The ability to characterize the mitochondria in single living cells may provide a powerful tool in clinical applications. We have recently developed a 2D (both polar angle and azimuth angle dependences) light scattering cytometric technique which we apply here to assess experimental 2D light scattering patterns from single biological cells (yeast and human). We compare these patterns to those obtained from simulations using a 3D Finite-Difference Time-Domain (FDTD) method and demonstrate that microstructure (e.g., the cytoplasm and/or nucleus) of cells generates fringes of scattered light, while in the larger human cells the light scattered by the mitochondria dominates the scatter pattern, forming compact regions of high intensity that we term ‘blobs’. These blobs provide information on the mitochondria within the cell and their analysis may ultimately be useful as a diagnostic technique.
© 2007 Optical Society of America
As demonstrated by the wide range of applications of cytometry, light scattering methods are a powerful means of acquiring cellular information [1–4]. Upon illuminating a biological cell with polarized laser light, the light is scattered over a wide solid angle and the spatial distribution of the scattered light is dependent on the cellular structure and composition at the micron and sub-micron scales. The nucleus, the largest organelle in a cell, plays an important role in the forward scatter at larger angles [2, 5], while the side scatter region is rich in information about the mitochondria [1, 2, 4–6]. Previous work in cytometry has been largely limited in terms of the solid angle over which the scatter patterns were obtained and has encountered difficulties since the cell morphology and orientation can cause dramatic variations in the 1D scatter spectra .
We have recently reported on an integrated microfluidic waveguide cytometer capable of obtaining 2D scatter patterns from single cells . The capture of 2D light scattering patterns provides far more information about biological cells and may allow the characterization of cells independently of their orientation. Although Mie theory [9, 10] is a useful starting point to predict light scatter by (typically) spherical structures, biological cells (with their organelles and non-spherical shapes) are far too complex to be modeled by Mie theory. Numerical simulations via Finite-Difference Time-Domain (FDTD) methods [1, 4, 11–13] have been applied to model scattering by such complex structures, allowing the study of biological cells with a spatial resolution of tens of nanometers, readily resolving the scattering effects by the various organelles (micron and sub-micron in scale) in a single cell. Although Lu et. al. have determined the FDTD 2D scatter patterns of a biconcave shaped human red blood cell , the bulk of these FDTD simulations have focused on the characterizations of the 1D scatter spectra to extract cellular information. To our knowledge, no previous work has compared the experimental 2D scatter patterns of biological cells with those obtained by 3D FDTD simulations. We use these comparisons to determine the origin of the major features in the scatter patterns.
In the present experimental and simulation work, our results show that the cytoplasm and the nucleus of single cells produce fringes in the 2D scatter patterns, while the mitochondria within the cell create a pattern of localized regions of higher intensity in the scatter patterns. We refer to these localized regions as ‘blobs’ and we are not aware of any prior reports in the literature of such observations. As expected, the scatter patterns produced both by the FDTD simulations and from experiments involving yeast cells, show fringes. We demonstrate that the different orientations of a yeast cell in the microchannel will change the fringe patterns, but will not generate blobs. By contrast, FDTD simulations and experimental scatter patterns from human (Raji) cells show scatter patterns that are dominated by blobs. We demonstrate here that the blobs provide information about the mitochondrial distribution in a human cell. Given the important role of mitochondria in human diseases such as cancer , aging , Parkinson’s and Alzheimer’s , a non-invasive method of characterizing mitochondria could be of great importance as a diagnostic tool.
2. Experimental and simulation methods
2.1 An integrated microfluidic waveguide cytometer
The illumination source used in the experiments is a 632.8nm HeNe laser (Melles Griot Laser Group). A microfluidic chip provides a microchannel that serves both as a waveguide to carry the laser light and as a fluidic channel to carry the cell. The microfluidic channel is etched with micron-scale observation windows to minimize contaminating signals from laser light scattered from sidewall roughness far from the biological cell under examination. The laser beam is prism-coupled into the microfluidic channel to illuminate a cell in the observation window area. Once a single cell is positioned in the observation window area it is observed from above by using a digital camera (Nikon Coolpix 990) mounted on a microscope (Nippon Kogaku 91219). The scatter pattern is simultaneously obtained by using a 2D CCD detector (ICX098BQ, Sony) from below the microchip. Additional higher resolution images are taken with a Zeiss Axiovert 200 microscope (Carl Zeiss, Germany). More information about this microfluidic 2D cytometry system can be found in our previous work .
The scattered signals used in this work are quite weak, and would typically require long integration times with a CCD that is cooled in order to minimize dark current. However, it is problematic to integrate a cooled CCD into a compact package several millimeters away from a microfluidic chip at room temperature. Instead, we chose a consumer grade, uncooled CCD that has a low dark current (using Sony’s HAD technology). It is simplest to obtain such a CCD as part of a consumer webcam (a Logitech Quickcam 4000). An 8-bit Analog/Digital converter is used with the CCD.
In order to ensure that we are not acquiring spurious signals, we have carefully calibrated the CCD system. The dark field value for each pixel was assessed by taking CCD images of the apparent scattered light intensity with the laser turned off and the CCD in a darkened state (i.e. making a dark field image). We assessed the production of stray light (e.g., light scattered from the microfluidic channel) by obtaining images from the CCD with the laser on but without anything in the observation window (we refer to these as ‘grey field’ images).
These dark field images show a constant low level of signal (approximately 3 counts) that do not require correction in subsequent images of the real scatter patterns. We checked variations in the pixel sensitivity by illuminating each pixel with the same intensity under an LED (EFR5366X, LED red clear 5mm) (forming a ‘white field’ image) and subtracting the dark field image. We find that the gain variations from pixel to pixel are negligible. The ‘grey field’ image intensity level is not as constant as the ‘white field’ image, however the ‘grey field’ image does not contain any blobs.
Finally, we ensure that scatter patterns containing either blobs or fringes could only be obtained when a scatterer is in the observation window (i.e. could not be generated by spurious signals). After positioning a cell in the window we verify its identity (as a cell rather than a dust particle, bubble or other contaminant) by photographing it from above, and then acquiring its scatter pattern by integrating for 1/15 seconds. The scatter patterns shown here are reproducible, representative and typically represented a signal of about 20 counts for a yeast cell, while the signal of the brighter mitochondrial blobs are typically 30~50 counts out of a maximum CCD resolution of 256 counts.
2.2 Visualization of single biological cell models
Programs were written to distribute organelles randomly throughout the cytoplasm of the cell. In our code, the nucleus and the mitochondria were assumed to be spherical while the cell itself could be ellipsoidal. The visualization is done by using the AVS software (Advanced Visual Systems Inc., USA). The different colors denote the refractive index for each kind of organelle. In order to study the cell orientation effects, ellipsoidal cells are used. The cell can be rotated through any polar angle θ about the x axis as shown in Supplementary Fig. S1. The axes in the artificial cell models show the cell orientations. If not otherwise specified, the nucleus and the cell itself are centered at the origin.
2.3 FDTD simulation of single biological cells in the waveguide cytometer
The simulations were performed on Silicon Graphics Inc. (SGI) parallel computers under WestGrid (a collaborative project that provides high performance computing and multimedia/visualization resources to researchers and educators across Western Canada, http://www.ualberta.ca/CNS/RESEARCH/WestGrid/) using the 3D FDTD program previously described by us . We have applied this FDTD code to our compact planar waveguide cytometer . For the larger Raji cells, a large memory allocation of 120Gb is typically required to perform a simulation, and approximately 40 hours of running time is needed for a space step of 50nm. For a smaller yeast cell, a memory of 12Gb is usually allocated and the simulation can be finished within 24 hours for a space step of 30nm. Supplementary Fig. S1 provides additional information regarding the FDTD geometry.
2.4 Yeast cell sample preparation
Saccharomyces cerevisiae yeast cells were prepared in our lab by resuspending the dried cells in filtered water (0.8µm filter, Millipore Corp.), diluting the resulting cells to a concentration of ~2000 cells/ml and sonicating for 2 minutes. The yeast cell sample was prepared and used the same day.
2.5 Raji cell sample preparation
The Raji cells were from a lymphoblast-like cell line created from a human Burkitt’s lymphoma (ATCC (American Type Culture Collection), CCL-86). The Raji cells we used were grown in 90% RPMI-1640 (Roswell Park Memorial Institute) medium with 2mM L-Glutamine (Gibco, Invitrogen Corporation, Canada) supplemented with 10% Fetal Bovine Serum (Gibco, Invitrogen Corporation, Canada) plus 10µg/mL Gentamicin (Gibco, Invitrogen Corporation, Canada) and 10mM Hepes Buffer (Gibco, Invitrogen Corporation, Canada). Cells were cultured in a six-well multiwell plate (Becton Dickinson, USA) at 105/mL for 3–4 days before reaching confluency. They were incubated at 370C (Forma II, Thermo Electron Corp., USA) in 5% CO2 (Praxair, Canada).
In order to more easily handle these biohazardous cells they were fixed (i.e. killed) using a formaldehyde fixation method. They were centrifuged at 2000 RPM for 5 minutes in a 15mL Falcon conical tube (Becton Dickinson, USA). The supernatant was then removed and cells were washed with 1x phosphate buffered saline solution (PBS). After removing the final supernatant, cells were re-suspended in a 2% formaldehyde solution (FA) (Fisher, Canada) at a concentration of 10M cells per 250µL FA, for 10 minutes at room temperature. The cells were again washed, centrifuged, and re-suspended in 1x PBS. The final cell concentration was transferred to a micro-centrifuge tube (Fisher Scientific, USA), stored on ice, and used the same day. In order to more easily use the Raji cells in a single-cell observation mode, the Raji cell solution was diluted to a density of 2000 Raji cells per milliliter. This method was far from ideal for producing viable cells, but it ensured that the cytometer only used intact cells by imaging each cell prior to acquiring the scatter pattern.
3.1 Effects of mitochondria upon the scatter patterns
For the purpose of studying light scattering, biological cells are described in terms of size and refractive index variations. Unless otherwise specified, for the FDTD simulations, the cytoplasm is taken to have a refractive index of 1.38 , the nucleus 1.39 , the mitochondria 1.42 , and the surrounding medium is a PBS of refractive index 1.334 . A program has been written to generate (3D) artificial biological cell models comprised of a membrane, a nucleus, cytoplasm and a random distribution of mitochondria throughout the cytoplasm. These 3D biological cells are visualized by using AVS software. The cell in Fig. 1 has a radius of 2µm, a nucleus with a radius of 1µm, and contains 40 mitochondria, each with a radius of 250nm. In Fig. 1(a), the cell only has a nucleus and cytoplasm, while Figure 1(b) shows a cell with only randomly distributed mitochondria (random seed I). In Fig. 1(c), the cell has randomly distributed mitochondria (random seed I), a nucleus, and cytoplasm. Figure 1(d) has randomly distributed mitochondria (random seed II), a nucleus, and cytoplasm. The FDTD method has been applied to simulate these models in an environment that includes the effects of the integrated microfluidic waveguide cytometer  (leading to a distortion of the scatter patterns). The planar waveguide structure is illustrated in Supplementary Fig. S2. The obtained 2D scatter patterns are in the zx plane, where z is the horizontal axis and x is the vertical axis.
Representative 2D FDTD scatter patterns are shown in Figs. 1(a′) to 1(d′) for the cell models shown in Figs. 1(a) to 1(d), respectively. As is seen in Fig. 1(a′), when there is only the cytoplasm and a nucleus in the cell, the 2D scatter pattern has only fringes. Figure 1(b′) corresponds to the case where only mitochondria are present and shows a 2D scatter pattern with a sparse distribution of localized regions of high intensity (i.e. ‘blobs’). Figures 1(a′) and (b′) show that the microstructures in the cell, such as the nucleus and the cytoplasm, give continuous fringes in the 2D scatter patterns, while the sub-micron scale mitochondria give a distribution of blobs. Figure 1(c′), corresponding to a cell model [Fig. 1(c)] containing cytoplasm, a nucleus, and mitochondria, shows scatter patterns containing both fringes and blobs. Figure 1(d′) also corresponds to a cell model [Fig. 1(d)] containing cytoplasm and a nucleus, but has a different distribution of mitochondria. Although Fig. 1(c′) and Fig. 1(d′) have the same number and position for the fringes, they have different distributions of the blobs. The representative figures shown in supplementary Fig. S3 indicate that changes in the position and size of the nucleus in a single cell will not generate blobs in the 2D scatter patterns.
3.2 Yeast cell 2D scatter patterns with fringes
The Saccharomyces cerevisiae yeast cells used in this study are ellipsoidal structures, with a thick cell wall of about several hundred nanometers, a nucleus size of about 1µm in radius, and a few to tens of mitochondria, each with a radius of several hundred nanometers [21, 22]. In this study, while a single cell is immobilized in the observation window area in the microfluidic channel, the side scatter pattern is obtained by using a 2D CCD detector located underneath the microchip.
In Fig. (2), we show the 2D scatter pattern with fringes from a single yeast cell immobilized in the observation window area. Images of the same yeast cell (not shown) taken with the Axiovert microscope show that the yeast cell is an elliptical structure, with a semimajor axis of approximately 3µm, and a semiminor axis of approximately 2.5µm.
The orientation of a cell has a strong effect on the 1D elastic light scattering spectra measured from the cell . In order to show how strongly the changes of a cell orientation affect the 2D scatter patterns, FDTD simulations are performed on the ellipsoidal yeast cell models. Figures 3(a) and 3(b) show AVS visualizations of the yeast cell models in the microchannel. The cell wall has a thickness of 200nm, the nucleus has a radius of 1.2µm, and the 40 randomly distributed mitochondria are assumed spherical with radii of 250nm. The refractive index for the cell wall is 1.45 , 1.42 for the mitochondria, 1.39 for the nucleus, and 1.38 for the cytoplasm. Each of the simulated yeast cells has the same number and distribution of mitochondria prior to rotation. Figures 3(c) and 3(d) show distinctly different fringe patterns that result from the different cell orientations in the microchannel. Comparing Figs. 3(c) and 3(d) with Fig. 2, we find similar fringe distributions for experimental and FDTD simulation results- there are around 5 fringes in the same CCD dimensions and neither Figure 3(c) nor 3(d) has obvious blobs. Supplementary Fig. S4 shows that cell orientation effects (without mitochondria) will not generate the blobs, but will give variations in the fringe distributions. Figure 2, Fig. 3, and supplementary Fig. S4 show that the yeast cell scatter patterns are dominated by the fringes from the microstructural cytoplasm, nucleus and cell wall. Neither simulation nor experimental scatter patterns from yeast cells give rise to blobs.
3.3 Human cell 2D scatter patterns with ‘blobs’
Light scattering measurements were made using Raji cells. As seen under the Axiovert microscope (not shown), the Raji cells that were used are spherical with a radius of about 8µm and are almost transparent in the PBS solution. Figure 4 shows the scatter pattern obtained from a Raji cell, consisting of a sparse distribution of blobs without any clear fringes.
In order to compare the experimental scatter pattern with FDTD simulations, we use the Raji cell models shown in Fig. 5. The Raji cell model has a radius of 8µm, a nucleus with a radius of 4µm as shown in Fig. 5(a). There are 300 randomly distributed mitochondria (an average number, as observed mitochondria numbers per cell varies in different cell types from 83~677)  with radii of 500nm (intermediate size within the observed range 0.25~1µm in radius) . The cell cytoplasm has a refractive index 1.35  (almost transparent in the PBS buffer), the nucleus has a refractive index of 1.39, and the mitochondria have a refractive index of 1.42. Typical human cells have a cell membrane of thickness 10nm  and with a refractive index 1.46 . FDTD simulations show that for a 1.5µm cell (radius), the 10nm cell membrane (volume ratio of 1.99%) contributes significantly less than the 1.5µm cell cytoplasm to the scattering. In the case of Raji cells, the cell membrane (10nm thickness) has a volume ratio of 0.375% to the whole Raji cell (8µm in radius). Thus we do not take into account the thin cell membrane in the FDTD calculations. To show the mitochondrial contribution to the 2D scatter patterns, various cell models are produced as shown in Figs. 5(a) and 5(b). Figure 5(b) has the same mitochondrial distribution as in Fig. 5(a) but without the microstructure of the cell. The corresponding 2D FDTD scatter patterns are shown in Figs. 5(c) and 5(d). Both simulation and experimental scatter patterns from Raji cells are dominated by blobs.
4. Discussion and conclusions
Our simulation results indicate (e.g. Fig. 1) that the microstructure generates fringes whereas the distributed mitochondria generate blobs. On this basis, we initially expected to see similar behavior with yeast and human cells. Instead we encountered two extremes with the yeast cells producing no blobs and human cells producing only blobs.
In contrast to the simulations of Fig. 1, there are no obvious blobs in the yeast cell 2D scatter patterns either experimentally as in Fig. 2, or in the simulations as in Fig. 3. This is despite having the same number and size of mitochondria in the simulations of Fig. 1 as in those of Fig. 3. We attribute this (with and without blobs) to the very strong role played by the yeast cell wall (not present in the simulation of Fig. 1). This wall is not only thick, but also occupies a substantially higher volume, with a higher refractive index, than the mitochondria. As in the yeast cell models used here, the volume ratio for the mitochondria (refractive index 1.42) to the whole yeast cell is 3.74%, and the volume ratio for the cell wall (refractive index 1.45) to the whole yeast cell is 21.77%. Additionally, the yeast mitochondria are smaller than those of the Raji cells and will scatter far less. As such it would appear that for yeast cells the microstructure dominates the side scatter patterns and no blobs are generated.
On the other hand, the experimental (Fig. 4) and the simulation (Fig. 5) results for human cells show no obvious fringes but a significant distribution of blobs. There are no clear fringes in Fig. 5(c), although the Raji cell model Fig. 5(a) has both the cytoplasm and the nucleus. Figure 5(d) has only the sparsely distributed blobs, as the corresponding cell model in Fig. 5(b) has only the randomly distributed mitochondria. From Fig. 4, Fig. 5(c) and Fig. 5(d), we find that the blobs dominate the Raji cell 2D scatter patterns. We attribute this to the larger number of mitochondria, their larger size (this leads to stronger side scatter), and the absence of a thick cell wall.
In order to quantify the reason for the observed fringes with yeast cells and the observed blobs with Raji cells, FDTD simulations are performed separately on various components of the yeast cell that is illustrated in Fig. 3(a). In the first case, only the 40 randomly distributed yeast mitochondria are considered without the presence of a nucleus, cytoplasm or the cell wall. In the second case, only the thick yeast cell wall is considered. The individual scatter pattern contributions from the 40 yeast mitochondria and the thick cell wall are shown in Figs. S5(a) and S5(b), respectively. From Fig. S5(a), the 40 yeast mitochondria give blobs in the 2D scatter patterns, while the yeast cell wall gives the fringes in the 2D scatter patterns. The representative intensity levels for the scatter patterns from different cell component are shown in Fig. S6. As shown in Fig. S6, the solid line is the intensity level of the 40 yeast mitochondria with an intensity peak of approximately 0.5×10-9 (arbitrary units.). The line of dashed plus symbols is the scatter intensity level from the yeast cell wall with an intensity peak of approximately 1.0×10-8 units, and the dotted line is the intensity level for the 300 Raji mitochondria [cell model Fig. 5(b)] with an intensity peak of approximately 3.0×10-8 units. From the different intensity levels we notice that the contribution to the scattering from the yeast cell wall is approximately 20 times larger than that from the 40 yeast mitochondria, while the scattering from the 300 Raji mitochondria is approximately 60 times larger than that from the 40 yeast mitochondria. The intensity level of the 300 Raji mitochondria is approximately 3 times larger than that of the yeast cell wall.
Experimentally, we obtain an intensity level of 20 counts for the yeast cell, and 30~50 counts for the Raji cell. Based on the experimental CCD counts, the yeast cell’s mitochondrial scattering will contribute only one count to the CCD detector. Such a low intensity level is undetectable in the experimental background and only the effects of the cell wall will be detected.
This new 2D cytometer was initially intended to provide a more complete characterization of individual cells . The present work demonstrates a novel method that provides information not only on the microstructure within the cell, but also on the distribution of mitochondria within the cell. Although imaging methods exist to detect mitochondria (e.g. with mitochondrially selective fluorescent dyes), these often perturb the function of the cell and are complicated by the size of the mitochondria being near or below the resolution limitation of the microscopes used. The present method, being based on scattering principles, will be relatively unaffected by such effects. It is apparent then that the scatter patterns from experiment and simulation are consistent in terms of overall behaviour (i.e. fringes versus blobs) and in terms of the relative magnitudes of the scatter intensities.
Further progress in the quantitative analysis of scattered light 2D angular spectra will rely on the proper identification of the characteristic features within the images that are most important and informative. For example, these could be methods based on work within specific angular ranges (as in ) in the scattered light distribution, which are sensitive to different characteristics of the cell. Such an analysis of experimental data will allow us to extract descriptive parameters from the images of both simulation and experiment, thereby allowing us to make quantitative comparisons and perhaps predict physical parameters. This image analysis research is underway but is beyond the scope of the present work.
Clearly there is much to do in terms of extracting information from the scatter patterns. An example is the challenge of solving an inverse problem — we can simulate the scatter pattern of a parameterized cell, but given the scatter pattern of a cell it is far from straightforward to obtain estimates of the optimal set of parameters to describe that cell. This is a problem often encountered in imaging applications and is an active area of research. We are investigating several approaches (with an emphasis upon less computationally challenging ones) to better analyze the 2D scatter patterns. With such techniques, a brief (< 1 second) capture of the scatter pattern could provide information on the mitochondria of a living (and unperturbed) cell. Such information on the mitochondria within a cell could be extremely important in the detection and monitoring of disease conditions such as cancer, where normal and cancerous cells are reported to have different mitochondrial distributions [28, 29].
The authors would like to thank WestGrid (Canada) for the parallel computation support. We thank Dr. Jon Johansson for the help in AVS visualization of 3D biological cells, Dr. L.M. Pilarski and Jana Lauzon for assistance with, and supply of, Raji cell samples. This work is supported by the Natural Science and Engineering Research Council (NSERC) of Canada, the Canadian Institutes of Health Research (CIHR) and the Canadian Institute for Photonic Innovations (CIPI).
References and links
1. A. Dunn and R. Richards-Kortum, “Three-dimensional computation of light scattering from cells,” IEEE J. Sel. Top. Quantum Electron. 2, 898–905 (1996). [CrossRef]
2. 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, 3586–3593 (1998). [CrossRef]
3. V. Backman, M. B. Wallace, L. T. Perelman, J. T. Arendt, R. Gurjar, M. G. Muller, Q. Zhang, G. Zonios, E. Kline, T. McGillican, S. Shapshay, T. Valdez, K. Badizadegan, J. M. Crawford, M. Fitzmaurice, S. Kabani, H. S. Levin, M. Seiler, R. R. Dasari, I. Itzkan, J. Van Dam, and M. S. Feld, “Detection of preinvasive cancer cells,” Nature 406, 35–36 (2000). [CrossRef] [PubMed]
6. J. D. Wilson, C. E. Bigelow, D. J. Calkins, and T. H. Foster, “Light scattering from intact cells reports oxidative-stress-induced mitochondrial swelling,” Biophys. J. 88, 2929–2938 (2005). [CrossRef] [PubMed]
7. D. Watson, N. Hagen, J. Diver, P. Marchand, and M. Chachisvilis, “Elastic light scattering from single cells: Orientational dynamics in optical trap,” Biophys. J. 87, 1298–1306 (2004). [CrossRef] [PubMed]
8. X. T. Su, W. Rozmus, C. Capjack, and C. J. Backhouse, “Side scatter light for micro-size differentiation and cellular analysis,” Proc. SPIE 6446, 64460W (2007). [CrossRef]
9. G. Mie, “Articles on the optical characteristics of turbid tubes, especially colloidal metal solutions,” Ann. Phys.-Berlin 25, 377–445 (1908). [CrossRef]
10. W. T. Grandy, Scattering of Waves from Large Spheres, (Cambridge University Press, Cambridge, 2000). [CrossRef]
11. R. Drezek, A. Dunn, and R. Richards-Kortum, “A pulsed finite-difference time-domain (FDTD) method for calculating light scattering from biological cells over broad wavelength ranges,” Opt. Express 6, 147–157 (2000). [CrossRef] [PubMed]
15. Y. Michikawa, F. Mazzucchelli, N. Bresolin, G. Scarlato, and G. Attardi, “Aging-dependent large accumulation of point mutations in the human mtDNA control region for replication,” Science 286, 774–779 (1999). [CrossRef] [PubMed]
16. P. A. Trimmer, R. H. Swerdlow, J. K. Parks, P. Keeney, J. P. Bennett, S. W. Miller, R. E. Davis, and W. D. Parker, “Abnormal mitochondrial morphology in sporadic Parkinson’s and Alzheimer’s disease cybrid cell lines,” Exp. Neurol. 162, 37–50 (2000). [CrossRef] [PubMed]
18. A. Brunstin and P. F. Mullaney, “Differential light-scattering from Spherical Mammalian-Cells,” Biophys. J. 14, 439–453 (1974). [CrossRef]
19. H. L. Liu, B. Beauvoit, M. Kimura, and B. Chance, “Dependence of tissue optical properties on solute-induced changes in refractive index and osmolarity,” J. Biomed. Opt. 1, 200–211 (1996). [CrossRef]
20. F. Giess, M. G. Friedrich, J. Heberle, R. L. Naumann, and W. Knoll, “The protein-tethered lipid bilayer: A novel mimic of the biological membrane,” Biophys. J. 87, 3213–3220 (2004). [CrossRef] [PubMed]
21. C. Charpentier, T. N. Vanlong, R. Bonaly, and M. Feuillat, “Alteration of cell-wall structure in Saccharomyces-Cerevisiae and Saccharomyces-Bayanus during autolysis,” Appl. Microbiol. Biotechnol. 24, 405–413 (1986). [CrossRef]
22. C. Bauer, V. Herzog, and M. F. Bauer, “Improved technique for electron microscope visualization of yeast membrane structure,” Microsc. microanal. 7, 530–534 (2001).
23. R. E. Marquis, “Immersion refractometry of isolated bacterial cell-walls,” J. Bacteriol. 116, 1273–1279 (1973). [PubMed]
27. J. S. Maier, S. A. Walker, S. Fantini, M. A. Franceschini, and E. Gratton, “Possible correlation between blood-glucose concentration and the reduced scattering coefficient of tissues in the near-infrared,” Opt. Lett. 19, 2062–2064 (1994). [CrossRef] [PubMed]
28. P. L. Gourley, J. K. Hendricks, A. E. McDonald, R. G. Copeland, K. E. Barrett, C. R. Gourley, and R. K. Naviaux, “Ultrafast nanolaser flow device for detecting cancer in single cells,” Biomed. Microdevices 7, 331–339 (2005). [CrossRef]