通过深度学习,通过单一图像推断克拉特林介导的内细胞分裂动态
Tianyao Wu1, Comert Kural1,2
1Department of Physics, Ohio State University, Columbus, Ohio 43210, USA.
The Journal of chemical physics
|October 15, 2025
概括
我们开发了一种深度学习方法,使用单个图像分析克拉斯林介导内细胞形成 (CME) 动态,克服了传统显微镜的局限性. 这种方法可以有效地,非侵入性地评估细胞过程.
科学领域:
- 细胞生物学 细胞生物学
- 生物物理学的生物物理.
- 计算生物学 计算生物学
背景情况:
- 克拉特林介导内细胞分裂 (CME) 对于细胞功能至关重要,但由于时间间隔显微镜等传统方法的局限性,对其进行研究具有挑战性.
- 现有的技术存在光毒性,时间低样本和计算复杂性,阻碍了对CME动态的实时分析.
研究的目的:
- 引入一种新的深度学习框架,从静态光图像中推断CME动态.
- 通过非侵入性实时评估内细胞过程,克服传统方法的局限性.
主要方法:
- 经过修改的U-Net深度学习架构被用于预测单个光图像中的clathrin外衣生长率标准偏差的空间地图.
- 该模型使用配对的图像数据和从实验追踪的内细胞事件中得出的标准偏差图进行了训练.
主要成果:
- 深度学习模型准确地回顾了CME的动态特征,包括迁移细胞的前后不对称性和对膜张力变化的反应.
- 结果与传统的时隔显微镜获得的指标有很强的一致性,验证了该模型的预测能力.
- 该框架成功地从静态成像数据中提取了动态生物物理信息.
结论:
- 深度学习提供了一种强大的,可扩展的方法来分析CME动态和相关的细胞下过程.
- 这种方法消除了轨迹重建和长时间成像的需要,促进了在各种生物环境中更广泛的应用.
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