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

9.2K 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

Sheathless Capillary Electrophoresis&#8211;Mass Spectrometry for Metabolic Profiling of Biological Samples

In metabolomics, a wide range of analytical techniques is used for the global profiling of (endogenous) metabolites in complex samples. In this paper, a protocol is presented for the analysis of anionic and cationic metabolites in biological samples by capillary electrophoresis&#8211;mass spectrometry (CE-MS). CE is well-suited for the analysis of highly polar and charged metabolites as compounds are separated on the basis of their charge-to-size ratio. A recently developed sheathless interfacing design, i.e., a porous tip interface, is used for coupling CE to electrospray ionization (ESI) MS. This interfacing approach allows the effective use of the intrinsically low-flow property of CE in combination with MS, resulting in nanomolar detection limits for a broad range of polar metabolite classes. The protocol presented here is based on employing a bare fused-silica capillary with a porous tip emitter at low-pH separation conditions for the analysis of a broad array of metabolite classes in biological samples. It is demonstrated that the same sheathless CE-MS method can be used for the profiling of cationic metabolites, including amino acids, nucleosides and small peptides, or anionic metabolites, including sugar phosphates, nucleotides and organic acids, by only switching the MS detection and separation voltage polarity. Highly information-rich metabolic profiles in various biological samples, such as urine, cerebrospinal fluid and extracts of the glioblastoma cell line, can be obtained by this protocol in less than 1 hr of CE-MS analysis.

Sheathless Capillary Electrophoresis&amp;#8211;Mass Spectrometry for Metabolic Profiling of Biological Samples

A protocol for metabolic profiling of biological samples by capillary electrophoresis&#8211;mass spectrometry using a sheathless porous tip interface design is presented.

In metabolomics, a wide range of analytical techniques is used for the global profiling of (endogenous) metabolites in complex samples. In this paper, a ...

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, ...

Time-resolved ElectroSpray Ionization Hydrogen-deuterium Exchange Mass Spectrometry for Studying Protein Structure and Dynamics

Intrinsically disordered proteins (IDPs) have long been a challenge to structural biologists due to their lack of stable secondary structure elements. Hydrogen-Deuterium Exchange (HDX) measured at rapid time scales is uniquely suited to detect structures and hydrogen bonding networks that are briefly populated, allowing for the characterization of transient conformers in native ensembles. Coupling of HDX to mass spectrometry offers several key advantages, including high sensitivity, low sample consumption and no restriction on protein size. This technique has advanced greatly in the last several decades, including the ability to monitor HDX labeling times on the millisecond time scale. In addition, by incorporating the HDX workflow onto a microfluidic platform housing an acidic protease microreactor, we are able to localize dynamic properties at the peptide level. In this study, Time-Resolved ElectroSpray Ionization Mass Spectrometry (TRESI-MS) coupled to HDX was used to provide a detailed picture of residual structure in the tau protein, as well as the conformational shifts induced upon hyperphosphorylation.

Conformational flexibility plays a critical role in protein function. Herein, we describe the use of time-resolved electrospray ionization mass spectrometry coupled to hydrogen-deuterium exchange for probing the rapid structural changes that drive function in ordered and disordered proteins.

Intrinsically disordered proteins (IDPs) have long been a challenge to structural biologists due to their lack of stable secondary structure elements. ...

Resolving Affinity Purified Protein Complexes by Blue Native PAGE and Protein Correlation Profiling

Most proteins act in association with others; hence, it is crucial to characterize these functional units in order to fully understand biological processes. Affinity purification coupled to mass spectrometry (AP-MS) has become the method of choice for identifying protein-protein interactions. However, conventional AP-MS studies provide information on protein interactions, but the organizational information is lost. To address this issue, we developed a strategy to unravel the distinct functional assemblies a protein might be involved in, by resolving affinity-purified protein complexes prior to their characterization by mass spectrometry. Protein complexes isolated through affinity purification of a bait protein using an epitope tag and competitive elution are separated through blue native electrophoresis. Comparison of protein migration profiles through correlation profiling using quantitative mass spectrometry allows assignment of interacting proteins to distinct molecular entities. This method is able to resolve protein complexes of close molecular weights that might not be resolved by traditional chromatographic techniques such as gel filtration. With little more work than conventional AP-geLC-MS/MS, we demonstrate this strategy may in many cases be adequate for obtaining protein complex topological information concomitantly to identifying protein interactions.

Here we present protocols for affinity purification of protein complexes and their separation by blue native PAGE, followed by protein correlation profiling using label free quantitative mass spectrometry. This method is useful to resolve interactomes into distinct protein complexes.

Most proteins act in association with others; hence, it is crucial to characterize these functional units in order to fully understand biological ...

Lipid Droplet Isolation for Quantitative Mass Spectrometry Analysis

Lipid droplets are vital to the replication of a variety of different pathogens, most prominently the Hepatitis C Virus (HCV), as the putative site of virion morphogenesis. Quantitative lipid droplet proteome analysis can be used to identify proteins that localize to or are displaced from lipid droplets under conditions such as virus infections. Here, we describe a protocol that has been successfully used to characterize the changes in the lipid droplet proteome following infection with HCV. We use Stable Isotope Labeling with Amino Acids in Cell Culture (SILAC) and thus label the complete proteome of one population of cells with &#34;heavy&#34; amino acids to quantitate the proteins by mass spectrometry. For lipid droplet isolation, the two cell populations (i.e.&#160;HCV-infected/&#34;light&#34; amino acids and uninfected control/&#34;heavy&#34; amino acids) are mixed 1:1 and lysed mechanically in hypotonic buffer. After removing the nuclei and cell debris by low speed centrifugation, lipid droplet-associated proteins are enriched by two subsequent ultracentrifugation steps followed by three washing steps in isotonic buffer. The purity of the lipid droplet fractions is analyzed by western blotting with antibodies recognizing different subcellular compartments. Lipid droplet-associated proteins are then separated by SDS-polyacrylamide gel electrophoresis (SDS-PAGE) followed by Coomassie staining. After tryptic digest, the peptides are quantified by liquid chromatography-electrospray ionization-tandem mass spectrometry (LC-ESI-MS/MS). Using this method, we identified proteins recruited to lipid droplets upon HCV infection that might represent pro- or antiviral host factors. Our method can be applied to a variety of different cells and culture conditions, such as infection with pathogens, environmental stress, or drug treatment.

Lipid droplets are important organelles for the replication of several pathogens, including the Hepatitis C Virus (HCV). We describe a method to isolate lipid droplets for quantitative mass spectrometry of associated proteins; it can be used under a variety of conditions, such as virus infection, environmental stress, or drug treatment.

Lipid droplets are vital to the replication of a variety of different pathogens, most prominently the Hepatitis C Virus (HCV), as the putative site of ...

Detection of Protein Ubiquitination Sites by Peptide Enrichment and Mass Spectrometry

The posttranslational modification of proteins by the small protein ubiquitin is involved in many cellular events. After tryptic digestion of ubiquitinated proteins, peptides with a diglycine remnant conjugated to the epsilon amino group of lysine (&#39;K-&#949;-diglycine&#39; or simply &#39;diGly&#39;) can be used to track back the original modification site. Efficient immunopurification of diGly peptides combined with sensitive detection by mass spectrometry has resulted in a huge increase in the number of ubiquitination sites identified up to date. We have made several improvements to this workflow, including offline high pH reverse-phase fractionation of peptides prior to the enrichment procedure, and the inclusion of more advanced peptide fragmentation settings in the ion routing multipole. Also, more efficient cleanup of the sample using a filter-based plug in order to retain the antibody beads results in a greater specificity for diGly peptides. These improvements result in the routine detection of more than 23,000 diGly peptides from human cervical cancer cells (HeLa) cell lysates upon proteasome inhibition in the cell. We show the efficacy of this strategy for in-depth analysis of the ubiquitinome profiles of several different cell types and of in vivo samples, such as brain tissue. This study presents an original addition to the toolbox for protein ubiquitination analysis to uncover the deep cellular ubiquitinome.

We present a method for the purification, detection, and identification of diGly peptides that originate from ubiquitinated proteins from complex biological samples. The presented method is reproducible, robust, and outperforms published methods with respect to the level of depth of the ubiquitinome analysis.

The posttranslational modification of proteins by the small protein ubiquitin is involved in many cellular events. After tryptic digestion of ...

Nitropeptide Profiling and Identification Illustrated by Angiotensin II

Protein nitration is one of the most important post-translational modifications (PTM) on tyrosine residues and it can be induced by chemical actions of reactive oxygen species (ROS) and reactive nitrogen species (RNS) in eukaryotic cells. Precise identification of nitration sites on proteins is crucial for understanding the physiological and pathological processes related to protein nitration, such as inflammation, aging, and cancer. Since the nitrated proteins are of low abundance in cells even under induced conditions, no universal and efficient methods have been developed for the profiling and identification of protein nitration sites. Here we describe a protocol for nitropeptide enrichment by using a chemical reduction reaction and biotin labeling, followed by high resolution mass spectrometry. In our method, nitropeptide derivatives can be identified with high accuracy. Our method exhibits two advantages compared to the previously reported methods. First, dimethyl labeling is used to block the primary amine on nitropeptides, which can be used to generate quantitative results. Second, a disulfide bond containing NHS-biotin reagent is used for the enrichment, which can be further reduced and alkylated to enhance the detection signal on a mass spectrometer. This protocol has been successfully applied to the model peptide Angiotensin II in the current paper.

Proteomic profiling of tyrosine-nitrated proteins has been a challenging technique due to the low abundance of the 3-nitrotyrosine modification. Here we describe a novel approach for nitropeptide enrichment and profiling by using Angiotensin II as the model. This method can be extended for other in vitro or in vivo systems.

Protein nitration is one of the most important post-translational modifications (PTM) on tyrosine residues and it can be induced by chemical actions of ...

A Plasma Sample Preparation for Mass Spectrometry using an Automated Workstation

Sample preparation for mass spectrometry analysis in proteomics requires enzymatic cleavage of proteins into a peptide mixture. This process involves numerous incubation and liquid transfer steps in order to achieve denaturation, reduction, alkylation, and cleavage. Adapting this workflow onto an automated workstation can increase efficiency and reduce coefficients of variance, thereby providing more reliable data for statistical comparisons between sample types. We previously described an automated proteomic sample preparation workflow1. Here, we report the development of a more efficient and better controlled workflow with the following advantages: 1) The number of liquid transfer steps is reduced from nine to six by combining reagents; 2) Pipetting time is reduced by selective tip pipetting using a 96-position pipetting head with multiple channels; 3) Potential throughput is increased by the availability of up to 45 deck positions; 4) Complete enclosure of the system provides improved temperature and environmental control and reduces the potential for contamination of samples or reagents; and 5) The addition of stable isotope labeled peptides, as well as &#946;-galactosidase protein, to each sample makes monitoring and quality control possible throughout the entire process. These hardware and process improvements provide good reproducibility and improve intra-assay and inter-assay precision (CV of less than 20%) for LC-MS based protein and peptide quantification. The entire workflow for digesting 96 samples in a 96-well plate can be completed in approximately 5 hours.

Here, we present an automated plasma protein digestion method for mass spectrometry-based quantitative proteomic analysis. In this protocol, the liquid transferring and incubation steps for protein denaturation, reduction, alkylation, and trypsin digestion reactions are streamlined and automated. It takes approximately five hours to prepare a 96-well plate with desired precision.

Sample preparation for mass spectrometry analysis in proteomics requires enzymatic cleavage of proteins into a peptide mixture. This process involves ...

Sample Preparation for Probe Electrospray Ionization Mass Spectrometry

Mass spectrometry (MS) is a powerful tool in analytical chemistry because it provides very accurate information about molecules, such as mass-to-charge ratios (m/z), which are useful to deduce molecular weights and structures. While it is essentially a destructive analytical method, recent advancements in the ambient ionization technique have enabled us to acquire data while leaving tissue in a relatively intact state in terms of integrity. Probe electrospray ionization (PESI) is a so-called direct method because it does not require complex and time-consuming pretreatment of samples. A fine needle serves as a sample picker, as well as an ionization emitter. Based on the very sharp and fine property of the probe tip, destruction of the samples is minimal, allowing us to acquire the real-time molecular information from living things in situ. Herein, we introduce three applications of PESI-MS technique that will be useful for biomedical research and development. One involves the application to solid tissue, which is the basic application of this technique for the medical diagnosis. As this technique requires only 10 mg of the sample, it may be very useful in the routine clinical settings. The second application is for in vitro medical diagnostics where human blood serum is measured. The ability to measure fluid samples is also valuable in various biological experiments where a sufficient volume of sample for conventional analytical techniques cannot be provided. The third application leans toward the direct application of probe needles in living animals, where we can obtain real-time dynamics of metabolites or drugs in specific organs. In each application, we can infer the molecules that have been detected by MS or use artificial intelligence to obtain a medical diagnosis.

This article introduces sample preparation methods for a unique real-time analytical method based on the ambient mass spectrometry. This method lets us perform real-time analysis of the biological molecules in vivo without any special pretreatments.

Mass spectrometry (MS) is a powerful tool in analytical chemistry because it provides very accurate information about molecules, such as mass-to-charge ...

Absolute Quantification of Cell-Free Protein Synthesis Metabolism by Reversed-Phase Liquid Chromatography-Mass Spectrometry

Cell-free protein synthesis (CFPS) is an emerging technology in systems and synthetic biology for the in vitro production of proteins. However, if CFPS is going to move beyond the laboratory and become a widespread and standard just in time manufacturing technology, we must understand the performance limits of these systems. Toward this question, we developed a robust protocol to quantify 40 compounds involved in glycolysis, the pentose phosphate pathway, the tricarboxylic acid cycle, energy metabolism and cofactor regeneration in CFPS reactions. The method uses internal standards tagged with 13C-aniline, while compounds in the sample are derivatized with 12C-aniline. The internal standards and sample were mixed and analyzed by reversed-phase liquid chromatography-mass spectrometry (LC/MS). The co-elution of compounds eliminated ion suppression, allowing the accurate quantification of metabolite concentrations over 2-3 orders of magnitude where the average correlation coefficient was 0.988. Five of the forty compounds were untagged with aniline, however, they were still detected in the CFPS sample and quantified with a standard curve method. The chromatographic run takes approximately 10 min to complete. Taken together, we developed a fast, robust method to separate and accurately quantify 40 compounds involved in CFPS in a single LC/MS run. The method is a comprehensive and accurate approach to characterize cell-free metabolism, so that ultimately, we can understand and improve the yield, productivity and energy efficiency of cell-free systems.

Here, we present a robust protocol to quantify 40 compounds involved in central carbon and energy metabolism in cell-free protein synthesis reactions. The cell-free synthesis mixture is derivatized with aniline for effective separation using reversed-phase liquid chromatography and then quantified by mass spectrometry using isotopically labelled internal standards.

Cell-free protein synthesis (CFPS) is an emerging technology in systems and synthetic biology for the in vitro production of proteins. However, if CFPS is ...