Structural and Computational Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany.
Faculty of Biosciences, Collaboration for joint PhD degree between EMBL and Heidelberg University, Heidelberg, Germany.
Mol Syst Biol. 2020 Oct;16(10):e9474. doi: 10.15252/msb.20209474.
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.
单细胞方法的出现为深入了解细胞周期铺平了道路，提供了前所未有的细节。由于细胞周期在几乎所有生物过程中的重要性，评估细胞周期进程对于全面的细胞特征描述至关重要。在这里，我们提出了 DeepCycle，这是一种从未分割的单细胞显微镜图像中估计细胞周期轨迹的深度学习方法，仅依赖于明场和核特异性荧光信号。我们在具有荧光 FUCCI2 系统的 MDCKII 细胞的 260 万张单细胞显微镜图像上评估了 DeepCycle。DeepCycle 提供了细胞图像的潜在表示，揭示了细胞周期的连续和封闭轨迹。此外，我们通过展示与从经历整个细胞周期的活细胞成像中实时测量的几乎完美的相关性，验证了 DeepCycle 轨迹。这是第一个能够仅基于贴壁细胞培养的未分割显微镜数据解析封闭细胞周期轨迹（包括细胞分裂）的模型。
