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

1. Cell culture

1. 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's modified Eagle'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.
2. Incubate cells for 2 days at 37 °C and 5% CO₂ in a humidified incubator.
3. Synchronize cell cultures to reach 70% confluency.
4. 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 °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.

2. Drug treatment

1. Remove cell cultures from the incubator and wash 2x with prewarmed PBS buffer (37 °C).
   NOTE: It is optimal to remove cells for drug treatment at a confluency of 50%-60%.
2. Divide cells into drug-treated and untreated subgroups in 35 mm culture dishes.
3. 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 µM. This will be the drug-treated group.
4. 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.
5. 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%.

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 described^7,8. 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.

1. Prior to spectral measurements, verify the alignment of the optics. A 50 µm pinhole can be used to verify that the pinhole and laser position match exactly. Enter the spectrophotometer slit when narrowed as much as possible.
2. 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.
3. Minimize the laser intensity at the sample to ~2.4 mW/µm² so that cells survive laser exposure.
4. Set up the microchamber at 5% CO₂ and 37 °C.
5. Once the microscope system is ready, remove cells from the incubator and immediately rinse cells 2x with warmed PBS (37 °C) buffer, then add 2 mL of warmed PBS (37 °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.
6. Add 10 µL of water onto the water-immersion objective lens and delicately place the glass-bottom cell dish onto the microscope stage.
7. 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.

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 literature^7,8. In this section, we briefly describe the approach used to analyze and interpret spectral Raman data obtained from living single cells.

1. 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.
2. Extract spectral data of cells and background (absence of cells) from each image using a homemade algorithm. Subtract the background signal from the cell'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.
3. 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.
4. 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.
5. 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.
6. 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.
7. 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.
8. 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 literature^11,12.

5. Single-cell sampling set-up and procedures

1. 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).
2. 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 µm. A capillary with a bore size of ~5 µm 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.
3. 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.
4. 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.
5. Move the capillary up on the z-axis. Then, detach the capillary from the capillary holder using forceps in preparation for MS analysis.

6. Mass spectrometry measurements

1. Calibrate the mass accuracy of the MS instrument according to the manufacturer's recommendations. After calibration, make sure that the mass error is no greater than 3 ppm.
2. 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 °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.
3. 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.
4. 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.
5. 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.
6. To avoid false positives, aspirate the media surrounding the cells treated with the drug using a 1 µm bore-size capillary with constant microscopic observation to avoid sampling any cellular parts.
7. Add 2 µL 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).
8. Add 2 µL 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.

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).

1. 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.
2. 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.
3. 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.