用明场图像预测细胞周期，让无标记成像也能精准追踪分裂过程。
Sequence models for continuous cell cycle stage prediction from brightfield images
- 采用序列模型分析连续明场图像，捕捉细胞周期动态变化。
- 在130万张图像上验证，模型可1小时内识别G1/S过渡等细微变化。
- 适合无需荧光标记的活细胞研究，推动无损动态监测发展。
理解细胞周期动态对研究生长、发育和疾病进展至关重要。尽管荧光蛋白报告系统（如Fucci）可实现活细胞周期相位的实时监测，但需基因工程且占用额外荧光通道，限制了其在复杂实验中的广泛应用。本研究全面评估了深度学习方法在非荧光明场成像下预测连续Fucci信号的性能，构建了包含130万张分裂RPE1细胞图像的大型数据集，覆盖完整的细胞周期轨迹。通过定量比较单帧模型、因果状态空间模型与双向Transformer模型，发现因果和Transformer类模型显著优于单帧及固定帧方法，可在1小时分辨率内预测肉眼不可见的G1/S转变。结果表明，序列模型对准确预测细胞周期动态至关重要，凸显其在无标记成像中的潜力。
原文摘要 · Abstract (English)
Understanding cell cycle dynamics is crucial for studying biological processes such as growth, development and disease progression. While fluorescent protein reporters like the Fucci system allow live monitoring of cell cycle phases, they require genetic engineering and occupy additional fluorescence channels, limiting broader applicability in complex experiments. In this study, we conduct a comprehensive evaluation of deep learning methods for predicting continuous Fucci signals using non-fluorescence brightfield imaging, a widely available label-free modality. To that end, we generated a large dataset of 1.3 M images of dividing RPE1 cells with full cell cycle trajectories to quantitatively compare the predictive performance of distinct model categories including single time-frame models, causal state space models and bidirectional transformer models. We show that both causal and transformer-based models significantly outperform single- and fixed frame approaches, enabling the prediction of visually imperceptible transitions like G1/S within 1h resolution. Our findings underscore the importance of sequence models for accurate predictions of cell cycle dynamics and highlight their potential for label-free imaging.
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