Imaging flow cytometry shows significant potential for increasing our understanding of heterogeneous and complex life systems and is useful for biomedical applications. Ghost cytometry is a recently proposed approach for directly analyzing compressively measured signals of cells, thereby relieving a computational bottleneck for real-time data analysis in high-throughput imaging cytometry. In our previous work, we demonstrated that this image-free approach could distinguish cells from two cell lines prepared with the same fluorescence staining method. However, the demonstration using different cell lines could not exclude the possibility that classification was based on non-morphological factors such as the speed of cells in flow, which could be encoded in the compressed signals. In this study, we show that GC can classify cells from the same cell line but with different fluorescence distributions in space, supporting the strength of our image-free approach for accurate morphological cell analysis. © 2020 International Society for Advancement of Cytometry.

中文翻译：

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使用 Ghost Cytometry 区分具有相似大体形态特征的细胞。

成像流式细胞术在增加我们对异质和复杂生命系统的理解方面显示出巨大的潜力，并且对生物医学应用非常有用。Ghost 流式细胞术是最近提出的一种直接分析细胞压缩测量信号的方法，从而缓解了高通量成像细胞术中实时数据分析的计算瓶颈。在我们之前的工作中，我们证明了这种无图像方法可以区分使用相同荧光染色方法制备的两种细胞系中的细胞。然而，使用不同细胞系的演示不能排除分类是基于非形态因素的可能性，例如细胞流动的速度，这些因素可以在压缩信号中编码。在这项研究中，我们表明 GC 可以对来自相同细胞系但在空间中具有不同荧光分布的细胞进行分类，支持我们的无图像方法进行准确形态学细胞分析的优势。© 2020 国际细胞计量学促进会。