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 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.
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'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).
1. Cell culture
2. Drug treatment
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.
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.
5. Single-cell sampling set-up and procedures
6. Mass spectrometry measurements
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).
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).
Figure 1: Cell picking system mounted on a microscope stage. Please click here to view a larger version of this figure.
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 ≤ 0.5) as described previously4. This figure has been modified from a previous publication4. Please click here to view a larger version of this figure.
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. Please click here to view a larger version of this figure.
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. Please click here to view a larger version of this figure.
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'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.
The authors have nothing to disclose.
The authors thank Toshio Yanagida for his support and RIKEN internal collaborative funds attributed to Dr. Arno Germond.
0.1% penicillin-streptomycin | Nacalai Tesque | 09367-34 | |
35mm glass bottom grid dish | Matsunami | ||
4-Hydroxy Tamoxifen standard | Sigma-Aldrich | 94873 | |
532 nm diode pumped solid-state laser | Ventus, Laser Quantum | ||
BIOS-L101T-S motorized microscope stage | OptoSigma | ||
CT-2 cellomics coated sampling capillaries | HUMANIX | ||
d5-Tamoxifen standard | Cambridge Isotope Laboratories | ||
Dimethyl sulfoxide LC-MS grade | Nacalai Tesque | D8418 | |
Dulbecco's Modified Eagle's medium | Sigma-Aldrich | D5796 | |
Eppendorf GELoader tips | Eppendorf | ||
fetal bovine serum | Hyclone laboratories | SH3006603 | |
FluoroBrite DMEM | Thermo Fisher Scientific | ||
Formic acid LC-MS grade | Sigma-Aldrich | 33015 | |
HepG2 cell line (RCB1886) | RIKEN cell bank center | RCB1886 | |
MC0-19A1C Incubator | Sanyo Electric Co. | MC0-19A1C | |
Methanol LC-MS grade | Sigma-Aldrich | 1060352500 | |
MMO-203 3-D Micromanipulator | Narshige | MMO-203 | |
NA:0.95, UPL40 water-immersion Olympus objective lens | Olympus | ||
Nanoflex nano-ESI adaptor | Thermo Fisher Scientific | ES071 | |
On-stage incubator | ibidi | ||
Pierce LTQ Velos ESI calibration solution | Thermo Fisher Scientific | 88323 | |
PIXIS BR400 cooled CCD camera | Princeton Instruments | ||
Q-Exactive Orbitrap | Thermo Fisher Scientific | ||
Rat-tail collagen coating solution | Cell Applications Inc. | ||
Tamoxifen standard | Sigma-Aldrich | 85256 |