The advent of single‐cell methods is paving the way for an in‐depth understanding of the cell cycle with unprecedented detail. Due to its ramifications in nearly all biological processes, the evaluation of cell cycle progression is critical for an exhaustive cellular characterization. Here, we present DeepCycle, a deep learning method for estimating a cell cycle trajectory from unsegmented single‐cell microscopy images, relying exclusively on the brightfield and nuclei‐specific fluorescent signals. DeepCycle was evaluated on 2.6 million single‐cell microscopy images of MDCKII cells with the fluorescent FUCCI2 system. DeepCycle provided a latent representation of cell images revealing a continuous and closed trajectory of the cell cycle. Further, we validated the DeepCycle trajectories by showing its nearly perfect correlation with real time measured from live‐cell imaging of cells undergoing an entire cell cycle. This is the first model able to resolve the closed cell cycle trajectory, including cell division, solely based on unsegmented microscopy data from adherent cell cultures.

中文翻译：

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DeepCycle 使用卷积神经网络从未分割的细胞图像重建循环细胞周期轨迹

单细胞方法的出现为以前所未有的细节深入了解细胞周期铺平了道路。由于其对几乎所有生物过程的影响，细胞周期进展的评估对于详尽的细胞表征至关重要。在这里，我们提出了 DeepCycle，一种深度学习方法，用于根据未分段的单细胞显微镜图像估计细胞周期轨迹，完全依赖于明场和细胞核特异性荧光信号。DeepCycle 使用荧光 FUCCI2 系统对 MDCKII 细胞的 260 万张单细胞显微镜图像进行了评估。DeepCycle 提供了细胞图像的潜在表示，揭示了细胞周期的连续且闭合的轨迹。此外，我们通过显示 DeepCycle 轨迹与经历整个细胞周期的细胞的活细胞成像实时测量的近乎完美的相关性来验证 DeepCycle 轨迹。这是第一个能够解析闭合细胞周期轨迹（包括细胞分裂）的模型，仅基于贴壁细胞培养物的未分段显微镜数据。