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Determining nuclear morphology using an improved angle-resolved low coherence interferometry system

John W. Pyhtila, Robert N. Graf, and Adam Wax

Open Access Open Access

Abstract

We outline the process for determining the morphology of subsurface epithelial cell nuclei using depth-resolved light scattering measurements. The measurements are accomplished using a second generation angle-resolved low coherence interferometry system. The new system greatly improves data acquisition and analysis times compared to the initial prototype system. The calibration of the new system is demonstrated in scattering studies to determine the size distribution of polystyrene microspheres in a turbid sample. The process for determining the size of cell nuclei is discussed by analyzing measurements of basal cells in a sub-surface layer of intact, unstained epithelial tissue.

©2003 Optical Society of America

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Figures (13)
Fig. 1.
Fig. 1. (a). Schematic of new a/LCI system. L1-L5 - lenses, BS1,BS2 - beamsplitters, D1,D2 - balanced detectors, RR - retroreflector. Shaded area shows scattered light. (b) Diagram illustrating angle selectivity by scanning lens L2 perpendicular to the beam. Light at the top of the collimated beam entering L3 is directed to the detector in the top figure. Light at the center is directed to the detector in the bottom figure.

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Fig. 2. (a)
Fig. 2. (a) Contour plot showing angular scattering by suspension of polystyrene microspheres as a function of depth. Light areas indicate increased intensity on a log scale. (b) Angular distributions vs. depth. Top left 0–100 µm, top right 100–200 µm, bottom left, 200–300 µm, bottom right 300–400 µm.

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Fig. 3.
Fig. 3. Fitted angular distribution from surface layer

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Fig. 4.
Fig. 4. Minimization of chi-squared value to determine best fit.

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Fig. 5.
Fig. 5. Raw and filtered scattering data from basal cell nuclei

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Fig. 6.
Fig. 6. Filtered data from Fig.5 with low order fit

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Fig. 7.
Fig. 7. Oscillatory component of data with best fit.

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Fig. 8.
Fig. 8. Chi-squared minimization to find best fit. The values are parabolic (solid line) near the minimum. The uncertainty is given by double the minimum value.

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Fig 9.
Fig 9. Original microsphere data and data adjusted by adding a low order polynomial

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Fig. 10.
Fig. 10. Best fit yields incorrect size when an incorrect slowly varying background is present

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Fig. 11.
Fig. 11. Best fit yields correct size when slowly varying background is removed using a low order polynomial prior to fitting. Readjusted data corrects the slowly varying background.

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Fig. 12.
Fig. 12. Micrograph of microspheres (bars=10, 50 µm)

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Fig. 13.
Fig. 13. Fourier transform of a portion of image in Fig. 12

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