数字细胞计数术：从全息层析成像中提取前向和侧向散射信号
Digital cytometry: extraction of forward and side scattering signals from holotomography
AI总结：
本研究利用散射理论和全息层析成像技术，从单个未标记细胞的三维折射率分布中提取前向和侧向散射信号，并引入数字加窗方法以辅助细胞类型分类，从而弥合了流式细胞术与显微成像之间的差距。
摘要：
流式细胞术是医学和生物学研究的基石技术，通过前向散射（FSC）和侧向散射（SSC）信号提供关于细胞大小和颗粒度的关键信息。尽管其应用广泛，但这些散射信号与相应显微图像之间的精确关系仍未被充分探索。在此，我们利用散射理论和全息层析成像（一种三维定量相位成像（QPI）技术）来研究这一内在关系。我们展示了通过分析由全息层析成像获得的三维折射率分布，从单个未标记细胞中提取FSC和SSC信号。此外，我们引入了一种对SSC信号进行数字加窗的方法，以促进有效的分割和基于形态学的细胞类型分类。我们的方法弥合了流式细胞术与显微成像之间的差距，为高精度且无需标记地分析细胞特性提供了新视角。
英文摘要：
Flow cytometry is a cornerstone technique in medical and biological research, providing crucial information about cell size and granularity through forward scatter (FSC) and side scatter (SSC) signals. Despite its widespread use, the precise relationship between these scatter signals and corresponding microscopic images remains underexplored. Here, we investigate this intrinsic relationship by utilizing scattering theory and holotomography, a three-dimensional quantitative phase imaging (QPI) technique. We demonstrate the extraction of FSC and SSC signals from individual, unlabeled cells by analyzing their three-dimensional refractive index distributions obtained through holotomography. Additionally, we introduce a method for digitally windowing SSC signals to facilitate effective segmentation and morphology-based cell type classification. Our approach bridges the gap between flow cytometry and microscopic imaging, offering a new perspective on analyzing cellular characteristics with high accuracy and without the need for labeling.
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