Label-free Imaging and Characterization of Cancer Cell Responses to

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Label-free imaging and characterization of cancer cell responses to polymethoxyflavones using Raman microscopy Hua Zhang, Jinkai Zheng, Anna Liu, Hang Xiao, and Lili He J. Agric. Food Chem., Just Accepted Manuscript • DOI: 10.1021/acs.jafc.6b03899 • Publication Date (Web): 07 Dec 2016 Downloaded from http://pubs.acs.org on December 7, 2016

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Journal of Agricultural and Food Chemistry

Label-free imaging and characterization of cancer cell responses to polymethoxyflavones using Raman microscopy

Hua Zhang,a Jinkai Zheng,b Anna Liu,c Hang Xiao,a,* Lili He.a,*

a

Department of Food Science, University of Massachusetts, Amherst, Massachusetts 01003, United States

b

Institute of Food Science and Technology, Chinese Academy of Agricultural Sciences, Beijing 100193, People’s Republic of China

c

Department of Mathematics and Statistics, University of Massachusetts, Amherst, Massachusetts 01003, United States

*

Corresponding author.

E-mail address: [email protected] (H. Xiao); [email protected] (L. He) .

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Abstract

We determined the cellular responses of human colon cancer HT29 and HCT116 cells to the treatments of Nobiletin (NBT) and 5-demethylnobiletin (5DN) using Raman microscopy. By evaluating at both single cell and cell population levels, it was found that NBT induced more changes in the peak intensity of nucleic acid than 5DN, while 5DN induced more changes in the peak intensity of localized lipid than NBT. This result indicates the different modes of inhibitory action of these two PMFs against colon cancer cells. Between the two colon cancer cells tested, HCT116 cells were more sensitive to both PMFs than HT29 cells. The Raman data were generally in a good agreement with the flow cytometry data. Our results demonstrate that Raman microscopy is able to provide macromolecular information of cellular responses to anticancer treatments.

Keywords: Raman microscopy; cell imaging; polymethoxyflavones; colon cancer cells; flow cytometry.

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Introduction Cellular imaging techniques based on vibrational spectroscopy have become powerful tools in cell biology because the molecular composition of subcellular compartments can be visualized nondestructively and without a chemical label.1 These properties have been highlighted when compared to electron microscopy and fluorescence microscopy which are invasive due to the necessity of fixation and/or the use of dyes or biomarkers. 2,3 After all these complex procedures, the cells are always dead. Raman spectroscopy is of particularly interest for cellular imaging due to its capacity to monitor live cells in aqueous media.4 Raman imaging can provide the overall biochemical profile (distribution and abundance) of a single cell, including lipids, proteins, and nucleic acids. Differences in the biochemical profiles can be utilized to distinguish different types of cells. In addition, the biochemical profile of a cell could change in response to different environmental factors such as cytotoxic agents in a time-dependent and/or a dose-dependent manner. The changes in cellular spectra and images can provide characteristic and predictive information for identification, quantification and discrimination of the environmental factors. This technique has been used for imaging the distribution of subcellular components,5 differencing among cells,6,7 discriminating different cell cycle phases,8 in-vivo tumor detection, 9 detection, discrimination and quantification of toxins,10-11 evaluation of drugs effects against cancer cells,12 and characterization of cell responses to chemical and environmental stress.10

Polymethoxyflavones (PMFs) are a group of bioactive nutraceutical compounds almost exclusively found in the citrus genus, particularly in the peels of sweet orange and mandarin.13 3



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They possess many valuable biological properties such as anti-allergic,14 anti-oxidant,15 anti-inflammatory,16 anti-proliferative,17 anti-cancer,18 antibacterial and biofilm,19,20 and anti-virial

activities.21

Nobiletin

(NBT)

and

its

monodemethylated

derivative,

5-demethylnobiletin (5DN) have been previously demonstrated to have anticancer activities against colon cancer cells.22,23 Moreover, it was found that 5DN was more potent in growth inhibition of colon cancer cells than NBT.24 A previous study using invasive labeling methods such as cell cycle and apoptosis analysis demonstrated their different modes of actions.25 NBT induced G0/G1 cell cycle arrest in HT29 cells. However, this arrest was not accompanied by an increase in apoptosis, indicating a cytostatic effect of NBT.26,27 5DN, on the other hand, caused cell cycle arrest at G2/M phase in both HT29 and HCT116 cells.18

In this study, we aimed to use Raman microscopy to characterize the cellular responses of two types of colon cancer cells, HT29 and HCT116 to NBT and 5DN at both single cell and population levels. Live cells in culture media were directly subjected to the Raman microscopic analysis. For the first time, we investigated the biochemical profiles of single cells in response to NBT and 5DN. Statistical analysis was conducted to evaluate certain specific marker peaks based on a population of the cancer cells. Cell cycle analysis was also performed to validate the Raman microscopic results. To the best of our knowledge, this is the first report that used Raman microscopy to characterize and compare the interactions between human cancer cells and anticancer phytochemicals.

Materials and methods PMFs and cell lines 4


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The isolation of NBT and the synthesis of 5DN were carried out as previously described.1 The primary stock solutions of NBT and 5DN were made by dissolving their solid powder in DMSO to yield a stock with a concentration of 100 mM and 20 mM, respectively. Human colon cancer cells (HT29 and HCT116) were purchased from ATCC and cultured in RPMI media supplemented with 5% heat-inactivated FBS 100 U/mL of penicillin and 0.1 mg/mL of streptomycin at 37 oC in humidified atmosphere with 5% CO2. All cells used were within 10 to 30 passages cultured in the 10 cm petri dishes.

Cell treatment for Raman measurement

Before Raman measurement, 10 mL (1 × 104 cells per mL) of cell suspension was transferred to a new petri dish where a gold coated microscopic slide was placed and then incubated for 24 hours at 37 oC to allow cell attachment on the gold slide. The use of a gold slide was to enhance the light scattering of the cells and minimize the background noise. The HT29 cells were treated with NBT (100 µM) or 5DN (20 µM) for 48 hours. The HCT116 cells were treated with NBT (100 µM) or 5DN (8 µM) for 48 hours. From the previous study,18 we learned 5DN is more effective than NBT, therefore we used higher concentration of NBT than 5DN in this study. The reason to further lower the concentration of 5DN for HCT 116 cells is

because the HCT116 cells are more sensitive to 5DN. Each treatment in cell culture was repeated three times independently.

Raman instrumentation and data analysis

A DXR Raman microscope (Thermo Fisher Scientific, Madison, WI) was used in this study. This instrument facilitates 780 nm excitation and 24 mW laser power through a 60× water 5



immersed objective (Olympus, Japan). Aperture was set to be 50 µm slit, grating 400 lines/mm and the spot size was 1.0 µm. Each spectrum was acquired in the 400-2000 cm-1 range. The acquisition time was 1s, repeated twice, and the spectrum was averaged automatically by the OMINICS software (Thermo Fisher Scientific). At the time for Raman measurement, the petri dish with the cell attached gold slide was immediately placed under the Raman microscope and a water immerse objective lens was used for measurement. The measurement took around 5 min for spectral collection and 15 min for cell imaging for a sample. The working distance of the objective lens to the surface of the gold slide was kept consistently at 1.5 mm to minimize the variance caused by different laser penetration depth in the cells.

For single cell imaging, we randomly selected cells within the range of 25 - 35 µm in diameter in each treatment, to minimize the possible spectral variance due to the different cell size. Each treatment was repeated three times independently. Raman mapping was performed by collecting about 150 spectra with a 5 µm step size over a cell in sequence. The images were integrated based on the a specific Raman peak shifts using the Atlµs function in the OMINICS software (Thermo Fisher Scientific). All the spectra were presented in the same common scale to compare, and no other data processing, including subtraction, was done.

For cell population analysis, we randomly collected at least 15 spectra from each cell and at least 5 cells were included in the analysis. Statistics results on the numerical comparison were presented as mean ± SD in the figures. A one-way ANOVA was used in the study. The Least Significant Difference (LSD) test was used to determine the difference between two data sets. A 0.5% significant level was used for all tests.

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The spectral data were averaged and further analyzed by principal component analysis (PCA) using the TQ Analyst software (Thermo Fisher Scientific). PCA is a mathematical procedure that uses orthogonal transformation to convert a set of observations of possibly correlated variables into a set of values of linearly uncorrelated variables called principal components. It is a common method for analyzing the overall spectral variance of the Raman data. Generally speaking, if there is no overlapping between the data cluster, then it means the spectral data is significantly different from each other. The PC1 axis shows a principal difference while the PC2 and PC3 showed the second and the third most variation. The eigenvalues of the PCA determine the contribution of each principal component. When the sum of the selected eigenvalues is higher than 70%, it means a sufficient portion of the variation of the data set had been extracted. Though the three independent replicas showed the same trend, it could not be plotted in one PCA plot due to the heterogeneity of cancer cells and the high sensitivity of Raman microscopy. Cells were more homogeneous from one replica and more heterogeneous from different replicas though under the same treatment. Therefore, there were 15 cells from one of three replicas shown in the PCA plot.

To compare spectral data between groups, a one-way ANOSIM (Analysis of Similarity) was used in the study.28 The comparison was based on 9999 permutations, the Euclidean distance as the dissimilarity metric and the first three principle component scores (PC1, PC2 and PC3). A 0.01% significant level was used. In ANOSIM, R value measures the difference between two groups under comparison. The range of R-value is (-1, 1). An R-value > 0.75 indicates clearly difference, R-value > 0.5 indicates difference with some overlapping while an R-value < 0.25 indicates almost no differences. R-values below 0 suggest more similarity 7



between groups than within groups and therefore a problem in experiment design.

Cell cycle analysis

HT29 (1×104 cells/well) and HCT116 (1×104 cells/well) cells were seeded in 6-well plates. After 24 hours of incubation for attachment, the HT29 cells were treated with NBT (100 µM) or 5DN (20 µM), and the HCT116 cells were treated with NBT (100 µM) or 5DN (8 µM). After another 24 hours, the floating cells and adherent cells, which were detached by brief trypsinization (0.25% trypsin-EDTA; Sigma-Aldrich), were collected. Cell pellets were washed with 1 mL of ice-cold PBS and then resuspended in 1 mL of 70% ethanol in -20℃ for at least 24 hours. After centrifugation (1,600 g, 1 min), the supernatant was removed and cells were incubated with 0.3 mL of PBS containing 30 mg RNAse (Sigma-Aldrich) and 3 mg propidium iodide (Sigma-Aldrich) for 30 min at room temperature. Single-cell suspension was generated by gentle pipettes. Cell cycle was analyzed using a BD LSR II cell analyzer at the analytical cytometry facility (University of Massachusetts Amherst), and data were processed using Modifit software. Statistics results on the numerical comparison in the figures were presented as mean ± SD. A one-way ANOVA was used in the study. The Least Significant Difference (LSD) test was used to determine the difference between two data sets. A p

Label-free Imaging and Characterization of Cancer Cell Responses to

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