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Biomedical Optics Express

Biomedical Optics Express

  • Editor: Joseph A. Izatt
  • Vol. 3, Iss. 9 — Sep. 1, 2012
  • pp: 2111–2120
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Cell death detection by quantitative three-dimensional single-cell tomography

Nai-Chia Cheng, Tsung-Hsun Hsieh, Yu-Ta Wang, Chien-Chih Lai, Chia-Kai Chang, Ming-Yi Lin, Ding-Wei Huang, Jeng-Wei Tjiu, and Sheng-Lung Huang  »View Author Affiliations


Biomedical Optics Express, Vol. 3, Issue 9, pp. 2111-2120 (2012)
http://dx.doi.org/10.1364/BOE.3.002111


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Abstract

Ultrahigh-resolution optical coherence tomography (UR-OCT) has been used for the first time to our knowledge to study single-cell basal cell carcinoma (BCC) in vitro. This noninvasive, in situ, label-free technique with deep imaging depth enables three-dimensional analysis of scattering properties of single cells with cellular spatial resolution. From three-dimensional UR-OCT imaging, live and dead BCC cells can be easily identified based on morphological observation. We developed a novel method to automatically extract characteristic parameters of a single cell from data volume, and quantitative comparison and parametric analysis were performed. The results demonstrate the capability of UR-OCT to detect cell death at the cellular level.

© 2012 OSA

1. Introduction

The aim of cancer therapies is mainly to stop cell proliferation or induce cell death. Cell death regulation is important for normal development and homeostasis [1

1. S. L. Spencer and P. K. Sorger, “Measuring and modeling apoptosis in single cells,” Cell 144(6), 926–939 (2011). [CrossRef] [PubMed]

]. Cancer cells escape from death signals and continue their abnormal proliferation. Therefore, the ability to induce death in cancer cells has been a crucial biomarker for the efficacy of chemotherapeutic agents. However, individual cancer cells, even from the same population, vary greatly in their response to cell death stimuli [2

2. K. E. Gascoigne and S. S. Taylor, “Cancer cells display profound intra- and interline variation following prolonged exposure to antimitotic drugs,” Cancer Cell 14(2), 111–122 (2008). [CrossRef] [PubMed]

4

4. S. V. Sharma, D. Y. Lee, B. Li, M. P. Quinlan, F. Takahashi, S. Maheswaran, U. McDermott, N. Azizian, L. Zou, M. A. Fischbach, K. K. Wong, K. Brandstetter, B. Wittner, S. Ramaswamy, M. Classon, and J. Settleman, “A chromatin-mediated reversible drug-tolerant state in cancer cell subpopulations,” Cell 141(1), 69–80 (2010). [CrossRef] [PubMed]

]. Some cancer cells are easily killed, but others remain resistant. This cell-to-cell variability might be attributed to genetic, epigenetic or proteomic factors, the presence of cancer stem cells and different stages of the cell cycle [5

5. A. A. Cohen, N. Geva-Zatorsky, E. Eden, M. Frenkel-Morgenstern, I. Issaeva, A. Sigal, R. Milo, C. Cohen-Saidon, Y. Liron, Z. Kam, L. Cohen, T. Danon, N. Perzov, and U. Alon, “Dynamic proteomics of individual cancer cells in response to a drug,” Science 322(5907), 1511–1516 (2008). [CrossRef] [PubMed]

10

10. S. L. Spencer, S. Gaudet, J. G. Albeck, J. M. Burke, and P. K. Sorger, “Non-genetic origins of cell-to-cell variability in TRAIL-induced apoptosis,” Nature 459(7245), 428–432 (2009). [CrossRef] [PubMed]

]. In the context of the tumor microenvironment, the reasons for variable cancer cell response to treatment further include variations in tissue drug concentration, local oxygen concentration, cytokine profile, interaction between cancer cells, and host immune response. Measuring the response at the single-cell level provides further pharmacokinetic and pharmacodynamic information, which aids drug development and regimen design [11

11. S. Earley, C. Vinegoni, J. Dunham, R. Gorbatov, P. F. Feruglio, and R. Weissleder, “In vivo imaging of drug-induced mitochondrial outer membrane permeabilization at single-cell resolution,” Cancer Res. 72(12), 2949–2956 (2012). [CrossRef] [PubMed]

,12

12. J. D. Orth, R. H. Kohler, F. Foijer, P. K. Sorger, R. Weissleder, and T. J. Mitchison, “Analysis of mitosis and antimitotic drug responses in tumors by in vivo microscopy and single-cell pharmacodynamics,” Cancer Res. 71(13), 4608–4616 (2011). [CrossRef] [PubMed]

]. Fluorescence microscopy can be used to detect cell death at the single-cell level after cancer cells have been labeled by genetically engineered reporters or exogenous fluorescent dyes [13

13. J. G. Albeck, J. M. Burke, B. B. Aldridge, M. Zhang, D. A. Lauffenburger, and P. K. Sorger, “Quantitative analysis of pathways controlling extrinsic apoptosis in single cells,” Mol. Cell 30(1), 11–25 (2008). [CrossRef] [PubMed]

16

16. R. Weissleder and M. J. Pittet, “Imaging in the era of molecular oncology,” Nature 452(7187), 580–589 (2008). [CrossRef] [PubMed]

]. Despite great success employing fluorescence microscopy in mouse intravital observations, the requirement for labeling and possible phototoxicity circumvents its use in human subjects. Furthermore, the limited imaging depth of fluorescence microscopy also places restrictions on clinical application. Therefore, developing a microscopic technology with noninvasive, in situ, label-free, single-cell spatial resolution may serve this long-term need. In this paper, we report the detection of cell death at the single-cell level using ultrahigh-resolution optical coherence tomography (UR-OCT).

Optical coherence tomography is widely used in clinical medicine, especially in ophthalmology and cardiology because of the ability of imaging deep within tissue [17

17. D. Huang, E. A. Swanson, C. P. Lin, J. S. Schuman, W. G. Stinson, W. Chang, M. R. Hee, T. Flotte, K. Gregory, C. A. Puliafito, and et, “Optical coherence tomography,” Science 254(5035), 1178–1181 (1991). [CrossRef] [PubMed]

]. After the first trial using OCT for three-dimensional tomography on cell-based tissue models [18

18. W. Tan, A. L. Oldenburg, J. J. Norman, T. A. Desai, and S. A. Boppart, “Optical coherence tomography of cell dynamics in three-dimensional tissue models,” Opt. Express 14(16), 7159–7171 (2006). [CrossRef] [PubMed]

], OCT emerged as a promising technique for detecting multiple cell activities and responses to environmental stimuli [19

19. X. Liang, B. W. Graf, and S. A. Boppart, “Imaging engineered tissues using structural and functional optical coherence tomography,” J. Biophotonics 2(11), 643–655 (2009). [CrossRef] [PubMed]

21

21. S. M. Rey, B. Povazay, B. Hofer, A. Unterhuber, B. Hermann, A. Harwood, and W. Drexler, “Three- and four-dimensional visualization of cell migration using optical coherence tomography,” J. Biophotonics 2(6-7), 370–379 (2009). [CrossRef] [PubMed]

]. Traditionally, clinical OCT is relatively low in resolution and mainly used to characterize architectural morphology [22

22. L. Liu, J. A. Gardecki, S. K. Nadkarni, J. D. Toussaint, Y. Yagi, B. E. Bouma, and G. J. Tearney, “Imaging the subcellular structure of human coronary atherosclerosis using micro-optical coherence tomography,” Nat. Med. 17(8), 1010–1014 (2011). [CrossRef] [PubMed]

]. An improvement in OCT technology currently provides axial resolution to approximately 1 μm and lateral resolution to 2 μm [22

22. L. Liu, J. A. Gardecki, S. K. Nadkarni, J. D. Toussaint, Y. Yagi, B. E. Bouma, and G. J. Tearney, “Imaging the subcellular structure of human coronary atherosclerosis using micro-optical coherence tomography,” Nat. Med. 17(8), 1010–1014 (2011). [CrossRef] [PubMed]

24

24. A. B. Vakhtin, D. J. Kane, W. R. Wood, and K. A. Peterson, “Common-path interferometer for frequency-domain optical coherence tomography,” Appl. Opt. 42(34), 6953–6958 (2003). [CrossRef] [PubMed]

]. In this report, we demonstrated that UR-OCT not only provides three-dimensional in situ single-cell imaging but is also able to delineate subcellular structure (i.e., the nucleus).

Dead cells cannot be differentiated from live cells based merely on size. Many parametric analytic methods have been used to address this issue, including speckle fluctuation in time-lapse images [25

25. P. O. Bagnaninchi, C. Holmes, N. Drummond, J. Daoud, and M. Tabrizian, “Two-dimensional and three-dimensional viability measurements of adult stem cells with optical coherence phase microscopy,” J. Biomed. Opt. 16(8), 086003 (2011). [CrossRef] [PubMed]

27

27. C. Joo, T. Akkin, B. Cense, B. H. Park, and J. F. de Boer, “Spectral-domain optical coherence phase microscopy for quantitative phase-contrast imaging,” Opt. Lett. 30(16), 2131–2133 (2005). [CrossRef] [PubMed]

]. It was confirmed that back-scattering signals are lower in apoptotic cells [28

28. Z. Darzynkiewicz, G. Juan, X. Li, W. Gorczyca, T. Murakami, and F. Traganos, “Cytometry in cell necrobiology: analysis of apoptosis and accidental cell death (necrosis),” Cytometry 27(1), 1–20 (1997). [CrossRef] [PubMed]

], which is most likely due to the perturbation of mitochondrial morphology during apoptosis [29

29. Y. Reis, M. Bernardo-Faura, D. Richter, T. Wolf, B. Brors, A. Hamacher-Brady, R. Eils, and N. R. Brady, “Multi-parametric analysis and modeling of relationships between mitochondrial morphology and apoptosis,” PLoS ONE 7(1), e28694 (2012). [CrossRef] [PubMed]

]. Nuclear disintegration after chromatin condensation provides high-signal-intensity peaks that facilitate the identification of apoptotic cells.

Other nonlinear optical techniques, such as second/third harmonic generation microscopy [30

30. M. Rehberg, F. Krombach, U. Pohl, and S. Dietzel, “Label-free 3D visualization of cellular and tissue structures in intact muscle with second and third harmonic generation microscopy,” PLoS ONE 6(11), e28237 (2011). [CrossRef] [PubMed]

], coherent anti-stoke Raman scattering microscopy [31

31. C. L. Evans, E. O. Potma, M. Puoris’haag, D. Côté, C. P. Lin, and X. S. Xie, “Chemical imaging of tissue in vivo with video-rate coherent anti-Stokes Raman scattering microscopy,” Proc. Natl. Acad. Sci. U.S.A. 102(46), 16807–16812 (2005). [CrossRef] [PubMed]

], and stimulated Raman scattering microscopy [32

32. X. Zhang, M. B. Roeffaers, S. Basu, J. R. Daniele, D. Fu, C. W. Freudiger, G. R. Holtom, and X. S. Xie, “Label-free live-cell imaging of nucleic acids using stimulated Raman scattering microscopy,” ChemPhysChem 13(4), 1054–1059 (2012). [CrossRef] [PubMed]

], also provide alternative choices for label-free imaging with subcellular spatial resolution. Because these techniques make use of nonlinear signals originating from light-material interactions within the specimen as a source of contrast, femtosecond or picosecond pulse lasers are usually used to efficiently excite nonlinear processes. In view of the high peak power of these pulse lasers, combined with the risk of damaging the specimen under illumination with high intensity, the application of these nonlinear microscopy techniques remains in the field of pre-clinical research.

In this study, we aimed to use a homemade UR-OCT system to image single-cell basal cell carcinoma (BCC) in three dimensions and differentiate between live and dead BCC cells by not only morphological recognition but also parametric analysis. A BCC cell line was used because BCC is the most common skin cancer, and we are familiar with it [33

33. J. W. Tjiu, Y. H. Liao, S. J. Lin, Y. L. Huang, W. L. Tsai, C. Y. Chu, M. L. Kuo, and S. H. Jee, “Cyclooxygenase-2 overexpression in human basal cell carcinoma cell line increases antiapoptosis, angiogenesis, and tumorigenesis,” J. Invest. Dermatol. 126(5), 1143–1151 (2006). [CrossRef] [PubMed]

,34

34. J. W. Tjiu, J. S. Chen, C. T. Shun, S. J. Lin, Y. H. Liao, C. Y. Chu, T. F. Tsai, H. C. Chiu, Y. S. Dai, H. Inoue, P. C. Yang, M. L. Kuo, and S. H. Jee, “Tumor-associated macrophage-induced invasion and angiogenesis of human basal cell carcinoma cells by cyclooxygenase-2 induction,” J. Invest. Dermatol. 129(4), 1016–1025 (2009). [CrossRef] [PubMed]

]. An image analysis approach was also developed to automatically extract deterministic information of a single cell.

2. Materials and methods

2.1. Sample preparation

The BCC cell line was tested to be free of mycoplasma and other trivial contaminants. BCC cells were cultured in RPMI 1640 (Invitrogen, Carlsbad, CA) supplemented with 10% fetal calf serum (FCS), 100 mg/ml penicillin and 100 mg/ml streptomycin and maintained in an incubator at 37 °C with 5% CO2. Prior to the experiment, grown cells were trypsinized and collected by centrifugation. Samples for OCT scanning were prepared by mixing a BCC cell suspension with thawed Matrigel solution (BD Bioscience, Bedford, MA) 1:1, and injecting 25 μl of the suspension, which corresponded to 5000 cells per sample, into round-grooved glass-slides. To prevent environmental effects during the experiment, all of the samples were fixed with 2% paraformaldehyde and mounted with cover-slips.

2.2. Confocal microscopy

The samples for confocal microscopy were prepared similarly to the typical procedure, except that the cells were stained before being injected into round-grooved glass-slides. Before staining, the BCC cells were suspended in Hank’s balanced salt solution (HBSS) supplemented with 2% FCS (HBSS+) and centrifuged to replace HBSS+ with the stain. To stain the nucleus and cell membrane, the BCC cells were incubated with Hoechst and CellMask (Invitrogen, Carlsbad, CA) solution (diluted 1:1 in HBSS+) for 15 minutes and 5 minutes, respectively, and then washed again in HBSS+. The samples were observed under a commercial confocal microscope system (LSM 510 META, Carl Zeiss, Oberkochen, Germany).

2.3. UR-OCT system

2.4. Data analysis algorithm and statistical methods

Several parameters were defined based on the intensity and spatial distribution of the back-scattering signal for single-cell analysis. Quantitative analysis could be performed because back-scattering signal characteristics were closely associated with morphological and physiological differences between live and dead BCC cells. In this study, each cell was analyzed in a three-dimensional manner. Two-dimensional image analysis was unable to completely describe a whole cell and may lead to biased conclusions. To avoid power fluctuation of the light source, the signal intensity was normalized by the reflection intensity of the lower surface of the cover glass. For each data volume, the normalized noise level was measured, and any pixel whose normalized intensity was 3 dB higher than the noise level was considered for signal analysis (Figs. 2a
Fig. 2 For each B-scan, intensity image (a) was transform into binary image by applying a threshold (b) which was higher than noise level by 3 dB on every pixels (c). The corresponding image of cell region which was found by automatically boundary detecting method (d). Cellular density can also calculated by dividing the area above the threshold and the total area of the bell shape intensity distribution diagram (e).
and 2b). Pixels whose intensity was lower than the 3 dB threshold were disregarded because we could not easily distinguish them from noise fluctuation. Therefore, a binary image called a “mask” can be constructed from each B-scan (Fig. 2c) by assigning considered pixels’ value to logical one and assigning disregarded pixels’ value to logical zero. In this way, the considered pixels and their total number inside a data volume for single-cell analysis were obtained. The signal average was defined by computing the mean value of considered pixel intensity for each cell. The signal average can be considered as the total scattering capability of a single cell to the incident light. To compute the spatial density of a cell, defined as cellular density, the cell volume must first be evaluated. This evaluation could be performed by identifying the cell region of each B-scan from its corresponding mask (Fig. 2d). For every column in a mask, the segment between the first and last nonzero pixel was regarded as within the cell region; thus, the pixel number of the cell region of a B-scan could be computed, and the cell volume was obtained by summing the total pixel number in cell regions. Cellular density was calculated as the ratio of the number of total analyzed pixels and the total pixel number in a cell volume (Fig. 2e). Finally, the average dynamic range of a cell was defined as the mean value of the dynamic range of each A-scan within the data volume. We averaged the dynamic range of each A-scan because the sampling rate was the highest along the z-axis. The average dynamic range can be thought of as the scattering capability of the main scatterers in a cell. Note that the dynamic range was defined as the ratio of the maxima and noise level of the A-scan. After obtaining the characteristic parameters of single BCC cells, Student’s t-test was used to evaluate the differences in these parameters between the normal group and the dead group. All of the tests were two-tailed, and a P-value < 0.05 was considered to be statistically significant. The linear relationship between these parameters was evaluated based on Pearson's linear regression model. All of the statistical tests were performed using STATA 8.2 software (StataCorp, College Station, TX).

3. Results

3.1. UR-OCT images

3.2. Confocal microscopy observation

A comparison of the UR-OCT en face image with a confocal microscope image of the same cell was also performed (Figs. 4a
Fig. 4 Co-registered BCC cells image of UR-OCT and confocal microscopy. En face image of UR-OCT (a) with corresponding CFM and CRM images (b, c) of BCC cells. The CFM and CRM images were acquired before UR-OCT imaging since the fluorescence decayed rapidly after staining procedure. Red arrows, strong scattering from small organelles.
-4c). Several BCC cells in which the nucleus and cell membrane were previously stained were imaged with UR-OCT, and several x-y planes were summed to provide an en face image, which was consistent with the image obtained with confocal microscopy (Fig. 4a). A confocal fluorescence microscope (CFM) image with the nucleus in blue and the plasma membrane in red was acquired (Fig. 4b). Note that the cytoplasm of these cells was also stained unexpectedly. Comparing Figs. 4a and 4b, the relative position and the shape of BCC cells in the UR-OCT image could be perfectly correlated to the CFM image. To illustrate the origin of back scattering, confocal reflectance microscope (CRM) image was also acquired (Fig. 4c). In the CRM image, we observed that the back-scattered signals were concentrated on some small organelles, and the position of the nucleus showed a hollow morphology. The phenomenon was also found in the UR-OCT image, suggesting that the main scatterers in UR-OCT were small organelles, such as mitochondria or the Golgi apparatus, instead of the relatively larger nucleus.

3.3. Signal characteristics of UR-OCT single-cell images

Based on the previously mentioned parameter definitions, the signal average of the live group was significantly higher than the dead group (live group, 0.0150±0.001; dead group, 0.0126±0.0011; P = 0.0006) (Fig. 5a
Fig. 5 Different properties between live and dead BCC cells. (a) Signal average; (b) cellular density; (c) average dynamic range; (d) cell volume of BCC cells. White bar, normal group (n = 7); black group, apoptotic group (n = 7); **P < 0.05. NS, not significant.
), suggesting that back-scattered light intensity was higher for live BCC cells. However, the cellular density of the live group was significantly lower than the dead group (live group, 0.41±0.045; dead group, 0.53±0.093; P = 0.0125) (Fig. 5b). Moreover, the average dynamic range of the three-dimensional image was also significantly lower for the live group than the dead group (live group, 4.75±0.057; dead group, 5.38±0.284; P = 0.0208) (Fig. 5c). The higher cellular density and dynamic range for the dead group were consistent with the characteristic properties of apoptotic cells. Nevertheless, there was no significant difference in cell volume between live and dead groups, showing the heterogeneous nature of the size distribution of original BCC cells (normal group, 2827.5 μm3; apoptotic group, 3210.7 μm3; P = 0.4229) (Fig. 5d).

The relationships of different parameters of BCC cells were further illustrated by scatter plots. In the dead group, the signal average was highly correlated with cellular density (Pearson’s correlation 0.894, P < 0.01), but it was highly negatively correlated with cellular density in the live group (Pearson’s correlation −0.783, P < 0.05) (Figs. 6a
Fig. 6 Correlation of signal average, cellular density, and average dynamic range between live and dead BCC cells. Scatter plots of cellular density and signal average, average dynamic range and signal average of live (a, c) and dead group (b, d). Significant positive correlations of the three parameters were observed in dead group.
and 6b). Moreover, the signal average had a high positive correlation with the average dynamic range in the dead group (Pearson’s correlation 0.772, P < 0.05) but appeared to be uncorrelated in the live group (Pearson’s correlation 0.046, P = 0.922) (Figs. 6c and 6d). The results suggested that the distinct relationships of these parameters could originate from intrinsic morphological differences between live and dead BCC cells.

4. Discussion

Further examination of the relationships between characteristic parameters of single cells provided even deeper insight into intracellular structure. It has been reported that the scattering cross section increases significantly as the nuclear-cytoplasmic ratio increases [38

38. R. Drezek, A. Dunn, and R. Richards-Kortum, “Light scattering from cells: finite-difference time-domain simulations and goniometric measurements,” Appl. Opt. 38(16), 3651–3661 (1999). [CrossRef] [PubMed]

]. In live BCC cells, cellular density decreased as the nuclear-cytoplasmic ratio increased. Therefore, the signal average should increase as cellular density decreases. In this study, the measured signal average was significantly negatively correlated with cellular density in live BCC cells, which indicated that light scattering is enhanced as the nuclear-cytoplasmic ratio increases. In addition, the size distribution of main scatterers in live BCC cells, such as mitochondria, should have high homogeneity, which means that these scatterers also have similar scattering capability. The experimental result showed that the average dynamic range was independent of the signal average for different live BCC cells, which suggests that the difference in scattering capability between different cells is most likely due to the difference between the numbers of scatterers inside the cells.

5. Conclusions

Three-dimensional single-cell imaging and the analysis of live and dead BCC cells utilizing UR-OCT were demonstrated for the first time. The apoptotic process involves a series of morphological changes, and there are many new scatterers generated. The morphological change-induced scatterers affected the scattering properties of dead cells. Therefore, the death of a single cell can be detected by not only direct imaging but also parametric analysis. In this study, we defined the signal average, cellular density, average dynamic range, and cell volume as characteristic parameters of single cells. Based on these parameters, three effective parameters were found, with P-value less than 0.05. Correlations between these parameters also provide complementary information to three-dimensional imaging. This technique is believed to be an important noninvasive methodology that provides morphological and quantitative information for detecting cell death at cellular level.

Acknowledgments

This work was supported by grants from the National Science Council, Executive Yuan, Taiwan (NSC 100-3113-P-002-008). The authors would like to thank Dr. Chung-Liang Chien and Chuan-Chuan Chao for assistance with confocal microscopy.

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P. O. Bagnaninchi, C. Holmes, N. Drummond, J. Daoud, and M. Tabrizian, “Two-dimensional and three-dimensional viability measurements of adult stem cells with optical coherence phase microscopy,” J. Biomed. Opt. 16(8), 086003 (2011). [CrossRef] [PubMed]

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C. Joo, T. Akkin, B. Cense, B. H. Park, and J. F. de Boer, “Spectral-domain optical coherence phase microscopy for quantitative phase-contrast imaging,” Opt. Lett. 30(16), 2131–2133 (2005). [CrossRef] [PubMed]

28.

Z. Darzynkiewicz, G. Juan, X. Li, W. Gorczyca, T. Murakami, and F. Traganos, “Cytometry in cell necrobiology: analysis of apoptosis and accidental cell death (necrosis),” Cytometry 27(1), 1–20 (1997). [CrossRef] [PubMed]

29.

Y. Reis, M. Bernardo-Faura, D. Richter, T. Wolf, B. Brors, A. Hamacher-Brady, R. Eils, and N. R. Brady, “Multi-parametric analysis and modeling of relationships between mitochondrial morphology and apoptosis,” PLoS ONE 7(1), e28694 (2012). [CrossRef] [PubMed]

30.

M. Rehberg, F. Krombach, U. Pohl, and S. Dietzel, “Label-free 3D visualization of cellular and tissue structures in intact muscle with second and third harmonic generation microscopy,” PLoS ONE 6(11), e28237 (2011). [CrossRef] [PubMed]

31.

C. L. Evans, E. O. Potma, M. Puoris’haag, D. Côté, C. P. Lin, and X. S. Xie, “Chemical imaging of tissue in vivo with video-rate coherent anti-Stokes Raman scattering microscopy,” Proc. Natl. Acad. Sci. U.S.A. 102(46), 16807–16812 (2005). [CrossRef] [PubMed]

32.

X. Zhang, M. B. Roeffaers, S. Basu, J. R. Daniele, D. Fu, C. W. Freudiger, G. R. Holtom, and X. S. Xie, “Label-free live-cell imaging of nucleic acids using stimulated Raman scattering microscopy,” ChemPhysChem 13(4), 1054–1059 (2012). [CrossRef] [PubMed]

33.

J. W. Tjiu, Y. H. Liao, S. J. Lin, Y. L. Huang, W. L. Tsai, C. Y. Chu, M. L. Kuo, and S. H. Jee, “Cyclooxygenase-2 overexpression in human basal cell carcinoma cell line increases antiapoptosis, angiogenesis, and tumorigenesis,” J. Invest. Dermatol. 126(5), 1143–1151 (2006). [CrossRef] [PubMed]

34.

J. W. Tjiu, J. S. Chen, C. T. Shun, S. J. Lin, Y. H. Liao, C. Y. Chu, T. F. Tsai, H. C. Chiu, Y. S. Dai, H. Inoue, P. C. Yang, M. L. Kuo, and S. H. Jee, “Tumor-associated macrophage-induced invasion and angiogenesis of human basal cell carcinoma cells by cyclooxygenase-2 induction,” J. Invest. Dermatol. 129(4), 1016–1025 (2009). [CrossRef] [PubMed]

35.

C. C. Tsai, T. H. Chen, Y. S. Lin, Y. T. Wang, W. Chang, K. Y. Hsu, Y. H. Chang, P. K. Hsu, D. Y. Jheng, K. Y. Huang, E. Sun, and S. L. Huang, “Ce3+:YAG double-clad crystal-fiber-based optical coherence tomography on fish cornea,” Opt. Lett. 35(6), 811–813 (2010). [CrossRef] [PubMed]

36.

I. Csiki, J. D. Morrow, A. Sandler, Y. Shyr, J. Oates, M. K. Williams, T. Dang, D. P. Carbone, and D. H. Johnson, “Targeting cyclooxygenase-2 in recurrent non-small cell lung cancer: a phase II trial of celecoxib and docetaxel,” Clin. Cancer Res. 11(18), 6634–6640 (2005). [CrossRef] [PubMed]

37.

K. L. Reckamp, K. Krysan, J. D. Morrow, G. L. Milne, R. A. Newman, C. Tucker, R. M. Elashoff, S. M. Dubinett, and R. A. Figlin, “A phase I trial to determine the optimal biological dose of celecoxib when combined with erlotinib in advanced non-small cell lung cancer,” Clin. Cancer Res. 12(11), 3381–3388 (2006). [CrossRef] [PubMed]

38.

R. Drezek, A. Dunn, and R. Richards-Kortum, “Light scattering from cells: finite-difference time-domain simulations and goniometric measurements,” Appl. Opt. 38(16), 3651–3661 (1999). [CrossRef] [PubMed]

39.

J. R. Mourant, J. P. Freyer, A. H. Hielscher, A. A. Eick, D. Shen, and T. M. Johnson, “Mechanisms of light scattering from biological cells relevant to noninvasive optical-tissue diagnostics,” Appl. Opt. 37(16), 3586–3593 (1998). [CrossRef] [PubMed]

40.

Z. Darzynkiewicz, E. Bedner, and P. Smolewski, “Flow cytometry in analysis of cell cycle and apoptosis,” Semin. Hematol. 38(2), 179–193 (2001). [CrossRef] [PubMed]

41.

R. Drezek, M. Guillaud, T. Collier, I. Boiko, A. Malpica, C. Macaulay, M. Follen, and R. Richards-Kortum, “Light scattering from cervical cells throughout neoplastic progression: influence of nuclear morphology, DNA content, and chromatin texture,” J. Biomed. Opt. 8(1), 7–16 (2003). [CrossRef] [PubMed]

OCIS Codes
(100.2960) Image processing : Image analysis
(170.1530) Medical optics and biotechnology : Cell analysis
(170.1870) Medical optics and biotechnology : Dermatology
(170.4500) Medical optics and biotechnology : Optical coherence tomography

ToC Category:
Cell Studies

History
Original Manuscript: June 13, 2012
Revised Manuscript: July 24, 2012
Manuscript Accepted: August 2, 2012
Published: August 13, 2012

Citation
Nai-Chia Cheng, Tsung-Hsun Hsieh, Yu-Ta Wang, Chien-Chih Lai, Chia-Kai Chang, Ming-Yi Lin, Ding-Wei Huang, Jeng-Wei Tjiu, and Sheng-Lung Huang, "Cell death detection by quantitative three-dimensional single-cell tomography," Biomed. Opt. Express 3, 2111-2120 (2012)
http://www.opticsinfobase.org/boe/abstract.cfm?URI=boe-3-9-2111


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  28. Z. Darzynkiewicz, G. Juan, X. Li, W. Gorczyca, T. Murakami, and F. Traganos, “Cytometry in cell necrobiology: analysis of apoptosis and accidental cell death (necrosis),” Cytometry27(1), 1–20 (1997). [CrossRef] [PubMed]
  29. Y. Reis, M. Bernardo-Faura, D. Richter, T. Wolf, B. Brors, A. Hamacher-Brady, R. Eils, and N. R. Brady, “Multi-parametric analysis and modeling of relationships between mitochondrial morphology and apoptosis,” PLoS ONE7(1), e28694 (2012). [CrossRef] [PubMed]
  30. M. Rehberg, F. Krombach, U. Pohl, and S. Dietzel, “Label-free 3D visualization of cellular and tissue structures in intact muscle with second and third harmonic generation microscopy,” PLoS ONE6(11), e28237 (2011). [CrossRef] [PubMed]
  31. C. L. Evans, E. O. Potma, M. Puoris’haag, D. Côté, C. P. Lin, and X. S. Xie, “Chemical imaging of tissue in vivo with video-rate coherent anti-Stokes Raman scattering microscopy,” Proc. Natl. Acad. Sci. U.S.A.102(46), 16807–16812 (2005). [CrossRef] [PubMed]
  32. X. Zhang, M. B. Roeffaers, S. Basu, J. R. Daniele, D. Fu, C. W. Freudiger, G. R. Holtom, and X. S. Xie, “Label-free live-cell imaging of nucleic acids using stimulated Raman scattering microscopy,” ChemPhysChem13(4), 1054–1059 (2012). [CrossRef] [PubMed]
  33. J. W. Tjiu, Y. H. Liao, S. J. Lin, Y. L. Huang, W. L. Tsai, C. Y. Chu, M. L. Kuo, and S. H. Jee, “Cyclooxygenase-2 overexpression in human basal cell carcinoma cell line increases antiapoptosis, angiogenesis, and tumorigenesis,” J. Invest. Dermatol.126(5), 1143–1151 (2006). [CrossRef] [PubMed]
  34. J. W. Tjiu, J. S. Chen, C. T. Shun, S. J. Lin, Y. H. Liao, C. Y. Chu, T. F. Tsai, H. C. Chiu, Y. S. Dai, H. Inoue, P. C. Yang, M. L. Kuo, and S. H. Jee, “Tumor-associated macrophage-induced invasion and angiogenesis of human basal cell carcinoma cells by cyclooxygenase-2 induction,” J. Invest. Dermatol.129(4), 1016–1025 (2009). [CrossRef] [PubMed]
  35. C. C. Tsai, T. H. Chen, Y. S. Lin, Y. T. Wang, W. Chang, K. Y. Hsu, Y. H. Chang, P. K. Hsu, D. Y. Jheng, K. Y. Huang, E. Sun, and S. L. Huang, “Ce3+:YAG double-clad crystal-fiber-based optical coherence tomography on fish cornea,” Opt. Lett.35(6), 811–813 (2010). [CrossRef] [PubMed]
  36. I. Csiki, J. D. Morrow, A. Sandler, Y. Shyr, J. Oates, M. K. Williams, T. Dang, D. P. Carbone, and D. H. Johnson, “Targeting cyclooxygenase-2 in recurrent non-small cell lung cancer: a phase II trial of celecoxib and docetaxel,” Clin. Cancer Res.11(18), 6634–6640 (2005). [CrossRef] [PubMed]
  37. K. L. Reckamp, K. Krysan, J. D. Morrow, G. L. Milne, R. A. Newman, C. Tucker, R. M. Elashoff, S. M. Dubinett, and R. A. Figlin, “A phase I trial to determine the optimal biological dose of celecoxib when combined with erlotinib in advanced non-small cell lung cancer,” Clin. Cancer Res.12(11), 3381–3388 (2006). [CrossRef] [PubMed]
  38. R. Drezek, A. Dunn, and R. Richards-Kortum, “Light scattering from cells: finite-difference time-domain simulations and goniometric measurements,” Appl. Opt.38(16), 3651–3661 (1999). [CrossRef] [PubMed]
  39. J. R. Mourant, J. P. Freyer, A. H. Hielscher, A. A. Eick, D. Shen, and T. M. Johnson, “Mechanisms of light scattering from biological cells relevant to noninvasive optical-tissue diagnostics,” Appl. Opt.37(16), 3586–3593 (1998). [CrossRef] [PubMed]
  40. Z. Darzynkiewicz, E. Bedner, and P. Smolewski, “Flow cytometry in analysis of cell cycle and apoptosis,” Semin. Hematol.38(2), 179–193 (2001). [CrossRef] [PubMed]
  41. R. Drezek, M. Guillaud, T. Collier, I. Boiko, A. Malpica, C. Macaulay, M. Follen, and R. Richards-Kortum, “Light scattering from cervical cells throughout neoplastic progression: influence of nuclear morphology, DNA content, and chromatin texture,” J. Biomed. Opt.8(1), 7–16 (2003). [CrossRef] [PubMed]

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