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首页> 外文会议>High-Speed Biomedical Imaging and Spectroscopy III: Toward Big Data Instrumentation and Management >Label-free cell-cycle analysis by high-throughput quantitative phase time-stretch imaging flow cytometry
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Label-free cell-cycle analysis by high-throughput quantitative phase time-stretch imaging flow cytometry

机译：通过高通量定量相时间拉伸成像流式细胞仪进行无标记细胞周期分析

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摘要

Biophysical properties of cells could complement and correlate biochemical markers to characterize a multitude of cellular states. Changes in cell size, dry mass and subcellular morphology, for instance, are relevant to cell-cycle progression which is prevalently evaluated by DNA-targeted fluorescence measurements. Quantitative-phase microscopy (QPM) is among the effective biophysical phenotyping tools that can quantify cell sizes and sub-cellular dry mass density distribution of single cells at high spatial resolution. However, limited camera frame rate and thus imaging throughput makes QPM incompatible with high-throughput flow cytometry - a gold standard in multiparametric cell-based assay. Here we present a high-throughput approach for label-free analysis of cell cycle based on quantitative-phase time-stretch imaging flow cytometry at a throughput of > 10,000 cells/s. Our time-stretch QPM system enables sub-cellular resolution even at high speed, allowing us to extract a multitude (at least 24) of single-cell biophysical phenotypes (from both amplitude and phase images). Those phenotypes can be combined to track cell-cycle progression based on a t-distributed stochastic neighbor embedding (t-SNE) algorithm. Using multivariate analysis of variance (MANOVA) discriminant analysis, cell-cycle phases can also be predicted label-free with high accuracy at >90% in G_1 and G_2 phase, and >80% in S phase. We anticipate that high throughput label-free cell cycle characterization could open new approaches for large-scale single-cell analysis, bringing new mechanistic insights into complex biological processes including diseases pathogenesis.
机译：细胞的生物物理特性可以补充和关联生化标记，以表征多种细胞状态。例如，细胞大小，干重和亚细胞形态的变化与细胞周期进程有关，而细胞周期进程通常是通过靶向DNA的荧光测量来评估的。定量相显微镜（QPM）是有效的生物物理表型分析工具之一，可以在高空间分辨率下量化单个细胞的细胞大小和亚细胞干质量密度分布。但是，有限的相机帧速率以及因此的成像吞吐量使QPM与高通量流式细胞仪不兼容-高通量流式细胞仪是基于多参数细胞的测定方法的金标准。在这里，我们提出了一种基于无定量分析的细胞周期的高通量方法，该方法基于定量相时间拉伸成像流式细胞术，吞吐量> 10,000个细胞/秒。我们的时间拉伸QPM系统即使在高速下也能实现亚细胞分辨率，从而使我们能够从振幅和相位图像中提取多种（至少24种）单细胞生物物理表型。这些表型可以基于t分布随机邻居嵌入（t-SNE）算法进行组合，以跟踪细胞周期进程。使用多变量方差分析（MANOVA）判别分析，还可以在G_1和G_2相中> 90％，在S相中> 80％的情况下，以无标签的高准确度预测细胞周期相。我们预计，高通量的无标记细胞周期表征将为大规模单细胞分析打开新方法，为包括疾病发病机制在内的复杂生物过程带来新的机理见解。

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