Optical Spectroscopy of Biological Cells

Transcription

1 Optical Spectroscopy of Biological Cells Adam Wax, * Michael G. Giacomelli, Thomas E. Matthews, Matthew T. Rinehart, Francisco E. Robles, and Yizheng Zhu Department of Biomedical Engineering, Duke University, Durham, North Carolina 27708, USA *Corresponding author: a.wax@duke.edu Received April 3, 2012; revised June 28, 2012; accepted June 29, 2012; published July 27, 2012 (Doc. ID ) Optical spectroscopy has seen expanding use for the study of biological cells in recent years. An overview of relevant spectroscopic techniques is presented, and applications to biological cells are reviewed. c 2012 Optical Society of America OCIS codes: , , Introduction Wavelength-Dependent Scattering Spectroscopy Confocal Light Scattering and Absorption Spectroscopy Fourier Domain Low-Coherence Interferometry and Spectroscopic Optical Coherence Microscopy Partial Wave Spectroscopy Angle-Dependent Scattering Spectroscopy Techniques Intensity-Based Methods Field-Based Methods Microscope-Based Techniques Light Scattering Models Phase-Based Spectroscopy of Cells Temporal Phase Fluctuations Fourier Transform Light Scattering Dispersion Spectroscopy Analysis Phase Measurements via Optical Spectroscopy Vibrational Spectroscopy Spontaneous Raman Microscopy Stimulated Raman Scattering Coherent Anti-Stokes Raman Scattering Pump Probe, Excited State Absorption, Transient Absorption Developing Spectroscopic Techniques Conclusion Acknowledgments References and Notes Advances in Optics and Photonics 4, (2012) doi: /aop /12/ /$15.00 c OSA 322

2 Optical Spectroscopy of Biological Cells Adam Wax, Michael G. Giacomelli, Thomas E. Matthews, Matthew T. Rinehart, Francisco E. Robles, and Yizheng Zhu 1. Introduction The evolution of optical microscopy has provided the enabling technology behind many of the key advances in cell biology. As new techniques are developed, they reveal unique aspects of cell structure and function. The versatility of the visible spectrum of light has long been exploited for these studies. For example, fluorescence microscopy exploits the Stokes shift to permit the illumination and collection of light in different spectral regions, providing high-contrast images. However, while optical spectroscopy has long been used to assess the properties of matter, such as atoms and molecules, it is only recently that the approach has been applied to study biological cells. Optical spectroscopy exploits the richness of the visible and nearinfrared spectra to provide a unique tool for studying the biophysical and biochemical properties of cells. The spectroscopic properties of biological cells can reveal detailed information on their structure and dynamics in a way that is not possible with traditional microscopy techniques. While histopathology can assess changes in structure and function by examination of fixed, stained, and sectioned cells, optical spectroscopy provides a noninvasive means of studying their development, formation, and function without the artifacts associated with this preparation. In addition, histological studies can obtain only a snapshot of the activity of individual cells, relying instead on large ensembles to develop a picture of the cells temporal evolution. In contrast, optical spectroscopy can be applied to cells with little to no preparation and can permit studies of the same live cells at extended time intervals. Application of optical spectroscopic methods to cellular imaging and analysis has led to cell biology studies of fundamental cell properties and preclinical discovery of disease mechanisms and has provided an avenue for translating detection of pathological conditions to the clinic. The development of biophotonics technologies has built on the foundation laid by these fundamental studies and has expanded in recent years to encompass a wide range of techniques, applied to a variety of different laboratory and clinical investigations. This review seeks to recapitulate the significant pioneering efforts in applying optical spectroscopy to cells, the key findings they have made possible, and their impact for cell biology and biomedicine. Advances in Optics and Photonics 4, (2012) doi: /aop

3 The optical spectroscopic tools discussed here share the common feature of exploiting the properties of light to learn about the structure, function and dynamics of biological cells. Spectroscopy typically connotes the idea of studying the changes in material properties with color. Indeed, wavelength-dependent studies of biological cells have revealed a wealth of information and birthed new clinical diagnostic methods. However, since biological cells are largely transparent, presenting few absorption features across the visible spectrum, the dominant mode for wavelength-dependent studies has been to examine the change in elastic scattering across the visible and near-infrared spectrum. Several techniques and advances in using wavelength-dependent scattering will be reviewed and compared. Alternatively to using wavelength dependence to learn about elastic scattering, the angular dependence of scattered light can reveal desired properties of cells. Both approaches share the common feature of investigating cell structure by observing the changes in the momentum of incident light. This approach has been used to provide structural information about cells as a means to create realistic biophysical models as input for solving the forward problem. Different configurations for measuring angular scattering are discussed here, as well as exemplary applications of each approach. As a third method to learn about cells via spectroscopy, the use of optical phase has also been pursued. Some of the first advances in microscopy for cell biology revolved about exploiting this optical phase to create contrast from the often nearly transparent biological cell. As technology advanced, improved methods for observing phase have been developed, permitting quantitative measurements. Here spectral information about the cell can take several forms. The frequency spectrum of phase fluctuations can reveal important information about the dynamic behavior of cells. These data may also be Fourier transformed to produce information analogous to angular-dependent light scattering. However, wavelength-dependent information can also be examined via measurements of optical phase by observing dispersion-inducing properties. Finally, in a complementary measurement process, the wavelength-dependent spectrum can instead be measured and analyzed to produce phase measurements. This review is focused on optical spectroscopic methods applied to biological cells. To make this vast area manageable, we have focused on a specific group of measurement approaches. However, there are many other optical techniques that can reveal cell structure and function. To connect with these techniques, the concluding sections include a brief look at the use of inelastic scattering, including Raman spectroscopy and related techniques, and a sampling of novel techniques that exploit the optical spectrum for contrast, including photoacoustic imaging of cells and application of plasmonic nanoparticles (NPs). 2. Wavelength-Dependent Scattering Spectroscopy The wavelength-dependent behavior of light scattered by living cells reveals structural and functional information that is not readily available with traditional microscopy methods. For example, periodic Advances in Optics and Photonics 4, (2012) doi: /aop

4 fine structures in the spectra of a field scattered by a cell can reveal information regarding nanometer-sized organelles that are well below the diffraction limit [1]. Such information may in turn be used to monitor subcellular content and gain insight into normal and pathogenic phenotypes without perturbing the cell or its environment. The first method to utilize the unique spectral features of light that has interacted with cells in order to recover structural information in vivo is light scattering spectroscopy (LSS) [2]. In this approach, measured spectra are analyzed to discriminate periodic fine structures that are well described by Mie theory, an analytical solution to the scattered field of a spherical scatterer using Maxwell s equations [2,3]. When applied to scattering by tissues, LSS must employ a method to discriminate contributions from singly scattered (nonrandomized) and diffusely scattered (randomized) fields, such as mathematical modeling (i.e., model-based) [2], polarization gating [4], or coherence gating [5]. LSS has shown very promising results for diagnosing different types of cancers in vivo [6], and has hence received much attention as a unique clinical tool for noninvasive diagnosis. However, this method is not without its limitations. For example, LSS suffers from poor spatial resolution, since measurements are subject to spatial averaging over relatively large areas. In this section, different techniques are reviewed that further the ability of LSS to provide morphological and functional information by using the spectra of elastically scattered light for cell imaging and analysis as a means to improve our understanding of the cellular origin of disease and inform the ability to diagnose diseases in the clinic Confocal Light Scattering and Absorption Spectroscopy Confocal light scattering and absorption spectroscopy (CLASS) is an extension of LSS in which the spectra originating from a small confocal volume are analyzed by using Mie Theory. The main difference between CLASS and LSS is that CLASS incorporates the capability to reject out-of-focus light as well as the high-resolution inherent to confocal microscopy, thus permitting the detection of various sized subcellular organelles inside individual living cells. The instrumentation for this method consists of a confocal microscope, where a broadband spectral source (either a Xe-arc lamp or a supercontinuum light source) delivers a tightly focused beam onto a sample. Scattered light is collected by means of the same microscope objective used for delivery, and then light is sent through a pinhole, for out-of-focus light rejection, and onto a spectrometer. In this approach, the use of a microscope objective introduces an angular dependence in the scattered field that must be taken into account and which results in an interesting trade-off between the resolution of the microscope and the sensitivity of the spectral analysis. To illustrate this trade-off, a brief theoretical treatment of the CLASS signal is presented. Consider the experimentally measured spectrum (ratio of the scattered spectrum to a reference spectrum), S, as a function Advances in Optics and Photonics 4, (2012) doi: /aop

5 Figure 1 1.0E 03 CLASS, NA = 0.75 CLASS, NA = 0.5 LSS, θ = E E 07 Region 1 Region 2 Region 3 1.0E /X 10 Theoretical scattering coefficients for three different NA s as a function of inverse size parameter, 1/x = λ/(2πδ). Calculations assume spherical scatterers with relative RI n = 1.06 and a medium with n = Region 1 corresponds to wavelengths ranging from 400 to 800 nm for a scatterer size of 900 nm. Regions 2 and 3 correspond to the same spectral region for scatterer sizes of 300 and 100 nm, respectively (from [1]). of wavelength, λ, and numerical aperture, NA [1]: S(λ, NA) = 0 ( ) λ I, n, NA N(δ)dδ + ε(λ), (1) δ where N(δ) is the organelles size distribution, I is the theoretical spectrum for a single scatterer with diameter δ, n is the refractive index (RI), and ε(λ) is the error. The theoretical spectrum may be described as [1] ( ) λ I, n, NA δ ( ) λ P( ˆk)P(ˆk )f δ, n, ˆk, ˆk dˆkdˆk 2, (2) where P is the pupil function, k and k are unit vectors in the direction of the incident and the scattered field, respectively, and f is the Mie theory amplitude function. Note that the solid angle subtended by the objective,, is set by the NA and is the same for the delivered and the scattered light. The integral over a wide range of angles is what differentiates the theoretical spectra of CLASS from LSS, which effectively accepts only the backscattered light (180 ). The resulting behavior of the measured signal with varying NA is illustrated in Fig. 1. Here, it is clear that the CLASS spectrum changes as a function of NA; specifically, as the NA increases, the periodic fine structures begin to wash out as a result of averaging over many angles. This dependence on results in the trade-off mentioned above between the spatial resolution of the microscope, which increases with higher NA, and the sensitivity to subwavelength scatterers, which decreases with increasing NA. Consequently, the objective used in the CLASS microscope is typically of intermediate NA, for example NA = 0.5 (dotted line in Fig. 1), which yields a lateral resolution of 600 nm and an axial resolution of 2 µm (experimental) [7]. Advances in Optics and Photonics 4, (2012) doi: /aop

6 Figure 2 (a) (c) (b) 0.02 (d) Reflectance 0.01 (e) Wavelength (nm) (a) Confocal microscopy image of a 535 nm bead, along with (b) the theoretical (solid curve) and measured spectra (points) (taken from [1]). (c) (d) Fluorescent polystyrene beads of various sizes imaged by fluorescence microscopy (left) and CLASS (middle). The right-hand images are overlays. (e) Human epithelial cells (16HBE14o) with the lysosomes stained with Lysotracker Red DND-99 (taken from [7], c 2007 by The National Academy of Sciences of the USA). These theoretical results also show a fundamental limit on the smallest scatterer size that can be uniquely identified with CLASS (and also LSS). This limit is set by Rayleigh scattering, where scatterers below 50 nm no longer produce periodic fine structures, but rather produce a size-independent spectrum, given by λ 4. This makes it impossible to distinguish among the structures of different scatterers in this range. In Fig. 1, this outcome is observed approximately from regions above an inverse size parameter (1/x = λ/(2πδ)) of 1. The capabilities of CLASS for cellular imaging were demonstrated in two studies by Fang et al. [1] and Itzkan et al. [7]. Here, imaging of polystyrene beads and human epithelial cells by using CLASS was compared with theory and fluorescence microscopy. First, the scattering spectra of polystyrene beads with different sizes, ranging from 535 to 1053 nm, were compared with theory. Figure 2(a) shows a confocal image of a 535 nm bead, and Fig. 2(b) plots the corresponding theoretical (solid curve) and experimental (points) spectra measured at the center of the bead (note that the two lines are in good agreement). After analysis of various populations of beads, it was determined that the accuracy of the method is better than 10 nm, and similar experiments showed results comparable with that of electron microscopy [1]. Next, fluorescent polystyrene beads were imaged: Fig. 2(c) presents images of 1.9 µm beads, where the fluorescent image (left) and the reconstructed CLASS image (middle) show excellent agreement in the overlay image (right) [7]. Here, the CLASS image is reconstructed by displaying a sphere with a diameter equal to that determined by the spectral analysis, the gray scale represents the RI, and the centroid is determined from the maxima of the CLASS signal. An important feature of CLASS can be seen in Fig. 2(d), which presents images of mixed beads with diameters 0.5 µm, 1.1 µm, and 1.9 µm. Here, it is again clear that both the CLASS and the fluorescent images are in good agreement, but note Advances in Optics and Photonics 4, (2012) doi: /aop

7 Figure 3 (a) S, a.u (b) λ (nm) (a) Human epithelial cell (right) and representative spectra from a large organelle with diameter 700 nm (red), and a smaller organelle with diameter 400 nm (blue). (b) Three (top) untreated cells and (bottom) cells treated with DHA to induce apoptosis (taken from [7], c 2007 by The National Academy of Sciences of the USA). that neither the relative brightness nor the apparent size of the object in the fluorescent image affects the size of the scatterer as measured by CLASS, confirming that this imaging method is only sensitive to the size of the scatterers. As a last demonstration of the capabilities of this method, images were acquired of human epithelial cells (16HBE14o) with the lysosomes (typically ranging in size from 0.1 to 1.2 µm) stained with Lysotracker Red DND-99. The fluorescent and CLASS images show good agreement [Fig. 2(e)] and demonstrate the ability to measure subwavelength scatterers within the cellular environment. One of the main advantages of CLASS is its ability to monitor changes in the organization of organelles in living cells in real time, which may be particularly useful for understanding the cell response to certain treatments. These capabilities are illustrated in Fig. 3 which shows images, with CLASS rather than using fluorescent markers, of the same 16HBE14o human cells, imaged after treatment with docosahexaenoic acid (DHA) to induce apoptosis [7]. Figure 3(b) shows three untreated (top) and treated (bottom) cells, along with [Fig. 3(a)] representative spectra from a large organelle (red curve), likely a mitochondrion, which is typically around nm, and a smaller organelle (blue curve) Advances in Optics and Photonics 4, (2012) doi: /aop

8 consisting, for example, of peroxisomes (<400 nm). As the images illustrate, it is clear that the cells on the bottom contain the shell-like structure characteristic of apoptosis, while the untreated cells on the top of the figure do not. To date, the ability of CLASS to monitor cellular changes without the need of tagging or fixation, or without compromising the integrity of the cell, has shown promising utility for imaging cells in various applications. In a recent study, Lim et al. showed that CLASS may be used to differentiate between fetal (and adult) nucleated red blood cells (fnrbc) [8]. This work is important because of increasing interest in noninvasive methods for obtaining the fetal genetic information contained in the fnrbc. Currently, aneuploidy (abnormal number of chromosomes) is diagnosed by using invasive methods like amniocentesis, chorionic villus sampling, or percutaneous umbilical blood sampling, which all carry a significant risk to the fetus. Other reported applications of CLASS include the separation of normal and premalignant cervical epithelial cells. Work in this area has shown promising results in improving the sensitivity of current cytology methods, in particular for differentiating high- and low-grade cervical squamous intraepithelial lesions [9] Fourier Domain Low-Coherence Interferometry and Spectroscopic Optical Coherence Microscopy As previously mentioned, coherence gating, achieved by low-coherence interferometry (LCI), is another method by which singly scattered light may be isolated for spectroscopic analysis. LCI is widely used for imaging in optical coherence tomography (OCT) to provide high-axialresolution, tomographic images of tissue in vivo [10]. Similar to CLASS, which benefits from the cross-sectional capability of confocal microscopy, Fourier domain LCI (flci) and spectroscopic spectral-domain optical coherence microscopy (OCM) use LCI to obtain spatially resolved spectroscopic information for cellular imaging. To understand these methods, first consider a Michelson interferometer with detection in the Fourier domain, as illustrated by Fig. 4. Here, a source with a wide bandwidth, S(λ), is split into a sample field, E S, and reference field, E R. E S is incident on a sample with RI n, and E R is reflected off a reference mirror. Light scattered by the sample (in this case discrete scatterers m = 1, 2, and 3 are considered) is recombined with the reference field at the beam splitter (BS) and detected with a spectrometer. A fast Fourier transform of the data (after interpolation from wavelength to a linear wave number vector) reveals optical path length differences, OPL = (z S z R ) n, between the scatterers and the reference mirror (this depth-resolved profile is known as an A-scan). Figure 4(c) illustrates the resulting A-scan after the interferogram has been corrected for background signals. This process provides cross-sectional images of the sample, but it does not provide spatially resolved spectral information. To achieve this, signals have to be further processed by using short-time Fourier transforms (STFTs), wavelet transforms, or a dual-window (DW) processing method. To illustrate this, consider that the light returning from the simulated sample above only returns or scatters part of the Advances in Optics and Photonics 4, (2012) doi: /aop

9 Figure 4 (a) n Z S (m) m = 3 m = 2 m = 1 Sample (b) Intensity (a.u.) l (2π/λ) E S Mirror Source E R Wavenumber (μm 1 ) S(λ) λ BS Z R E tot = E R + E S Intensity (a.u.) m = 1 m = 2 m = 3 FT [l' (2π/λ)] Spectrometer E R E S ΔOPL (μm) (a) Schematic of Fourier domain OCT system using a Michelson interferometer geometry and a source with power spectral density S(λ). (b) Interferogram resampled into a linear wavenumber vector. (c) A-scan: absolute value of the Fourier transform of (b) after background subtraction (taken from [11]). Figure 5 (a) (b) (c) Intensity (a.u.) m = 1 m = 2 m = 3 Intensity (a.u.) FT Wavenumber (μm 1 ) Wavenumber (μm 1 ) Wavenumber (μm 1 ) ΔOPL (μm) (a) Relative wavenumber (or wavelength) intensity returning from three simulated scatterers; (b) Spectral OCT processing using STFT. (c) Resulting time frequency distribution (taken from [11]). spectrum, as shown in Fig. 5(a). A STFT entails using a window, in this case a Gaussian window, that truncates the spectral bandwidth of the measured interferogram, from which a fast Fourier transform is computed, which again reveals the location of scatterers (i.e., depth information), but now the relative magnitude at each point in depth depends on the intensity of the wavenumbers (or wavelengths) of the truncated spectral region. This window is swept across the full bandwidth, and a fast Fourier transform is computed at each step to yield a map that describes the spectrum returning from each scattering point. This map is known as a time frequency distribution. Note that STFTs and wavelet transforms suffer from an inherent trade-off between the spatial and the spectral resolution, which may be avoided by using the DW processing method, where simultaneous application of two appropriately chosen windows recovers high spatial and axial resolution [12]. Advances in Optics and Photonics 4, (2012) doi: /aop

10 Figure 6 Intensity (a.u.) (a) Intensity Wavenumber (b) Intensity (a.u.) * n * d = µm d = 6.86 µm (n = 1.395) Wavenumber (µm 1 ) Correlation distance (µm) (a) Local oscillations from T84 epithelial cells; (b) correlation plot with a peak indicating an average size of the nuclei of 6.86 µm (modified from [14]). The flci approach utilizes the spatially resolved spectra computed from a STFT or DW method to provide information regarding the temporal coherence induced by samples, thereby yielding micromorphological information of cells [13]. This process may be understood by considering the fact that dominant scatterers, such as cell nuclei, contain two scattering components one that originates from the front surface and another from the back surface that will interact and produce periodic oscillations (known as local oscillations) that are proportional to the size of the scatterer (see inset of Fig. 6). These oscillations are typically masked in the DC portion of the interferograms and are thus impossible to observe by using conventional OCT or LCI methods. However, by looking at the depth-resolved spectrum, flci recovers these signals. Graf et al. used this method to measure the size of cell nuclei in vitro [14]. In this particular study, a common-path interferometer was used with a Xe-arc lamp source, which grants access to the wide visible spectrum. T84 epithelial cells were grown in a chambered coverglass and imaged with the low-coherence interferometer. After processing with a STFT, the depth-resolved spectrum, corresponding to the location of the cells, was normalized by the spectrum obtained from a blank coverslip. The resulting spectrum is shown in Fig. 6(a), which clearly depicts the induced temporal coherence effects (local oscillations) of the scatterers. In this case, the estimated size is 6.86 µm, as revealed by the peak of the Fourier transform of the local oscillations (known as a correlation plot). The average result of 11 different cell samples was compared with the average size given by fluorescence and confocal microscopy. For this comparison, the cells were stained with Hoechst stain and LDS-751 nuclear stain for fluorescent and confocal imaging, respectively. Comparison of these findings with confocal fluorescence imaging shows that flci accurately measures the size of the nucleus along the longitudinal direction, which is a result of the small acceptance angle of the interferometer and the axial sectioning capabilities of LCI. This in vitro cell study confirmed the potential of using flci to measure the size of cell nuclei and has served as the basis for a number of studies that demonstrate its capability in turbid media [15], and also confirmed the potential for detecting early signs of cancerous development in animal models [16,17]. Xu et al. used a combination of confocal microscopy, OCT, and flci to provide enhanced cellular contrast without the need of exogenous Advances in Optics and Photonics 4, (2012) doi: /aop

11 Figure 7 (a) (c) (b) (d) (e) 10 Spectral intensity (db. normalized) cell periphery cell center Digital frequency (a) Image of live fibroblast cells in culture using spectroscopic spectral-domain OCM and (b) corresponding flci image. (c) Multiphoton image and (d) overlay with the spectroscopic spectral-domain OCM. (e) Average correlation plots from the cell periphery (blue) and center (red) (taken from [18]). agents [18]. In this method, named spectroscopic spectral-domain OCM, a Michelson interferometer is equipped with a high-na (0.95, water immersion) objective in order to obtain high lateral resolution and, more important, decouple the spatial and spectral information (and thus avoid the trade-off resulting from processing with STFTs and wavelet transforms). In theory, this interferometric approach could also be used for CLASS if a low-na objective were used to prevent washout of the periodic fine structures. Moreover, use of LCI, with detection in the Fourier domain, affords this method an up to three times better signal-to-noise ratio compared with conventional confocal microscopy [19], resulting in increased penetration depth in scattering media. In addition, because the spatial and spectral components are decoupled, flci can readily be applied (without processing with STFTs or the DW method) to highlight regions where dominant scatterers are present. Figure 7(a) shows an image of live fibroblast cells in culture using spectroscopic spectral-domain OCM along with the flci image [Fig. 7(b)], which shows remarkable sensitivity to the cell nuclei. Figure 7(e) shows the average correlation plots from the cell periphery (blue) and center (red), which exhibit behavior similar to that observed in Fig. 6(b). To ensure that the flci results did in fact correspond to the cell nuclei, multiphoton images were also acquired. Note that in order to obtain these multiphoton images, the cells had to be stained with exogenous agents (e.g., Hoechst stain). Figures 7(c) and 7(d) again confirm that flci does identify the nuclei without the need of any exogenous labels. Other results using this method have shown enhanced contrast compared with confocal microscopy in rat tissue, particularly for regions containing muscle Partial Wave Spectroscopy In the methods described thus far, the spectra of scattered light has been processed to yield the size of scatterers; in partial wave spectroscopy (PWS), however, the spectra are used to describe the Advances in Optics and Photonics 4, (2012) doi: /aop

12 statistical properties of intracellular RI fluctuations, quantified by a disorder strength parameter. In this method, fluctuations in the returned spectrum are hypothesized to arise from the interference of photons reflected from RI fluctuations within a scattering object [20]. As such, this type of phenomena may be described by one-dimensional propagating waves (also known as partial waves), which have enhanced sensitivity to subwavelength correlation lengths [21]. Theoretically, there is no limitation to the minimum scales that can be assessed with this approach; therefore PWS is not only sensitive to the nucleus and other small organelles, like flci and CLASS, but is also sensitive to the fundamental building blocks of cells, such as protein complexes, cytoskeleton, intracellular membranes, and nucleosomes. The instrumentation for this method consists of a very simple microscope, where light from a Xe-arc lamp source is delivered and collected by a 0.4 NA microscope objective. Then the scattered light is imaged onto the entrance slit of an imaging spectrograph (spectral resolution of 3 nm) or passed through a tunable filter (spectral resolution of 7 nm) and detected by a CCD camera. In both cases the analyzed spectral region is from 500 to 670 nm. Essentially, this is the same setup as CLASS with the exception of the confocal pinhole. To obtain the fluctuating part of the reflection coefficient, R(λ), the raw spectra are normalized by the source spectrum and low-pass filtered to remove spectral noise, and a low-order polynomial is used to remove further variations from the source [20]. Figure 8(a) shows a raw spectrum and the resulting R(λ). To obtain the disorder strength from the spectrum obtained for a given diffraction-limited spot, PWS utilizes principles of the mesoscopic light transport theory [21], where, for 1D waves, the root-mean square average of R(k = 2π/λ) may be described by the ratio of the thickness of the scatterer (L) and the scattering coefficient (ξ) ( R(k) = L/ξ). For correlation lengths (l c ) of RI fluctuations smaller than the inverse wavenumber of light (i.e., k l c < 1), the scattering coefficient may be described by a simple relation that depends only on the average RI (n 0 ) and the disorder strength L d. Further, L may be estimated from the autocorrelation function of R(k), a process conceptually similar to flci. Thus, the disorder strength may be described as [22] L d = n 2 I c n2 0 2k 2 R ( k) 2 ln(c( k)), (3) where n 2 is the variance of the RI fluctuations and C( k) is the autocorrelation function of R(k). Note that l c and n 2 describe the statistical properties of intracellular RI fluctuations and that they are always coupled in L d. Biologically speaking, n 2 may be interpreted as the local concentration of intracellular solids, whereas l c is a measure of the average size of structures within an imaged region. As a first demonstration of the PWS approach, Subramanian et al. used simulations and controlled experiments with polystyrene beads to demonstrate that the sensitivity of the method is well below 20 nm (and as low as 5 nm using finite-difference time-domain simulations), thus confirming theoretical expectations [20]. As hypothesized, these results demonstrate the ability to assess differences in the nanomorphology of Advances in Optics and Photonics 4, (2012) doi: /aop

13 Figure 8 (a) (b) (c) R(λ) Reflection of 1 cell Reflection of N channels/pixels Reflection intensity I (λ) of a single channel/pixel (amplified) EGFR Control CSK Glass Fluctuating part of reflection coefficient :R(λ) Wavelength λ (nm) Wavelength λ (nm) (μm) x10 4 I (λ) a.u. EGFR Control CSK Cytoplasm Nucleus Standard deviation of (μm) (μm) p-value<0.001 EGFR Control CSK p-value<0.001 p-value<0.001 EGFR Control CSK (a) Processing of a raw spectrum to obtain the fluctuating part of the reflection coefficient, R(λ). (b) H&E stained (top) and PWS (bottom) images of HT29 human colonic adenocarcinoma cells after three different treatments are used to alter the rate of carcinogenesis (see text for details). (c) Average (top) and standard deviation (bottom) of the disorder strength over the entire cell for the three different groups (N = 50 for each group) (taken from [20], c 2012 by the National Academy of Sciences). structures and can thus be used to determine changes in the building blocks of cells. In particular, this method can be used to detect early signs of carcinogenesis, among other diseases where the nanoscale architecture may be disrupted. To confirm this, HT29 human colonic adenocarcinoma cells were analyzed because their malignant behavior may be reduced or accelerated by using different well-established treatments [20]. Here, three treatments were employed: (1) an empty vector was used that did not affect the cells; (2) the human suppressor gene CSK was knocked down to accelerate malignant behavior; and (3) the epidermal growth factor receptor (EGFR) was knocked down to reduce malignant behavior. Figure 8(b) illustrates the qualitative result, where the three different treatments show vastly different behaviors with respect to their disorder strength; however, their micromorphology, as seen by the confocal microscopy images (after staining), does not appear to contain any significant differences. Quantitatively, the average disorder strength over the entire cell and its corresponding standard deviation show highly statistically significant differences between the three groups [Fig. 8(c)], underscoring the sensitivity to changes that are undetectable with current microscopy methods. In an effort to better understand which specific structures contribute to the disorder strength of a cell, Damania et al. conducted a study to test the contributions arising from the cytoskeleton [23]. Here, HT29 cells and CSK shrna-transfected HT29 cells were used, where the latter cell type exhibits more aggressive cancerous behavior. As expected, without additional treatment, HT29 cells show a statistically significant lower disorder strength than the CSK shrna-transfected HT29 cells, both in the nucleus and in the cytoplasm. However, to truly assess the cytoskeleton s contributions, the cells were subjected to a treatment using cytochalasin D, which affects the actin filaments Advances in Optics and Photonics 4, (2012) doi: /aop

14 ,, Figure 9 (a) N CN C Glass i (µm) 6 (b) m < (e) > (c) 21 * p-value < * p-value < N CN C N CN C m < (e) > (a) Bright field (top) and PWS (bottom) images of pancreatic cells from three different groups: normal cells from normal patients (N), and normal and malignant cells from patients with cancer (CN and C, respectively). (b) Average (left) and standard deviation (right) of the disorder strength over the entire cell for the three different groups (N 650 cells) (taken from [22]). found through the entire cell, and another treatment using colchicine, which affects microtubules found only in the cytoplasm. In both cases, the treatment hinders polymerization; thus the disorder strength is expected to be reduced in the targeted regions. The results show that when cytochalasin D was used both cell types exhibit reduced L d compared with their untreated counterparts; in addition, the two cell types were no longer statistically significantly different. On the other hand, when colchicine was used, L d was found to be unchanged compared with the untreated counterparts. Thus the two cell types were statistically different, while the cytoplasm showed much lower L d and statistically insignificant differences between the two cell types. This study confirmed that the cytoskeleton plays a major role in the measured cellular disorder strength. Significantly, this study suggests that the cytoskeleton may serve an indicator of the early carcinogenesis, which is not at all surprising given this organelle s paramount role: it provides the cell s shape and structure, and in the nucleus it is involved in DNA cross-linking, transcription control, and chromosome morphology. As a last note on PWS, further studies have been conducted in human patients with pancreatic cancer [22]. Pancreatic cells were obtained by using fine needle aspiration. Three different pancreatic cell types were tested: normal cells from normal patients (N), and normal and malignant cells from patients with cancer (CN and C, respectively). Figure 9 Advances in Optics and Photonics 4, (2012) doi: /aop

15 shows the results, where the L d images, represented as the mean and standard deviation over the entire cell, clearly distinguish between the three groups even though the bright field images do not show this difference. Using a predictive linear regression model, 100% sensitivity and 100% specificity was achieved, thereby emphasizing the potential of this method as a tool for early cancer detection. In summary, the wavelength-dependent behavior of scattered light yields access to structural and functional information that cannot be resolved with conventional microscopy. In addition, detailed quantitative information is obtained without altering cells natural living conditions. As we have discussed here, these unique features provide remarkable sensitivity to even the fundamental building blocks of cells, thus allowing the earliest response to many cellular abnormalities to be detected. In particular, many of the methods described here have focused on cancer because of the drastic improvement in the survival rate of patients when the disease is diagnosed early; however, these methods can be used to monitor many more atypical conditions owing to the wealth of information they provide and the flexibility of their optical approach. 3. Angle-Dependent Scattering Spectroscopy Techniques In contrast to wavelength-dependent techniques, which use broadband radiation, angle-dependent scattering spectroscopy typically employs a plane wave of monochromatic light as illumination. When the plane wave interacts with biological cells, a complicated scattering pattern is generated that is composed of scattered fields originating from many different components, including whole cells, cell nuclei, small organelles, and protein complexes. The resulting angle-dependent scattering pattern contains contributions from an enormous number of individual scatterers and may appear completely random at first glance. However, the contribution from the various cellular components can be separated by observing that the scattering pattern follows specific trends depending on the size and general shape of individual scatterers. For many years, the examination of the angular scattering patterns of biological materials was limited to isolated, individual cells, as in flow cytometry. For the simplified problem of a single cell, a few measurements can be made that roughly establish the size and composition of the cell. For example, the amount of forward or backscattered light can be used to estimate the approximate size of a cell, while the amount of side-scattered light can be used to estimate the granularity of cells with great accuracy [24]. Beginning in the late 1990s, a number of optical techniques emerged that could relate the organization of many cells in tissue to the angular distribution of single scattered light without the restrictions of cytometry. The emergence of these techniques was made possible by the intricate dependence of scattering angle on the size of a scatterer relative to the wavelength of light. This dependence is characterized by three scattering regimes. The first, describing subwavelength scatterers in the Rayleigh regime, is characterized by nearly isotropic scattering. Advances in Optics and Photonics 4, (2012) doi: /aop

16 Intermediate scatterers that are several wavelengths in size compose the regime that is well described by the Mie scattering formalism, and they exhibit significantly stronger forward and backward scattering than side scattering. Finally, the geometrical optics regime describes scattering by much larger objects, where light passes directly forwards and backwards according to the laws of reflection and refraction [3] Intensity-Based Methods Although flow cytometry historically exploited the differences between Mie and Rayleigh scattering patterns to sort and classify cell types, existing techniques had a number of limitations. First, they were limited to single cells isolated from their environment. Second, they performed only coarse angular measurements of scattered light, providing limited sensitivity to variations in structure. In contrast, newer techniques permitted high-resolution, potentially in vivo measurements coupled with more sophisticated electromagnetic modeling of scattered fields. Early studies on the angular scattering by cells included goniometric measurements of cell suspensions by Drezek et al. [25] and Mourant et al. [26]. These studies were among the first to investigate angular scattering by cells but provided a new avenue for investigating structure by using angle-dependent light scattering. In roughly the same time period, a new technique was also advanced based on measuring two-dimensional angular optical scattering (TAOS) [27]. In TAOS, individual particles, cells or aggregates of particles in air fall through a laser beam. A Fourier transforming lens and a two-dimensional array detector are used to record the angular scattering pattern in two dimensions (Fig. 10), typically in a side-scattering geometry. A sizing metric was introduced based on the number of oscillations in the angular scattering pattern. Although designed for atmospheric monitoring of pathogens rather than cellular imaging, TAOS was one of the first methods to combine high-resolution angular spectroscopy with sophisticated light scattering models [28]. A variation on LSS (see Section 2), scattering angle sensitive LSS (a/lss) combines two-dimensional angle-resolved detection with polarization and spectrally resolved detection [29,30]. This is accomplished by Figure 10 (a) Scanning electron microscope images of Bacillus subtilis spores. (b) TAOS measurements in the near-forward scattering direction. (c) TAOS measurements in the near-backward scattering direction. Adapted with permission from [27]. Advances in Optics and Photonics 4, (2012) doi: /aop

17 repeatedly acquiring angle-resolved measurements, similarly to TAOS, but using various polarizing and spectral filters. Unlike TAOS, measurements are taken centered around the backscattering angle. As in LSS, Mie theory is used to invert the spectrally resolved intensity values to recover scatterer sizes. However, because multiple scattering is highly depolarizing, polarization sensitivity can be used to exclude multiply scattered light, potentially allowing in situ measurements in thick tissue without the confounding effects of multiple scattering. Similarly, because scatterers in the Mie and Rayleigh regimes scatter most strongly in different directions, angle-resolved detection allows the LSS processing to consider only the contribution from structures of a specific size scale (e.g., cell nuclei) while partially rejecting other contributions. Using a/lss and Mie theory, Backman and colleagues demonstrated measurement of the sizes of living cell nuclei in the presence of confounding multiple scattering and absorption [31]. In subsequent work, an improved version of the a/lss technique called four-dimensional elastic LSS (4D-ELF) was introduced. In the 4D-ELF technique, rather than using spectral filters, an imaging spectrometer placed in the Fourier plane of a lens is used to record extremely high-resolution spectral and angle-resolved measurements. Because the imaging spectrometer can resolve the scattering angle in only one dimension, the polarization state of the illumination light is rotated to scan through the azimuthal scattering dimension. Simultaneously, a polarizing filter on the collection side is used to resolve polarization. The 4D-ELF system was used in a series of experiments to detect nanoscale changes in cellular organization that precede colon cancer in a rat cancer model [30,32] Field-Based Methods Concurrently with the development of a/lss, a related technique, angle-resolved LCI (a/lci) was introduced [33]. Like a/lss, a/lci uses angular resolution for isolating scatterers on specific size scales. Unlike a/lss, only a single spectral window and polarization are acquired, and angular variations alone are used to perform inverse processing. A key advantage of the a/lci technique is that LCI, the operating principle behind OCT [34] is used to provide depth sectioning, isolate single scattered light, and increase sensitivity. The basic setup of a/lci systems is an interferometer using a low-coherence light source such as a superluminescent diode (SLD). The far-field diffraction pattern is Fourier transformed by using a lens as in TAOS and then overlapped with a reference field. Finally, either scanning optics [35] or an array of detectors [36] are used to detect the interferogram generated by the combination of the diffraction pattern and the reference field. Thus, a/lci combines many of the advantages of both a/lss and OCT. A crucial realization in the development of a/lci was that the ability to separate the contribution from small and larger scatterers using measurements acquired at different scattering angles could be generalized by using the two-point correlation function. In a straightforward derivation, Wax et al. showed that band pass filtering the angular spectrum of scattered light isolates the contribution of scatterers Advances in Optics and Photonics 4, (2012) doi: /aop

18 Figure 11 Size (µm) mosm 400 mosm 500 mosm (a) (b) a/lci measurements Image analysis measurements Comparison of measurements of porcine chondrocyte cell nuclei using image analysis (dark gray) and a/lci (light gray). Confocal images of stained chondrocyte cell nucleus equilibrated at (a) 500 mosm and (b) 330 mosm. A statistically significant (p < 0.05) increase in nuclear size with decreasing osmolarity is observed. Taken from [38], c 2012 Elsevier Inc. at specific length scales [33]. Consequently, light scattered from cell nuclei can be isolated directly from the ensemble scattering of all cellular components, allowing highly accurate measurement and inversion of nuclear scattering to recover geometry. In a series of experiments using cells cultured on a regularly spaced grid substrate, Pyhtila and colleagues demonstrated that a/lci measurements could isolate both the contribution from cell nuclei and from the spatial arrangement of cells in a single measurement [37]. Application of a/lci for cell studies saw application to cell mechanics and organization (Fig. 11). Chalut et al. conducted a series of studies on the biomechanics of cells using a/lci to study in real time the deformation of chondrocyte cell nuclei in response to osmotic changes in their environment (Fig. 11) [38]. These were followed by studies on the effect of nanotopological cues on the organization of cell nuclei [39]. Finally, this work culminated in the demonstration that angle-resolved light scattering could be used to detect the reorganization of cellular components that precedes apoptotic cell death following treatment with several common chemotherapy drugs (Fig. 12) [40]. Another approach based on measuring the scattered field using interferometry is Fourier holographic light scattering angular spectroscopy [41]. This approach uses a novel scheme in which holography is used to record the complex field amplitude in a Fourier plane. Because this scheme records not only scattering amplitude but phase information as well, the Fourier plane can be propagated to generate an image of the sample. By selecting a subset of the image and employing Fresnel propagation, angular and spatial resolution can be achieved in a single measurement, allowing for segmentation of a sample. Combined with Mie theory, the technique was shown to be capable of accurately determining the size of beads subtending approximately 2 3 pixels worth of the image plane [42]. Highly accurate sizing of incompletely resolved scatterers is possible because of the Fourier transform relationship between the image and angular planes. Using the technique, Hillman et al. demonstrated automated size measurements of red blood cells with an impressively wide field of view (Fig. 13) [42]. Advances in Optics and Photonics 4, (2012) doi: /aop

19 Figure 12 Paclitaxel treated (5 nm) Untreated (a) (b) 2.4 Mass fractal dimension (c) (d) 0.8 t = 0 h t = 3 h t = 6 h t = 12 h t = 24 h Fractal dimension of MCF-7 cells shows a significant change in response to 5 nm treatment with Paclitaxel at t = 3, 6, 12, and 24 h post treatment. indicates statistical significance (p < 0.05) and indicates high statistical significance (p < 0.001). Representative images from (a), (b) DAPI (4,6-diamidino-2-phenylindole) and (c), (d) MitoTracker stained (a), (c) control and (b), (d) paclitaxel-treated MCF-7 cells. Scalebars, 10 µm. Adapted from [40], courtesy of American Association for Cancer Research. Figure 13 (a) (c) Region 1 Region 2 Region 3 Region 4 Region 5 (b) (d) Fourier-holographic light scattering angular spectroscopy images of red blood cells. (a) Computed intensity image. (b) Intensity image classified by scatterer size. (c) Enlarged view of the circled regions. (d) Angular scattering patterns of the selected regions with the best fit size selected. Adapted with permission from [42] Microscope-Based Techniques The ability of angle-resolved scattering to rapidly characterize the structure of subcellular features has led to a number of microscope-based Advances in Optics and Photonics 4, (2012) doi: /aop

20 Figure Scattered light intensity (ADU) Treated cells Control cells Angle (deg) Angularly resolved light scattering from untreated EMT6 cells ( ) and photodynamically insulted EMT6 cells ( ) [47]. The treated cells scatter less light at small angles and have a more pronounced forward peak. instruments, applied to cell biology studies. Several research efforts have used microscope-based light scattering instruments to monitor cellular features such as mitochondrial morphology. Boustany et al. used optical scattering imaging to monitor calciuminduced alterations in mitochondrial morphology [43]. Optical scattering imaging uses an inverted microscope setup to monitor the ratio of wide to narrow angle scattering, achieved by placing a variable iris in the focal plane of the objective. Later implementations substituted a digital micromirror device to permit detection of different scattering angles [44]. Application of this instrument included studies of BCL-xL which is important for understanding apoptosis [45], and combination with fluorescence microscopy, which closely linked these measurements with changes in mitochondrial morphology [46]. Wilson et al. further pursued angle-resolved light scattering as a tool for monitoring mitochondrial morphology [47]. While the instrumentation used by this group was goniometric, the ability to localize mitochondria and lysosomes was likewise verified by using fluorescence microscopy [48]. Further application of the approach demonstrated subcellular changes due to photodynamic therapy (Fig. 14) [49]. Another novel microscope-based approach is the integrated Ramanand angular-scattering microscopy system (IRAM) developed by Berger et al. [50]. In contrast to other two-dimensional angle-resolved systems, which generally use plane wave illumination, IRAM uses a high-na objective to tightly focus the illumination beam. Generalized Lorenz Mie theory is used to account for the extremely wide range of illumination angles when inverse analysis is performed [50,51]. The extremely high NA permits collection of a wide range of angles and collection of sufficient power for Raman scattering detection (Section 5). Using IRAM, Smith et al. demonstrated detection of CD8+ lymphocytes based on changes in elastic and inelastic scattering [52]. Advances in Optics and Photonics 4, (2012) doi: /aop

21 3.4. Light Scattering Models The interpretation of angle-resolved scattering data requires a forward model that describes how particular structures scatter light. A set of solutions to this forward problem can then be used to infer the most probable scatterer geometry for a given measurement. A model can be as simple as calibration measurements taken with known beads or cells, or can involve complex computational models. To date, most methods have used Mie theory, an analytic model of scattering from spheres of known size and RI. Although few natural objects are precisely spherical, many are approximately so, and can be modeled as spheres of equivalent volume with reasonable accuracy. The development of angle-resolved techniques created interest in determining precisely how applicable Mie theory is to biological scatterers and in developing alternative techniques. A number of groups have evaluated Mie theory as a light scattering model in comparison with aspherical phantoms or cells of known shape [53,54], the finite-difference time-domain method [55,56], and the T-matrix [53,57 60]. While these results have shown that Mie theory is generally applicable to ensembles of moderately aspherical scatterers [53,61], it has a number of limitations, including reduced accuracy for highly aspherical objects and no capability to model the shape or orientation of aspherical scatterers. As an alternative to Mie theory, the T-matrix method (also known as the extended-boundary-condition method) has attracted considerable attention, beginning with its development in the 1990s [27,57,58,62,63]. However, because of the higher computational cost, and the much larger range of parameters needed to describe aspherical scatterers, it was not until the mid-2000s that the T-matrix was first applied to determine scatterer size and shape from angle-resolved scattering measurements by Dubovik [64]. Following this initial work, the T-matrix formalism was used to analyze a/lci measurements to assess the size and shape of cell nuclei (Fig. 15) [61]. These results showed that by modeling cell nuclei as Figure 15 (a) (b) 10.0 Quantitative image analysis a/lci-t-matrix a/lci-mie Theory µm Equal volume diameter (µm) Equal volume diameter Aspect ratio Aspect ratio (a) Images of MCF-7 breast cancer cell nuclei labeled with DAPI stain. (b) Cell size and shape as determined by quantitative image analysis, a/lci with Mie theory, and a/lci with the T-matrix formalism. Mie theory and T-matrix both give good agreement with quantitative image analysis, but only T-matrix can provide measurements of the cell aspect ratio. Adapted with permission from [61]. Advances in Optics and Photonics 4, (2012) doi: /aop

22 spheroids rather than spheres, additional structural information could be recovered from a limited number of angular measurements. In summary, measurements of the angle dependence of scattered light can be exploited to measure structural and functional information in ways that are not possible through conventional microscopic imaging. Subcellular information such as the size, shape, and orientation of cell nuclei and the organization of smaller structures such as mitochondria and lysosomes can also be recovered. As a noninvasive optical approach, this quantitative structural information is obtained without perturbing the cell, allowing for continual studies of individual cells in response to environmental stimuli. 4. Phase-Based Spectroscopy of Cells The recent development of quantitative phase imaging techniques have provided an advantage for cell studies over traditional phase-based microscopy techniques. By permitting quantitative analysis of in vitro cell cultures, these techniques have both served to improve sensitivity in measuring parameters relevant to cell biology and have also brought about the definition of new useful parameters based on optical spectroscopy. Temporal fluctuation analysis discerns cell features through the Fourier spectrum of phase variations. Spatial phase distributions can likewise be analyzed to obtain light scattering spectra via Fourier transform light scattering (FTLS) techniques. Phase measurements can also be interpreted across wavelength in dispersion-based phase measurements. Finally, optical spectroscopy can also be used as a method to obtain phase measurements with high stability and unique capabilities Temporal Phase Fluctuations Cells are dynamic objects that exhibit movements over a broad range of time scales. Within an individual cell, the cytoskeletal activity [65], membrane fluctuations and dynamics [66,67], and molecularly driven transport [68] all contribute to changes in the three-dimensional composition that occur over the subminute time scale. In an attempt to characterize these relatively high-speed dynamics, many mathematical descriptions of physical models, for example, molecular motor [69] and lipid bilayer models [70 72], have been developed. While methods of measuring various biophysical model parameters include micropipette aspiration [73,74] and microscopic cantilevered force transduction devices [75,76], only recently have noncontact optical methods been applied to quantitatively study dynamic temporal fluctuations of in vitro cells. Popescu et al. [77] first demonstrated that transmission quantitative phase images of cells could be used to investigate thermal membrane fluctuations in a noncontact fashion, and with improved sensitivity over other previously presented optical measurements [78,79]. In this work, interferometric information was acquired and processed by using Hilbert phase microscopy to yield quantitative phase data [80,81]. Advances in Optics and Photonics 4, (2012) doi: /aop

23 The mean-squared spatiotemporal displacement spectra were measured in discocyte, echinocyte, spherocyte, and glutaraldehyde-fixed erythrocytes. From these spectra, the tension coefficients for each of these morphologies were calculated by using a temporally independent spatial-frequency decay fit [82], and show an increasing trend in tension as erythrocytes progress from discocytes to spherocytes. The temporal-frequency fluctuation power spectra that were presented also showed distinct frequency-dependent decay rates associated with each of these morphologies, which were attributed to cytoskeletal differences. Adapting the coherence formalism from electromagnetics as described by Mandel and Wolf [83], Popescu et al. then presented the concept of measuring spatio-temporal coherence properties of membrane fluctuations in erythrocytes [84]. In this work, membrane height fluctuations were used to calculate spatially resolved instantaneous restoring forces and coherence times. The subsequent combination of the fluctuation-dissipation theorem [85,86], the Kramers Kronig relations [87], and the generalized Stokes Einstein equation [86] allows retrieval of the complex spatiotemporal response function and also the viscoelastic modulus. Recent measurements of temporal power spectra and spatial restoring forces have been analyzed to characterize new versus old red blood cells via phase contrast recordings [88]. Other measurements have further quantified membrane properties and elasticity of red blood cells undergoing morphological changes [89] and have demonstrated differences in average fluctuation levels in the presence or absence of adenosine 5 -triphosphate [90]. Cellular membrane stiffness has also been observed to increase in populations of sickle cell erythrocytes [91]. While most biological studies employing phase-based temporal spectra measurements have focused on morphological features of erythrocytes, one study of cardiomyocyte contraction dynamics [92] uses quantitative phase rather than thickness maps to calculate parameters based on displacement correlation spectra (Fig. 16). These parameters characterize contraction dynamics and show significant dependences on the environmental temperature. Spectral analysis of temporal fluctuations can also be applied to multiple cells in the context of early tumor growth models (Fig. 16) [93]. Multicellular tumor spheroids are clusters of cells that can be grown up to 1 mm in size in vitro and develop a necrotic core as the diameter of the spheroids expands and restricts oxygen transport to central cells. These model early stage avascular tumor nodules [94,95]. Nolte and colleagues have developed motility contrast imaging as a tool for investigating speckle fluctuations. In this technique, reflectance-mode off-axis holographic imaging is combined with low-coherence light to optically section tumor spheroid samples as in time-domain OCT. The motility of the clustered cells is determined at each point by calculating the coherently detected speckle fluctuations. From this motility measure, the necrotic core of tumor spheroids can be distinguished from the proliferating outer envelope of cells, and a decrease in the normalized standard deviation of the speckle fluctuations results from the administration of chemotherapeutic drugs [93]. More recently, this group has studied changes in cell motility in response to osmolarity, Advances in Optics and Photonics 4, (2012) doi: /aop

24 Figure 16 (a) (b) (c) (d) Healthy tumor (e) 79 min later (g) 21 min later (f) Nocodazole 119 min later (h) Nocodazole Nocodazole (a) (d) From [92], phase images of a cardiomyocyte at (a) 30 C and (b) 23 C; (c) and (d) are respective average phase displacements observed during contractile motion. (e) (h) Time-course images of a tumor spheroid, showing decreases in coherently detected motility contrast images after administration of nocodazole; from [93]. temperature, and ph changes by using this temporal-spectrum analysis technique [96,97]. Phase profiles have also been used to examine longer-time-scale changes in studies of articular chondrocyte osmotic swell dynamics [98], cellcycle-dependent growth in Escherichia coli [99], and growth dynamics of the yeast Schizosaccharotnyces pombe [100] Fourier Transform Light Scattering In addition to temporal fluctuation spectroscopy, the spatial-frequency spectra of optical phase images have been used to quantitatively study cells. FTLS is an approach for studying inhomogeneous and dynamic media that combines holographic microscopy with light scattering analysis techniques [101,102]. Ding et al. demonstrated that rapid-acquisition FTLS could be used to recover both spatial-frequency spectra that agree with Mie scattering angular oscillations and temporal-frequency spectra relating to viscous fluid dynamic models [103]. Spectra of 3 µm microspheres and red blood cells were originally presented. This system used diffraction phase microscopy to collect transmission phase information. FTLS was also used to measure physiological differences in red blood cell membrane structure in response to Plasmodium falsiparum infection [104] and more recently to ATP depletion [105]. FTLS has also been applied to relate phase spectra of thin tissue samples, e.g., histological slices that are thinner than a single cell layer, to the bulk tissue scattering mean-free path l s and anisotropy coefficient g [106]. The scattering-phase theorem was extended [107] as a formal model for relating quantitative optical field phase measurements to spatially mapped measurements of l s and g and applied to tissue slices [108]. These parameters were calculated for 9 µm 9 µm windows across samples as large as several square millimeters, thereby yielding scattering parameters on the cellular scale over a histologically relevant Advances in Optics and Photonics 4, (2012) doi: /aop

25 Figure 17 (a) SLIM (b) H & E (c) (d) (e) φ[rad] g (a) Spatial light interference microscopy image of a human prostate biopsy; (b) corresponding H&E stained slice. (c) (e) rat liver tissue slice: (c) phase image; (d) scattering mean-free path, l s ; (e) anisotropy coefficient, g. From [109], c 2011 Society of Photo-Optical Instrumentation Engineers (SPIE). area. In a recent publication, Wang et al. used this method to map scattering parameters in histological tissue biopsy slides and demarcate cancerous regions (Fig. 17) [109]. While this work also identified populations of various cell types, segmentation was performed by using shape and RI rather than scattering parameters Dispersion Spectroscopy Analysis Spectroscopic features have also been used in quantitative phase measurements of cellular phenomena. Material dispersion has become a source of interest for quantitative phase measurements and can provide some label-free molecular specificity in otherwise transparent samples. In 2000, Yang et al. presented interferometric phase-dispersion microscopy using 400 and 800 nm illumination sources simultaneously coupled into a common optical setup [110]. By interferometrically measuring the difference in phase between these two wavelengths, the dispersion can be calculated. In this study an unstained sample of brain matter was imaged and yielded differing dispersion values for adjacent sections of gray and white matter. This technique of dispersion-based imaging was extended to low-coherence reflectionbased tomography [111] and also transmission-geometry quantitative phase measurements [112]. More recently, dispersion features in cells have been quantitatively measured. Park et al. used diffraction phase microscopy with a white light source and bandpass illumination filters to investigate spectral properties of red blood cells at multiple discrete wavelength ranges [113]. These experiments demonstrated wavelength-dependent RI changes that were related to hemoglobin concentration. Furthermore, the spatially resolved hemoglobin concentrations and mean cell volumes of individual red blood cells were calculated and reported to agree with normal physiological ranges [114]. Fu et al. used dual-wavelength quantitative phase measurements at 310 and 400 nm to spatially map dispersion in living HeLa cells [Figs. 18(a) 18(c)] [115]. Measurements of dilutions of DNA and protein solutions yielded refractive increment values at each wavelength and were used as an alternative image contrast mechanism. While different values of dispersion were observed Advances in Optics and Photonics 4, (2012) doi: /aop

26 Figure 18 (a) (b) 0.5 (d) RegA + OPA 310 nm 400 nm XY sample stage 40X OBJ NA = 0.6 Tube lens F = 20 cm 1 st order: sample 0 th order: reference G F = 10 cm SPF F = 15 cm CCD (c) z (μm) (e) z (μm) Ideal Measured +/ stdev Wavelength (nm) (a) Two-wavelength diffraction phase microscopy optical system. (b) Quantitative phase image of HeLa cells at λ = 310 nm. (c) Dispersion ratio of quantitative phase images at λ = 310 nm and λ = 400 nm. (d) Quantitative height profile of a red blood cell. (e) Measured n from a single point in (d) (blue curve) compared with theoretical dispersion computed by a modified Kramers Kronig relation [117]. (a) (c) From [115]; (d) (e) from [116]. Δn ( ) x (μm) y (μm) at each point with individual cell boundaries, the signal-to-noise ratio was not high enough to quantify concentrations of biomolecules in vitro. One method that makes use of measurements across the entire visible spectrum is nonlinear phase dispersion spectroscopy [116]. Initial work in developing this technique showed that LCI measurements can be used to separate absorption and scattering from the extinction coefficient [118]. Subsequently, Robles et al. analyzed the nonlinear dispersion term observed in spectral-domain phase microscopy measurements of red blood cells [116]. In addition to quantitative topographical maps, nonlinear spectral oscillations corresponding to Kramers Kronig [117] predictions for oxy-hemoglobin [Figs. 18(d) 18(e)] were calculated at each location and were used to quantify oxy-hemoglobin concentrations Phase Measurements via Optical Spectroscopy Several spectral-domain techniques have been developed for cell imaging. These techniques use similar image processing such as Fourerdomain OCT to recover depth-resolved reflectance information [119,120]. The phase term is then calculated by taking the angle of the complex reflectance at a specific depth. One method of detection uses the reflection from a coverslip as a common-path phase reference and calculates a differential phase between the phase at this surface and that at the cell membrane surface [121,122]. In both [121 and 122] the phase sensitivity is described as being proportional to 1/ SNR [121], which corresponds to tens of picometers for state-of-the-art systems. Choma et al. used spectral-domain phase microscopy to measure the temporal changes in cell membrane position of an isolated cardiomyocyte as it spontaneously contracted [121]; Joo et al. measured the optical pathlength through an individual epithelial cell and were able to see distinct pathlength differences between cytoplasmic regions and intracellular organelles, such as the nuclei [Fig. 19(c)] [122]. Advances in Optics and Photonics 4, (2012) doi: /aop

27 Figure 19 (nm) (a) (b) 6 (c) ΔL DIC (nm) Nuclei 4 Δp (nm) (s) σ = 84 pm Background σ = 36 pm y (μm) x (μm) Time (s) (a b) Live cardiomyocyte measurements, originally from [123]. (a) Spectraldomain differential interference contrast image of a cardiomyocyte; double arrow indicates shear direction. Scale bar 20 µm (b) Top plot, stochastic beating events at the point encircled in (a); inset, enlarged view of the two beating events. Bottom plot, background signal taken at the upper-left corner of the image. (c) Surface plot of a human epithelial cheek cell as imaged by spectral-domain optical phase contrast microscopy; from [122]. More recently, Zhu et al. extended spectral-domain processing techniques to differential interference contrast microscopy as a method of quantitatively computing phase gradient images of cells with high sensitivity [123]. In this work, the differential phases from two adjacent points on a sample are spectrally resolved and averaged, producing a reported 36 pm sensitivity. In this system, the digital processing used to recover quantitative differential interference contrast images is greatly simplified in comparison with spectral-domain phase microscopy systems, since the differential measurement is optically encoded and requires no phase reference. This system is also capable of investigating cellular dynamics, as demonstrated by time-resolved nanometer-scale changes in the cardiomyocyte membrane during contractile motion [Figs. 19(a) 19(c)]. Spectrally resolved techniques have also been applied to the problem of unwrapping 2π ambiguities in cell phase measurements, since biological samples can introduce phase delays larger than a single wavelength. Quantitative phase microscopy at discrete wavelengths has utility in phase unwrapping algorithms [124] and has been applied to cell images [125]. However, the field of phase unwrapping encompasses a diverse range of methods, and cells generally have smooth phase profiles that can be unwrapped by many other two-dimensional algorithms [126]. The added complexity of a second laser source and additionally recorded interferograms typically deters the use of two-wavelength phase unwrapping for biological applications. Additional techniques have also been developed for spectrally resolved phase measurements of samples that are illuminated by a broadband source, as is the case with OCT and related imaging technologies. One system adds a quadratic phase term by introducing a dispersive element to the sample arm of an interferometer [127]. The recovered phase profile can be integrated to produce a relative height measurement that does not suffer from 2π ambiguities. Another alternative is to perform spectral phase unwrapping under the assumption that the sample dispersion does not cause changes in phase greater than 2π between adjacently measured spectral bins [128]. Advances in Optics and Photonics 4, (2012) doi: /aop

28 5. Vibrational Spectroscopy Vibrational-based imaging methods are popular because all molecules contain vibrational transitions that can be accessed with reasonable energies ( cm 1 or ,000 nm). Many of these vibrations have narrow spectral peaks, which allows for easier spectroscopy of mixed samples, such as cells. Molecular vibrations are commonly measured by performing direct, one-photon absorption of the transition (infrared spectroscopy) or measuring the energy shift of inelastically scattered photons (spontaneous Raman spectroscopy). For a given vibrational transition to be infrared active, it must involve a change in the dipole moment. To be Raman active, a vibrational transition must involve a change in the polarizability of a molecule [129]. It is important to note that some Raman peaks may not appear in an infrared absorption spectrum and vice versa, and some vibrations may not appear in either. Infrared absorption measurements of cells and tissue fall into two general categories: microspectroscopy, where a spectrum is acquired from a small volume, and imaging techniques, where images are reconstructed based on infrared absorption features. Typically, to acquire an image a laser or another infrared light source is raster scanned across the sample, and the transmitted intensity for each wavelength is acquired at each point. Fourier transform infrared spectrometers are popular because the instrument allows the full spectrum to be acquired in parallel with a point detector, as infrared array detectors are often expensive or difficult to manufacture. Light sources for infrared spectroscopy are also difficult to come by, and synchrotrons provide some of the best sources (bright and highly focused). Fourier transform infrared microspectroscopy of cells has been used for diverse applications, finding use in classifying cancers cells, characterizing Alzheimer s disease, diagnosing melanoma, and studying receptor signaling, drug toxicity, and cell motility [ ]. The uses of Fourier transform infrared microspectroscopy and a full listing of organic functional group vibrational transitions can be found in other reviews [133] Spontaneous Raman Microscopy Raman microscopy and microspectroscopy avoids many of the limitations of infrared microscopy. As in infrared microspectroscopy, a pointscan Raman image is acquired by raster scanning a laser focus across the sample and confocally collecting the scattered light. The technique has been widely developed and applied to a number of problems, from cellular imaging to noninvasive in vivo animal studies, disease diagnosis, and endoscopy [ ]. Chan et al., for example, report extremely sensitive classification of stem cells from other immortalized cell lines through the use of Raman microspectroscopy [144]. The extraordinarily low conversion efficiencies for Raman scattering lead to typical image acquisition times of the order of a few seconds per pixel [145]. The Raman efficiency can be improved by many orders of magnitude through the use of the surface enhanced Raman scattering effect [146]; however, because it involves applying an exogenous contrast Advances in Optics and Photonics 4, (2012) doi: /aop

29 Figure cm cm cm cm 1 Raman images of budding yeast cells acquired at four Raman shifts: 1446 cm 1, belonging to the CH bend (lipids), 1583 cm 1 belonging to porphyrin in-plane C=C stretch (cytochrome c), and 1665 cm 1, which is assigned to the superposition of the cis C=C stretch of unsaturated lipids and the C=O stretching amide I mode of proteins. The 1602 cm 1 band is associated with metabolism of a living cell. Scale bar is 4 µm. Adapted from [147]. agent, it will not be reviewed here. Another method for increasing image acquisition speed is to parallelize the process through a multifocal technique and multichannel detector. Such a multifocus (40+foci) system was recently reported by several groups including Okuno et al. and Kong et al. [145,147]. Figure 20 shows such an image acquired in 20 s from a multifocal system Stimulated Raman Scattering As mentioned above, spontaneous Raman scattering is a very weak process. The scattering efficiency can be vastly increased by exploiting the phenomenon of stimulated Raman scattering (SRS). Similarly to stimulated emission of fluorescence, illumination with light at the Stokes wavelength increases the probability of Raman scattering. The probability per unit time of photons being scattered into the Stokes mode by a molecule is directly dependent on the number of incident Stokes photons: P Raman = σ Raman n pump (n Stokes + 1), where σ Raman is the Raman scattering cross section, and n pump and n Stokes are the number of incident photons at the pump and Stokes wavelengths (illustrated in Fig. 21) [148]. While SRS was first observed in 1962 [149], has been used in various fields since, and is well described [150], it was the recent development of ultrafast SRS that led to fast cellular imaging with a spectroscopic vibrational contrast [151,152]. In an ultrafast SRS experiment, pulsed lasers are used as the pump and Stokes light sources. This gives increased signal because SRS is a multiphoton process and nonlinearly dependent on the total peak Advances in Optics and Photonics 4, (2012) doi: /aop

30 Figure 21 1 virtual level ω ω ω 2 2 ω 2ω ω Stokes ω ω anti-stokes Stokes ω 3 ω ω 1 ω pump ω pump ω ω 1 ω 4 0 Two-photon absorption Sum frequency absorption Δv Stimulated Raman scattering Δv Coherent anti-stokes Raman scattering Second harmonic generation Four wave mixing Nonlinear processes. intensity. As shown above, the SRS signal is linearly proportional to both the pump and Stokes intensities, and so it has a quadratic dependence on total intensity. Pulsed excitation allows for high instantaneous intensity while maintaining a moderate average intensity to avoid damaging the sample. Multiphoton nonlinear imaging modalities inherently offer three-dimensional sectioning because the signal generation is confined to the region of greatest intensity (the focal volume) [153]. These modalities also function in turbid media (such as tissue) because scattered signal may safely be assumed to have originated in the focal volume. The image is constructed by point scanning the focal volume across the sample. Unlike the more commonly used multiphoton techniques of two-photon excited fluorescence and second-harmonic generation, SRS does not generate light at a new wavelength, but instead shifts power from the pump beam to the Stokes beam. Sensitive detection techniques are therefore necessary to discriminate detected power changes in the pump or Stokes beams from other sources of gain or loss (such as absorption, laser intensity noise, loss due to scattering, and fluorescence). The Xie group implemented the method of modulation transfer to perform an essentially background free measurement. By modulating either the pump or Stokes beam at a high frequency (1 20 MHz) and detecting transfer of this modulation to the other beam with a lock-in amplifier, the SRS signal can be acquired with nearly shot-noise-limited sensitivity ( I/I < 10 8 ) [154]. The Xie group study presented images of a human lung cancer cell (A549) acquired using SRS tuned to match two different vibrational transitions. The Stokes beam was the 1064 nm fundamental output of a mode-locked Nd:YVO 4 pulsed laser, and the pump beam was derived from a synchronously pumped optical parametric oscillator. Pulse durations were 7 ps, chosen to allow for a high peak intensity but a narrow spectrum (3 cm 1 ). Images were acquired when the energy difference between the pump and Stokes beams was 2920 cm 1, which matches vibrational resonances in both fatty acids and nuclear material and at 3015 cm 1, the resonant energy of the alkene =CH 2 stretch, found in unsaturated fatty acids but not saturated ones. The stronger cytoplasm signal in the 2920 cm 1 image demonstrates that most of the fatty acids outside the lipid droplets were saturated. Microspectroscopy spectra from SRS of the nuclear and lipid droplet regions confirmed the lack of unsaturated lipids characteristic of nuclear material. Advances in Optics and Photonics 4, (2012) doi: /aop

31 SRS imaging is not restricted to transmission images of cells. Because it is a nonlinear technique, it is possible to acquire three dimensional data from tissue using an epi-illumination geometry and measuring backscattered light. The Xie group demonstrated this to acquire Raman tissue images at high speed using the setup described in [154]. As in transmission mode imaging, light was focused into the sample by the objective. Instead of collecting the signal light with the same objective, however, they used a large-area photodiode in direct contact with the sample. Because much of the SRS signal is initially forward propagating and relies on being backscattered to exit the tissue surface, the SRS light emerges over a much larger area than fluorescence, which is emitted isotropically. The large-area photodetector is ideally suited to collect the SRS signal. Special filters that can reject backscattered excitation light over a large angular range with high efficiency were required. In addition the photodetector was designed with a hole in the center to pass the pump and Stokes beams. Using this setup, video rate images were acquired with optical powers of mw. Images of the epidermis in living mice with resonances tuned to 2845, 3250 and 2950 cm 1 allowed selective highlighting of oily material in sebaceous glands, proteins, and red blood cells in capillaries. Recently, Fu et al. demonstrated an imaging SRS method for acquiring multiple resonances simultaneously, which they call multiplex SRS microscopy [155]. An acousto-optic tunable filter was used to spectrally select a number of spectral bands from a broadband femtosecond source. The energy difference between each pump band and the probe pulse determined the Raman resonance to be excited. Each pump band was modulated at a different frequency, and signal from individual Raman shifts was extracted by demodulation, allowing for simultaneous measurement. They demonstrated the ability of this technique to extract quantitative chemical concentration information and then to image algae cells and unmix chlorophyll, lipids, and proteins. It is often the case that multiple chemical species in a sample will contain resonances at the same frequency, which results in images with ambiguous contrast. To increase contrast specificity, one can acquire images at multiple resonances of the target molecule, or perform microspectroscopy on a region of interest. The method of spectrally tailored excitation SRS microscopy was developed as an alternative solution [156]. Instead of two spectrally narrow pulses, a broadband femtosecond pulse is substituted for either the pump or the Stokes pulse. This allows excitation of a spectrally broad set of vibrational resonances in the sample. Using a pulse shaper, the spectral intensity profile of the broadband pulse can then be tailored based on the Raman spectrum of the target and confounding molecules. By rapidly switching back and forth between two differently shaped excitations and locking onto the difference signal, signal from the target molecule can imaged while contributions from confounding molecules are minimized. A broadband pulse is shaped to have two peaks, for example, at ω 1 and ω 2, which are resonant with a target molecule. The broadband pulse is then shaped twice to contain peaks at ω 3 and ω 4, which are resonant only with confounding species. The second spectrum is weighted based on the relative resonances of the four peaks with the confounding species, in such a way that the difference signal between Advances in Optics and Photonics 4, (2012) doi: /aop

32 Figure 22 (a) Intensity (a.u.) Laser spectrum Oleic acid Stearic acid Protein (b) Effective excitation (a.u.) 1 0 Oleic acid Stearic acid Protein Raman shift (cm 1 ) Raman shift (cm 1 ) (c) (d) (e) 1 Spectrally tailored SRS images. (a) Spontaneous Raman spectra for four species, normalized to the laser excitation spectrum. (b) Derived spectral masks for tailored excitation. (c) Spectrally tailored SRS image of protein, (d) oleic acid, and (e) stearic acid in C. elegans. Scale bar is 25 µm. Adapted from [156], c 2011 Nature Publishing Group. the first and second tailored spectra cancels out the contributions from the confounding species. Example spectra are presented in Fig. 22. Both oleic acid (blue spectrum) and stearic acid (purple spectrum) have no isolated Raman active resonances in the cm 1 spectral region. After normalizing the expected SRS responses of oleic acid and stearic acid to the broadband pump spectrum, an excitation mask can be generated to select the signal of cholesterol alone [Fig. 22(b)]. Using this scheme permitted detection of a signal that was linear with the target molecular concentration and independent from the concentration of confounding molecules. Signals up to 2000 times stronger from confounding species were successfully suppressed. This method was extended to in vivo imaging of lipid trafficking in Caenorhabditis elegans. Three images were acquired by using a broadband pulse for excitation in the cm 1 spectral window. The spectrally tailored excitation SRS images are shown in Figs. 22(c) 22(e) Coherent Anti-Stokes Raman Scattering Another popular nonlinear imaging technique that exploits Raman contrast is the method of coherent anti-stokes Raman scattering (CARS) microscopy. In a CARS experiment, the sample is again illuminated with light at three frequencies: ω pump, ω probe, and ω Stokes. Typically, for simplicity the pump and probe beams are degenerate. The energy difference between the pump and the Stokes beams corresponds to a vibrational transition in the sample. Unlike spontaneous and stimulated Raman scattering, CARS is a coherent process where the sample vibrations oscillate in phase, leading to constructive interference and a net third-order polarization at the anti-stokes frequency. Also, unlike Advances in Optics and Photonics 4, (2012) doi: /aop

33 Figure 23 6 Intracellular droplet 5 Extracellular lump (a') CARS intensity (arb.units) Stratified laminae Needle-like debris Tissue fat (b') (c') (d') 1 0 Tissue matrix sym asym CH 2 sym asym CH 3 (e') (f') Raman shift (cm 1 ) CARS imaging and spectra of atherosclerotic plaques. (a) Lipid-rich foam cells, where lipid droplets are small white circles; (b) extracellular lipid deposits, (c) stratified laminae lipid crystals, (d) needle shaped lipid crystals, (e) large fat deposits in connective tissue, (f) low lipid extracellular medium (darker areas, arrows). Scale bars are 30 µm. CARS spectra from selected regions are shown in the right panel. Adapted from [164]. SRS, however, CARS generates new frequencies of light at the anti-stokes frequency given by ω AS = 2ω pump ω Stokes (illustrated in Fig. 22). These new frequencies are blueshifted from the illumination and therefore are naturally separated from any linear fluorescent background. This property allows for very sensitive measurements without the need for lock-in detection. CARS images can often be acquired from thin samples with submilliwatt levels of power [157,158]. Pulsed excitation is used to provide high peak power with low average power, and picosecond sources are chosen because of their narrow spectral width compared with femtosecond sources. CARS microscopy of cells was first demonstrated in 1982, based on the 2450 cm 1 resonance of D 2 O [159]. It has become a staple cellular imaging technique for its high sensitivity and molecular contrast. One of the most common vibrational modes for CARS imaging is the CH 2 stretch of aliphatic groups (lipids) in the region of cm 1, which highlights cell membranes and other lipid bilayers as well as intracellular lipid droplets (Fig. 23) [160]. Other useful vibrational bands include the broad OH stretch mode near 3000 cm 1 (water) [161], the phosphate vibration at 1095 cm 1 (DNA/RNA) [141,162], and the C=O stretch of the amide I band at cm 1 (protein) [157]. CARS can be used to extract various chemical properties of tissue, such as the oxygenation state of blood [163]. Because CARS is a nonlinear method, it can provide three-dimensional optical sectioning in tissue, just as SRS can. Advances in Optics and Photonics 4, (2012) doi: /aop

34 As stated above, the CARS signal depends on an induced third-order sample polarization, P (3), driven by the pump and Stokes beams: P (3) (ω AS ) χ (3) E 2 pump E Stokes, where χ (3) is the third-order nonlinear susceptibility of the sample [165]. The intensity of the generated CARS field is proportional to the square of the induced polarization: I CARS χ (3) 2 I 2 pump I Stokes [166]. This nonlinear dependence on the total applied intensity gives CARS, like SRS, all the advantages of nonlinear imaging. Unlike SRS, CARS has a quadratic instead of linear dependence on sample concentration because of its quadratic dependence on χ (3). Another consequence of the χ (3) dependency is the presence of a nonresonant background to CARS images, which renders interpretation and quantification difficult and distorts the lineshapes obtained from CARS microspectroscopy. CARS is fundamentally a four-wave mixing process and does not deposit energy into the sample. The presence of a vibrational transition resonant with the pump and Stokes beams energy mismatch greatly enhances the CARS signal, but it is not necessary. The electronic response of the material to the pump and Stokes beams arises from this nonresonant portion of χ (3). The complete form of χ (3) is given by χ (3) = χ (3) NR + χ (3) (3) R /( iɣ), where χ NR is the nonresonant susceptibility and is the detuning of the pump and Stokes energy difference from a Raman resonance with bandwidth Ɣ [158]. Mixing between the resonant and nonresonant portions of χ (3) gives rise to a CARS spectral dependency with a dispersive lineshape centered on a Raman resonance. This background signal redshifts the observed CARS spectrum and introduces ambiguity when one is attempting to compare CARS spectra with known spontaneous Raman spectra. The ambiguity is especially strong when images or spectra are acquired in regions with many closely spaced resonances. A number of efforts have been made to suppress the nonresonant background. Polarization CARS accomplishes this by exploiting the fact that the induced resonant and nonresonant third-order polarizations in the sample have different orientations and can be suppressed with an analyzer [157,167]. However this scheme both attenuates the resonant signal and reduces the efficiency of suppression by birefringence or scattering in the sample. Another approach is to use the time dependence of the resonant signal to isolate it [168,169]. Interferometric CARS separates the resonant susceptibility because it contains both real and imaginary components, whereas the nonresonant susceptibility is purely real. The spontaneous Raman spectrum is proportional to Im(χ (3) R ) [170,171]. The imaginary part can be measured interferometrically by mixing with a known heterodyne reference field at the anti-stokes frequency ω AS. Problems with this method arise from varying indices of refraction in the sample and the experimental complexity of an interferometric setup. Frequency modulation CARS rapidly modulates the frequency of either the pump or the Stokes beam and therefore the Raman shift with which it is resonant. This translates into intensity modulations of the resonant CARS signal. Lock-in detection is necessary to extract the signal, but this carries the advantage of removing low-frequency noise [172]. Phase cycling CARS provides similar advantages to interferometric CARS, but uses pulse shaping techniques to generate a local oscillator in the sample Advances in Optics and Photonics 4, (2012) doi: /aop

35 medium itself from the nonresonant portion of ω AS [173,174]. A temporal offset in the excitation pulses can differentiate the induced vibrational excitation from fast-decaying nonresonant coherence [168]. Combining this method with a broadband Stokes pulse, Selm et al. were able to acquire undistorted Raman spectra from biological samples [169]. The Offerhaus group recently introduced a method they term vibrational molecular interferometry, which allows for Raman microspectroscopy in a CARS-like experiment. The sample is illuminated with three beams (ω 1, ω 2, ω 3 ) at the frequencies which would be used in a CARS experiment (ω 1 = ω pump, ω 2 = ω Stokes, ω 3 = ω AS ). However, the pump beam is frequency shifted by an acousto-optic modulator. Similarly to frequency modulation CARS, this translates into amplitude modulations. In the case of vibrational molecular interferometry the amplitude modulation evolves at the beat frequency of the two possible Stokes Raman pathways given by ω 1 ω 2 and ω 3 ω 1. All three beams are collected after the sample, and their amplitude modulation is measured with lock-in detection [175]. Methods for obtaining quantitative spectra from CARS are reviewed in greater depth by Day et al. [176]. As described above, classic CARS experiments are narrowband techniques that produce images based on a specific vibrational contrast. In order to collect a spectrum at each pixel and produce hyperspectral images, multiplex CARS was developed [164,177,178]. One of the narrowband pulses, typically the Stokes pulse, is replaced by a pulse with broad spectral content. Alternatively, the pump pulse can be replaced with a broadband pulse if a third narrowband pulse is used at the probe. This scheme allows for an anti-stokes signal from vibrational transitions with a range of energies, depending on the bandwidth of the broadband pulse. The energies of the probed vibrations are therefore encoded in the spectral content of the anti-stokes signal. Figure 23 shows Raman spectra and images collected of atherosclerotic plaques Pump Probe, Excited State Absorption, Transient Absorption Molecular contrast can also be achieved based on transient properties. Pump probe imaging exploits various transient behaviors of chromophores to gain chemically specific information. A pump probe experiment starts by pumping the sample at an absorption wavelength, which generates transient excited states. The dynamics of these states can then be probed at a second wavelength. Such pump probe experiments have several degrees of freedom; with the results depending on two spectral dimensions and one temporal dimension, they can be highly chemically selective. Pump probe experiments, which include transient absorption measurements, have long been a spectroscopic staple. Recently detection of a pump probe signal by modulation transfer has been expanded into a nonlinear imaging modality by Fu et al. [179]. Like the case of stimulated Raman imaging, pump probe methods for measuring transient changes in the absorptive properties of a material require the detection of very small intensity changes (of the order of 10 6 or smaller) in the probe beam. This was requirement was met by using lock-in detection to Advances in Optics and Photonics 4, (2012) doi: /aop

36 Figure 24 (a) 2 1 ω probe 0 Excited state absorption Decreased probe transmission ω ω ω pump probe ω pump ω probe pump ω pump Δv ωprobe Δv Stimulated Two-photon excited emission stimulated emission Increased probe transmission Ground state depletion Signal (au) Eumelanin Pheomelanin Pump: 810 nm Probe: 720 nm Interpulse delay (ps) (d) (e) (f) (g) (b) (c) OxyHb Pump: 650 nm Probe: 775 nm DeoxyHb Interpulse delay (ps) Pump probe imaging of biological pigments. (a) Various pump probe processes that give rise to observed probe beam modulation. (b) Transient dynamics of eumelanin and pheomelanin and (c) oxyhemoglobin and deoxyhemoglobin. (d) Image of a melanoma showing increased eumelanin content in nests (red, 100% eumelanin, blue, 100% pheomelanin). (e) Image showing relatively uniformly pigmented pheomelanic nevus. Scale bars, 100 µm. (f) Pump probe image of red blood cells. (g) In vivo image of microvasculature based on hemoglobin contrast. Adapted from [180,182,185]. Images from 185, c 2011 American Association for the Advancement of Science. monitor the transfer of a high-frequency (megahertz range) intensity modulation from the pump beam to the probe beam. We refer to this method as pump probe imaging because it can be used for more processes than just transient absorption [illustrated in Fig. 24(a)]. To achieve the highest temporal resolution, a picosecond or femtosecond laser is used as the light source. The first event is absorption of the pump pulse by a chromophore in the sample. This creates a real excited state population, which has a finite lifetime. The sample is then illuminated by a time-delayed probe pulse, which interacts with the excited state in some fashion. This interaction leads to either gain or loss of probe intensity. An event such as excited state absorption leads to decreased probe transmission, and stimulated emission or ground state depletion leads to increased probe transmission. Lock-in detection is phase sensitive and so can discriminate between these two families of interactions. By scanning the pump probe interpulse time delay, it is possible to monitor the excited state lifetimes and decay dynamics. The pump probe signal is linearly proportional to both the pump and the probe beam intensities, which gives it a quadratic total dependence on intensity. Like two-photon excitation fluorescence and SRS, it is a nonlinear imaging method that can provide three-dimensional optical sectioning in turbid media. Epi-mode detection of backscattered probe light allows noninvasive in vivo imaging of tissue. Imaging of red blood cells, microvasculature networks, and nonfluorescent chromoproteins has been demonstrated in vivo [ ]. The pump probe transient dynamics of oxyhemoglobin versus deoxyhemoglobin [Fig. 24(c)] also allow for the differentiation of venules from arterioles [183]. Advances in Optics and Photonics 4, (2012) doi: /aop

37 Pump probe imaging carries a number of advantages for cell and tissue imaging and spectroscopy. It is a chemically selective contrast mechanism, like Raman, which allows for intrinsic molecular imaging. However Raman spectra of biological samples are often crowded and very difficult to interpret, because many different compounds share similar organic functional groups and therefore have overlapping spectral features. Pump probe imaging adds the temporal dimension of transient dynamics, as well as two spectral dimensions, which allows for separations of chemicals that would otherwise be impossible [184]. Recently Matthews et al. reported the ability to discriminate eumelanin from pheomelanin (two pigments commonly found in human skin and melanoma), which was not previously possible in a microscopic image. Pump probe imaging of melanin revealed chemical and structural changes in melanoma, as compared with melanocytic nevi and other pigmented skin lesions [Figs. 24(d) 24(e)]. 6. Developing Spectroscopic Techniques In addition to the aforementioned techniques, which have been extensively researched, other novel spectroscopic approaches have attracted attention and demonstrated promising potential for molecular and functional imaging of live cells. Traditional spectroscopic methods examine the intrinsic optical properties of cells, but exogenous contrast agents, such as nanoparticles (NPs), open a new dimension for observation. Nobel metal NPs display a unique property of localized surface plasmon resonance, where increased optical scattering and absorption can be observed at a resonant optical wavelength. Compared with fluorescence dyes and quantum dots, NPs produce brighter signal and are generally nontoxic and free of photobleaching and blinking. Hence, immuno-targeted NPs can be used as effective contrast agents for molecular imaging to characterize the presence and quantity of biomolecules [ ]. Significantly, the scattering spectrum of a NP is highly sensitive to the local environment it resides in, providing a means to observe molecular dynamics with high contrast. For example, a molecular binding event occurring near the surface of a NP modifies its scattering spectrum, specifically the position of the resonance peak, and so does the existence of a second NP in its proximity. Based on this concept, dark-field hyperspectral microscopy has revealed binding-induced spectroscopic distinction between anti-epidermal growth factor receptors (anti-egfrs)-conjugated gold NPs and those free of labeling after both are subject to incubation with EGFR-overexpressing cancer cells [189]. This approach provides an avenue for high-sensitivity measurement of intracellular RI as an indicator for molecular events. Using similar schemes, dimerization of human epidermal growth factor receptor 2 (HER-2) [190] and real-time trafficking of EGFR have been investigated [191]. Both studies reported a strong redshift in the resonant wavelength of immuno-labeled gold NPs (Fig. 25). In other variations of the technique, polarization-dependent scattering of NPs was exploited to improve image signal-to-noise ratio [192] or to potentially monitor interparticle distances for cell receptor study [193]. Additionally, simultaneous imaging of multiple Advances in Optics and Photonics 4, (2012) doi: /aop

38 Figure 25 (a) (b) Scattering intensity (a.u.) (c) (d) Scattering intensity (a.u.) Wavelength (nm) Wavelength (nm) (a) Representative image and (b) scattering spectrum of HER-2-overexpressing SK-BR-3 cells bound with HER-2 Ab immunolabeled 60 nm gold NPs. The peak wavelength of the scattering is at ± 26.0 nm. (c) Image and (d) spectrum of A549 cells, which have a significantly lower level of HER-2 expression, bound with the same NPs. The peak wavelength of the scattering is at ± 20.1 nm. The redshifted spectrum of the NPs bound to SK-BR-3 is due to plasmonic coupling of the labels, indicating close proximity and thus dimerization of the receptors. Scalebars, 10 µm. From Ref. [190], c 2011 American Chemical Society. molecular processes is also made possible by use of multiple species of NPs with distinct spectral scattering peaks [194]. While enhanced scattering permits direct observation of NPs, they can be indirectly detected as well by using enhanced absorption via the photothermal effect, where photons absorbed by a nanoparticle are converted to heat and hence induce local changes to the RI in the immediate vicinity. These RI variations can be readily detected by a variety of optical phase imaging techniques, often with markedly higher sensitivity. For example, based on photothermal interference contrast, imaging of single 5 nm gold NPs on glass substrates was demonstrated and immuno-labeled 10 nm NPs were used to map membrane protein distribution in fixed cells [195,196]. Live cell imaging was also demonstrated with a laser-induced scattering technique, which boosts detection sensitivity to allow for substantially reduced excitation energy amiable to living specimens [197]. Recently, phase-sensitive optical coherence tomography has been validated as a viable molecular imaging modality using NP-generated photothermal phase signals in cell monolayers and 3D cell constructs to characterize EGFR expression [198,199] (Fig. 26). Another closely related method is photoacoustic spectroscopy, which also relies on wavelength-dependent absorption as contrast but detects acoustic signals generated by absorbed energy. By measuring acoustic Advances in Optics and Photonics 4, (2012) doi: /aop

39 Figure 26 (a) (b) Depth (mm) Lateral position (mm) Depth (mm) Lateral position (mm) (c) Depth (mm) Lateral position (mm) (d) Photothermal signal (a.u.) EGFR+ nanosphere- EGFRnanosphere+ EGFR+ nanosphere+ EGFR expression in 3D cell constructs: EGFR+ MDA-MB-468 cells (a) with and (c) without anti-egfr-conjugated NPs, and (b) EGFR MDA-MB-435 cells with anti-egfr-conjugated NPs. (d) Significant higher photothermal signal is observed in (a) as compared with the two controls in (b) and (c). (, p < ). From Ref. [198], c 2008 American Chemical Society. magnitude at different excitation wavelengths, a photoacoustic spectrum can be recorded and used to identify chemical species and quantify their concentrations. Photoacoustic spectroscopy has a long and rich history dating back to the late nineteenth century and has been be applied to all phases of materials [ ]. Its primary utility in biomedicine comes in the form of photoacoustic tomography, whereby not only the structural distribution of a chemical but its quantitative concentration may be mapped in three dimensions [203,204]. The main intrinsic photoacoustic contrasts for biomedical imaging are hemoglobin or melanin absorption, earning the technique major in vivo utility for functional imaging of blood vessels, brain hemodynamics, and tumor angiogenesis, among many others. Photoacoustic imaging at the subcellular level has also been reported for melanoma cells and red blood cells [ ]. It should be noted that, given that the focus in these studies is to image rather than quantify, the excitation sources are usually a single wavelength laser instead of a multiwavelength or broadband nature. If needed, a true spectroscopic approach will gather more molecular information and improve the characterization of these cells. Further, enhanced functionalities and performance can be achieved by using exogenous contrast agents including organic dyes, nanoparticles, reporter genes, or fluorescent proteins [204]. Here, we briefly covered several optical molecular imaging techniques that may be broadly categorized as optical spectroscopy and their applications for cellular imaging. These developing methods are different from and perhaps more sophisticated than traditional optical spectroscopy in that they are inspired by a combination of fast-growing developments in other fields, such as nanoplasmonics and photothermal Advances in Optics and Photonics 4, (2012) doi: /aop

40 and photoacoustic technologies. While these new modalities will continue to mature, we expect other synergistic efforts to emerge and further push the envelope of optical spectroscopy as a versatile tool for biomedical research. 7. Conclusion A review of optical spectroscopy applied to biological cells, broadly defined, has been presented. Table 1 summarizes these techniques, including their spectroscopic method and key areas of applications. There are three key properties of optical spectroscopy that make it well suited for the study of cells. First, optical spectroscopy is noninvasive and thus does not typically perturb cells during measurement. This offers the unique opportunity to observe cells in their natural state and avoid the artifacts of other imaging methods. This particular aspect allows these techniques to be translated to clinical applications where exogenous labeling is not possible. In comparison, cell biology studies often employ fluorescent labels to highlight specific cellular components and processes. The availability of a wide range of fluorescent tags, in addition to endogenous fluorescent features, makes label-free optical spectroscopic techniques less appealing to the cell biologist, as the need to avoid labeling is not acute. In order for these optical spectroscopic techniques to be more widely adopted by cell biologists, they must be made user-ready and commercially available. While several efforts to commercialize these optical spectroscopic techniques have begun, the confusing array of competing techniques and their limitations, in that they typically focus on only a narrow range of features, makes adoption by the research market difficult. A second aspect of optical spectroscopic techniques that makes them well suited for cell study is that the wavelengths of optical radiation in the visible and near-infrared spectrum are well matched to the size of structures within cells. Thus, LSS methods [209] can provide relevant information on their structure and function. Again, this aspect makes these techniques well suited for clinical applications where detailed analysis of cellular structure is otherwise impossible. However, there is also a need for such information in cell biology studies. In this case, these optical spectroscopy approaches have not been widely adopted because of the lack of high-throughput systems. For example, flow cytometry is widely used by many modern biomedical laboratories, primarily because sophisticated systems have been reduced to clinical instrumentation that can be operated by a technician. In order for the optical spectroscopic approaches discussed here to gain similar adoption, there is not only a need to provide easily operated systems but also to show that there is a compelling advantage over established technologies such as flow cytometry. Such advantages would include the ability to provide more precise information that was previously unavailable, but must also provide that information in a cost-effective, easily used format. Finally, the recent advances in developing enabling optical technologies, because of their widespread use for telecommunication, has provided a wealth of optical light sources, active and passive components, and Advances in Optics and Photonics 4, (2012) doi: /aop

41 Table 1. Summary of Optical Spectroscopic Techniques for Study of Biological Cells Technique Spectroscopic Method Key Applications LSS (LSS) Confocal LSS (CLASS) Fourier domain low-coherence interferometry (flci) Partial wave spectroscopy (PWS) RI (TAOS) Angle sensitive LSS (a/lss) Four-dimensional elastic LSS (4D-ELF) Angle-resolved low coherence interferometry (a/lci) Fourier holographic light scattering angular spectroscopy Optical scattering imaging Integrated Raman- and angular-scattering microscopy system (IRAM) Hilbert phase microscopy Motility contrast imaging Fourier transform light scattering (FTLS) Dispersion spectroscopy analysis Raman microspectroscopy Stimulated Raman scattering (SRS) Coherent anti Stokes Raman scattering (CARS) microscopy Transient absorption spectroscopy Wavelength-dependent elastic backscattering Confocal gating of localized wavelength-dependent elastic backscattering Coherence gating of wavelength-dependent elastic backscattering Wavelength-dependent analysis of reflected light Angle-dependent elastic scattering Angle-dependent elastic scattering of polarized light Combined analysis of wavelength, angle and polarization-dependent backscattering Coherence gating of angle-dependent elastic backscattering Spatially resolved measurements of angle-dependent scattering Selection of angle-dependent backscattering using Fourier plane masking Combination of angle-resolved elastic scattering with inelastic, Raman spectroscopy Spatial and temporal analysis of phase fluctuations Coherence gating of temporal intensity fluctuations Fourier transformed phase data Wavelength-dependent phase changes Wavelength-dependent inelastic scattering Pump probe technique for detecting inelastic scattering transitions Pump probe technique for observing anti-stokes (blueshifted) inelastic scattering Ultrafast measurement of absorption dynamics using pump probe techniques Detecting dysplastic cells within tissues by observing enlarged nuclei Observation of apoptosis via modified mitochondrion morphology; discrimination of fetal nucleated red blood cells Detecting dysplastic cells within thick tissues by observing enlarged nuclei Identifying diseased tissues by analysis of cytology samples Analysis of aerosols; detection of cell granularity Detecting dysplastic cells in ex vivo tissues Early detection of precancerous changes in colonic epithelium Detection of dysplastic cell in thick tissues by observing enlarged nuclei; detection of apoptosis by nuclear and mitochondrial fragmentation; detection of cell nuclei deformation due to environmental changes Determining size of red blood cells Observation of changes in mitochondrion morphology due to onset of apoptosis Identification of CD8+ lymphocytes Analysis of red blood cell pathology, particularly malarial infection Response of tumor spheroids to chemotherapeutic agents Analysis of histological sections Mapping RI and absorption profiles of cells Identification and localization of biomolecules within cells Imaging of specific biomolecule distributions within cells Fluorescence-free imaging of biomolecules within cells Detection of melanoma via discrimination of eumelanin from pheomelanin detectors that are easily accessible to researchers in the field. The availability of such resources permits simple, rapid prototyping of advanced optical spectroscopy schemes and has led to the development of a wide range of new techniques. As discussed above, for these techniques to be adopted, the pathway for translation and commercialization must be taken, but for many researchers in the field, and indeed for Advances in Optics and Photonics 4, (2012) doi: /aop

42 licensing companies and potential investors, it is not clear when such translation is warranted. In today s age of interdisciplinary research, there appears to be another need that has not been widely met: the focus of research on practical technologies which can be advanced by including commercialization experts on research teams. The optical spectroscopic methods shown here have significant potential to lead to important advances in cell biology and biomedicine. Further development of optical spectroscopy for application to the study of the cell continues in many research laboratories today and point to new avenues for generating fundamental knowledge and incisive tools in these fields. To realize this potential, successful translation of these novel technologies to common use is required. Indeed, the model of including physicians as collaborators to make clinical translation possible can be extended to form new types of collaboration to bring in the skills and knowledge of not just cell biologists but also entrepreneurs with expertise beyond that of scientists and engineers. Acknowledgments Support from the National Institutes of Health (R ) and the National Science Foundation (MRI and CBET ) is gratefully acknowledged. References and Notes 1. 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 microscopy, Appl. Opt. 46(10), (2007). 2. L. Perelman, V. Backman, M. Wallace, G. Zonios, R. Manoharan, A. Nusrat, S. Shields, M. Seiler, C. Lima, T. Hamano, I. Itzkan, J. Van Dam, J. M. Crawford, and M. S. Feld, Observation of periodic fine structure in reflectance from biological tissue: a new technique for measuring nuclear size distribution, Phys. Rev. Lett. 80(3), (1998). 3. H. C. van de Hulst, Light Scattering by Small Particles (Dover, New York, 1957). 4. V. Backman, R. Gurjar, K. Badizadegan, I. Itzkan, R. R. Dasari, L. T. Perelman, and M. S. Feld, Polarized light scattering spectroscopy for quantitative measurement of epithelial cellular structures in situ, IEEE J. Sel. Top. Quantum Electron. 5(4), (1999). 5. C. H. Yang, A. Wax, I. Georgakoudi, E. B. Hanlon, K. Badizadegan, R. R. Dasari, and M. S. Feld, Interferometric phase-dispersion microscopy, Opt. Lett. 25(20), (2000). 6. V. Backman, M. B. Wallace, L. T. Perelman, J. T. Arendt, R. Gurjar, M. G. Müller, Q. Zhang, G. Zonios, E. Kline, J. A. McGilligan, 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(6791), (2000). Advances in Optics and Photonics 4, (2012) doi: /aop

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56 Adam Wax, Ph.D., received dual B.S. degrees in 1993, one in electrical engineering from Rensselaer Polytechnic Institute, Troy, NY, and one in physics from the State University of New York at Albany, and the Ph.D. degree in physics from Duke University, Durham, NC, in He joined the George R. Harrison Spectroscopy Laboratory at the Massachusetts Institute of Technology as a postdoctoral fellow of the National Institutes of Health immediately after his doctorate. Dr. Wax joined the faculty of the Department of Biomedical Engineering at Duke University in the fall of In 2006, Dr. Wax founded Oncoscope, Inc. to commercialize early cancer detection technology developed in his laboratory. In 2010, he was named as Fellow of the Optical Society of America and SPIE, the International Society for Optics and Photonics. He is currently the Theodore Kennedy associate professor of biomedical engineering at Duke University and Chairman of Oncoscope, Inc. His research interests are in the use of light scattering and interferometry to probe the biophysical properties of cells for both diagnosis of disease and fundamental cell biology studies. Michael G. Giacomelli, Ph.D., attended the University of Arizona where he graduated cum laude in 2006 with Bachelor of Science degrees in Computer Engineering and in Computer Science. He received a Masters of Science in Electrical Engineering and his Ph.D. in Biomedical Engineering from Duke University. His research interests include light scattering, interferometry, and endoscopic imaging. Thomas E. Matthews, Ph.D., attended the University of Vermont, where he received a bachelor s of art in chemistry and a bachelor s of science in biochemistry in In 2011 he earned a Ph.D. in chemistry at Duke University. During his graduate work he developed novel nonlinear spectroscopy and imaging methods, with applications including detection of skin cancer based on chemical changes in melanins. Currently he is a postdoctoral researcher in biomedical engineering at Duke University, developing tissue Raman and interferometric light scattering methods. Matthew T. Rinehart is a Ph.D. student in biomedical engineering at Duke University, where he is also president of the OSA/SPIE student chapters. He attended Duke University as an undergraduate, receiving bachelor degrees in biomedical engineering and electrical engineering. His current research interests include the development of technology and techniques of quantitative phase microscopy and quantitative phase spectroscopy, as well as exploring the applications of these tools to measuring dynamic biological systems. Advances in Optics and Photonics 4, (2012) doi: /aop

57 Francisco E. Robles, Ph.D., was born and raised in Mexico City. In 2007, he graduated summa cum laude from North Carolina State University with two Bachelor of Science degrees, one in Physics and another in Nuclear Engineering, and a minor in Mathematics. In 2011, he received his Ph.D. from Duke University in Medical Physics under the supervision of Dr. Adam Wax. Currently he is a Postdoctoral Associate at Duke University under the supervision of Dr. Warren S. Warren. His research interests are in novel microscopy methods that provide functional and molecular contrast in order to gain new insight into various diseases to achieve earlier and better diagnosis. Yizheng Zhu, Ph.D., obtained his doctorate in Electrical Engineering from Virginia Tech in 2007, where he developed micro/nano fiber-optic sensors for a variety of industrial applications. He received postdoctoral training in the Department of Biomedical Engineering at Duke University, where he is currently a research scientist. His research interests include novel fiber-optic systems for biomedical imaging, optical microscopy, and biosensing. He is the author or co-author of more than 40 journal and proceedings articles. Advances in Optics and Photonics 4, (2012) doi: /aop