Robust classification of cell cycle phase and biological feature extraction by image-based deep learning
Robust classification of cell cycle phase and biological feature extraction by image-based deep learning
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DOI：
10.1091/mbc.e20-03-0187
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发表时间：
2020-06-15
影响因子：
3.3
通讯作者：
Takao, Daisuke
中科院分区：
文献类型：
--
作者：
Nagao, Yukiko;Sakamoto, Mika;Takao, Daisuke
在整个细胞周期中，亚细胞组织经历了重大的时空变化，原则上可能包含可能代表细胞周期阶段的生物学特征。我们应用基于卷积神经网络的分类器，从细胞核、高尔基体和微管细胞骨架染色的荧光显微镜图像中提取这些假定特征。我们证明，细胞图像可以根据G1/S和G2细胞周期时相进行强有力的分类，而不需要特定的细胞周期标记。分类模型的Grad-CAM分析使我们能够提取特定亚细胞特征的几对量化参数，作为细胞周期时相的良好分类器。这些结果共同证明了基于机器学习的图像处理对于以无偏见和数据驱动的方式提取隐藏在感兴趣的细胞现象下的生物特征是有用的。
Across the cell cycle, the subcellular organization undergoes major spatiotemporal changes that could in principle contain biological features that could potentially represent cell cycle phase. We applied convolutional neural network-based classifiers to extract such putative features from the fluorescence microscope images of cells stained for the nucleus, the Golgi apparatus, and the microtubule cytoskeleton. We demonstrate that cell images can be robustly classified according to G1/S and G2 cell cycle phases without the need for specific cell cycle markers. Grad-CAM analysis of the classification models enabled us to extract several pairs of quantitative parameters of specific subcellular features as good classifiers for the cell cycle phase. These results collectively demonstrate that machine learning-based image processing is useful to extract biological features underlying cellular phenomena of interest in an unbiased and data-driven manner.