Increasing interest in the role of lipids in cancer cell proliferation and resistance to drug therapies has motivated the need to develop better tools for cellular lipid analysis. Quantification of lipids in cells is typically done by destructive chromatography protocols that do not provide spatial information on lipid distribution and prevent dynamic live cell studies. Methods that allow the analysis of lipid content in live cells are therefore of great importance. Using micro-Raman spectroscopy and coherent anti-Stokes Raman scattering (CARS) microscopy, we generated a lipid profile for breast (T47D, MDA-MB-231) and prostate (LNCaP, PC3) cancer cells upon exposure to medroxyprogesterone acetate (MPA) and synthetic androgen R1881. Combining Raman spectra with CARS imaging, we can study the process of hormone-mediated lipogenesis. Our results show that hormone-treated cancer cells T47D and LNCaP have an increased number and size of intracellular lipid droplets and higher degree of saturation than untreated cells. MDA-MB-231 and PC3 cancer cells showed no significant changes upon treatment. Principal component analysis with linear discriminant analysis of the Raman spectra was able to differentiate between cancer cells that were treated with MPA, R1881, and untreated.
A single exposure of digital Gabor holography (DGH) is used for simultaneous three-dimensional measurement of
particle position. The particle sample is set up such that its position can be electro-mechanically manipulated using
calibrated piezoelectric transducers in both the lateral and axial directions. The central position of the reconstructed
image of the particle is determined by low-pass filtering, thresholding, and center-of-mass calculation. We have obtained
less than 20 nm resolution in both the lateral and axial directions in a direct and unambiguous manner. The method is
applied to calibration of optical trap strength.
It has recently been demonstrated that diode laser bars can be used to not only optically trap red blood cells in flowing
microfluidic systems but also, stretch, bend, and rotate them. To predict the complex cell behavior at different locations
along a linear trap, 3D optical force characterization is required. The driving force for cells or colloidal particles within
an optical trap is the thermal Brownian force where particle fluctuations can be considered a stochastic process. For
optical force quantification, we combine diode laser bar optical trapping with Gabor digital holography imaging to
perform subpixel resolution measurements of micron-sized particles positions along the laser bar. Here, diffraction
patterns produced by trapped particles illuminated by a He-Ne laser are recorded with a CMOS sensor at 1000 fps where
particle beam position reconstruction is performed using the angular spectrum method and centroid position detection.
3D optical forces are then calculated by three calibration methods: the equipartition theorem, Boltzmann probability
distribution, and power spectral density analysis for each particle in the trap. This simple approach for 3D tracking and
optical control can be implemented on any transmission microscope by adding a laser beam as the illumination source
instead of a white light source.
We report red blood cell (RBC) stretching using a Zeiss Axioplan microscope, modified for phase contrast and optical
trapping using a 808 nm diode laser bar, as a tool to characterize RBC dynamics along a linear optical trap. Phase
contrast offers a convenient method of converting small variations of refractive index into corresponding amplitude
changes, differentially enhancing the contrast near cell edges. We have investigated the behavior of RBCs within both
static and dynamic microfluidic environments with a linear optical stretcher. Studies within static systems allow
characterization of cell interactions with the line optical force field without the complicating forces associated with
hydrodynamics. In flowing, dynamic systems, cells stretch along the optical trap down microfluidic channels and are
eventually released to recover their original shape. We record the dynamic cell response with a CMOS camera at 250 fps
and extract cell contours with sub-pixel accuracy using derivative operators. To quantify cell deformability, we measure
the major and minor axes of individual cells both within and outside of the trap, which also allows measurement of cell
relaxation. In these studies, we observe that cell rotation, stretching, and bending along the linear optical trap, are tightly
coupled to the modulation of optical power and cell speed inside our microfluidic systems.
We present three-dimensional imaging of artificial fingerprints using the Digital Interference Holography (DIH) scanner.
DIH is based on a multiwavelength optical sensing technique that can be used to build holographically the three
dimensional structure of the fingerprints.
Many holograms (~50) were acquired by a CCD camera by scanning a range of wavelengths. Each hologram was
numerically reconstructed and then superposed yielding tomographic images which represented the artificial fingerprint
structure. The axial resolution is a parameter that depends on the wavelength scanning range and is about 5 μm. The
light source was a solid state pumped dye laser with a tunable wavelength range of 550 nm to 600 nm. Holograms were
captured by a monochrome CCD camera (Sony XC-ST50, with 780 × 640 pixels and a pixel size of ~ 9 μm). An image
acquisition board (NI IMAQ PCI-1407) digitized the image with 8 bit resolution. All software was developed in house
with the NI LabView.
We used a Michelson interferometer in a backscattering geometry and the reconstruction of the optical field was done
using the angular spectrum algorithm.
Our goal is to identify and quantify, Level 1 (pattern), Level 2 (minutia points), and Level 3 (ridge contours) features
from the amplitude images, using the DIH technique and fingerprints recognition. The results could be used in the two
fingerprint matching phases, identification and verification.
An improved digital interference holography (DIH) technique suitable for fundus images is proposed. This technique
incorporates a dispersion compensation algorithm to compensate for the unknown axial length of the eye. Using this
instrument we acquired successfully tomographic fundus images in human eye with narrow axial resolution less than 5μm. The optic nerve head together with the surrounding retinal vasculature were constructed. We were able to quantify a
depth of 84μm between the retinal fiber and the retinal pigmented epithelium layers. DIH provides high resolution 3D
information which could potentially aid in guiding glaucoma diagnosis and treatment.