Department of Mechanical Engineering, Johns Hopkins University, Baltimore, MD 21218, USA.
Institute for NanoBioTechnology, Johns Hopkins University, Baltimore, MD 21218, USA.
J Cell Sci. 2020 Apr 14;133(7):jcs245050. doi: 10.1242/jcs.245050.
Measuring the physical size of a cell is valuable in understanding cell growth control. Current single-cell volume measurement methods for mammalian cells are labor intensive, inflexible and can cause cell damage. We introduce CTRL: Cell Topography Reconstruction Learner, a label-free technique incorporating the deep learning algorithm and the fluorescence exclusion method for reconstructing cell topography and estimating mammalian cell volume from differential interference contrast (DIC) microscopy images alone. The method achieves quantitative accuracy, requires minimal sample preparation, and applies to a wide range of biological and experimental conditions. The method can be used to track single-cell volume dynamics over arbitrarily long time periods. For HT1080 fibrosarcoma cells, we observe that the cell size at division is positively correlated with the cell size at birth (sizer), and there is a noticeable reduction in cell size fluctuations at 25% completion of the cell cycle in HT1080 fibrosarcoma cells.
测量细胞的物理大小对于理解细胞生长控制是有价值的。目前用于哺乳动物细胞的单细胞体积测量方法既繁琐又不灵活，而且可能会对细胞造成损伤。我们引入 CTRL：细胞形貌重建学习者，这是一种无标记技术，结合深度学习算法和荧光排除法，仅从相差显微镜图像重建细胞形貌并估计哺乳动物细胞的体积。该方法具有定量准确性，需要最少的样品制备，适用于广泛的生物学和实验条件。该方法可用于跟踪任意长时间内的单细胞体积动态变化。对于 HT1080 纤维肉瘤细胞，我们观察到细胞在分裂时的大小与细胞在出生时的大小（sizer）呈正相关，并且在 HT1080 纤维肉瘤细胞中细胞周期完成 25%时细胞大小波动明显减小。
