We present an optical holographic micro-tomographic technique for imaging both the three-dimensional structures and dynamics of biological cells. Optical light field images of a sample, illuminated by a plane wave with various illumination angles, are measured in a common-path interferometry, and thus both the three-dimensional refractive index tomogram and two-dimensional dynamics of live biological cells are measured with extremely high sensitivity. The applicability of the technique is demonstrated through quantitative and measurements of morphological, chemical, and mechanical parameters at the individual cell level.
© 2014 Optical Society of America
In the last decade, there have been significant advances in quantitative phase imaging (QPI) techniques, which have potential for diverse applications in various research fields [1–3]. Due to its non-invasiveness and quantitative and label-free imaging capability, QPI has played important roles in several emerging biophysical studies including the pathophysiology of human red blood cells (RBCs) [4–9], the live cell imaging , the investigation of bacteria  and neuron cells , the measurements of angle resolved light scattering from individual cells [13–16], and the measurements of cellular growth and division [17, 18].
QPI techniques based on a common-path interferometry design have recently achieved extremely high phase-sensitivity [19–24]; sensitivity of optical path lengths of about a few milliradian can be realized, enabling quantification of subtle cell membrane motions [25, 26]. One the other hand, tomographic QPI techniques have been employed to measure the 3D tomograms of individual biological samples [27–37]. Tomographic QPI techniques, typically based on a Mach-Zehnder interferometer, measure multiple 2D optical fields with various illumination angles to reconstruct the 3D tomogram of a sample based on appropriate algorithms. These two technical advances have significantly extended the applications of the QPI including measurement of the 3D structures of living cells and the dynamic fluctuation in cell membranes [5–7, 38].
None of the existing techniques, however, precisely measure both 3D tomograms and 2D dynamics of live individual biological cells using a single optical setup. Current common-path QPI techniques can measure the 2D dynamic phase images of a sample with high stability. However, the measured phase images are translated into physical thickness information assuming the refractive index (RI) of the sample, because the RI of the sample should be independently measured. Existing QPI tomography methods typically employ Mach-Zehnder interferometry and suffer from phase noise, which impedes stable measurement of a sample with high stability. One of the unexplored applications of QPI is to simultaneously measure the local RI of individual cells and the dynamic membrane fluctuation of the cells at the single cell level. This can offer the possibility of investigating the subtle alterations associated with pathophysiology of several diseases at the single cell level . Although there is strong motivation for single cell profiling, achieving a full-field common-path tomographic phase imaging technique has been regarded as technically challenging. To fully profile individual cells, morphological, chemical, and mechanical parameters should be quantified at the individual cell level, which requires measuring both the 3-D RI tomogram and dynamic fluctuations at the individual cell level. For this purpose, optical instruments should provide 3-D RI tomography capability while providing common-path interferometry for highly stable dynamic phase measurement.
Here, a novel common-path quantitative phase tomography, referred to as common-path diffraction optical tomography (cDOT), is presented to measure both the 3D RI tomogram and 2D dynamic phase images of a sample. The angle of the beam impinging onto a sample is scanned over a wide range for tomographic measurements, and the beam diffracted from the sample is de-scanned to ensure common-path interferometry. This non-invasive and label-free technique simultaneously characterizes the 3D structures and 2D dynamics of individual cells. We demonstrate the capability of cDOT by measuring morphological, chemical, and mechanical parameters of healthy human red blood cells (RBCs) at the single cell level. In addition, we also show the tomographic image of a hepatocyte cell as a model eukaryotic cell.
2. Methods and results
2.1. Common-path diffraction optical tomography
The experimental scheme of cDOT is based on the principles of common-path laser-interferometric microscopy and optical diffraction tomography (Fig. 1). A sample, which is positioned between the condenser lens (CL) and objective lens (OL), is illuminated using a spatially filtered plane-wave laser beam with specific angles of illumination. The angle of the impinging beam is systematically controlled by rotating a two-axis galvanometer mirror (GM1). GM2, which is located at the conjugated plane to the sample and GM1, is synchronized with GM1 such that the angle of the beam reflected from GM2 remains unchanged regardless of the illumination angle. Then, after passing through the sample, the beam can be precisely quantified using a common-path interferometric microscope using the principle of diffraction phase microscopy, the details of which can be found elsewhere [19, 20]. Finally, a spatially modulated hologram is recorded onto an image sensor from which a full-field optical-field image with both amplitude and phase information is quantitatively retrieved using a phase retrieval algorithm .
The synchronized angle-scanning and common-path interferometry are finely tuned to work together, which allows simultaneous measurement of the 3D RI tomography and the dynamic 2D membrane fluctuations of individual biological cells. By changing the angles of the illumination impinging on the sample, cDOT measures multiple 2D optical fields with different illumination angles from which 3D RI tomograms of the sample are reconstructed using optical diffraction tomography [41–43]. In order to measure the 2D dynamic images of the cell, the illumination angle is fixed at normal to the sample and cDOT can measure high-speed dynamic 2D optical fields from which the dynamic fluctuations in the RBC membrane can be retrieved.
2.2. Experimental setup
A diode-pumped solid-state (DPSS) laser (λ = 532 nm, 50 mW, Cobolt, Solna, Sweden) was used as an illumination source for an inverted microscope (IX73, Olympus Inc., Center Valley, PA, USA). The laser beam was first spatially filtered by a pinhole with a diameter of 25 μm. The collimated laser beam was steered by a two-axis galvanometric mirror (GM1, GVS012/M, Thorlabs, USA), and then projected onto a sample plane via a 4-f telescopic lens system composed of a lens (L1) and a condenser lens [CL, UPLFLN 60 × , numerical aperture (NA) = 0.9, Olympus, Japan] with a tube lens (f = 200 mm). A sample was prepared and sandwiched between two cover glasses separated by a thin spacer of double side tape. At the sample plane, the illumination angle of the beam can be rapidly scanned by GM1. The diffracted beam from the sample was collected by a high-NA objective lens (OL, UPLSAPO 60 × , oil immersion, NA = 1.42, Olympus, Japan).
To maintain an optical axis for common-path interferometry, the second two-axis galvanometric mirror (GM2, GVS012/M, Thorlabs, USA) was synchronized with GM1. The mirror of GM2 rotates exactly as much as rotated by GM1 but in the opposite direction, such that the beam reflected from GM2 maintains an optical axis regardless of the illumination angle at the sample plane. GM1 and GM2 are placed at the conjugate imaging plane of the sample. Thus, the rotation angle of the GM1 is linearly related with that of GM2. To physically synchronize the galvo mirrors, we first set the rotation angle of GM1 to be the maximum in the kx axis which is determined by the NA of two objective lenses. Then, we set the angle of GM2 so that the beam reflected from GM2 maintains unchanged. After repeating this procedure for both the kx and ky axes, we can synchronize the rotation angle of the galvano mirror.
The optical field of the diffracted beam is quantitatively and precisely measured by a common-path interferometry setup. Here, cDOT employs the principle of diffraction phase microscopy to construct a common-path interferometry. The beam from a sample is diffracted by a transmission grating (70 grooves mm−1, #46-067, Edmund Optics Inc., NJ, USA). Among several orders of diffracted beams, only the 0th and 1st orders of the diffracted beams are used and the others are blocked. The 0th order diffraction beam is spatially filtered by a 4-f lens system with a spatial filter to serve as a reference plane wave at the image plane. The 1st order beam is directly projected onto the image plane. At the image plane, the sample and reference beams interfere with a small angle difference defined by the spatial period of the grating and the 4-f lens system, forming spatially-modulated interferograms. The interferograms are recorded by a scientific complementary metal-oxide semiconductor camera (Neo sCMOS, ANDOR Inc., Northern Ireland, UK) with a pixel size of 6.5 μm and × 240 total magnification of the imaging system.
2.3. Verification of the angle synchronization in cDOT
In cDOT, GM1 is used to vary the angle of the illumination beam impinging onto a sample. For a common-path interferometry, GM2 is synchronized with GM1 such that the optical axis of the beam passing the sample remains unchanged. To verify the synchronization between GM1 and GM2, representative logarithmic Fourier spectra of the measured optical fields with representative illumination angles are shown in Figs. 2(a) to 2(d). The Fourier spectra were obtained by 2D Fourier transform of the measured optical field images. The maximum intensity of the Fourier spectra corresponds to an unscattered light field, and thus the position that yields the maximum intensity indicates the spatial frequency of the unscattered light field. As can be seen in Fig. 2(b), when the angle of the illumination beam is set to 0°, the position of the maximum intensity is located at the exact center, or the DC point in the Fourier spectra. When the illumination angle is changed to −30° or 30° [Figs. 2(a) or 2(c)], the maximum intensity is still located at the DC point. This is because GM2 compensates the angle of the beam path to the same degree that the angle is rotated by GM1. When synchronized, the beam will pass the pinhole between two lenses (L7 & 8) and the beam intensity will be high at the camera plane; otherwise, if it is not synchronized, the beam will not pass through the pinhole and will have low intensity, as shown in Fig. 2(d).
Figure 2(e) shows the maximum intensity of Fourier spectra as a function of the illumination angle controlled by GM1. Over a range of illumination angles from −43° to 43° (inside the medium), high light intensity was observed. Regardless of the illumination angle, the maxima of the Fourier spectra are found at the center of the spectra, which indicates that GM1 and GM2 are precisely synchronized with each other. The temporal changes in the intensity with the fixed angle illumination only vary within 0.8%. This demonstrates that over this angle range the two galvanometric mirrors (GM1 & 2) are precisely synchronized with each other such that the common path interferometry works for various illumination angles. This angle range is comparable with the upper limit determined by the NA of the condenser and objective lenses. This shows that the two galvanometric mirrors are synchronized sufficiently to fully utilize the NA of the condenser and objective lenses.
2.4. 3-D tomographic reconstruction of refractive index
To verify the 3D imaging capability of cDOT, we first measured the 3D RI tomogram of a polystyrene microsphere. Multiple optical fields of a sample illuminated with various angles were recorded in cDOT, from which the 3D RI tomograms of the sample were reconstructed using an optical diffraction tomography algorithm. The detailed optical diffraction tomography algorithm for retrieving the 3D RI map of a sample can be found elsewhere [28, 41]. In short, the spatial frequencies of diffracted optical fields from a sample at a specific illumination angle are mapped onto a hemispheric surface in the frequency domain called an Ewald sphere. Multiple 2D optical fields with various illumination angles are mapped onto multiple Ewald spheres with corresponding translations in Fourier space, resulting in 3D Fourier spectra. The inverse 3D Fourier transform of the Fourier spectra then provides the 3D RI tomogram of a sample. Due to the weak scattering nature of most biological cells, the first Rytov approximation is applied to simplify the relationship between incident and scattered light fields in the optical diffraction tomography algorithm. Unlike conventional optical diffraction tomography, cDOT considers the rotation of diffracted fields due to the rotation of GM2. The optical field from each illumination is rotated on the surface of the Ewald sphere corresponding to the angle of incident illumination; this can be approximated as the translation of an optical field in a 2-D plane because the rotation angle is less than 1° due to the angular de-magnification at the plane of GM2. Due to the limited accepted angle of the optical system, reconstructed 3-D Fourier spectra have missing information which is so-called the missing cone. This missing cone information was filled using the iterative non-negativity algorithm [29, 30, 43].
In order to obtain 3D RI tomograms of polystyrene beads, cDOT measures multiple 2D optical-field images of the sample with different angles of illumination; the 3D RI tomogram of the sample is then reconstructed using a diffraction optical tomography algorithm . Typically 300 holograms are recorded while the illumination angles draw a spiral trajectory within a range from −35° to 35° in the 2-D sample plane (inside the medium). The total measurement time is less than 0.5 s. The 3D RI tomogram of the polystyrene microsphere with a diameter of 3 μm (79166, Sigma-Aldrich Inc., USA) submersed in immersion oil (noil = 1.518 at λ = 532 nm) is presented in Fig. 3. Cross-sectional views of the 3D RI tomogram of the bead demonstrate the high resolution capability of cDOT, and both the morphology and the RI values of the beads show good agreement with expected values and previous reports [41, 43].
2.5. 3D RI maps and dynamic membrane fluctuations of a RBC
In order to obtain 3D RI tomograms of individual RBCs, RBCs from a healthy individual were prepared according to the standard protocol , and then diluted in Dulbecco`s Phosphate Buffered Saline (DPBS, nDPBS = 1.337 at λ = 532 nm) before the measurement. The same measurement procedure as used to measure the polystyrene microsphere with cDOT was performed. The 3D RI tomograms of a RBC from a healthy individual are presented in Fig. 4. Cross-sectional views of the measured 3D RI tomograms are presented in Figs. 4(a) to 4(c). The RI values of the cytoplasm of a RBC can be directly translated into the Hb concentration, because the cytoplasm of a RBC mainly consists of Hb solution. The RI tomograms of RBCs from the healthy individual exhibited the characteristic biconcave shape. The reconstructed morphologies and the RI values exhibit good agreement with the known reference range . The image shown in Fig. 4(d) is a rendered isosurface of the 3D RI tomogram of the RBC, where rendering was performed using commercial software (Amira 5, Visage Imaging Inc., San Diego, CA, USA).
Furthermore, quantitative mechanical information related to the cell deformability is simultaneously obtained by measuring dynamic fluctuations in the membrane of the RBC. To probe the dynamic membrane fluctuation, 256 optical field images of a RBC with a normal incident illumination beam were recorded at a temporal resolution of 125 frames/s. From the measured optical field images, the instantaneous phase delay, Δϕ(x,y;t), is retrieved using the following relation with the cell height profile, h(x,y;t):
Figure 4(e) presents the dynamic membrane fluctuations in several locations in the RBC, as indicated in Figs. 4(a) to 4(d). A position on the convex area of the RBC (position A) and a position on the dimple area of the cell (position B) exhibits mean fluctuation of 41 nm and 54 nm, respectively. These values of membrane fluctuations are in good agreements with previous reports [4, 45]. A position in the background exhibits fluctuation of 6.8 nm, demonstrating the high sensitivity of the common-path interferometry.
2.6. 3D RI maps of hepatocytes
To extend the applicability of cDOT for various biological samples, we measured 3D RI tomograms of individual hepatocyte cells. Hepatocyte cells (Huh-7 cell line, Apath, Brooklyn, NY, USA) were prepared according to the standard protocol . In brief, Huh-7 cells were maintained in Dulbecco's Modified Eagle Medium (DMEM, Gibco, Big Cabin, Oklahoma, USA) supplemented with 10% heat-inactivated fetal bovine serum, 4500 mg/L D-glucose, L-glutamine, 110 mg/L sodium pyruvate, sodium bicarbonate, 100 U/mL penicillin, and 100 μg/mL streptomycin. The cells were subcultivated for 4 hours before experiments, and then diluted in Dulbecco’s phosphate buffered saline (DPBS, nDPBS = 1.337 at λ = 532 nm) before measurements. The same cDOT measurement procedures were performed as previously described. The 3D RI tomograms of a hepatocyte cell in DPBS buffer are presented in Fig. 5. The spherical objects having relatively high RI inside cytoplasm are subcellular organelles. The reconstructed RI tomogram measured by cDOT shows similar quality with that measured by Mach-Zehnder-type quantitative phase tomography . Although the hepatocyte cell is relatively thin, cDOT is capable to resolve the internal structures.
3. Discussions and conclusions
This paper presents a precise and sensitive optical holographic technique that is well suited for studying biological alterations at the single cell level. By integrating optical diffraction tomography into a common-path interferometer, we demonstrate that cDOT can measure the 3D RI tomography as well as membrane dynamic fluctuations of individual biological cells. 3D RI tomograms of individual biological samples are obtained in diffraction-limited resolution. Furthermore, the dynamic membrane fluctuations of the cells were simultaneously measured with sub-10-nm sensitivity. We demonstrate the capability of cDOT by measuring both 3D RI tomograms and the dynamic membrane fluctuation of individual human RBCs. The present method employs a pair of synchronized galvanometer mirrors, which enabled common-path optical diffraction tomography. This approach can also be combined with other modalities in quantitative phase imaging including spectroscopic measurement [47–51], polarization-sensitive phase imaging [52, 53], and reflection geometry .
The primary advantage of cDOT is its ability to analyze single cell profiles of RBCs, and cDOT is expected to find immediate applications in hematology. cDOT can provide not only routine red cell indices (such as cell volume, Hb contents, and Hb concentration) but also other indices such as sphericity, surface area, and membrane fluctuation. The structural, chemical, and mechanical information of RBCs is important for hemodiagnosis, and cDOT can potentially be used to simultaneously measure these parameters at the individual cell level. Although only healthy RBCs were examined to demonstrate the proof of principle, this method is readily applicable to other RBC-related diseases such as malaria, sickle cell disease, and thalassemia [8, 16, 55, 56]. Furthermore, we demonstrated that cDOT can also be utilized for other eukaryotic cell type. In order for cDOT to be extended to clinical applications, high-throughput measurement capability is necessary. Currently, the measurement time for probing one cell is nearly 3 sec, but this time is not limited. By employing the principles of flow cytometry and optimizing the measurement instruments, the measurement speed can be significantly enhanced. Furthermore, cDOT can also be applied to other cell types including white blood cells, circulating tumor cells, and endothelial cells for biophysical and medical purposes.
The authors thank K.R. Lee for helpful discussions. This work was supported by KAIST, the Korean Ministry of Education, Science and Technology (BRL 2009-0087691), and National Research Foundation (NRF) of Korea (2012R1A1A1009082, 2012K1A3A1A09055128, M3C1A1-048860, 2013M3C1A3000499, NRF-2012-M3C1A1-048860, 2013R1A1A3011886). Y.P. acknowledges support from TJ ChungAm Foundation. K.K. is supported by Global Ph.D. Fellowship from NRF.
References and links
1. G. Popescu, Quantitative Phase Imaging of Cells and Tissues (McGraw-Hill Professional, 2011).
2. K. Lee, K. Kim, J. Jung, J. H. Heo, S. Cho, S. Lee, G. Chang, Y. J. Jo, H. Park, and Y. K. Park, “Quantitative phase imaging techniques for the study of cell pathophysiology: from principles to applications,” Sensors (Basel) 13(4), 4170–4191 (2013). [CrossRef] [PubMed]
3. M. K. Kim, Digital Holography and Microscopy: Principles, Techniques, and Applications (Springer Verlag, 2011), Vol. 162.
4. G. Popescu, Y. Park, W. Choi, R. R. Dasari, M. S. Feld, and K. Badizadegan, “Imaging red blood cell dynamics by quantitative phase microscopy,” Blood Cells Mol. Dis. 41(1), 10–16 (2008). [CrossRef] [PubMed]
5. Y. K. Park, C. A. Best, K. Badizadegan, R. R. Dasari, M. S. Feld, T. Kuriabova, M. L. Henle, A. J. Levine, and G. Popescu, “Measurement of red blood cell mechanics during morphological changes,” Proc. Natl. Acad. Sci. U.S.A. 107(15), 6731–6736 (2010). [CrossRef] [PubMed]
6. Y. Park, C. A. Best, T. Kuriabova, M. L. Henle, M. S. Feld, A. J. Levine, and G. Popescu, “Measurement of the nonlinear elasticity of red blood cell membranes,” Phys. Rev. E Stat. Nonlin. Soft Matter Phys. 83(5), 051925 (2011). [CrossRef] [PubMed]
7. Y. K. Park, M. Diez-Silva, G. Popescu, G. Lykotrafitis, W. S. Choi, M. S. Feld, and S. Suresh, “Refractive index maps and membrane dynamics of human red blood cells parasitized by Plasmodium falciparum,” Proc. Natl. Acad. Sci. U.S.A. 105(37), 13730–13735 (2008). [CrossRef] [PubMed]
8. N. T. Shaked, L. L. Satterwhite, M. J. Telen, G. A. Truskey, and A. Wax, “Quantitative microscopy and nanoscopy of sickle red blood cells performed by wide field digital interferometry,” J. Biomed. Opt. 16, 030506 (2011).
9. B. Rappaz, A. Barbul, A. Hoffmann, D. Boss, R. Korenstein, C. Depeursinge, P. J. Magistretti, and P. Marquet, “Spatial analysis of erythrocyte membrane fluctuations by digital holographic microscopy,” Blood Cells Mol. Dis. 42(3), 228–232 (2009). [CrossRef] [PubMed]
10. B. Kemper, A. Vollmer, C. E. Rommel, J. Schnekenburger, and G. von Bally, “Simplified approach for quantitative digital holographic phase contrast imaging of living cells,” J. Biomed. Opt. 16, 026014 (2011).
11. B. Rappaz, E. Cano, T. Colomb, J. Kühn, V. Simanis, P. J. Magistretti, P. Marquet, and C. Depeursinge, “Noninvasive characterization of the fission yeast cell cycle by monitoring dry mass with digital holographic microscopy,” J. Biomed. Opt. 14, 034049 (2009).
12. P. Jourdain, N. Pavillon, C. Moratal, D. Boss, B. Rappaz, C. Depeursinge, P. Marquet, and P. J. Magistretti, “Determination of transmembrane water fluxes in neurons elicited by glutamate ionotropic receptors and by the cotransporters KCC2 and NKCC1: a digital holographic microscopy study,” J. Neurosci. 31(33), 11846–11854 (2011). [CrossRef] [PubMed]
13. H. Ding, E. Berl, Z. Wang, L. J. Millet, M. U. Gillette, J. Liu, M. Boppart, and G. Popescu, “Fourier Transform Light Scattering of Biological Structure and Dynamics,” IEEE J. Sel. Top. Quantum Electron. 16(4), 909–918 (2010). [CrossRef]
14. Y. K. Park, C. A. Best-Popescu, R. R. Dasari, and G. Popescu, “Light scattering of human red blood cells during metabolic remodeling of the membrane,” J. Biomed. Opt. 16(1), 011013 (2011). [CrossRef] [PubMed]
15. Y. K. Park, M. Diez-Silva, D. Fu, G. Popescu, W. Choi, I. Barman, S. Suresh, and M. S. Feld, “Static and dynamic light scattering of healthy and malaria-parasite invaded red blood cells,” J. Biomed. Opt. 15(2), 020506 (2010). [CrossRef] [PubMed]
17. G. Popescu, Y. Park, N. Lue, C. Best-Popescu, L. Deflores, R. R. Dasari, M. S. Feld, and K. Badizadegan, “Optical imaging of cell mass and growth dynamics,” Am. J. Rhysiology Cell Physiol. 295, 538–544 (2008).
18. M. Mir, Z. Wang, Z. Shen, M. Bednarz, R. Bashir, I. Golding, S. G. Prasanth, and G. Popescu, “Optical measurement of cycle-dependent cell growth,” Proc. Natl. Acad. Sci. U.S.A. 108(32), 13124–13129 (2011). [CrossRef] [PubMed]
21. V. Chhaniwal, A. S. G. Singh, R. A. Leitgeb, B. Javidi, and A. Anand, “Quantitative phase-contrast imaging with compact digital holographic microscope employing Lloyd’s mirror,” Opt. Lett. 37(24), 5127–5129 (2012). [CrossRef] [PubMed]
22. V. Mico, Z. Zalevsky, and J. García, “Common-path phase-shifting digital holographic microscopy: a way to quantitative phase imaging and superresolution,” Opt. Commun. 281(17), 4273–4281 (2008). [CrossRef]
24. S. Bernet, A. Jesacher, S. Fürhapter, C. Maurer, and M. Ritsch-Marte, “Quantitative imaging of complex samples by spiral phase contrast microscopy,” Opt. Express 14(9), 3792–3805 (2006). [CrossRef] [PubMed]
25. Y. K. Park, C. A. Best, and G. Popescu, “Optical sensing of red blood cell dynamics,” in Mechanobiology of Cell-cell and Cell-matrix Interactions (Springer, 2011), p. 279.
26. S. Oh, C. Fang-Yen, W. Choi, Z. Yaqoob, D. Fu, Y. Park, R. R. Dassari, and M. S. Feld, “Label-free imaging of membrane potential using membrane electromotility,” Biophys. J. 103(1), 11–18 (2012). [CrossRef] [PubMed]
27. G. G. Levin, G. N. Vishnyakov, C. S. Zakarian, A. V. Likhachov, V. V. Pickalov, G. I. Kozinets, J. K. Novoderzhkina, and E. A. Streletskaya, “Three-dimensional limited-angle microtomography of blood cells: experimental results,” in Proceedings of SPIE, 1998), 159.
28. V. Lauer, “New approach to optical diffraction tomography yielding a vector equation of diffraction tomography and a novel tomographic microscope,” J. Microsc. 205(2), 165–176 (2002). [CrossRef] [PubMed]
30. K. Kim, H. Yoon, M. Diez-Silva, M. Dao, R. R. Dasari, and Y. Park, “High-resolution three-dimensional imaging of red blood cells parasitized by Plasmodium falciparum and in situ hemozoin crystals using optical diffraction tomography,” J. Biomed. Opt. 19(1), 011005 (2014). [CrossRef] [PubMed]
31. M. Debailleul, V. Georges, B. Simon, R. Morin, and O. Haeberlé, “High-resolution three-dimensional tomographic diffractive microscopy of transparent inorganic and biological samples,” Opt. Lett. 34(1), 79–81 (2009). [CrossRef] [PubMed]
34. T. Kim, R. J. Zhou, M. Mir, S. D. Babacan, P. S. Carney, L. L. Goddard, and G. Popescu, “White-light diffraction tomography of unlabelled live cells,” Nat. Photonics 8(3), 256–263 (2014). [CrossRef]
35. Z. Wang, D. L. Marks, P. S. Carney, L. J. Millet, M. U. Gillette, A. Mihi, P. V. Braun, Z. Shen, S. G. Prasanth, and G. Popescu, “Spatial light interference tomography (SLIT),” Opt. Express 19(21), 19907–19918 (2011). [CrossRef] [PubMed]
36. M. Mir, S. D. Babacan, M. Bednarz, M. N. Do, I. Golding, and G. Popescu, “Visualizing Escherichia coli Sub-Cellular Structure Using Sparse Deconvolution Spatial Light Interference Tomography,” PLoS ONE 7(6), e39816 (2012). [CrossRef] [PubMed]
37. F. Charrière, N. Pavillon, T. Colomb, C. Depeursinge, T. J. Heger, E. A. Mitchell, P. Marquet, and B. Rappaz, “Living specimen tomography by digital holographic microscopy: morphometry of testate amoeba,” Opt. Express 14(16), 7005–7013 (2006). [CrossRef] [PubMed]
38. Y. K. Park, C. A. Best, T. Auth, N. S. Gov, S. A. Safran, G. Popescu, S. Suresh, and M. S. Feld, “Metabolic remodeling of the human red blood cell membrane,” Proc. Natl. Acad. Sci. U.S.A. 107(4), 1289–1294 (2010). [CrossRef] [PubMed]
41. K. Kim, K. S. Kim, H. Park, J. C. Ye, and Y. Park, “Real-time visualization of 3-D dynamic microscopic objects using optical diffraction tomography,” Opt. Express 21(26), 32269–32278 (2013). [CrossRef] [PubMed]
42. E. Wolf, “Three-dimensional structure determination of semi-transparent objects from holographic data,” Opt. Commun. 1(4), 153 (1969). [CrossRef]
43. Y. J. Sung, W. Choi, C. Fang-Yen, K. Badizadegan, R. R. Dasari, and M. S. Feld, “Optical diffraction tomography for high resolution live cell imaging,” Opt. Express 17(1), 266–277 (2009). [CrossRef] [PubMed]
44. K. Kaushansky, Williams Hematology (McGraw-Hill Medical New York, 2010).
45. Y. Kim, K. Kim, and Y. Park, “Measurement Techniques for Red Blood Cell Deformability: Recent Advances,” in Blood Cell - An Overview of Studies in Hematology, T. E. Moschandreou, ed. (INTECH, 2012), pp. 167–194.
46. J. Park, W. Kang, S. W. Ryu, W. I. Kim, D. Y. Chang, D. H. Lee, Y. Park, Y. H. Choi, K. Choi, E. C. Shin, and C. Choi, “Hepatitis C virus infection enhances TNFα-induced cell death via suppression of NF-κB,” Hepatology 56(3), 831–840 (2012). [CrossRef] [PubMed]
47. Y. Park, T. Yamauchi, W. Choi, R. Dasari, and M. S. Feld, “Spectroscopic phase microscopy for quantifying hemoglobin concentrations in intact red blood cells,” Opt. Lett. 34(23), 3668–3670 (2009). [CrossRef] [PubMed]
48. Y. Jang, J. Jang, and Y. Park, “Dynamic spectroscopic phase microscopy for quantifying hemoglobin concentration and dynamic membrane fluctuation in red blood cells,” Opt. Express 20(9), 9673–9681 (2012). [CrossRef] [PubMed]
50. J. H. Jung, J. Jang, and Y. Park, “Spectro-refractometry of individual microscopic objects using swept-source quantitative phase imaging,” Anal. Chem. 85(21), 10519–10525 (2013). [CrossRef] [PubMed]
53. Y. Kim, J. Jeong, J. Jang, M. W. Kim, and Y. Park, “Polarization holographic microscopy for extracting spatio-temporally resolved Jones matrix,” Opt. Express 20(9), 9948–9955 (2012). [CrossRef] [PubMed]
54. C. Edwards, A. Arbabi, G. Popescu, and L. L. Goddard, “Optically monitoring and controlling nanoscale topography during semiconductor etching,” Light: Sci. Appl. 1(9), e30 (2012). [CrossRef]
55. H. S. Byun, T. R. Hillman, J. M. Higgins, M. Diez-Silva, Z. Peng, M. Dao, R. R. Dasari, S. Suresh, and Y. K. Park, “Optical measurement of biomechanical properties of individual erythrocytes from a sickle cell patient,” Acta Biomater. 8(11), 4130–4138 (2012). [CrossRef] [PubMed]