-spectroscopy-mass-spectrometry-platform-to-study

Research

JoVE Journal

Biochemistry

9.3K Views.  Biodynamics Research Center (BDR), RIKEN. This protocol presents an integrated Raman spectroscopy-mass spectrometry (MS) platform that is capable of achieving single-cell resolution. Raman spectroscopy can be used to study cellular response to drugs, while MS can be used for targeted and quantitative analysis of drug uptake and metabolism.

Video: An Integrated Raman Spectroscopy and Mass Spectrometry Platform to Study Single-Cell Drug Uptake, Metabolism, and Effects

Fabricating a UV-Vis and Raman Spectroscopy Immunoassay Platform

Immunoassays are used to detect proteins based on the presence of associated antibodies. Because of their extensive use in research and clinical settings, a large infrastructure of immunoassay instruments and materials can be found. For example, 96- and 384-well polystyrene plates are available commercially and have a standard design to accommodate ultraviolet-visible (UV-Vis) spectroscopy machines from various manufacturers. In addition, a wide variety of immunoglobulins, detection tags, and blocking agents for customized immunoassay designs such as enzyme-linked immunosorbent assays (ELISA) are available.
Despite the existing infrastructure, standard ELISA kits do not meet all research needs, requiring individualized immunoassay development, which can be expensive and time-consuming. For example, ELISA kits have low multiplexing (detection of more than one analyte at a time) capabilities as they usually depend on fluorescence or colorimetric methods for detection. Colorimetric and fluorescent-based analyses have limited multiplexing capabilities due to broad spectral peaks. In contrast, Raman spectroscopy-based methods have a much greater capability for multiplexing due to narrow emission peaks. Another advantage of Raman spectroscopy is that Raman reporters experience significantly less photobleaching than fluorescent tags1. Despite the advantages that Raman reporters have over fluorescent and colorimetric tags, protocols to fabricate Raman-based immunoassays are limited. The purpose of this paper is to provide a protocol to prepare functionalized probes to use in conjunction with polystyrene plates for direct detection of analytes by UV-Vis analysis and Raman spectroscopy. This protocol will allow researchers to take a do-it-yourself approach for future multi-analyte detection while capitalizing on pre-established infrastructure.

Nanoparticle-based optical probes have been designed as a vehicle for detecting antigens using Raman and UV-Vis spectroscopy. Here we describe a protocol for preparing such probes for a UV-Vis/Raman spectroscopy immunoassay in such a way to incorporate future multiplexing capabilities.

Immunoassays are used to detect proteins based on the presence of associated antibodies. Because of their extensive use in research and clinical settings, ...

Non-contact, Label-free Monitoring of Cells and Extracellular Matrix using Raman Spectroscopy

Non-destructive, non-contact and label-free technologies to monitor cell and tissue cultures are needed in the field of biomedical research.1-5 However, currently available routine methods require processing steps and alter sample integrity. Raman spectroscopy is a fast method that enables the measurement of biological samples without the need for further processing steps. This laser-based technology detects the inelastic scattering of monochromatic light.6 As every chemical vibration is assigned to a specific Raman band (wavenumber in cm-1), each biological sample features a typical spectral pattern due to their inherent biochemical composition.7-9 Within Raman spectra, the peak intensities correlate with the amount of the present molecular bonds.1 Similarities and differences of the spectral data sets can be detected by employing a multivariate analysis (e.g. principal component analysis (PCA)).10
Here, we perform Raman spectroscopy of living cells and native tissues. Cells are either seeded on glass bottom dishes or kept in suspension under normal cell culture conditions (37 &deg;C, 5% CO2) before measurement. Native tissues are dissected and stored in phosphate buffered saline (PBS) at 4 &deg;C prior measurements. Depending on our experimental set up, we then either focused on the cell nucleus or extracellular matrix (ECM) proteins such as elastin and collagen. For all studies, a minimum of 30 cells or 30 random points of interest within the ECM are measured. Data processing steps included background subtraction and normalization.

Raman spectroscopy is a suitable technique for the non-contact, label-free analysis of living cells, tissue-engineered constructs and native tissues. Source-specific spectral fingerprints can be generated and analyzed using multivariate analysis.

Non-destructive, non-contact and label-free technologies to monitor cell and tissue cultures are needed in the field of biomedical research.1-5 However, ...

Bioengineering

A Versatile Automated Platform for Micro-scale Cell Stimulation Experiments

Study of cells in culture (in vitro analysis) has provided important insight into complex biological systems. Conventional methods and equipment for in vitro analysis are well suited to study of large numbers of cells (&ge;105) in milliliter-scale volumes (&ge;0.1 ml). However, there are many instances in which it is necessary or desirable to scale down culture size to reduce consumption of the cells of interest and/or reagents required for their culture, stimulation, or processing. Unfortunately, conventional approaches do not support precise and reproducible manipulation of micro-scale cultures, and the microfluidics-based automated systems currently available are too complex and specialized for routine use by most laboratories. To address this problem, we have developed a simple and versatile technology platform for automated culture, stimulation, and recovery of small populations of cells (100 - 2,000 cells) in micro-scale volumes (1 - 20 &mu;l). The platform consists of a set of fibronectin-coated microcapillaries ("cell perfusion chambers"), within which micro-scale cultures are established, maintained, and stimulated; a digital microfluidics (DMF) device outfitted with "transfer" microcapillaries ("central hub"), which routes cells and reagents to and from the perfusion chambers; a high-precision syringe pump, which powers transport of materials between the perfusion chambers and the central hub; and an electronic interface that provides control over transport of materials, which is coordinated and automated via pre-determined scripts. As an example, we used the platform to facilitate study of transcriptional responses elicited in immune cells upon challenge with bacteria. Use of the platform enabled us to reduce consumption of cells and reagents, minimize experiment-to-experiment variability, and re-direct hands-on labor. Given the advantages that it confers, as well as its accessibility and versatility, our platform should find use in a wide variety of laboratories and applications, and prove especially useful in facilitating analysis of cells and stimuli that are available in only limited quantities.

We have developed an automated cell culture and interrogation platform for micro-scale cell stimulation experiments. The platform offers simple, versatile, and precise control in cultivating and stimulating small populations of cells, and recovering lysates for molecular analyses. The platform is well suited to studies that use precious cells and/or reagents.

Study of cells in culture (in vitro analysis) has provided important insight into complex biological systems. Conventional methods and equipment for in ...

Microprobe Capillary Electrophoresis Mass Spectrometry for Single-cell Metabolomics in Live Frog (Xenopus laevis) Embryos

The quantification of small molecules in single cells raises new potentials for better understanding the basic processes that underlie embryonic development. To enable single-cell investigations directly in live embryos, new analytical approaches are needed, particularly those that are sensitive, selective, quantitative, robust, and scalable to different cell sizes. Here, we present a protocol that enables the in situ analysis of metabolism in single cells in freely developing embryos of the South African clawed frog (Xenopus laevis), a powerful model in cell and developmental biology. This approach uses a capillary microprobe to aspirate a defined portion from single identified cells in the embryo, leaving neighboring cells intact for subsequent analysis. The collected cell content is analyzed by a microscale capillary electrophoresis electrospray ionization (CE-ESI) interface coupled to a high-resolution tandem mass spectrometer. This approach is scalable to various cell sizes and compatible with the complex three-dimensional structure of the developing embryo. As an example, we demonstrate that microprobe single-cell CE-ESI-MS enables the elucidation of metabolic cell heterogeneity that unfolds as a progenitor cell gives rise to descendants during development of the embryo. Besides cell and developmental biology, the single-cell analysis protocols described here are amenable to other cell sizes, cell types, or animal models.

Microprobe Capillary Electrophoresis Mass Spectrometry for Single-cell Metabolomics in Live Frog Xenopus laevis Embryos

We describe steps that enable fast in situ sampling of a small portion of an individual cell with high precision and minimal invasion using capillary-based micro-sampling, to facilitate chemical characterization of a snapshot of metabolic activity in live embryos using a custom-built single cell capillary electrophoresis and mass spectrometry platform.

The quantification of small molecules in single cells raises new potentials for better understanding the basic processes that underlie embryonic ...

Chemistry

High-throughput and Comprehensive Drug Surveillance Using Multisegment Injection-Capillary Electrophoresis-Mass Spectrometry

New analytical methods are urgently needed to enable high-throughput, yet comprehensive drug screening, given an alarming opioid and prescription drug crisis in public health. Conventional urine drug testing based on a two-tier immunoassay screen followed by a gas chromatography-tandem mass spectrometry (GC-MS/MS) or liquid chromatography-tandem mass spectrometry (LC-MS/MS) method are expensive and prone to bias while being limited to targeted panels of known drugs of abuse (DoA). Herein, we outline an improved method for drug surveillance that allows for the resolution and detection of an expanded panel of DoA and their metabolites when using multisegment injection-capillary electrophoresis-mass spectrometry (MSI-CE-MS). Multiplexed separations of ten urine samples with a quality control by CE (&lt; 3 min/sample) in conjunction with full-scan data acquisition using a time-of-flight mass spectrometer (TOF-MS) under positive ion mode detection allows for the identification and quantification of DoA above recommended cut-off levels. An excellent resolution of drug isomers and isobars, including background interferences, are achieved when using MSI-CE-MS with an electrokinetic spacer between sample segments, where accurate mass/molecular formula together with the comigration of a matching deuterated internal standard and the detection of one or more bio-transformed metabolites facilitate DoA identification over a wider detection window. Additionally, urine samples can be analyzed directly without enzyme deconjugation for the rapid screening without complicated sample workup. MSI-CE-MS enables the surveillance of a broad spectrum of DoA that is required for the treatment monitoring of high-risk patients, including confirming prescribed drug adherence, revealing illicit drug use/substitution, and evaluating optimal dosage regimes as required for new advances in precision medicine.

Here we describe a high-throughput method for comprehensive drug surveillance that allows for the improved resolution and detection of large panels of drugs of abuse and their metabolites, with quality control based on multisegment injection-capillary electrophoresis-mass spectrometry.

New analytical methods are urgently needed to enable high-throughput, yet comprehensive drug screening, given an alarming opioid and prescription drug ...

Integrated Cell Manipulation Platform Coupled with the Single-probe for Mass Spectrometry Analysis of Drugs and Metabolites in Single Suspension Cells

Single cell mass spectrometry (SCMS) enables sensitive detection and accurate analysis of broad ranges of cellular species on the individual-cell level. The single-probe, a microscale sampling and ionization device, can be coupled with a mass spectrometer for on-line, rapid SCMS analysis of cellular constituents under ambient conditions. Previously, the single-probe SCMS technique was primarily used to measure cells immobilized onto a substrate, limiting the types of cells for studies. In the current study, the single-probe SCMS technology has been integrated with a cell manipulation system, typically used for in vitro fertilization. This integrated cell manipulation and analysis platform uses a cell-selection probe to capture identified individual floating cells and transfer the cells to the single-probe tip for microscale lysis, followed by immediate mass spectrometry analysis. This capture and transfer process removes the cells from the surrounding solution prior to analysis, minimizing the introduction of matrix molecules in the mass spectrometry analysis. This integrated setup is capable of SCMS analysis of targeted patient-isolated cells present in body fluids samples (e.g., urine, blood, saliva, etc.), allowing for potential applications of SCMS analysis to human medicine and disease biology.

An integrated cell manipulation platform is developed for use in conjunction with a single-probe mass spectrometry setup for the on-line analysis of individual suspension cells under ambient conditions.

Single cell mass spectrometry (SCMS) enables sensitive detection and accurate analysis of broad ranges of cellular species on the individual-cell level. ...

Bioorthogonal Chemical Imaging of Cell Metabolism Regulated by Aromatic Amino Acids

Essential aromatic amino acids (AAAs) are building blocks for synthesizing new biomasses in cells and sustaining normal biological functions. For example, an abundant supply of AAAs is important for cancer cells to maintain their rapid growth and division. With this, there is a rising demand for a highly specific, noninvasive imaging approach with minimal sample preparation to directly visualize how cells harness AAAs for their metabolism in situ. Here, we develop an optical imaging platform that combines deuterium oxide (D2O) probing with stimulated Raman scattering (DO-SRS) and integrates DO-SRS with two-photon excitation fluorescence (2PEF) into a single microscope to directly visualize the metabolic activities of HeLa cells under AAA regulation. Collectively, the DO-SRS platform provides high spatial resolution and specificity of newly synthesized proteins and lipids in single HeLa cell units. In addition, the 2PEF modality can detect autofluorescence signals of nicotinamide adenine dinucleotide (NADH) and Flavin in a label-free manner. The imaging system described here is compatible with both in vitro and in vivo models, which is flexible for various experiments. The general workflow of this protocol includes cell culture, culture media preparation, cell synchronization, cell fixation, and sample imaging with DO-SRS and 2PEF modalities.

We present a protocol to directly visualize metabolic activities in cells regulated by amino acids using deuterium-oxide (heavy water D2O) probed stimulated Raman scattering (DO-SRS) microscopy, which is integrated with two-photon excitation fluorescence microscopy (2PEF).

Essential aromatic amino acids (AAAs) are building blocks for synthesizing new biomasses in cells and sustaining normal biological functions. For example, ...

Label-Free SERS Analysis: Au@Carbon Dot Nanoprobes for Molecular Fingerprinting

Label-Free Surface-Enhanced Raman Scattering Bioanalysis Based on Au@Carbon Dot Nanoprobes

Surface-enhanced Raman scattering (SERS) technology has attracted more and more attention in the biomedical field due to its ability to provide molecular fingerprint information of biological samples, as well as its potential in single-cell analysis. This work aims to establish a simple strategy for label-free SERS bioanalysis based on Au@carbon dot nanoprobes (Au@CDs). Here, polyphenol-derived CDs are utilized as the reductant to rapidly synthesize core-shell Au@CD nanostructures, which allows powerful SERS performance even when the concentration of methylene blue (MB) is as low as 10-9 M, due to the cooperative Raman enhancement mechanism. For bioanalysis, Au@CDs can serve as a unique SERS nanosensor to identify the cellular components of biosamples (e.g., cancer cells and bacteria). The molecular fingerprints from different species can be further distinguished after combination with the principal component analysis. In addition, Au@CDs also enable label-free SERS imaging to analyze intracellular composition profiles. This strategy offers a feasible, label-free SERS bioanalysis, opening up a new prospect for nanodiagnosis.

Author Spotlight: Advancing SERS Technology: Au@Carbon Dot Nanoprobes for Label-Free Analysis and Imaging 

In this study, we developed a low-cost surface-enhanced Raman scattering (SERS)-based fingerprint nanoprobe with favorable biocompatibility to show label-free live cell bioimaging and detect two bacterial strains, showing in detail how to get SERS spectra of living cells in a non-destructive method.

Surface-enhanced Raman scattering (SERS) technology has attracted more and more attention in the biomedical field due to its ability to provide molecular ...

Applications of the Single-probe: Mass Spectrometry Imaging and Single Cell Analysis under Ambient Conditions

Mass spectrometry imaging (MSI) and in-situ single cell mass spectrometry (SCMS) analysis under ambient conditions are two emerging fields with great potential for the detailed mass spectrometry (MS) analysis of biomolecules from biological samples. The single-probe, a miniaturized device with integrated sampling and ionization capabilities, is capable of performing both ambient MSI and in-situ SCMS analysis. For ambient MSI, the single-probe uses surface micro-extraction to continually conduct MS analysis of the sample, and this technique allows the creation of MS images with high spatial resolution (8.5 &#181;m) from biological samples such as mouse brain and kidney sections. Ambient MSI has the advantage that little to no sample preparation is needed before the analysis, which reduces the amount of potential artifacts present in data acquisition and allows a more representative analysis of the sample to be acquired. For in-situ SCMS, the single-probe tip can be directly inserted into live eukaryotic cells such as HeLa cells, due to the small sampling tip size (&lt; 10 &#181;m), and this technique is capable of detecting a wide range of metabolites inside individual cells at near real-time. SCMS enables a greater sensitivity and accuracy of chemical information to be acquired at the single cell level, which could improve our understanding of biological processes at a more fundamental level than previously possible. The single-probe device can be potentially coupled with a variety of mass spectrometers for broad ranges of MSI and SCMS studies.

Here, we present protocols to perform both ambient mass spectrometry imaging (MSI) of tissues and in-situ live single cell MS (SCMS) analysis using the single-probe, which is a miniaturized multifunctional device for MS analysis.

Mass spectrometry imaging (MSI) and in-situ single cell mass spectrometry (SCMS) analysis under ambient conditions are two emerging fields with great ...

Biology

Rapid Antimicrobial Susceptibility Testing by Stimulated Raman Scattering Imaging of Deuterium Incorporation in a Single Bacterium

To slow and prevent the spread of antimicrobial resistant infections, rapid antimicrobial susceptibility testing (AST) is in urgent need to &#65279;quantitatively determine the &#65279;antimicrobial effects on pathogens. &#65279;It typically takes days to complete the AST by conventional methods based on the long-time culture, and they do not work directly for clinical samples. Here, we report a rapid AST &#65279;method enabled by stimulated Raman scattering (SRS) imaging of deuterium oxide (D2O) metabolic incorporation. Metabolic incorporation of D2O into biomass and the metabolic activity inhibition upon exposure to antibiotics at the single bacterium level are monitored by SRS imaging. The&#65279; single-cell metabolism inactivation concentration (SC-MIC) of bacteria upon exposure to antibiotics can be obtained after a total of 2.5 h of sample preparation and detection. Furthermore, this rapid AST method is directly applicable to bacterial samples in complex biological environments, such as urine or whole blood. SRS metabolic imaging of deuterium incorporation is transformative for rapid single-cell phenotypic AST in the clinic.

This protocol presents rapid antimicrobial susceptibility testing (AST) assay within 2.5 h by single-cell-stimulated Raman scattering imaging of D2O metabolism. This method applies to bacteria in the urine or whole blood environment, which is transformative for rapid single-cell phenotypic AST in the clinic.