研究目的
To develop and evaluate realistic optical cell models (OCMs) for rapid and label-free cell assay using diffraction imaging, focusing on the effects of cell morphology and refractive index distribution on diffraction patterns.
研究成果
The study demonstrates that realistic OCMs with nonspherical, irregular organelle shapes are essential for simulating diffraction patterns comparable to measured data. However, current models cannot fully replicate the diffraction patterns and intensity ratios of measured data, indicating the need for further refinement, including the incorporation of additional scattering-relevant organelles.
研究不足
The study acknowledges the difficulty in fully capturing the complexity of intracellular organelles and their refractive index distributions, which may affect the accuracy of simulated diffraction patterns. Additionally, the computational cost for large cell structures is high.
1：Experimental Design and Method Selection:
The study employs a hybrid model combining light scattering simulation using the discrete-dipole-approximation (ADDA) and ray-tracing based on geometric optics to simulate diffraction imaging.
2：Sample Selection and Data Sources:
Two prostate cell types, PC3 human prostate cancer cells and PCS normal human prostate epithelial cells, were used. Confocal fluorescent image stacks of doubly stained cells were acquired for 3D reconstruction.
3：List of Experimental Equipment and Materials:
A laser scanning confocal microscope (LSM 510, Zeiss), a p-DIFC system with a continuous-wave linear polarized laser beam of λ=532 nm, and an imaging unit consisting of an infinity-corrected 50x objective, a polarizing beam splitter, tube lenses, and CCD cameras.
4：Experimental Procedures and Operational Workflow:
Cells were prepared in suspension, injected into a p-DIFC system, and imaged. Light scattering by cells was simulated using ADDA, followed by ray-tracing to obtain calculated diffraction images.
5：Data Analysis Methods:
Diffraction patterns were characterized using a gray-level co-occurrence matrix (GLCM) algorithm, with selected parameters for quantitative comparison between calculated and measured data.
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