Abstract

To explore the effect in nasopharyngeal carcinoma (NPC) cells after treatment with chemodrugs, Raman profiles were characterized by laser tweezer Raman spectroscopy. Two NPC cell lines (CNE2 and C666-1) were treated with gemcitabine, cisplatin, and paclitaxel, respectively. The high-quality Raman spectra of cells without or with treatments were recorded at the single-cell level with label-free laser tweezers Raman spectroscopy (LTRS) and analyzed for the differences of alterations of Raman profiles. Tentative assignments of Raman peaks indicated that the cellular specific biomolecular changes associated with drug treatment include changes in protein structure (e.g. 1655 cm−1), changes in DNA/RNA content and structure (e.g. 830 cm−1), destruction of DNA/RNA base pairs (e.g. 785 cm−1), and reduction in lipids (e.g. 970 cm−1). Besides, both principal components analysis (PCA) combined with linear discriminant analysis (LDA) and the classification and regression trees (CRT) algorithms were employed to further analyze and classify the spectral data between control group and treated group, with the best discriminant accuracy of 96.7% and 90.0% for CNE2 and C666-1 group treated with paclitaxel, respectively. This exploratory work demonstrated that LTRS technology combined with multivariate statistical analysis has promising potential to be a novel analytical strategy at the single-cell level for the evaluation of NPC-related chemotherapeutic drugs.

© 2020 Optical Society of America under the terms of the OSA Open Access Publishing Agreement

1. Introduction

Nasopharyngeal carcinoma (NPC) is a common head and neck malignant tumor arising from the nasopharyngeal mucosa with the highest incidence rate in the east and southeast Asia, especially in south China [1]. Patients diagnosed with early stages (stages I and II) are able to reach long-term survival, however, approximately 80% of patients are diagnosed at advanced stages associated with poor outcomes [2]. Therefore, much effort should be made to improve the management and treatment for NPC patients with advanced stages. Different from the simple treatment (only radiotherapy) for early-stage patients, the treatment for locoregionally advanced patients requires radiotherapy combined with chemotherapy, including concurrent chemoradiotherapy, adjuvant chemotherapy, and induction chemotherapy to improve the recurrence-free survival and overall survival rate of these patients [3]. In clinical practice, gemcitabine, cisplatin, and paclitaxel are commonly used chemotherapeutic drugs for NPC. Although considerable progress has been made in the use of chemotherapy for NPC, the development of tumor cell drug resistance remains a therapeutic barrier. Therefore, it is of profound significance to explore the mechanism of action of different chemotherapeutic drugs for NPC cells via the effect in drug-induced cells.

Many studies in the past have contributed to the molecular action mechanisms of drugs in cancer chemotherapy. For instance, once taken up by cells, gemcitabine undergoes a series of phosphorylation by deoxycytidine kinase (DCK) to produce monophosphate (dFdCMP) and then by other pyrimidine kinases to its active diphosphate (dFdCDP) and triphosphate (dFdCTP) derivatives. dFdCTP damages DNA through a series of different mechanisms and dFdCDP enhances the effect of dFdCTP by directly inhibiting the RR subunit M1 (RRM1), which mainly happens in the G1/S phase [4,5]. It is also shown that cisplatin enters cells through multiple pathways. Due to the relatively low concentration of chloride ions in the cytoplasm, water gradually replaces the chloride ligands of cisplatin, resulting in highly reactive hydrated cisplatin. Aqueous cisplatin covalently binds to DNA to form a plurality of different DNA-cisplatin adducts, which cause DNA damage response, induce mitochondrial apoptosis and disrupt DNA replication and transcription, eventually leading to cell death [68]. For paclitaxel, it has been demonstrated that the main action of this drug is in interaction with β-tubulin in the microtubule network. Paclitaxel acts on the tubulin system, which can promote tubulin polymerization, assembly into microtubules, and inhibit the disintegration of microtubules. This results in the stabilization of microtubules and the loss of normal function of the spindle in the phase of G2/M, thereby inhibiting the mitosis and effectively preventing the proliferation of cancer cells [9,10]. At present, there are many methods to study the effect of chemotherapeutic drugs on cells, and it is very important to monitor the changes in intracellular characteristics. Shukla et al. [11] applied flow cytometry sorting, immunohistochemistry, chromatography, mass spectrometry, and other methods to confirm that gemcitabine-resistant pancreatic cancer cells targeting HIF-1α or de novo pyrimidine biosynthesis to increase the efficacy of gemcitabine. Ho et al. [12] used a flow cytometer to determine the cell cycle and apoptotic rate and used western blot to detect apoptosis and the expression of cell cycle related proteins. It was found that synergistically enhanced the antitumor effect of cisplatin on cisplatin-resistant T24R2 bladder cancer cells. Fisi et al. [13] used flow cytometry to discover that sequential treatment based on cell cycle characteristics can improve the cytotoxic effect of paclitaxel. However, at present, most methods commonly used in pharmacological, histological, and cytological research destroy cell samples. We need a convenient, non-destructive and rapid technology to study the mechanism of drugs at the cellular level and to explore the chemodrugs-induced effect in cells.

In the past decades, Raman spectroscopy (RS) based on inelastic scattering is emerging as a promising analytical technique in the field of biomedicine owing to a finger-like spectral pattern associated with various biomolecules [14]. Similar to infrared spectroscopy technology commonly used for cell analysis, RS can be used for exploring the information about cellular molecular structure, composition, and intermolecular interaction via the vibrations or rotations of the molecule. Furthermore, RS holds unique advantages over traditional infrared spectroscopy, such as higher spatial resolution and less absorption to water, enabling it to offer more cellular biochemical information as well as to greatly avoid the interference from intercellular or intracellular water during cell analysis [15]. Besides, RS is superior to fluorescence spectroscopy in biomedical applications, owing to its narrow bands generated by various biomolecules [16]. Attributing to the above characteristics, RS has been widely used to sensitively monitor subtle changes in the content of proteins, nucleic acids, and lipids in cells at the molecular level. Recently, some studies on the evaluation of chemotherapeutic drugs through cell assay using RS technology have shown attractive results [1720]. For example, Raman has been used to evaluate the efficacy of fluorouracil, cisplatin, and camptothecin on human breast adenocarcinoma (MCF-7) cells [21], monitor the chemical effects of cisplatin and 5-fluorouracil (5-FU) on human oral squamous carcinoma (HSC-3) cells [22], analyze the action of cisplatin on A549 adenocarcinoma cells [23], and explore the changes in lung cancer Calu-1 cells caused by gemcitabine [24]. Notably, Zoladek et. al. developed a novel system combing micro-Raman spectroscopy with an environmental enclosure to maintain target cells under sterile physiological conditions during measurement for accurate and non-invasive time-course imaging of apoptotic cells [25]. Although showing significant promising, some of these assays required cell fixation procedure (paraformaldehyde-fixed or formalin-fixed) prior to Raman measurement, especially for non-adherent, which has been demonstrated to cause significant changes to the original cellular profile [26,27]. This shortcoming would inevitably lead to the potential misinterpretation of cellular Raman data. In addition, other studies on living cells without fixation procedures need to rely on the cellular natural adherent property of the adherent cell line to perform stable Raman measurement, making it less suitable for non-adherent cell study. These limitations would hinder further applications of Raman-based cell assay for biomedical detection.

One of the alternative methods to overcome these limitations is the use of laser tweezers Raman spectroscopy (LTRS) technology which integrates laser trapping with confocal Raman spectroscopy to achieve single-cell trapping and perform Raman measurement simultaneously in suspension [28]. Due to the fact that the single cell can be non-invasively trapped and suspended away from any solid surface by a focused laser without disrupting cellular biological activity, the Raman signals of the single-cell under LTRS can precisely reflect cellular biomolecular information in comparison to traditional micro-Raman measurement [29]. Recently, the development of LTRS technology and its application in many diverse fields of biological science have made considerable progress. So far, LTRS has been extensively applied in the study of microbial cells, human peripheral blood cells, and cancer cells [3033]. Harvey et al. [33] showed that LTRS can be used to distinguish live prostate cancer and bladder cell lines (PC-3 and MGH-U1 respectively), and establish a principal component-linear discriminate analysis (PCA-LDA) model with a classification sensitivity of 93% and a specificity of 98%. Liu et al. [32] showed that colon cancer cells with single-base mutations in the KRAS gene fragment can be identified using the LTRS system. Besides, Chan et al. used LTRS to study the doxorubicin-induced leukemic T lymphocytes, and unique spectral variations after treatment suggested the changes in cellular lipid, protein, and DNA [34]. These results demonstrated the great potential of LTRS to be a novel, convenient and powerful tool for cell sorting and intracellular dynamics monitoring.

The main purpose of the present study was to explore the biochemical changes of NPC cells and assess the effects of chemotherapeutic drugs on cells using LTRS technology. A series of common chemotherapeutic drugs (gemcitabine, cisplatin, and paclitaxel) were applied to treat NPC cell lines (CNE2 and C666-1 NPC cell lines), respectively, and subsequently measured the Raman signals using LTRS followed by multivariate statistical analysis. It is expected to provide benefits for the study of the mechanism of action of drugs on cells and the improvement of the efficacy of chemotherapeutic drugs.

2. Materials and methods

2.1 Cell culture

Two human NPC cell lines, radiation-sensitive CNE2 and EBV-positive C666-1 (Shanghai Institute of Cells, Chinese Academy of Sciences, China), were involved in this study due to that they represent a common etiology in NPC and are widely used for NPC study. The cell lines were cultured in RPMI-1640 medium supplemented with 10% fetal bovine serum (FBS) and 100 U/ml of Penicillin and Streptomycin, grown in a humidified 5% CO2 at 37°C. The medium was changed every 2 days, and once the cell concentration at the bottom of the culture disk reached 75%–90%, the cells were passaged. Cells in the logarithmic growth phase were divided into the control group and three experimental groups, then 2µg/ml gemcitabine, 4 µg/ml cisplatin and 20 µg/ml paclitaxel were added to experimental groups respectively. The choice of drug dose is based on the inhibitory concentration, IC50, reported by the previous Refs. [3538]. In this work, we used the high dose of each drug to ensure the effects. After 48-hour treatment, the adherent cells were digested into suspension by trypsin and then centrifuged at the rate of 1000 rpm for 5 min. The supernatant was removed immediately and the precipitates were resuspended in standard phosphate-buffered saline (PBS) solution to maintain the cell's activity prior to LTRS assessment.

2.2 Laser tweezer Raman spectroscopy (LTRS) system and Raman measurement

Figure 1 shows the schematic of our home-made LTRS system. A 785nm diode laser beam was emitted from a laser source and pass through two convex lenses (11mm and 13mm focal lengths respectively) with a 10µm diameter pinhole to expand and reshape the incident laser beam into a 6mm diameter circular spot, which was necessary for stable cell trapping. The reshaped laser beam was further filtered by a bandpass filter to block laser except that with 785nm. A beam splitting dichroic mirror and several mirrors were employed to direct the laser beam into an inverted biological microscope (IX71; Olympus, Center Valley, PA, USA). The laser beam was further focused by the oil immersion objective (100×; N.A. = 1.3; Olympus, USA) to generate an optical trap with the size of 1 um above the high-purity quartz cover glass with a thickness of 80 µm placed on a 3D stage. Target cells on the sample holder will be captured by the 785nm laser with the spatial resolution of 2 µm, and excited by the same laser beam to generate the Raman signal with the laser power of 2mW at the sample. In order to confirm the cell trapping, the laser focal position was changed via the 3D stage under white light imaging to see whether the trapped cell would follow this point. The back-scattered Raman signals were collected by the same objective and passed through the dichroic mirror, a filter, lens and a single fiber to a transmissive holographic (Holospec-f/2.2-NIR) coupled to a back-illuminated, deep-depletion near-infrared (NIR) intensified CCD detector (Princeton Instruments), which was cooled to −120 °C prior to Raman measurement. Raman spectrum of each cell was acquired for 40 s in the wavenumber of 400-1800cm-1 under dark environment. A total of 30 CNE2 and 30 C666-1 treated with three chemotherapeutic drugs (gemcitabine, cisplatin, and paclitaxel) and without drugs (control), respectively, were detected in turn by LTRS. The software package WinSpec32 (Princeton Instruments, Trenton, NJ, USA) was employed for spectral acquisition and analysis. And the silicon wafer with Raman signals at 520cm−1 was used for system calibration.

 

Fig. 1. Schematic of the home-made laser tweezers Raman spectroscopy (LTRS) system. A 785 nm diode laser beam was delivered to an inverted microscope for both trapping NPC cells and generating the Raman signals from cells. Backward Raman scattering signals are recorded by a spectrometer combined with CCD. In this schematic, M: mirror; L: lens; PH: pinhole; F: filter; DM: dichroic mirror; MO: microscope objective; SH: sample holder.

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2.3 Data processing and multivariate statistical analysis

To acquire pure cell Raman signals, the raw Raman spectral data were preprocessed with background removal using a Vancouver Raman Algorithm based on a fifth-order polynomial fitting method [39]. The background-subtracted Raman spectra then were normalized to the integrated area under the curve in the wavenumber range of 400-1800 cm−1 to reduce the influence of spectral intensity variability generated by this system.

Principal components analysis (PCA) combined with linear discriminant analysis (LDA) and decision tree (DT) were used for analyzing the spectral data by the SPSS software package (SPSS Inc., Chicago, IL, USA). Firstly, we used PCA-LDA [4042] to investigate whether there were significant statistical differences between the control and experimental groups. The standardized data set of the Raman spectrum was input into SPSS for analysis. Independent samples T-test was used to determine the most diagnostic PC scores (p<0.05). The obtained PCs were input to an LDA model for correctly predicting the principal components of cells under different conditions. Then, based on the intensity of each band in the entire Raman spectrum, a DT classification model was constructed from the data, and the classification and regression trees (CRT) algorithm calculation tree was used to illustrate the variable importance.

3. Results and discussions

3.1 Spectral analysis

As shown in Fig. 2(A) and (C), the Raman profiles representing the effect in NPC cancer cells at 48 hours after the treatment of chemodrugs could be detected with LTRS technology, especially in the spectrum range of 400-1800cm−1. A small standard deviation (SD) of each spectral data indicated a great precision of Raman spectra. Although similar spectral patterns of four groups (control cells and cells treated with different drugs), there were many clearly detectable differences in peak intensity, as shown by the difference spectrum in Fig. 2(B) and 2(D) determined by comparing the average spectrum of control and treated cell group. Interestingly, in both CNE2 and C666-1 cells, compared to the control group, the alteration patterns of Raman profiles between cells treated with gemcitabine and cisplatin were very similar, while that with paclitaxel was quite different from gemcitabine and cisplatin. These differences of Raman profiles might relate to the actions of three drugs on different biological points, since both gemcitabine and cisplatin on DNA synthesis while paclitaxel on microtubule of M phase.

 

Fig. 2. Mean normalized Raman spectra and the difference spectrum calculated for the CNE2 (A, B) and C666-1 cell lines (C, D) without or with treatment chemodurgs. The shaded areas (grey) represent the standard deviation of the means.

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Generally, Raman peaks are generated by different cellular components, and the corresponding intensity of each peak is dominated by the relative concentration of a single component and the relative Raman scattering cross section. The main band positions and the corresponding assignments are given in Table 1, according to the previous reports [29,32,43,44], representing the molecular basis of the observed spectral peaks.

Tables Icon

Table 1. The Raman peak positions and tentative assignment of major vibrational bands observed in CNE2 and C666-1 cell lines treated with different chemotherapeutic drugsa

Notably, there was a decrease in the Raman signals of the paclitaxel-treated group in 1264 and 1655 cm−1 and an increase at 458, 492, 785, 830, and 853 cm−1. This means that amide III (1264 cm−1) and amide I (1655 cm−1) decreased, while tryptophan (458 cm−1), tyrosine (492 cm−1), U, T, C (785 cm−1), O-P-O stretch and tyrosine (830 cm−1), and ring breathing mode of tyrosine and C-C stretch of proline ring (853 cm−1) all increased under the action of paclitaxel. This points to changes in proteins and amino acids, confirming the effect of paclitaxel on tubulin. This might provide a new characteristic signature for defining new drug that acts on microtubule like paclitaxel. Similarly, the highly similar Raman profiles of gemcitabine and cisplatin might provide a characteristic signature for defining drugs that acts on the DNA synthesis phase.

This can provide an opportunity to explore the mechanism of chemotherapeutic drugs or the choice of biomarkers. Also, it should be noted that all of the above are just simple peak intensity descriptions with limited Raman peak information under observation. Some biomarker-based peaks may overlap, so multivariate statistical analysis is needed to analyze the spectral data to explore more potential characteristic information.

3.2 Statistical analysis

A multivariate statistics algorithm with PCA-LDA analysis was used to analyze the identity between the control group and the three drugs treated groups. This algorithm is widely used in biomedical applications as a powerful spectral analysis method [4547]. The normalized data of the whole Raman spectra was input into the SPSS software for factor analysis. As shown in Fig. 3, the first two principal components (PCs) contained the greatest effects and explained the maximum variance in the PCA process, therefore, the effects and variances of PCs>3 were neglected. Similar to Fig. 2(B) and (D), the score plots of PC1 versus PC2 in control and paclitaxel-treated samples were greatly clustered into two separate regions (Fig. 3(C) and (F)), which was better separated than those of cells treated with gemcitabine (Fig. 3(A) and (D)) or the cisplatin (Fig. 3(B) and (E)). Again, this may be caused by the different mechanisms of drug action. Both gemcitabine and cisplatin act on DNA damage, while paclitaxel inhibits mitosis by regulating tubulin, showing different degrees of separation from the control group in two NPC cell lines.

 

Fig. 3. Score plots of PC1 versus PC2 of whole Raman data for the different treatments of CNE2 (A-C) andC666-1 (D-F) cells (% explained variance in parenthesis).

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Figure 4 displays the loading plots of PC 1 and PC2 calculated from the Raman spectra of control vs. CNE2 (A-C) and C666-1(D-F) cells treated by gemcitabine, cisplatin, and paclitaxel. As we can see, some distinct peaks (e.g. 830, 1264, and 1655 cm−1) generated by PCs are similar to those of cell Raman spectra in Fig. 2. More importantly, some diagnostic variables (e.g. 621, 970, and 1335 cm−1) that are non-significant in the Raman difference spectrum, can be well revealed by the PC loading. These results indicate PC loading is capable of identifying the discriminating features in the underlying spectroscopic data and revealing the diagnostically significant spectral features for cell classification under different chemotherapeutic drugs treatment. The possible reason for these characteristics of PC loading might be that the variations in the data are maximized when the PCA process reduces the dimensionality of Raman data into linear combinations of a few orthogonal components (PCs) [48,49]. To further develop sophisticated multivariate spectral diagnostic algorithms, all the two diagnostically significant PCs are fed into the LDA model with the leave-one-out cross-validation method for cell classification. The sensitivity, specificity, and accuracy obtained are summarized in Table 2.

 

Fig. 4. The first two diagnostically significant principal components (PCs) calculated from Raman spectra of control vs. CNE2 (A-C) and C666-1(D-F) cells treated by different chemotherapeutic drugs (gemcitabine, cisplatin, and paclitaxel).

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Tables Icon

Table 2. Classification results of Raman spectra prediction of the four groups using PCA-LDA

In CNE2 and C666-1 cells, the diagnostic sensitivities for identifying the cells in control and gemcitabine group were 86.7% and 86.7%; in control and cisplatin group 100% and 80.0% and in control and paclitaxel group were 100% and 86.7%, respectively. The corresponding diagnostic specificities for each combination were 90.0% and 86.7%, 90.0%,76.7%, and 93.3% and 93.3%, respectively. It was found that both in CNE2 or C666-1 cell lines, the classification of the control and the paclitaxel group was the best, consistent with the above results of PCA-based classification model (Fig. 3). In the comparison of the gemcitabine group and the cisplatin group, the gemcitabine group and the paclitaxel group, and the cisplatin group and the paclitaxel group, the accuracy of CNE2 and C666-1 were 91.7% and 80.0%, 98.3% and 100.0%, and 93.3% and 100.0%, respectively. Due to that, the paclitaxel inhibits mitosis and both gemcitabine and cisplatin cause DNA damage, the classification accuracy was higher in the paclitaxel than the other two drugs. These results indicated that through the processing of PCA-LDA, the information in Raman spectrum data could be fully utilized for the classification of NPC cells under the influence of different drugs.

In order to further explore specific biomolecular changes induced by drugs, the deep analysis of Raman peaks is needed. The band data was used to construct a decision tree (DT) classification model by the classification and regression trees (CRT) algorithm [50] for prediction and classification. DT model is a tree structure in which each internal node represents a test on an attribute, each branch represents a test output, and each leaf node represents a category. The CRT algorithm uses the Gini index to select attributes to divide child nodes to build a DT model. Then trim the tree, that is, trim the internal nodes to prevent overfitting, which can improve the generalization ability of the model. In the process of building the model, the contribution of the bands is its importance as an independent variable after normalization. These bands play a key role in the classification.

The most important top ten bands in each model are listed in Fig. 5. It can be found that under the same contrast, the most important spectral bands of CNE2 and C666-1 cell lines are different. For example, in the comparison between the control group and the gemcitabine group, the top ten most important independent variables in CNE2 and C666-1 cell lines were the bands at 1655, 1123, 1264, 970, 1156, 901, 1335, 458, 933, 1521 cm−1 and 830, 492, 458, 1123, 1552, 1655, 752, 1335, 901, 1297 cm−1, respectively. Although CNE2 and C666-1 cell lines are both NPC cells, there were still some differences in biological characteristics. Therefore, even under the same conditions, their most important bands were different. In the comparison of different groups of the same cell line, the important bands were also different. The intensity of these bands illustrated the changes in the biological characteristics of cells under the action of different drugs.

 

Fig. 5. The contribution of each band as an independent variable in the models after normalization of CNE2 (A-C) and C666-1 (D-F) cell lines. The importance of each independent Raman band in establishing the DT models, between the control group and the gemcitabine group, the control group, and the cisplatin group, and the control group and the paclitaxel group.

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Next, the important bands obtained by CRT are sorted and classified according to their assignment. Since NPC cells are basically composed of three macromolecules including protein, nucleic acid, and lipid, bands are divided into these three types. Through the band intensity and their differences between different groups, we explored the changes in cells treated with drugs. As shown in Fig. 6, the intensity of the important bands is plotted as a histogram. Then Student’s t-test was used to analyze the significant differences of band data between the control group and the experimental groups. p<0.05 was labeled as *, p<0.01 was labeled as **.

 

Fig. 6. Comparison of the mean intensities between four groups in (A) CNE2 and (B) C666-1 cell lines. Histogram showing the quantitative contribution of proteins, nucleic acids, and lipids in the cellular spectral information. *: p<0.05: **: p<0.01.

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In the CNE2 and C666-1 cell lines, the intensity of the same peak position was compared. It was found that the content of the substances in the two cell lines was similar, and the changes in the intensity mostly in the same direction, indicating that the components of NPC cell lines were similar, and the effects of drugs on cells were also consistent.

In the bands that represented proteins, it was found in three experimental groups that the intensity of the 1123 cm−1 (C-C stretching mode of lipids & protein and C-N stretch) peak was significantly increased, while 1264 cm−1 (amide III) and 1655 cm−1 (amide I) peaks were decreased, indicating that the α-helix structure of the protein might be changed. The peak intensity of the 752 cm−1 (symmetric breathing of tryptophan) and 1612 cm−1 (C = C stretching mode of tyrosine & tryptophan) increased, reflecting the changes in the amino acid residues of the protein molecule. In addition, in the paclitaxel group, the intensity increased at 458 cm−1 (tryptophan), 492 cm−1 (tyrosine), 621 cm−1 (C-C twisting mode of phenylalanine), and 853 cm−1 (ring breathing mode of tyrosine & C-C stretch of proline ring) and decreased at 933 cm−1 (C-C stretching mode of proline). All of these changes led to disturbances in the structure of the proteins. The gemcitabine and cisplatin caused the conformation change of DNA, weaken protein synthesis, and induced apoptosis. Paclitaxel binds to tubulin to stabilize polymerization and prevent depolymerization. It breaks the dynamic balance of tubulin and tubulin dimers, which causes tumor cells to fail to form spindle apparatus during the process of mitosis, thereby inhibiting cell division and proliferation, and ultimately leading to the death of cancer cells. All three drugs changed the structure of the protein to varying degrees as demonstrated in Fig. 6.

In the nucleic acids’ changes, a decrease in 901 cm−1 (backbone deoxy rib) was observed in three groups. In the gemcitabine and cisplatin groups, the peaks at 785 cm−1 (U, C, T), decreased at 830 cm−1 (O-P-O stretch) and increased at 1335 cm−1 (A, G). The different patterns were seen in the paclitaxel group. The intensity of the peak at 785 cm−1 has been proven to determine the DNA content of cells [51]. These show that the content of the DNA/RNA and the structure of the double helix may have changed and the destruction of DNA/RNA base pairs affects the way of DNA/RNA replication. The main action mechanism of gemcitabine is via dFdCTP toxic effects. Gemcitabine also enhances topoisomerase-I cleavage during DNA/RNA replication, leading to DNA strand breaks. In addition, gemcitabine causes reactive oxygen species (ROS) stress, which can damage DNA. These mechanisms are based on DNA damage and subsequent cell apoptosis [4]. Cisplatin exerts an anti-cancer effect through a complex signaling pathway, mainly through intra-chain cross-linking to form DNA-cisplatin adducts. It can also cause DNA damage through oxidative stress, which in turn activates the apoptotic pathway and causes cell death [7]. Gemcitabine and cisplatin acted on the G1/S phase and inhibited DNA replication. Paclitaxel acts on tubulin at the G2/M phase, and tubulin plays a key role in the DNA isolation process. Due to their different mechanisms of action, the Raman changes in DNA caused by them were also different. Next, we observed a decrease in the peak at 970 cm−1 (chain C-C) in three groups, reflecting changes in lipids. Phospholipids are the main component of the cell membrane, presenting the integrated structure of the cell membrane. The three drugs cause alterations at 970 cm−1, suggesting that the cell membrane permeability might increase.

Taken together, we found that three chemodrugs induced biological changes in protein DNA and lipids could be sensitively detected by alterations of the Raman profile. The characteristic alterations of the Raman profile could reflect drug acting at different biological points, which might help to clarify the action mechanism of chemodrugs.

4. Conclusions

In summary, the LTRS technology capable of achieving single-cell analysis at the molecular level was used to investigate the effect in NPC cell lines (CNE2 and C666-1) treated with three chemotherapeutic drugs (gemcitabine, cisplatin, and paclitaxel). Through the analysis of Raman spectra, the characteristic changes of NPC cell lines after drug treatment were observed. Using the PCA-LDA statistical algorithm, cells treated with drugs can be well classified and identified from those without drugs, with the best accuracy of 96.7% and 90.0% for CNE2 and C666-1 group treated with paclitaxel, respectively. By analyzing the content of important bands in the process of constructing the CRT model, we found that three drugs mainly contributed to cellular changes in protein structure, changes in DNA/RNA content and structure, destruction of DNA/RNA base pairs, and reduction in lipids. These findings would be useful information for further understanding the biologic mechanism of NPC-related chemotherapeutic drugs, improving efficacy and making optimal treatment strategy. Next, we will continue to optimize this LTRS system, such as the design of a multi-beam-based LTRS system and development of unique Raman probes, to reveal more comprehensive biochemical information inside the single cell after treatment in a rapid and accurate manner.

Funding

Fujian Provincial Health Technology Project (2018-CX-13); Joint Funds for the innovation of science and Technology, Fujian province (2018Y9105); United Fujian Provincial Health and Education Project for Tackling the Key Research, China (2019-WJ-03); National Natural Science Foundation of China (11974077, U1605253); Fujian Provincial Key Laboratory of Translational Cancer Medicine and Science and Technology Program of Fujian Province, China (2018Y2003).

Disclosures

The authors declare that there are no conflicts of interest related to this article.

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10. X. Shi and X. Sun, “Regulation of paclitaxel activity by microtubule-associated proteins in cancer chemotherapy,” Cancer Chemother. Pharmacol. 80(5), 909–917 (2017). [CrossRef]  

11. S. K. Shukla, V. Purohit, K. Mehla, V. Gunda, N. V. Chaika, E. Vernucci, R. J. King, J. Abrego, G. D. Goode, A. Dasgupta, A. L. Illies, T. Gebregiworgis, B. Dai, J. J. Augustine, D. Murthy, K. S. Attri, O. Mashadova, P. M. Grandgenett, R. Powers, Q. P. Ly, A. J. Lazenby, J. L. Grem, F. Yu, J. M. Matés, J. M. Asara, J.-W. Kim, J. H. Hankins, C. Weekes, M. A. Hollingsworth, N. J. Serkova, A. R. Sasson, J. B. Fleming, J. M. Oliveto, C. A. Lyssiotis, L. C. Cantley, L. Berim, and P. K. Singh, “MUC1 and HIF-1alpha Signaling Crosstalk Induces Anabolic Glucose Metabolism to Impart Gemcitabine Resistance to Pancreatic Cancer,” Cancer Cell 32(1), 71–87.e7 (2017). [CrossRef]  

12. J.-N. Ho, S.-S. Byun, S. Lee, J. J. Oh, S. K. Hong, S. E. Lee, and J. S. Yeon, “Synergistic antitumor effect of triptolide and cisplatin in cisplatin resistant human bladder cancer cells,” J. Cell Biol. 193(3), 1016–1022 (2015). [CrossRef]  

13. V. Fisi, E. Kátai, P. Bogner, A. Miseta, and T. Nagy, “Timed, sequential administration of paclitaxel improves its cytotoxic effectiveness in a cell culture model,” Cell Cycle 15(9), 1227–1233 (2016). [CrossRef]  

14. C. Zong, M. Xu, L.-J. Xu, T. Wei, X. Ma, X.-S. Zheng, R. Hu, and B. Ren, “Surface-Enhanced Raman Spectroscopy for Bioanalysis: Reliability and Challenges,” Chem. Rev. 118(10), 4946–4980 (2018). [CrossRef]  

15. I. W. De Silva, A. R. Kretsch, H.-M. Lewis, M. Bailey, and G. F. Verbeck, “True one cell chemical analysis: a review,” Analyst 144(16), 4733–4749 (2019). [CrossRef]  

16. D. Lin, Q. Wu, S. Qiu, G. Chen, S. Feng, R. Chen, and H. Zeng, “Label-free liquid biopsy based on blood circulating DNA detection using SERS-based nanotechnology for nasopharyngeal cancer screening,” Nanomedicine (N. Y., NY, U. S.) 22, 102100 (2019). [CrossRef]  

17. H. J. Byrne, F. Bonnier, and Z. Farhane, “Two-dimensional correlation analysis of Raman microspectroscopy of subcellular interactions of drugs in vitro,” J. Biophotonics 12(3), e201800328 (2019). [CrossRef]  

18. Z. Farhane, F. Bonnier, and H. J. Byrne, “An in vitro study of the interaction of the chemotherapeutic drug Actinomycin D with lung cancer cell lines using Raman micro-spectroscopy,” J. Biophotonics 11(1), e201700112 (2018). [CrossRef]  

19. S. F. El-Mashtoly, H. K. Yosef, D. Petersen, L. Mavarani, A. Maghnouj, S. Hahn, C. Kötting, and K. Gerwert, “Label-free Raman spectroscopic imaging monitors the integral physiologically relevant drug responses in cancer cells,” Anal. Chem. 87(14), 7297–7304 (2015). [CrossRef]  

20. H. Salehi, E. Middendorp, I. Panayotov, P.-Y. C. Dutilleul, A.-G. Vegh, S. K. Ramakrishnan, C. Gergely, and F. J. Cuisinier, “Confocal Raman data analysis enables identifying apoptosis of MCF-7 cells caused by anticancer drug paclitaxel,” J. Biomed. Opt. 18(5), 056010 (2013). [CrossRef]  

21. S.-S. Li, Q.-Y. Guan, G. Meng, X.-F. Chang, J.-W. Wei, P. Wang, B. Kang, J.-J. Xu, and H.-Y. Chen, “Revealing chemical processes and kinetics of drug action within single living cells via plasmonic Raman probes,” Sci. Rep. 7(1), 2296 (2017). [CrossRef]  

22. L. A. Austin, B. Kang, and M. A. El-Sayed, “A new nanotechnology technique for determining drug efficacy using targeted plasmonically enhanced single cell imaging spectroscopy,” J. Am. Chem. Soc. 135(12), 4688–4691 (2013). [CrossRef]  

23. H. Nawaz, F. Bonnier, A. D. Meade, F. M. Lyng, and H. J. Byrne, “Comparison of subcellular responses for the evaluation and prediction of the chemotherapeutic response to cisplatin in lung adenocarcinoma using Raman spectroscopy,” Analyst 136(12), 2450–2463 (2011). [CrossRef]  

24. F. Draux, C. Gobinet, J. Sulé-Suso, M. Manfait, P. Jeannesson, and G. D. Sockalingum, “Raman imaging of single living cells: probing effects of non-cytotoxic doses of an anti-cancer drug,” Analyst 136(13), 2718–2725 (2011). [CrossRef]  

25. A. Zoladek, F. C. Pascut, P. Patel, and I. Notingher, “Non-invasive time-course imaging of apoptotic cells by confocal Raman micro-spectroscopy,” J. Raman Spectrosc. 42(3), 251–258 (2011). [CrossRef]  

26. J. W. Chan, D. S. Taylor, and D. L. Thompson, “The effect of cell fixation on the discrimination of normal and leukemia cells with laser tweezers Raman spectroscopy,” Biopolymers 91(2), 132–139 (2009). [CrossRef]  

27. A. N. Kuzmin, A. Pliss, and P. N. Prasad, “Changes in biomolecular profile in a single nucleolus during cell fixation,” Anal. Chem. 86(21), 10909–10916 (2014). [CrossRef]  

28. J. W. Chan, “Recent advances in laser tweezers Raman spectroscopy (LTRS) for label-free analysis of single cells,” J. Biophotonics 6(1), 36–48 (2013). [CrossRef]  

29. C. Krafft, I. W. Schie, T. Meyer, M. Schmitt, and J. Popp, “Developments in spontaneous and coherent Raman scattering microscopic imaging for biomedical applications,” Chem. Soc. Rev. 45(7), 1819–1849 (2016). [CrossRef]  

30. S. Wang, B. Setlow, P. Setlow, and Y.-Q. Li, “Uptake and levels of the antibiotic berberine in individual dormant and germinating Clostridium difficile and Bacillus cereus spores as measured by laser tweezers Raman spectroscopy,” J. Antimicrob. Chemother. 71(6), 1540–1546 (2016). [CrossRef]  

31. J. Lin, L. Shao, S. Qiu, X. Huang, M. Liu, Z. Zheng, D. Lin, Y. Xu, Z. Li, Y. Lin, R. Chen, and S. Feng, “Application of a near-infrared laser tweezers Raman spectroscopy system for label-free analysis and differentiation of diabetic red blood cells,” Biomed. Opt. Express 9(3), 984–993 (2018). [CrossRef]  

32. M. Liu, X. Liu, Z. Huang, X. Tang, X. Lin, Y. Xu, G. Chen, H. F. Kwok, Y. Lin, and S. Feng, “Rapid discrimination of colon cancer cells with single base mutation in KRAS gene segment using laser tweezers Raman spectroscopy,” J. Biophotonics 12(3), e201800332 (2019). [CrossRef]  

33. T. J. Harvey, C. Hughes, A. D. Ward, E. C. Faria, A. Henderson, N. W. Clarke, M. D. Brown, R. D. Snook, and P. Gardner, “Classification of fixed urological cells using Raman tweezers,” J. Biophotonics 2(1-2), 47–69 (2009). [CrossRef]  

34. T. J. Moritz, D. S. Taylor, D. M. Krol, J. Fritch, and J. W. Chan, “Detection of doxorubicin-induced apoptosis of leukemic T-lymphocytes by laser tweezers Raman spectroscopy,” Biomed. Opt. Express 1(4), 1138–1147 (2010). [CrossRef]  

35. C. Xue, Y. Tian, J. Zhang, Y. Zhao, J. Zhan, W. Fang, and L. Zhang, “In vitro and in vivo efficacy of afatinib as a single agent or in combination with gemcitabine for the treatment of nasopharyngeal carcinoma,” Drug Des., Dev. Ther. 10, 1299–1306 (2016). [CrossRef]  

36. X. Ren, X. Yang, B. Cheng, X. Chen, T. Zhang, Q. He, B. Li, Y. Li, X. Tang, X. Wen, Q. Zhong, T. Kang, M. Zeng, N. Liu, and J. Ma, “HOPX hypermethylation promotes metastasis via activating SNAIL transcription in nasopharyngeal carcinoma,” Nat. Commun. 8(1), 14053 (2017). [CrossRef]  

37. H. Peng, J. Zhang, P.-P. Zhang, L. Chen, L.-L. Tang, X.-J. Yang, Q.-M. He, X. Wen, Y. Sun, N. Liu, Y.-Q. Li, and J. Ma, “ARNTL hypermethylation promotes tumorigenesis and inhibits cisplatin sensitivity by activating CDK5 transcription in nasopharyngeal carcinoma,” J. Exp. Clin. Cancer Res. 38(1), 11 (2019). [CrossRef]  

38. Y. Zhang, J. Fan, Y. Fan, L. Li, X. He, Q. Xiang, J. Mu, D. Zhou, X. Sun, Y. Yang, G. Ren, Q. Tao, and T. Xiang, “The new 6q27 tumor suppressor DACT2, frequently silenced by CpG methylation, sensitizes nasopharyngeal cancer cells to paclitaxel and 5-FU toxicity via β-catenin/Cdc25c signaling and G2/M arrest,” Clin. Epigenet. 10(1), 26 (2018). [CrossRef]  

39. J. Zhao, H. Lui, D. I. McLean, and H. Zeng, “Automated autofluorescence background subtraction algorithm for biomedical Raman spectroscopy,” Appl. Spectrosc. 61(11), 1225–1232 (2007). [CrossRef]  

40. Q. Wu, S. Qiu, Y. Yu, W. Chen, H. Lin, D. Lin, S. Feng, and R. Chen, “Assessment of the radiotherapy effect for nasopharyngeal cancer using plasma surface-enhanced Raman spectroscopy technology,” Biomed. Opt. Express 9(7), 3413–3423 (2018). [CrossRef]  

41. D. Lin, S. Feng, J. Pan, Y. Chen, J. Lin, G. Chen, S. Xie, H. Zeng, and R. Chen, “Colorectal cancer detection by gold nanoparticle based surface-enhanced Raman spectroscopy of blood serum and statistical analysis,” Opt. Express 19(14), 13565–13577 (2011). [CrossRef]  

42. I. Aguilar-Hernández, D. L. Cárdenas-Chavez, T. López-Luke, A. García-García, M. Herrera-Domínguez, E. Pisano, and N. Ornelas-Soto, “Discrimination of radiosensitive and radioresistant murine lymphoma cells by Raman spectroscopy and SERS,” Biomed. Opt. Express 11(1), 388–405 (2020). [CrossRef]  

43. J. De Gelder, K. De Gussem, P. Vandenabeele, and L. Moens, “Reference database of Raman spectra of biological molecules,” J. Raman Spectrosc. 38(9), 1133–1147 (2007). [CrossRef]  

44. Z. Movasaghi, S. Rehman, and I. U. Rehman, “Raman Spectroscopy of Biological Tissues,” Appl. Spectrosc. Rev. 42(5), 493–541 (2007). [CrossRef]  

45. K. Lin, W. Zheng, C. M. Lim, and Z. Huang, “Real-time In vivo Diagnosis of Nasopharyngeal Carcinoma Using Rapid Fiber-Optic Raman Spectroscopy,” Theranostics 7(14), 3517–3526 (2017). [CrossRef]  

46. A. Ghosh, S. Raha, S. Dey, K. Chatterjee, A. Roy Chowdhury, and A. Barui, “Chemometric analysis of integrated FTIR and Raman spectra obtained by non-invasive exfoliative cytology for the screening of oral cancer,” Analyst 144(4), 1309–1325 (2019). [CrossRef]  

47. Q. Zheng, J. Li, L. Yang, B. Zheng, J. Wang, N. Lv, J. Luo, F. L. Martin, D. Liu, and J. He, “Raman spectroscopy as a potential diagnostic tool to analyse biochemical alterations in lung cancer,” Analyst 10.1039/c1039an02175b (2019).

48. S. Duraipandian, M. S. Bergholt, W. Zheng, K. Y. Ho, M. Teh, K. G. Yeoh, J. B. Y. So, A. Shabbir, and Z. Huang, “Real-time Raman spectroscopy for in vivo, online gastric cancer diagnosis during clinical endoscopic examination,” J. Biomed. Opt. 17(8), 081418 (2012). [CrossRef]  

49. M. Ringnér, “What is principal component analysis?” Nat. Biotechnol. 26(3), 303–304 (2008). [CrossRef]  

50. Y. Chen, Z. Wang, Y. Huang, S. Feng, Z. Zheng, X. Liu, and M. Liu, “Label-free detection of hydrogen peroxide-induced oxidative stress in human retinal pigment epithelium cells via laser tweezers Raman spectroscopy,” Biomed. Opt. Express 10(2), 500–513 (2019). [CrossRef]  

51. S. Ahlawat, A. Chowdhury, A. Uppal, N. Kumar, and P. K. Gupta, “Use of Raman optical tweezers for cell cycle analysis,” Analyst 141(4), 1339–1346 (2016). [CrossRef]  

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  18. Z. Farhane, F. Bonnier, and H. J. Byrne, “An in vitro study of the interaction of the chemotherapeutic drug Actinomycin D with lung cancer cell lines using Raman micro-spectroscopy,” J. Biophotonics 11(1), e201700112 (2018).
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  19. S. F. El-Mashtoly, H. K. Yosef, D. Petersen, L. Mavarani, A. Maghnouj, S. Hahn, C. Kötting, and K. Gerwert, “Label-free Raman spectroscopic imaging monitors the integral physiologically relevant drug responses in cancer cells,” Anal. Chem. 87(14), 7297–7304 (2015).
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  24. F. Draux, C. Gobinet, J. Sulé-Suso, M. Manfait, P. Jeannesson, and G. D. Sockalingum, “Raman imaging of single living cells: probing effects of non-cytotoxic doses of an anti-cancer drug,” Analyst 136(13), 2718–2725 (2011).
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  29. C. Krafft, I. W. Schie, T. Meyer, M. Schmitt, and J. Popp, “Developments in spontaneous and coherent Raman scattering microscopic imaging for biomedical applications,” Chem. Soc. Rev. 45(7), 1819–1849 (2016).
    [Crossref]
  30. S. Wang, B. Setlow, P. Setlow, and Y.-Q. Li, “Uptake and levels of the antibiotic berberine in individual dormant and germinating Clostridium difficile and Bacillus cereus spores as measured by laser tweezers Raman spectroscopy,” J. Antimicrob. Chemother. 71(6), 1540–1546 (2016).
    [Crossref]
  31. J. Lin, L. Shao, S. Qiu, X. Huang, M. Liu, Z. Zheng, D. Lin, Y. Xu, Z. Li, Y. Lin, R. Chen, and S. Feng, “Application of a near-infrared laser tweezers Raman spectroscopy system for label-free analysis and differentiation of diabetic red blood cells,” Biomed. Opt. Express 9(3), 984–993 (2018).
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  33. T. J. Harvey, C. Hughes, A. D. Ward, E. C. Faria, A. Henderson, N. W. Clarke, M. D. Brown, R. D. Snook, and P. Gardner, “Classification of fixed urological cells using Raman tweezers,” J. Biophotonics 2(1-2), 47–69 (2009).
    [Crossref]
  34. T. J. Moritz, D. S. Taylor, D. M. Krol, J. Fritch, and J. W. Chan, “Detection of doxorubicin-induced apoptosis of leukemic T-lymphocytes by laser tweezers Raman spectroscopy,” Biomed. Opt. Express 1(4), 1138–1147 (2010).
    [Crossref]
  35. C. Xue, Y. Tian, J. Zhang, Y. Zhao, J. Zhan, W. Fang, and L. Zhang, “In vitro and in vivo efficacy of afatinib as a single agent or in combination with gemcitabine for the treatment of nasopharyngeal carcinoma,” Drug Des., Dev. Ther. 10, 1299–1306 (2016).
    [Crossref]
  36. X. Ren, X. Yang, B. Cheng, X. Chen, T. Zhang, Q. He, B. Li, Y. Li, X. Tang, X. Wen, Q. Zhong, T. Kang, M. Zeng, N. Liu, and J. Ma, “HOPX hypermethylation promotes metastasis via activating SNAIL transcription in nasopharyngeal carcinoma,” Nat. Commun. 8(1), 14053 (2017).
    [Crossref]
  37. H. Peng, J. Zhang, P.-P. Zhang, L. Chen, L.-L. Tang, X.-J. Yang, Q.-M. He, X. Wen, Y. Sun, N. Liu, Y.-Q. Li, and J. Ma, “ARNTL hypermethylation promotes tumorigenesis and inhibits cisplatin sensitivity by activating CDK5 transcription in nasopharyngeal carcinoma,” J. Exp. Clin. Cancer Res. 38(1), 11 (2019).
    [Crossref]
  38. Y. Zhang, J. Fan, Y. Fan, L. Li, X. He, Q. Xiang, J. Mu, D. Zhou, X. Sun, Y. Yang, G. Ren, Q. Tao, and T. Xiang, “The new 6q27 tumor suppressor DACT2, frequently silenced by CpG methylation, sensitizes nasopharyngeal cancer cells to paclitaxel and 5-FU toxicity via β-catenin/Cdc25c signaling and G2/M arrest,” Clin. Epigenet. 10(1), 26 (2018).
    [Crossref]
  39. J. Zhao, H. Lui, D. I. McLean, and H. Zeng, “Automated autofluorescence background subtraction algorithm for biomedical Raman spectroscopy,” Appl. Spectrosc. 61(11), 1225–1232 (2007).
    [Crossref]
  40. Q. Wu, S. Qiu, Y. Yu, W. Chen, H. Lin, D. Lin, S. Feng, and R. Chen, “Assessment of the radiotherapy effect for nasopharyngeal cancer using plasma surface-enhanced Raman spectroscopy technology,” Biomed. Opt. Express 9(7), 3413–3423 (2018).
    [Crossref]
  41. D. Lin, S. Feng, J. Pan, Y. Chen, J. Lin, G. Chen, S. Xie, H. Zeng, and R. Chen, “Colorectal cancer detection by gold nanoparticle based surface-enhanced Raman spectroscopy of blood serum and statistical analysis,” Opt. Express 19(14), 13565–13577 (2011).
    [Crossref]
  42. I. Aguilar-Hernández, D. L. Cárdenas-Chavez, T. López-Luke, A. García-García, M. Herrera-Domínguez, E. Pisano, and N. Ornelas-Soto, “Discrimination of radiosensitive and radioresistant murine lymphoma cells by Raman spectroscopy and SERS,” Biomed. Opt. Express 11(1), 388–405 (2020).
    [Crossref]
  43. J. De Gelder, K. De Gussem, P. Vandenabeele, and L. Moens, “Reference database of Raman spectra of biological molecules,” J. Raman Spectrosc. 38(9), 1133–1147 (2007).
    [Crossref]
  44. Z. Movasaghi, S. Rehman, and I. U. Rehman, “Raman Spectroscopy of Biological Tissues,” Appl. Spectrosc. Rev. 42(5), 493–541 (2007).
    [Crossref]
  45. K. Lin, W. Zheng, C. M. Lim, and Z. Huang, “Real-time In vivo Diagnosis of Nasopharyngeal Carcinoma Using Rapid Fiber-Optic Raman Spectroscopy,” Theranostics 7(14), 3517–3526 (2017).
    [Crossref]
  46. A. Ghosh, S. Raha, S. Dey, K. Chatterjee, A. Roy Chowdhury, and A. Barui, “Chemometric analysis of integrated FTIR and Raman spectra obtained by non-invasive exfoliative cytology for the screening of oral cancer,” Analyst 144(4), 1309–1325 (2019).
    [Crossref]
  47. Q. Zheng, J. Li, L. Yang, B. Zheng, J. Wang, N. Lv, J. Luo, F. L. Martin, D. Liu, and J. He, “Raman spectroscopy as a potential diagnostic tool to analyse biochemical alterations in lung cancer,” Analyst 10.1039/c1039an02175b (2019).
  48. S. Duraipandian, M. S. Bergholt, W. Zheng, K. Y. Ho, M. Teh, K. G. Yeoh, J. B. Y. So, A. Shabbir, and Z. Huang, “Real-time Raman spectroscopy for in vivo, online gastric cancer diagnosis during clinical endoscopic examination,” J. Biomed. Opt. 17(8), 081418 (2012).
    [Crossref]
  49. M. Ringnér, “What is principal component analysis?” Nat. Biotechnol. 26(3), 303–304 (2008).
    [Crossref]
  50. Y. Chen, Z. Wang, Y. Huang, S. Feng, Z. Zheng, X. Liu, and M. Liu, “Label-free detection of hydrogen peroxide-induced oxidative stress in human retinal pigment epithelium cells via laser tweezers Raman spectroscopy,” Biomed. Opt. Express 10(2), 500–513 (2019).
    [Crossref]
  51. S. Ahlawat, A. Chowdhury, A. Uppal, N. Kumar, and P. K. Gupta, “Use of Raman optical tweezers for cell cycle analysis,” Analyst 141(4), 1339–1346 (2016).
    [Crossref]

2020 (1)

2019 (10)

A. Ghosh, S. Raha, S. Dey, K. Chatterjee, A. Roy Chowdhury, and A. Barui, “Chemometric analysis of integrated FTIR and Raman spectra obtained by non-invasive exfoliative cytology for the screening of oral cancer,” Analyst 144(4), 1309–1325 (2019).
[Crossref]

Y. Chen, Z. Wang, Y. Huang, S. Feng, Z. Zheng, X. Liu, and M. Liu, “Label-free detection of hydrogen peroxide-induced oxidative stress in human retinal pigment epithelium cells via laser tweezers Raman spectroscopy,” Biomed. Opt. Express 10(2), 500–513 (2019).
[Crossref]

Y.-P. Chen, A. T. C. Chan, Q.-T. Le, P. Blanchard, Y. Sun, and J. Ma, “Nasopharyngeal carcinoma,” Lancet 394(10192), 64–80 (2019).
[Crossref]

M. F. Ji, W. Sheng, W. M. Cheng, M. H. Ng, B. H. Wu, X. Yu, K. R. Wei, F. G. Li, S. F. Lian, P. P. Wang, W. Quan, L. Deng, X. H. Li, X. D. Liu, Y. L. Xie, S. J. Huang, S. X. Ge, S. L. Huang, X. J. Liang, S. M. He, H. W. Huang, S. L. Xia, P. S. Ng, H. L. Chen, S. H. Xie, Q. Liu, M. H. Hong, J. Ma, Y. Yuan, N. S. Xia, J. Zhang, and S. M. Cao, “Incidence and mortality of nasopharyngeal carcinoma: interim analysis of a cluster randomized controlled screening trial (PRO-NPC-001) in southern China,” Ann. Oncol. 30(10), 1630–1637 (2019).
[Crossref]

Y. Zhang, L. Chen, G.-Q. Hu, N. Zhang, X.-D. Zhu, K.-Y. Yang, F. Jin, M. Shi, Y.-P. Chen, W.-H. Hu, Z.-B. Cheng, S.-Y. Wang, Y. Tian, X.-C. Wang, Y. Sun, J.-G. Li, W.-F. Li, Y.-H. Li, L.-L. Tang, Y.-P. Mao, G.-Q. Zhou, R. Sun, X. Liu, R. Guo, G.-X. Long, S.-Q. Liang, L. Li, J. Huang, J.-H. Long, J. Zang, Q.-D. Liu, L. Zou, Q.-F. Su, B.-M. Zheng, Y. Xiao, Y. Guo, F. Han, H.-Y. Mo, J.-W. Lv, X.-J. Du, C. Xu, N. Liu, Y.-Q. Li, M. L. K. Chua, F.-Y. Xie, Y. Sun, and J. Ma, “Gemcitabine and Cisplatin Induction Chemotherapy in Nasopharyngeal Carcinoma,” N. Engl. J. Med. 381(12), 1124–1135 (2019).
[Crossref]

I. W. De Silva, A. R. Kretsch, H.-M. Lewis, M. Bailey, and G. F. Verbeck, “True one cell chemical analysis: a review,” Analyst 144(16), 4733–4749 (2019).
[Crossref]

D. Lin, Q. Wu, S. Qiu, G. Chen, S. Feng, R. Chen, and H. Zeng, “Label-free liquid biopsy based on blood circulating DNA detection using SERS-based nanotechnology for nasopharyngeal cancer screening,” Nanomedicine (N. Y., NY, U. S.) 22, 102100 (2019).
[Crossref]

H. J. Byrne, F. Bonnier, and Z. Farhane, “Two-dimensional correlation analysis of Raman microspectroscopy of subcellular interactions of drugs in vitro,” J. Biophotonics 12(3), e201800328 (2019).
[Crossref]

M. Liu, X. Liu, Z. Huang, X. Tang, X. Lin, Y. Xu, G. Chen, H. F. Kwok, Y. Lin, and S. Feng, “Rapid discrimination of colon cancer cells with single base mutation in KRAS gene segment using laser tweezers Raman spectroscopy,” J. Biophotonics 12(3), e201800332 (2019).
[Crossref]

H. Peng, J. Zhang, P.-P. Zhang, L. Chen, L.-L. Tang, X.-J. Yang, Q.-M. He, X. Wen, Y. Sun, N. Liu, Y.-Q. Li, and J. Ma, “ARNTL hypermethylation promotes tumorigenesis and inhibits cisplatin sensitivity by activating CDK5 transcription in nasopharyngeal carcinoma,” J. Exp. Clin. Cancer Res. 38(1), 11 (2019).
[Crossref]

2018 (6)

Y. Zhang, J. Fan, Y. Fan, L. Li, X. He, Q. Xiang, J. Mu, D. Zhou, X. Sun, Y. Yang, G. Ren, Q. Tao, and T. Xiang, “The new 6q27 tumor suppressor DACT2, frequently silenced by CpG methylation, sensitizes nasopharyngeal cancer cells to paclitaxel and 5-FU toxicity via β-catenin/Cdc25c signaling and G2/M arrest,” Clin. Epigenet. 10(1), 26 (2018).
[Crossref]

J. Lin, L. Shao, S. Qiu, X. Huang, M. Liu, Z. Zheng, D. Lin, Y. Xu, Z. Li, Y. Lin, R. Chen, and S. Feng, “Application of a near-infrared laser tweezers Raman spectroscopy system for label-free analysis and differentiation of diabetic red blood cells,” Biomed. Opt. Express 9(3), 984–993 (2018).
[Crossref]

Z. Farhane, F. Bonnier, and H. J. Byrne, “An in vitro study of the interaction of the chemotherapeutic drug Actinomycin D with lung cancer cell lines using Raman micro-spectroscopy,” J. Biophotonics 11(1), e201700112 (2018).
[Crossref]

C. Zong, M. Xu, L.-J. Xu, T. Wei, X. Ma, X.-S. Zheng, R. Hu, and B. Ren, “Surface-Enhanced Raman Spectroscopy for Bioanalysis: Reliability and Challenges,” Chem. Rev. 118(10), 4946–4980 (2018).
[Crossref]

N. Sarin, F. Engel, F. Rothweiler, J. Cinatl, M. Michaelis, R. Frötschl, H. Fröhlich, and G. V. Kalayda, “Key Players of Cisplatin Resistance: Towards a Systems Pharmacology Approach,” Int. J. Mol. Sci. 19(3), 767 (2018).
[Crossref]

Q. Wu, S. Qiu, Y. Yu, W. Chen, H. Lin, D. Lin, S. Feng, and R. Chen, “Assessment of the radiotherapy effect for nasopharyngeal cancer using plasma surface-enhanced Raman spectroscopy technology,” Biomed. Opt. Express 9(7), 3413–3423 (2018).
[Crossref]

2017 (6)

K. Lin, W. Zheng, C. M. Lim, and Z. Huang, “Real-time In vivo Diagnosis of Nasopharyngeal Carcinoma Using Rapid Fiber-Optic Raman Spectroscopy,” Theranostics 7(14), 3517–3526 (2017).
[Crossref]

M. Amrutkar and I. P. Gladhaug, “Pancreatic Cancer Chemoresistance to Gemcitabine,” Cancers 9(12), 157 (2017).
[Crossref]

X. Shi and X. Sun, “Regulation of paclitaxel activity by microtubule-associated proteins in cancer chemotherapy,” Cancer Chemother. Pharmacol. 80(5), 909–917 (2017).
[Crossref]

S. K. Shukla, V. Purohit, K. Mehla, V. Gunda, N. V. Chaika, E. Vernucci, R. J. King, J. Abrego, G. D. Goode, A. Dasgupta, A. L. Illies, T. Gebregiworgis, B. Dai, J. J. Augustine, D. Murthy, K. S. Attri, O. Mashadova, P. M. Grandgenett, R. Powers, Q. P. Ly, A. J. Lazenby, J. L. Grem, F. Yu, J. M. Matés, J. M. Asara, J.-W. Kim, J. H. Hankins, C. Weekes, M. A. Hollingsworth, N. J. Serkova, A. R. Sasson, J. B. Fleming, J. M. Oliveto, C. A. Lyssiotis, L. C. Cantley, L. Berim, and P. K. Singh, “MUC1 and HIF-1alpha Signaling Crosstalk Induces Anabolic Glucose Metabolism to Impart Gemcitabine Resistance to Pancreatic Cancer,” Cancer Cell 32(1), 71–87.e7 (2017).
[Crossref]

X. Ren, X. Yang, B. Cheng, X. Chen, T. Zhang, Q. He, B. Li, Y. Li, X. Tang, X. Wen, Q. Zhong, T. Kang, M. Zeng, N. Liu, and J. Ma, “HOPX hypermethylation promotes metastasis via activating SNAIL transcription in nasopharyngeal carcinoma,” Nat. Commun. 8(1), 14053 (2017).
[Crossref]

S.-S. Li, Q.-Y. Guan, G. Meng, X.-F. Chang, J.-W. Wei, P. Wang, B. Kang, J.-J. Xu, and H.-Y. Chen, “Revealing chemical processes and kinetics of drug action within single living cells via plasmonic Raman probes,” Sci. Rep. 7(1), 2296 (2017).
[Crossref]

2016 (6)

C. Krafft, I. W. Schie, T. Meyer, M. Schmitt, and J. Popp, “Developments in spontaneous and coherent Raman scattering microscopic imaging for biomedical applications,” Chem. Soc. Rev. 45(7), 1819–1849 (2016).
[Crossref]

S. Wang, B. Setlow, P. Setlow, and Y.-Q. Li, “Uptake and levels of the antibiotic berberine in individual dormant and germinating Clostridium difficile and Bacillus cereus spores as measured by laser tweezers Raman spectroscopy,” J. Antimicrob. Chemother. 71(6), 1540–1546 (2016).
[Crossref]

C. Xue, Y. Tian, J. Zhang, Y. Zhao, J. Zhan, W. Fang, and L. Zhang, “In vitro and in vivo efficacy of afatinib as a single agent or in combination with gemcitabine for the treatment of nasopharyngeal carcinoma,” Drug Des., Dev. Ther. 10, 1299–1306 (2016).
[Crossref]

V. Fisi, E. Kátai, P. Bogner, A. Miseta, and T. Nagy, “Timed, sequential administration of paclitaxel improves its cytotoxic effectiveness in a cell culture model,” Cell Cycle 15(9), 1227–1233 (2016).
[Crossref]

S. Xie, A. Ogden, R. Aneja, and J. Zhou, “Microtubule-Binding Proteins as Promising Biomarkers of Paclitaxel Sensitivity in Cancer Chemotherapy,” Med. Res. Rev. 36(2), 300–312 (2016).
[Crossref]

S. Ahlawat, A. Chowdhury, A. Uppal, N. Kumar, and P. K. Gupta, “Use of Raman optical tweezers for cell cycle analysis,” Analyst 141(4), 1339–1346 (2016).
[Crossref]

2015 (3)

Y. Binenbaum, S. Na’ara, and Z. Gil, “Gemcitabine resistance in pancreatic ductal adenocarcinoma,” Drug Resist. Updates 23, 55–68 (2015).
[Crossref]

J.-N. Ho, S.-S. Byun, S. Lee, J. J. Oh, S. K. Hong, S. E. Lee, and J. S. Yeon, “Synergistic antitumor effect of triptolide and cisplatin in cisplatin resistant human bladder cancer cells,” J. Cell Biol. 193(3), 1016–1022 (2015).
[Crossref]

S. F. El-Mashtoly, H. K. Yosef, D. Petersen, L. Mavarani, A. Maghnouj, S. Hahn, C. Kötting, and K. Gerwert, “Label-free Raman spectroscopic imaging monitors the integral physiologically relevant drug responses in cancer cells,” Anal. Chem. 87(14), 7297–7304 (2015).
[Crossref]

2014 (1)

A. N. Kuzmin, A. Pliss, and P. N. Prasad, “Changes in biomolecular profile in a single nucleolus during cell fixation,” Anal. Chem. 86(21), 10909–10916 (2014).
[Crossref]

2013 (3)

J. W. Chan, “Recent advances in laser tweezers Raman spectroscopy (LTRS) for label-free analysis of single cells,” J. Biophotonics 6(1), 36–48 (2013).
[Crossref]

L. A. Austin, B. Kang, and M. A. El-Sayed, “A new nanotechnology technique for determining drug efficacy using targeted plasmonically enhanced single cell imaging spectroscopy,” J. Am. Chem. Soc. 135(12), 4688–4691 (2013).
[Crossref]

H. Salehi, E. Middendorp, I. Panayotov, P.-Y. C. Dutilleul, A.-G. Vegh, S. K. Ramakrishnan, C. Gergely, and F. J. Cuisinier, “Confocal Raman data analysis enables identifying apoptosis of MCF-7 cells caused by anticancer drug paclitaxel,” J. Biomed. Opt. 18(5), 056010 (2013).
[Crossref]

2012 (3)

D.-W. Shen, L. M. Pouliot, M. D. Hall, and M. M. Gottesman, “Cisplatin resistance: a cellular self-defense mechanism resulting from multiple epigenetic and genetic changes,” Pharmacol. Rev. 64(3), 706–721 (2012).
[Crossref]

L. Galluzzi, L. Senovilla, I. Vitale, J. Michels, I. Martins, O. Kepp, M. Castedo, and G. Kroemer, “Molecular mechanisms of cisplatin resistance,” Oncogene 31(15), 1869–1883 (2012).
[Crossref]

S. Duraipandian, M. S. Bergholt, W. Zheng, K. Y. Ho, M. Teh, K. G. Yeoh, J. B. Y. So, A. Shabbir, and Z. Huang, “Real-time Raman spectroscopy for in vivo, online gastric cancer diagnosis during clinical endoscopic examination,” J. Biomed. Opt. 17(8), 081418 (2012).
[Crossref]

2011 (4)

D. Lin, S. Feng, J. Pan, Y. Chen, J. Lin, G. Chen, S. Xie, H. Zeng, and R. Chen, “Colorectal cancer detection by gold nanoparticle based surface-enhanced Raman spectroscopy of blood serum and statistical analysis,” Opt. Express 19(14), 13565–13577 (2011).
[Crossref]

H. Nawaz, F. Bonnier, A. D. Meade, F. M. Lyng, and H. J. Byrne, “Comparison of subcellular responses for the evaluation and prediction of the chemotherapeutic response to cisplatin in lung adenocarcinoma using Raman spectroscopy,” Analyst 136(12), 2450–2463 (2011).
[Crossref]

F. Draux, C. Gobinet, J. Sulé-Suso, M. Manfait, P. Jeannesson, and G. D. Sockalingum, “Raman imaging of single living cells: probing effects of non-cytotoxic doses of an anti-cancer drug,” Analyst 136(13), 2718–2725 (2011).
[Crossref]

A. Zoladek, F. C. Pascut, P. Patel, and I. Notingher, “Non-invasive time-course imaging of apoptotic cells by confocal Raman micro-spectroscopy,” J. Raman Spectrosc. 42(3), 251–258 (2011).
[Crossref]

2010 (1)

2009 (2)

T. J. Harvey, C. Hughes, A. D. Ward, E. C. Faria, A. Henderson, N. W. Clarke, M. D. Brown, R. D. Snook, and P. Gardner, “Classification of fixed urological cells using Raman tweezers,” J. Biophotonics 2(1-2), 47–69 (2009).
[Crossref]

J. W. Chan, D. S. Taylor, and D. L. Thompson, “The effect of cell fixation on the discrimination of normal and leukemia cells with laser tweezers Raman spectroscopy,” Biopolymers 91(2), 132–139 (2009).
[Crossref]

2008 (1)

M. Ringnér, “What is principal component analysis?” Nat. Biotechnol. 26(3), 303–304 (2008).
[Crossref]

2007 (3)

J. De Gelder, K. De Gussem, P. Vandenabeele, and L. Moens, “Reference database of Raman spectra of biological molecules,” J. Raman Spectrosc. 38(9), 1133–1147 (2007).
[Crossref]

Z. Movasaghi, S. Rehman, and I. U. Rehman, “Raman Spectroscopy of Biological Tissues,” Appl. Spectrosc. Rev. 42(5), 493–541 (2007).
[Crossref]

J. Zhao, H. Lui, D. I. McLean, and H. Zeng, “Automated autofluorescence background subtraction algorithm for biomedical Raman spectroscopy,” Appl. Spectrosc. 61(11), 1225–1232 (2007).
[Crossref]

Abrego, J.

S. K. Shukla, V. Purohit, K. Mehla, V. Gunda, N. V. Chaika, E. Vernucci, R. J. King, J. Abrego, G. D. Goode, A. Dasgupta, A. L. Illies, T. Gebregiworgis, B. Dai, J. J. Augustine, D. Murthy, K. S. Attri, O. Mashadova, P. M. Grandgenett, R. Powers, Q. P. Ly, A. J. Lazenby, J. L. Grem, F. Yu, J. M. Matés, J. M. Asara, J.-W. Kim, J. H. Hankins, C. Weekes, M. A. Hollingsworth, N. J. Serkova, A. R. Sasson, J. B. Fleming, J. M. Oliveto, C. A. Lyssiotis, L. C. Cantley, L. Berim, and P. K. Singh, “MUC1 and HIF-1alpha Signaling Crosstalk Induces Anabolic Glucose Metabolism to Impart Gemcitabine Resistance to Pancreatic Cancer,” Cancer Cell 32(1), 71–87.e7 (2017).
[Crossref]

Aguilar-Hernández, I.

Ahlawat, S.

S. Ahlawat, A. Chowdhury, A. Uppal, N. Kumar, and P. K. Gupta, “Use of Raman optical tweezers for cell cycle analysis,” Analyst 141(4), 1339–1346 (2016).
[Crossref]

Amrutkar, M.

M. Amrutkar and I. P. Gladhaug, “Pancreatic Cancer Chemoresistance to Gemcitabine,” Cancers 9(12), 157 (2017).
[Crossref]

Aneja, R.

S. Xie, A. Ogden, R. Aneja, and J. Zhou, “Microtubule-Binding Proteins as Promising Biomarkers of Paclitaxel Sensitivity in Cancer Chemotherapy,” Med. Res. Rev. 36(2), 300–312 (2016).
[Crossref]

Asara, J. M.

S. K. Shukla, V. Purohit, K. Mehla, V. Gunda, N. V. Chaika, E. Vernucci, R. J. King, J. Abrego, G. D. Goode, A. Dasgupta, A. L. Illies, T. Gebregiworgis, B. Dai, J. J. Augustine, D. Murthy, K. S. Attri, O. Mashadova, P. M. Grandgenett, R. Powers, Q. P. Ly, A. J. Lazenby, J. L. Grem, F. Yu, J. M. Matés, J. M. Asara, J.-W. Kim, J. H. Hankins, C. Weekes, M. A. Hollingsworth, N. J. Serkova, A. R. Sasson, J. B. Fleming, J. M. Oliveto, C. A. Lyssiotis, L. C. Cantley, L. Berim, and P. K. Singh, “MUC1 and HIF-1alpha Signaling Crosstalk Induces Anabolic Glucose Metabolism to Impart Gemcitabine Resistance to Pancreatic Cancer,” Cancer Cell 32(1), 71–87.e7 (2017).
[Crossref]

Attri, K. S.

S. K. Shukla, V. Purohit, K. Mehla, V. Gunda, N. V. Chaika, E. Vernucci, R. J. King, J. Abrego, G. D. Goode, A. Dasgupta, A. L. Illies, T. Gebregiworgis, B. Dai, J. J. Augustine, D. Murthy, K. S. Attri, O. Mashadova, P. M. Grandgenett, R. Powers, Q. P. Ly, A. J. Lazenby, J. L. Grem, F. Yu, J. M. Matés, J. M. Asara, J.-W. Kim, J. H. Hankins, C. Weekes, M. A. Hollingsworth, N. J. Serkova, A. R. Sasson, J. B. Fleming, J. M. Oliveto, C. A. Lyssiotis, L. C. Cantley, L. Berim, and P. K. Singh, “MUC1 and HIF-1alpha Signaling Crosstalk Induces Anabolic Glucose Metabolism to Impart Gemcitabine Resistance to Pancreatic Cancer,” Cancer Cell 32(1), 71–87.e7 (2017).
[Crossref]

Augustine, J. J.

S. K. Shukla, V. Purohit, K. Mehla, V. Gunda, N. V. Chaika, E. Vernucci, R. J. King, J. Abrego, G. D. Goode, A. Dasgupta, A. L. Illies, T. Gebregiworgis, B. Dai, J. J. Augustine, D. Murthy, K. S. Attri, O. Mashadova, P. M. Grandgenett, R. Powers, Q. P. Ly, A. J. Lazenby, J. L. Grem, F. Yu, J. M. Matés, J. M. Asara, J.-W. Kim, J. H. Hankins, C. Weekes, M. A. Hollingsworth, N. J. Serkova, A. R. Sasson, J. B. Fleming, J. M. Oliveto, C. A. Lyssiotis, L. C. Cantley, L. Berim, and P. K. Singh, “MUC1 and HIF-1alpha Signaling Crosstalk Induces Anabolic Glucose Metabolism to Impart Gemcitabine Resistance to Pancreatic Cancer,” Cancer Cell 32(1), 71–87.e7 (2017).
[Crossref]

Austin, L. A.

L. A. Austin, B. Kang, and M. A. El-Sayed, “A new nanotechnology technique for determining drug efficacy using targeted plasmonically enhanced single cell imaging spectroscopy,” J. Am. Chem. Soc. 135(12), 4688–4691 (2013).
[Crossref]

Bailey, M.

I. W. De Silva, A. R. Kretsch, H.-M. Lewis, M. Bailey, and G. F. Verbeck, “True one cell chemical analysis: a review,” Analyst 144(16), 4733–4749 (2019).
[Crossref]

Barui, A.

A. Ghosh, S. Raha, S. Dey, K. Chatterjee, A. Roy Chowdhury, and A. Barui, “Chemometric analysis of integrated FTIR and Raman spectra obtained by non-invasive exfoliative cytology for the screening of oral cancer,” Analyst 144(4), 1309–1325 (2019).
[Crossref]

Bergholt, M. S.

S. Duraipandian, M. S. Bergholt, W. Zheng, K. Y. Ho, M. Teh, K. G. Yeoh, J. B. Y. So, A. Shabbir, and Z. Huang, “Real-time Raman spectroscopy for in vivo, online gastric cancer diagnosis during clinical endoscopic examination,” J. Biomed. Opt. 17(8), 081418 (2012).
[Crossref]

Berim, L.

S. K. Shukla, V. Purohit, K. Mehla, V. Gunda, N. V. Chaika, E. Vernucci, R. J. King, J. Abrego, G. D. Goode, A. Dasgupta, A. L. Illies, T. Gebregiworgis, B. Dai, J. J. Augustine, D. Murthy, K. S. Attri, O. Mashadova, P. M. Grandgenett, R. Powers, Q. P. Ly, A. J. Lazenby, J. L. Grem, F. Yu, J. M. Matés, J. M. Asara, J.-W. Kim, J. H. Hankins, C. Weekes, M. A. Hollingsworth, N. J. Serkova, A. R. Sasson, J. B. Fleming, J. M. Oliveto, C. A. Lyssiotis, L. C. Cantley, L. Berim, and P. K. Singh, “MUC1 and HIF-1alpha Signaling Crosstalk Induces Anabolic Glucose Metabolism to Impart Gemcitabine Resistance to Pancreatic Cancer,” Cancer Cell 32(1), 71–87.e7 (2017).
[Crossref]

Binenbaum, Y.

Y. Binenbaum, S. Na’ara, and Z. Gil, “Gemcitabine resistance in pancreatic ductal adenocarcinoma,” Drug Resist. Updates 23, 55–68 (2015).
[Crossref]

Blanchard, P.

Y.-P. Chen, A. T. C. Chan, Q.-T. Le, P. Blanchard, Y. Sun, and J. Ma, “Nasopharyngeal carcinoma,” Lancet 394(10192), 64–80 (2019).
[Crossref]

Bogner, P.

V. Fisi, E. Kátai, P. Bogner, A. Miseta, and T. Nagy, “Timed, sequential administration of paclitaxel improves its cytotoxic effectiveness in a cell culture model,” Cell Cycle 15(9), 1227–1233 (2016).
[Crossref]

Bonnier, F.

H. J. Byrne, F. Bonnier, and Z. Farhane, “Two-dimensional correlation analysis of Raman microspectroscopy of subcellular interactions of drugs in vitro,” J. Biophotonics 12(3), e201800328 (2019).
[Crossref]

Z. Farhane, F. Bonnier, and H. J. Byrne, “An in vitro study of the interaction of the chemotherapeutic drug Actinomycin D with lung cancer cell lines using Raman micro-spectroscopy,” J. Biophotonics 11(1), e201700112 (2018).
[Crossref]

H. Nawaz, F. Bonnier, A. D. Meade, F. M. Lyng, and H. J. Byrne, “Comparison of subcellular responses for the evaluation and prediction of the chemotherapeutic response to cisplatin in lung adenocarcinoma using Raman spectroscopy,” Analyst 136(12), 2450–2463 (2011).
[Crossref]

Brown, M. D.

T. J. Harvey, C. Hughes, A. D. Ward, E. C. Faria, A. Henderson, N. W. Clarke, M. D. Brown, R. D. Snook, and P. Gardner, “Classification of fixed urological cells using Raman tweezers,” J. Biophotonics 2(1-2), 47–69 (2009).
[Crossref]

Byrne, H. J.

H. J. Byrne, F. Bonnier, and Z. Farhane, “Two-dimensional correlation analysis of Raman microspectroscopy of subcellular interactions of drugs in vitro,” J. Biophotonics 12(3), e201800328 (2019).
[Crossref]

Z. Farhane, F. Bonnier, and H. J. Byrne, “An in vitro study of the interaction of the chemotherapeutic drug Actinomycin D with lung cancer cell lines using Raman micro-spectroscopy,” J. Biophotonics 11(1), e201700112 (2018).
[Crossref]

H. Nawaz, F. Bonnier, A. D. Meade, F. M. Lyng, and H. J. Byrne, “Comparison of subcellular responses for the evaluation and prediction of the chemotherapeutic response to cisplatin in lung adenocarcinoma using Raman spectroscopy,” Analyst 136(12), 2450–2463 (2011).
[Crossref]

Byun, S.-S.

J.-N. Ho, S.-S. Byun, S. Lee, J. J. Oh, S. K. Hong, S. E. Lee, and J. S. Yeon, “Synergistic antitumor effect of triptolide and cisplatin in cisplatin resistant human bladder cancer cells,” J. Cell Biol. 193(3), 1016–1022 (2015).
[Crossref]

Cantley, L. C.

S. K. Shukla, V. Purohit, K. Mehla, V. Gunda, N. V. Chaika, E. Vernucci, R. J. King, J. Abrego, G. D. Goode, A. Dasgupta, A. L. Illies, T. Gebregiworgis, B. Dai, J. J. Augustine, D. Murthy, K. S. Attri, O. Mashadova, P. M. Grandgenett, R. Powers, Q. P. Ly, A. J. Lazenby, J. L. Grem, F. Yu, J. M. Matés, J. M. Asara, J.-W. Kim, J. H. Hankins, C. Weekes, M. A. Hollingsworth, N. J. Serkova, A. R. Sasson, J. B. Fleming, J. M. Oliveto, C. A. Lyssiotis, L. C. Cantley, L. Berim, and P. K. Singh, “MUC1 and HIF-1alpha Signaling Crosstalk Induces Anabolic Glucose Metabolism to Impart Gemcitabine Resistance to Pancreatic Cancer,” Cancer Cell 32(1), 71–87.e7 (2017).
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Cao, S. M.

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Figures (6)

Fig. 1.
Fig. 1. Schematic of the home-made laser tweezers Raman spectroscopy (LTRS) system. A 785 nm diode laser beam was delivered to an inverted microscope for both trapping NPC cells and generating the Raman signals from cells. Backward Raman scattering signals are recorded by a spectrometer combined with CCD. In this schematic, M: mirror; L: lens; PH: pinhole; F: filter; DM: dichroic mirror; MO: microscope objective; SH: sample holder.
Fig. 2.
Fig. 2. Mean normalized Raman spectra and the difference spectrum calculated for the CNE2 (A, B) and C666-1 cell lines (C, D) without or with treatment chemodurgs. The shaded areas (grey) represent the standard deviation of the means.
Fig. 3.
Fig. 3. Score plots of PC1 versus PC2 of whole Raman data for the different treatments of CNE2 (A-C) andC666-1 (D-F) cells (% explained variance in parenthesis).
Fig. 4.
Fig. 4. The first two diagnostically significant principal components (PCs) calculated from Raman spectra of control vs. CNE2 (A-C) and C666-1(D-F) cells treated by different chemotherapeutic drugs (gemcitabine, cisplatin, and paclitaxel).
Fig. 5.
Fig. 5. The contribution of each band as an independent variable in the models after normalization of CNE2 (A-C) and C666-1 (D-F) cell lines. The importance of each independent Raman band in establishing the DT models, between the control group and the gemcitabine group, the control group, and the cisplatin group, and the control group and the paclitaxel group.
Fig. 6.
Fig. 6. Comparison of the mean intensities between four groups in (A) CNE2 and (B) C666-1 cell lines. Histogram showing the quantitative contribution of proteins, nucleic acids, and lipids in the cellular spectral information. *: p<0.05: **: p<0.01.

Tables (2)

Tables Icon

Table 1. The Raman peak positions and tentative assignment of major vibrational bands observed in CNE2 and C666-1 cell lines treated with different chemotherapeutic drugsa

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Table 2. Classification results of Raman spectra prediction of the four groups using PCA-LDA

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