Label-free cell cycle analysis for high-throughput imaging flow cytometry.
Label-free cell cycle analysis for high-throughput imaging flow cytometry.
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DOI：
10.1038/ncomms10256
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发表时间：
2016-01-07
影响因子：
16.6
通讯作者：
Rees P
中科院分区：
文献类型：
--
作者：
Blasi T;Hennig H;Summers HD;Theis FJ;Cerveira J;Patterson JO;Davies D;Filby A;Carpenter AE;Rees P
成像流式细胞术结合了传统流式细胞术的高通量能力和单细胞成像。在这里，我们演示了DNA含量的无标记预测和有丝分裂细胞周期相的量化，方法是将有监督机器学习应用于从Brightfield提取的形态特征和从成像流式细胞仪提取的通常被忽略的细胞暗视野图像。这种方法便于对细胞的非破坏性监测，避免了荧光染色的潜在混杂效应，同时最大限度地利用了可用的荧光通道。该方法在哺乳动物细胞周期分析中是有效的，无论是固定的还是活的，并准确地评估了细胞周期有丝分裂期阻滞剂的影响。由于同样的方法在预测分裂酵母的DNA含量方面是有效的，它很可能在其他类型的细胞中有广泛的应用。成像流式细胞术能够高通量地获取生物细胞的荧光、明场和暗场图像。在这里，布拉西等人。证明在明场和暗场图像上应用机器学习算法可以在不需要荧光染色的情况下检测细胞表型，从而实现无标记分析。
Imaging flow cytometry combines the high-throughput capabilities of conventional flow cytometry with single-cell imaging. Here we demonstrate label-free prediction of DNA content and quantification of the mitotic cell cycle phases by applying supervised machine learning to morphological features extracted from brightfield and the typically ignored darkfield images of cells from an imaging flow cytometer. This method facilitates non-destructive monitoring of cells avoiding potentially confounding effects of fluorescent stains while maximizing available fluorescence channels. The method is effective in cell cycle analysis for mammalian cells, both fixed and live, and accurately assesses the impact of a cell cycle mitotic phase blocking agent. As the same method is effective in predicting the DNA content of fission yeast, it is likely to have a broad application to other cell types. Imaging flow cytometry enables high-throughput acquisition of fluorescence, brightfield and darkfield images of biological cells. Here, Blasi et al. demonstrate that applying machine learning algorithms on brightfield and darkfield images can detect cellular phenotypes without the need for fluorescent stains, enabling label-free assays.