A microscopy system has been constructed that is capable of simultaneously acquiring both Raman spectra and angle-resolved elastic-scattering patterns in either epi- or transillumination modes with a spot size. The benefits and drawbacks of the epi- and transillumination modalities are discussed. Validation studies have been performed on single beads of a few micrometers in size, as well as on ensembles of submicrometer particles. In addition, transilluminated Raman and elastic-scattering spectra were obtained from single granulocytes and peripheral blood monocytes. Both the Raman- and the elastic-scattering channels show clear differences between the two types of immune cells.
© 2009 Optical Society of America
Over the past several years there has been a growing interest in analyzing the light scattering signatures of biological samples. Two such scattering processes are elastic scattering, which characterizes the morphology and refractive index of a sample, and Raman scattering, which characterizes the chemical com position of the sample. Elastic scattering has been used to observe differences between cancerous and noncancerous tissues  and to assess changes in organelle morphology in response to photodynamic therapy in cancer cells [2, 3], among other applications. Raman spectroscopy has similarly been used for a wide variety of applications, some examples of which are to measure chemical differences between cancerous and noncancerous tissues [4, 5], to simultaneously quantify multiple analytes in whole blood , and to assist in the understanding of the biochemical basis for disease . Recently there has been an increasing focus on acquiring scattering signals from single cells. A number of studies have used elastically scattered light to characterize the morphology of individual cells [8, 9], while others have investigated the chemical differences between cells using Raman spectroscopy [10, 11]. Both of these scattering techniques have the advantage that they require no external labeling or sample preparation. This allows one to make measurements of single cells such that the cells retain their full functionality and are not influenced by the measurement .
Earlier we reported on an instrument that could acquire both Raman- and elastic-scattering signals simultaneously . By combining the two modalities into a single instrument one can characterize both the chemistry and the morphology of a single microscopic sample without the need for external labels. Furthermore, the sample can be studied at several timepoints to obtain information about its temporal dynamics. This could be particularly valuable for studying cellular changes, such as those that immune cells undergo during their responses to stimuli.
The chief difference between the integrated Raman- and angular-scattering microscope (IRAM) system and other angular scattering microscopes [14, 15, 16] is that we incorporate a more tightly focused beam (spot size of compared to hundreds of micrometers) into our system to obtain the high photon flux through the sample required for Raman microspectroscopy. Where most groups utilize Mie theory, which describes scattering of plane waves by spheres of uniform refractive index, we must incorporate an extension to Mie theory in our analysis to account for the nonnegligible cone angle of our Gaussian beam [17, 18].
Our initial validation results were on single spheres with diameters of 2 to . In order to extend our work to the size range of cellular organelles, we must validate our instrument on systems composed of multiple submicrometer scatterers. Moving from one to multiple scatterers introduces two new challenges in a focused geometry. First, the contributions from the limited number of scatterers may interfere in ways that do not average out as they do when the contributing volume is larger. Additionally, scatterers at different spatial locations in the sample (and thus at different locations within the focal volume of the system) will experience a field with different amplitude and phase profile in a focused beam. Xu et al. investigated these problems theoretically for the case of wavelength-dependent scattering and concluded that, if sufficient incoherent averaging takes place, either by averaging over many scatterers, or equivalently over many orientations of a small number of scatterers, the coherent effects introduced by a multiparticle system can be mitigated .
In this paper we present a validation of our system on single polystyrene beads in epi- and transillumination geometries, with remarks on the benefits and challenges of both modalities, followed by an experimental validation of the IRAM system’s ability to size and chemically characterize particles in single- and mixed-population suspensions in transillumination. Finally, we present initial data taken on two classes of immune cells, peripheral blood monocytes and granulocytes, with both the Raman- and the elastic-scattering channels showing clear differences between the two types.
2. Materials and Methods
2A. IRAM System
The IRAM system, shown schematically in Fig. 1, was constructed by coupling a single-spatial-mode, laser source (Innovative Photonic Solutions, Monmouth Junction, New Jersey) into an Eclipse TE2000-S inverted microscope (Nikon Instruments, Japan). Two illumination geometries were established, selectable by a flip mirror. In the epi-illumination mode, light was delivered to the sample through the microscope objective located below the sample stage, with a resulting optical power at the sample of . In the transillumination mode, light was delivered to the sample from above by a low NA achromatic doublet mounted in place of the manufacturer-supplied condenser lens, with a power at the sample of . In both illumination modes, the scattered light was collected by the microscope’s objective lens from below. A water immersion objective (, maximum collection angle of ) was used for the studies of single polystyrene beads, while the other studies reported below were done using a oil immersion objective (, maximum collection angle of ). In the epi- illumination mode the beam was coupled into the back aperture of the objective through steering mirrors and a dichroic beam splitter (Semrock, Rochester, New York). The beam underfilled the objective by a factor of about 6 to obtain an approximately beam waist in the sample plane. In the transillumination mode, the focal length of the doublet was chosen to give the same beam waist in the sample plane as the epi-illumination mode.
A small fraction of the elastically scattered light was picked off by a pair of Fresnel beam samplers (Newport 20B10NC.2, Irvine, California) oriented orthogonally to each other to provide a polarization- insensitive collection path. A relay lens imaged the back aperture of the microscope objective onto a custom-printed mask, obscuring the center of the Fourier plane (angular range to ), to block either the unscattered laser light (in the transillumination mode) or the Fresnel reflection from the center of the microscope objective (in the epi-illumination mode). A second relay lens imaged the masked plane onto a CCD (Andor Technology, Belfast, Ireland). Calibration of angles in the Fourier plane was accomplished by diffracting a collimated beam of light off of a grating of known pitch placed in the sample plane of the microscope objective. The Raman-scattered light was separated from the elastically scattered and unscattered light by the dichroic beam splitter and then passed through a pair of holographic notch filters (Kaiser Optical Systems, Ann Arbor, Michigan) to further filter out the laser line. The filtered light was then coupled into a spectrograph via a multimode optical fiber and dispersed onto a CCD array (both Andor Technology, Belfast, Ireland). Calibration of wavenumber in the spectrograph was accomplished through measurement of light from a neon lamp and Raman scattering from indene as wavelength and wavenumber standards, respectively.
2B. Experiments on Polystyrene Beads
Dilute suspensions of beads were created by mixing polystyrene microspheres (Duke Scientific, Fremont, California) with deionized water to create suspensions with number densities of scatterers not exceeding approximately . The suspensions were made extremely dilute to prevent multiple scattering from dominating the elastic channel signal. These suspensions were then placed within a sample chamber composed of either a thick piece of FEP Teflon (Integument, Tonawanda, New York) for the single bead measurements or a #1 thickness, () round quartz slide for the other experiments, both placed in a metal coverslip holder (Molecular Probes, Carlsbad, California) whose sides are high enough to accommodate a large () volume of liquid in the chamber. The large volume moves the air–water interface far from the microscope’s focal plane, reducing backreflection effects in the epi-illumination mode. Having the Teflon was crucial to eliminate backreflections that can interfere with the scattered signal in the epi- illumination direction.
In order to compare the epi- and transillumination modes we studied elastic scattering from the same polystyrene bead in both geometries. The laser beam was sufficiently powerful and focused to trap the particles laterally. However, in the axial direction both the trapping force and the gravity are weak compared to the scattering force experienced by the high index contrast beads. In the transillumination mode this does not pose a problem because the beads’ downward motion is halted by the presence of the coverslip. In the epi-illumination direction, however, free-floating beads brought into focus will be pushed upward by the scattering force and escape the trap. For this reason, for the single particle studies the suspended beads were allowed to dry onto the coverslip, causing them to adhere. The sample was then rehydrated and a single, adhered bead was then brought into the center of the laser beam’s focus, and the elastic-scattering patterns and Raman spectra were recorded in both epi- and transillumination geometries.
For multiparticle studies it was necessary for the beads to stay free-floating in suspension in order to average out coherent artifacts caused by interferences between the individual beads’ scattering. As this presents a problem for the epi-illumination mode, where the scattering force pushes free-floating beads away from focus, the multiparticle studies were performed only in the transillumination mode with the laser beam focused at the coverslip–water interface. Over time free-floating beads would enter the optical trap and move about within the trap due to Brownian motion. After waiting several seconds, until approximately 3 to 10 particles had entered the trap, a multiparticle elastic scattergram was recorded. It was important to allow the integration time to encompass several seconds, to ensure that the beads averaged over several different orientations during the course of the measurement. Several such measurements were combined to average over more particles and orientations.
2C. Preparation of Cellular Samples
Whole blood samples were obtained from human donors. After separating the cell population from the plasma, red blood cells were lysed by suspending the cells in of Gey’s lysing solution. The remaining white blood cells were washed and resuspended in a buffer composed of Hank’s Balanced Salt Solution (HBSS) with 1% Bovine Serum Albumin (BSA). The cells were then submitted to a FACSAria cell sorter (BD Biosciences, San Jose, California), and pure populations of granulocytes and peripheral blood monocytes were extracted. The cells were then suspended in the HBSS/BSA buffer in concentrations of approximately . For each cell population, of the cell suspension was placed in the same quartz coverslip chamber described above and was allowed to settle to the bottom of the chamber prior to measurement. Cells were placed beneath the laser focus, and IRAM measurements were taken. The position of the laser spot was varied manually on the scale of to further average out coherent artifacts from scatterers that do not move on the time scale of the measurements.
2D. Data Analysis
For the single particle studies, angular scattergrams and Raman spectra such as those shown in Fig. 2 were collected. Raman spectral processing involved fitting of background spectra (quartz, water, and immersion oil) as well as a 5th order polynomial to the data using standard least squares. The resultant spectrum was then prepared for presentation by processing with the iterative polynomial fitting algorithm developed by Lieber and Mahadevan-Jansen . The elastic-scattering data were processed by direct background subtraction. A matrix of theoretical curves T versus θ, ϕ, and radius (a) was calculated. One-dimensional slices through the data, corresponding to scattering versus polar angle θ for three different azimuthal angle ϕ values, were compared to theory simultaneously using a global metric  given in Eq. (1):2c, 2f. A beam waist of radius was assumed, along with a refractive index of 1.33 for water and 1.577 for polystyrene.
For multiparticle studies, scattergrams were obtained that averaged over many particles in many orientations to help reduce oscillations caused by coherent interferences between the scattered light from each individual particle. To further reduce coherent artifacts, the data were reduced into four pie-slice-shaped bins, corresponding to azimuthal angles of to , to , to , and to . Because of twofold reflection symmetry in the scattering patterns (seen clearly in Fig. 2), angles in the other three polar quadrants were mapped into these four bins and averaged. The binning is shown schematically in Fig. 3b. The resulting data consist of four vectors of intensity versus θ, shown as black dots in Fig. 3c. The influence of unrelated baseline drifts was removed by mean-centering both experimental and theoretical curves. In order to compare the data measured from these distributions with theory, we follow Wilson and Foster in assuming a lognormal functional form for the particle dis tributions . Our model accepts two distributions with five free parameters (two means, two standard deviations, and the relative amplitude of the two populations). A lookup table of scattering intensities versus scattering angle and radius I was generated using MATLAB (The MathWorks, Natick, Massachusetts) according to the generalized Lorenz–Mie theory , as outlined in a previous publication . The same beam waist and refractive index parameters were assumed for this study as for the above study on single spheres. For each proposed distribution of particle sizes, an accompanying theoretical scattering signal T was generated by weighting the lookup table by the distribution’s number density versus radius, as shown in Eq. (2):23]; oscillatory signatures of whole-cell scattering are readily measured from a yeast cell (data not shown), with its thick cell wall and fewer subcellular components, but are not observed from immune cells. Similar to the analysis of single spheres, the quality of fit is determined by comparing the least-squares scaled version of T, , to S via a metric: 24] is performed on the five-dimensional surface. To reduce the likelihood of being caught in a local minimum, the search is started from 20 random initial guesses, with the location of the minimum value across all 20 searches being chosen as the answer.
For the immune cells, Raman and angular scattering data were acquired. The Raman data were processed in the same fashion as that described for the single polystyrene beads, except no iterative polynomial fit was performed and each curve was normalized to the area under the peak. The angular scattering data were processed identically to the multiparticle suspensions of polystyrene beads. For the cellular data, however, the refractive indices assumed were 1.38 for cytoplasm and 1.4 for subcellular organelles .
3A. Epi- Versus Transillumination IRAM Measurements of Single Polystyrene Beads
3A1. Angle-Resolved Elastic Scattering
Scattering patterns in the forward and backward direction have different sensitivities to particle shape, with nonspherical particles scattering in the forward direction like their surface-area-equivalent spheres regardless of their orientation . Biological samples often contain scatterers, such as mitochondria, that are highly nonspherical and whose orientations are randomized; the success of attempting an orientation-independent Mie-type analysis on such scatterers will be greatly improved by measuring scattering data in the forward direction, corresponding to the transillumination mode of our microscope. Figure 2 compares elastic-scattering data obtained from the same single polystyrene bead in the two modes, epi- and transillumination. Figure 2a shows the 2-D angular scattergram (i.e., scattered intensity versus angle in the Fourier plane) in the backward direction, while Fig. 2d shows the scattergram for the forward-directed scattering. Figures 2b, 2e are the associated theory plots, chosen by the minimum value as described above. The diameters predicted by the epi- and transillumination modalities agree to within , with the agreement being similar across several individual beads measured (data not shown). Plots of are shown in Figs. 2c, 2f for the epi- and transillumination modes, respectively. Each plot shows a clear maximum corresponding to the best-fit sphere radius. We note that, although the “sidelobe” centered at in Fig. 2f rises to within 50% of the maximum, it represents a visibly inferior fit (data not shown). We further note that the width of the maximum “lobe” in transillumination is approximately an order of magnitude larger than the lobe in epi- illumination. This reflects the fact that the backscattered signal changes more dramatically than the forward-scattered signal for a given change in size. A movie depicting the evolution of the forward and backward scattering across a change in radius, showing the full 2-D patterns and also a representative 1-D slice through the data, is provided as Media 1 , two stills from which are shown in Fig. 4. Note that even for a small change in radius (), the backscattered signal is significantly different, while the forward-scattered signal remains nearly identical.
The differing sensitivity to size and shape represents a trade-off between the epi- and the transillumination modalities. As noted, particle scattering in the forward direction can be successfully analyzed by Mie theory even in the case of highly nonspherical scatterers [2, 26]. However, there is a corresponding decrease in sensitivity to particle size that may hamper one’s ability to detect very slight changes in sample morphology. Having an instrument capable of measuring a sample in both modalities allows one to choose the most appropriate modality for different experiments. Additionally, measuring a single sample both ways in succession can increase the confidence that the size predictions are accurate and robust.
3A2. Forward-Directed Raman Scattering
To make simultaneous Raman- and angular- scattering measurements in the transillumination geometry, it was necessary to collect the forward- directed Raman-scattering signal. This is rarely done in practice because of the challenge in filtering the unscattered laser light from the Raman signal. In order to obtain high quality spectra without interference from the unscattered laser light, it was necessary to add a second holographic notch filter to the Raman collection system, offering another 6 OD at the laser wavelength. Once this adjustment was made, however, high quality Raman spectra in transillumination mode could be acquired. A spectrum of polystyrene consistent with others in the literature  was obtained from the bead measured in Fig. 2 and is shown in Fig. 5.
3B. Forward-Directed Elastic Scattering of Multiparticle Suspensions
3B1. Monodisperse Suspensions
Figures 3, 6 summarize results from making transilluminated elastic-scattering measurements of four different monodisperse suspensions of polystyrene beads with manufacturer-specified mean sizes of 330, 500, 820, and . Figure 3b depicts the Fourier plane sectioned into four bins to illustrate the azimuthal averaging we perform on our data, as discussed in Subsection 2D. Figure 3c shows experimental data from each of the bins (circles), the expected scattering signal given the manufacturer’s specifications (solid curve), and the best fit from the distribution chosen through the downhill simplex search (dashed curve). Note that the dashed and solid curves lie on top of each other to within the resolution of the figure. Figure 6 compares the extracted particle distributions for all four samples (dashed curves) to the manufacturer-specified distributions (solid curves). In each case the distribution was characterized to within of the manufacturer-specified mean. The extracted means and standard deviations are also summarized in Table 1.
3B2. Mixed-Population Suspensions
Figure 7 summarizes results from measuring three different mixed-population suspensions, constructed using bead diameter mixtures 330/820, 500/820, and beads. Figure 7a shows the azimuthal bin labeled “1” in Fig. 3b for experimental data from the mixture (black dots). As in Fig. 3c, the best fit of theory to experiment is shown (dashed curve) along with the curve corresponding to the manufacturer’s specifications. Additionally, we have plotted the individual contributions of the two subpopulations to the total scattered signal, as estimated by the best-fit procedure. The extracted size distribution from this data set is shown in Fig. 7b (dashed curve) alongside the manufacturer’s specifications (solid curve). The extracted populations for the other two mixtures are similarly shown in Figs. 7c, 7d. The results are summarized numerically in Table 2. The two-population fit is more difficult than the one-population fit because similar line shapes may be generated by several different mixed populations. Nevertheless, we are able to accurately extract the individual means and standard deviations of the multicomponent mixtures with approximately precision. Unsurprisingly, the fits are more accurate for the larger of the two particle sizes in each mixture due to the fact that signals from the larger particles have more features present to distinguish them.
3C. IRAM Measurements of Single Human Immune Cells
Results from IRAM measurements of single cells are summarized in Figs. 8, 9. Figures 8a, 8b show the averaged elastic scattergrams collected for four granulocytes and four lymphocytes, respectively. Figures 8c, 8d show the averaged experimental data corresponding to azimuthal bin “1” for granulocytes and lymphocytes (black dots), respectively. As in Fig. 7a, we also show the best fits of theory to experiment and the relative contributions of the two scattering subpopulations. The extracted distributions (i.e., a pair of lognormal distributions) for the averaged data from each type of cell are shown in Figs. 8e, 8f as the thick dark curves. Extractions for each individual cell are shown by the lighter curves, showing slight variability from cell to cell, while still maintaining consistent differences between cell types. For the granulocytes, populations were predicted with mean diameters of around 700 and . For lymphocytes, these numbers were around 350 and . The results are summarized numerically in Table 3. Figure 9 shows the mean Raman spectra for four lymphocytes (top curve) and four granulocytes (bottom curve), with the spectra offset for clarity. Areas of clear spectral differences are noted by asterisks, with assignments of spectral differences summarized in Table 4. The standard deviations in each spectrum are depicted by the shaded areas.
In the single particle scattering study shown in Fig. 2, elastic scattering from a polystyrene bead was collected and analyzed in both epi- and transillumination modes. Although the two modes provide similar predictions of bead diameter in this case, it is clear that the uncertainty in the bead’s size is larger in transillumination simply from looking at the dif ferences in their associated metrics [Figs. 2c, 2f]. As discussed above, the qualitative change in the scattering pattern for a given shift in radius is much smaller in the forward direction than in the backward direction. However, along with this decreased sensitivity to size, the forward-scattering signal carries a corresponding insensitivity to shape that is advantageous when considering bio logical samples of unknown composition. For the cell analysis reported above it was crucial that we adapted our system to the transillumination modality in order to obtain fits of the experimental data to generalized Lorenz–Mie theory.
The epi-illumination mode has other problems associated with it. An index-matching sample substrate is crucial for reducing backreflections in this geometry. However, the Teflon film we used to date to provide an index match to water has its own scattering “haze” that covers the Fourier plane, due to weak scattering from inhomogeneities in the film. This haze does not pose a problem for larger scatterers, whose signals are orders of magnitude stronger than the haze, but it can coherently interfere with weaker scattering signals from both smaller (subwavelength) particles as well as particles where the index contrast is low (such as in cells), precluding a robust Mie-type analysis. This problem could be overcome if a more optically clear index matching substrate were used. In transillumination, where the backreflection was not a concern, the Teflon could be replaced by quartz or glass.
Although coherent interferences from backreflected signals and issues of scatterer nonsphericity may prevent analysis of the acquired scattering signal using Mie theory, it is important to consider that Mie-type analysis is only one method appropriate for analyzing light scattering. Although it provides quantities that are physical and easy to understand, there are other ways of modeling the complex scattering that occurs in biological samples that do not require assumptions about scatterer shape or density, such as fractal analysis  or finite-difference time-domain computational approaches [23, 29]. Addi tionally there are wholly empirical techniques such as simple pattern recognition that can be employed as well . Especially in cases where the signals from a certain sample type are consistent, despite the presence of artifacts, and pattern recognition can be employed, the backscattered signal may be more sensitive to subtle changes than the forward-scattered light.
In our single-population studies of bead suspensions we reported an agreement to within approximately with respect to the manufacturer’s specifications. As can be seen in Fig. 3c, the best-fit curve chosen by the downhill simplex search (dashed curve) is so close to the curve corresponding to the manufacturer’s specification (solid curve) that they cannot be distinguished within the noise of the data. We therefore believe that the discrepancies between the extracted fits and the manufacturer’s specifications represent fundamental limits on the accuracy of the IRAM system for the current set of collection angles and analysis method.
Over the range of angles measured by our system, particles with diameters smaller than or comparable to the illumination wavelength generate scattering patterns that lack the same highly rippled structure seen in the scattering patterns of larger beads. In some cases their azimuthally binned scattering patterns are distinguished simply by a slope and intercept. As a consequence of the few features, the fitting problem remains highly sensitive to noise and can be close to degenerate even under noiseless conditions. This is especially challenging in the case of trying to fit scattering data to more than one population distribution. Even when using just two distributions, we find that many different parameters can produce equivalent fits to a single azimuthal bin. Fortunately, scattergrams arising from subwavelength beads show rotational asymmetry, in contrast to the ringlike patterns that characterize forward scattering from larger particles. Requiring a simultaneous fit to four azimuthal sections is crucial for converging on two-population solutions; our results are stable to within . Stable three-population fits would probably require a larger angular range to be acquired. However, others have reported that angular scattering from cells can be well described by two log-normal populations [31, 32], with a three- population fit not altering the results. This provides some justification for us to apply our analysis to studies of single-cell scattering.
Our single-cell studies examined four individual lymphocytes and four individual granulocytes. These two cell types were chosen for validation studies because of their known morphological and chemical differences. In each case, biologically plausible distributions were obtained. Granulocytes, as their name suggests, are highly granular, with several hundred submicrometer granules in their cytoplasm, and have a low nucleus-to-cytoplasm ratio . There are three types of granule: nucleated, azurophilic, and specific. The ratios of these types within a granulocyte vary according to granulocyte type and maturation. Neutrophils are the most abundant granulocytes found in human blood and contain primarily rod-shaped specific granules and large azurophilic granules, whose sizes are in the range of 500 to . Neutrophils have also been recently recognized to have a mitochondrial network . We therefore tentatively assign the population centered around in the granulocyte population to the azurophilic granules and specific granules that are abundant in mature neutrophils. We assign the population centered at to either mitochondria  or the individual nucleic lobes found in granulocytes.
Lymphocytes are, in their resting state, composed primarily of a large spherical nucleus surrounded by a small ring of cytoplasm. They have very large nucleus-to-cytoplasm ratios. Despite their relative paucity of cytoplasm, they still contain several organelles. Mayhew et al. reported that peripheral-blood-derived lymphocytes have, on average, 38 mitochondria per cell . In addition, despite their generally being considered agranular, lymphocytes also contain several secretory lysosomes per cell [37, 38], whose sizes are of the order of . Therefore we tentatively assign the extracted population centered at to the lysosomes and the larger population centered at to mitochondria. In addition, because lymphocytes contain so many fewer scatterers than granulocytes, we also notice a marked decrease in total scattered intensity between the two cell types. This reduced number of scatterers also resulted in less wash-out of the coherent interferences between scatterers, something that may account for the greater variability in the extracted populations for individual lymphocytes compared to the granulocytes.
Raman scattering results also present plausible differences between the two cell types. In the case of lymphocytes the Raman spectrum is primarily probing the cell’s large nucleus, while nuclear components are mostly lacking in the granulocyte. This hypothesis is supported by the main differences between the two cells correlating strongly with signals from DNA bases, as summarized in Table 4 . For each of these peaks, they are either not present at all in the granulocyte spectrum or else are significantly stronger in the lymphocyte spectrum.
The results presented here represent very encouraging validations and first biological results for the IRAM system. In particular, differences in the size distributions computed for the two cell types (peaks at 730 versus ) correlate well with reported size differences in their granular content, as observed by electron microscopy. The extracted values also correlate well with previous values reported in the literature [33, 35, 40]. Extracting size param eters from single cells could be particularly valu able for dynamically monitoring changes within single cells. Characterizations of developmental processes in individual immune cells are envisioned as future steps in this work.
The authors thank Brian McIntyre for his assistance in printing the mask used in our system, Sally Quataert and Terry Wightman for access to cell samples, and Wayne Knox and Li Ding for providing the hydrogel grating. This work was supported in part by National Science Foundation (NSF) grant CBET-0754698.
1. A. Wax, J. W. Pyhtila, R. N. Graf, R. Nines, C. W. Boone, R. R. Dasari, M. S. Feld, V. E. Steele, and G. D. Stoner, “Prospective grading of neoplastic change in rat esophagus epithelium using angle-resolved low-coherence interferometry,” J. Biomed. Opt. 10, 051604 (2005). [CrossRef] [PubMed]
2. 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]
4. M. Gniadecka, P. A. Philipsen, S. Sigurdsson, S. Wessel, O. F. Nielsen, D. H. Christensen, J. Hercogova, K. Rossen, H. K. Thomsen, R. Gniadecki, L. K. Hansen, and H. C. Wulf, “Melanoma diagnosis by Raman spectroscopy and neural networks: Structure alterations in proteins and lipids in intact cancer tissue,” J. Invest. Dermatol. 122, 443–449 (2004). [CrossRef] [PubMed]
5. A. Nijssen, T. C. B. Schut, F. Heule, P. J. Caspers, D. P. Hayes, M. H. A. Neumann, and G. J. Puppels, “Discriminating basal cell carinoma from its surrounding tissue by Raman spectroscopy,” J. Invest. Dermatol. 119, 64–69 (2002). [CrossRef] [PubMed]
6. A. M. K. Enejder, T.-W. Koo, J. Oh, M. Hunter, S. Sasic, M. S. Feld, and G. L. Horowitz, “Blood analysis by Raman spectroscopy,” Opt. Lett. 27, 2004–2006 (2002). [CrossRef]
7. K. U. Schallreuter, M. Zschiesche, J. Moore, A. Panske, N. A. Hibberts, F. H. Herrmann, H. R. Metelmann, and J. Sawatzki, “In vivo evidence for compromised phenylalanine metabolism in vitiligo,” Biochem. Biophys. Res. Commun. 243, 395–399 (1998). [CrossRef] [PubMed]
8. H. Fang, L. Qiu, E. Vitkin, M. M. Zaman, C. Andersson, S. Salahuddin, L. M. Kimerer, P. B. Cipolloni, M. D. Modell, B. S. Turner, S. E. Keates, I. Bigio, I. Itzkan, S. D. Freedman, R. Bansil, E. B. Hanlon, and L. T. Perelman, “Confocal light absorption and scattering spectroscopic (CLASS) microscopy,” Appl. Opt. 46, 1760–1769 (2007). [CrossRef] [PubMed]
9. W. Choi, C.-C. Yu, C. Fang-Yen, K. Badizadegan, R. R. Dasari, and M. S. Feld, “Field-based angle-resolved light scattering study of single live cells,” Opt. Lett. 33, 1596–1598 (2008). [CrossRef] [PubMed]
10. J. W. Chan, D. S. Taylor, T. Zwerdling, S. M. Lane, K. Ihara, and T. Huser, “Micro-Raman spectroscopy detects individual neoplastic and normal hematopoietic cells,” Biophys. J. 90, 648–656 (2006). [CrossRef]
12. M. D. Mannie, T. J. McConnell, C. Xie, and Y.-Q. Li, “Activation-dependent phases of t cells distinguished by use of optical tweezers and near infrared Raman spectroscopy,” J. Immunol. Methods 297, 53–60 (2005). [CrossRef] [PubMed]
14. W. J. Cottrell, J. D. Wilson, and T. H. Foster, “Microscope enabling multimodality imaging, angle-resolved scattering, and scattering spectroscopy,” Opt. Lett. 32, 2348–2350 (2007). [CrossRef] [PubMed]
15. M. T. Valentine, A. K. Popp, D. A. Weitz, and P. D. Kaplan, “Microscope-based static light-scattering instrument,” Opt. Lett. 26, 890–892 (2001). [CrossRef]
16. N. N. Boustany, R. Drezek, and N. V. Thakor, “Calcium- induced alterations in mitochondrial morphology quantified in situ with optical scatter imaging,” Biophys. J. 83, 1691–1700 (2002). [CrossRef] [PubMed]
21. P. R. Bevington, Data Reduction and Error Analysis for the Physical Sciences (McGraw-Hill, 1969).
23. R. Drezek, A. Dunn, and R. Richards-Kortum, “Light scattering from cells: Finite-difference time-domain simulations and goniometric measurements,” Appl. Opt. 38, 3651–3661 (1999). [CrossRef]
24. W. H. Press, S. A. Teukolsky, W. T. Vetterling, and B. P. Flannery, Numerical Recipes in C (Cambridge University Press, 1992).
26. C. F. Bohren and D. R. Huffman, Absorption and Scattering by Small Particles (Wiley Interscience, 1983).
27. B. Jasse, R. S. Chao, and J. L. Koenig, “Laser Raman scattering in uniaxially oriented atactic polystyrene,” J. Polym. Sci. 16, 2157–2169 (1978).
29. R. S. Brock, X.-H. Hu, D. A. Weidner, J. R. Mourant, and J. Q. Lu, “Effect of detailed cell structure on light scattering distribution: FDTD study of a B-cell with 3D structure constructed from confocal images,” J. Quant. Spectrosc. Radiat. Transfer 102, 25–36 (2006). [CrossRef]
30. P. P. Banada, S. Guob, B. Bayraktar, E. Baeb, B. Rajwa, J. P. Robinson, E. D. Hirleman, and A. K. Bhunia, “Optical forward-scattering for detection of Listeria monocytogenes and other Listeria species,” Biosens. Bioelectron. 22, 1664–1671 (2007). [CrossRef]
32. J. R. Mourant, T. M. Johnson, S. Carpenter, A. Guerra, T. Aida, and J. P. Freyer, “Polarized angular dependent spectroscopy of epithelial cells and epithelial cell nuclei to determine the size scale of scattering structures,” J. Biomed. Opt. 7, 378–387 (2002). [CrossRef] [PubMed]
33. P. Brederoo, J. van der Meulen, and A. M. Mommaas- Kienhuis, “Development of the granule population in neutrophil granulocytes from human bone marrow,” Cell Tissue Res. 234, 469–496 (1983). [CrossRef] [PubMed]
34. G. Fossati, D. A. Moulding, D. G. Spiller, R. J. Moots, M. R. H. White, and S. W. Edwards, “The mitochondrial network of human neutrophils: Role in chemotaxis, phagocytosis, respiratory burst activation, and commitment to apoptosis,” J. Immunol. 170, 1964–1972 (2003). [PubMed]
36. T. M. Mayhew, A. J. Burgess, C. D. Gregory, and M. E. Atkinson, “On the problem of counting and sizing mitochondria: a general reappraisal based on ultrastructural studies of mammalian lymphocytes,” Cell Tissue Res. 204, 297–303 (1979). [CrossRef] [PubMed]
37. Y. Sadahira, K. Akisada, T. Sugihara, S. Hata, K. Uehira, N. Muraki, and T. Manabe, “Comparative ultrastructural study of cytotoxic granules in nasal natural killer cell lymphoma, intestinal T-cell lymphoma, and anaplastic large cell lymphoma,” Virchows Archiv . 438, 280–288 (2001). [CrossRef] [PubMed]
39. N. Uzunbajakava, A. Lenferink, Y. Kraan, B. Willekens, G. Vrensen, J. Greve, and C. Otto, “Nonresonant Raman imaging of protein distribution in single human cells,” Biopolymers 72, 1–9 (2003). [CrossRef]
40. J. D. Wilson, W. J. Cottrell, and T. H. Foster, “Index-of- refraction-dependent subcellular light scattering observed with organelle-specific dyes,” J. Biomed. Opt. 12, 014010 (2007). [CrossRef] [PubMed]