基于深度学习的图像分类揭示了病毒感染期间细胞死亡命运的异质执行
Edoardo Centofanti1, Alon Oyler-Yaniv1, Jennifer Oyler-Yaniv1
1The Department of Systems Biology at Harvard Medical School, Boston, MA 02115.
Molecular biology of the cell
|January 22, 2025
概括
深度学习图像分析显示,死亡的感染简单疹病毒-1 (HSV-1) 的细胞尽管具有复杂的分子信号,但表现出不同的形态. 这种基于图像的表型化有助于理解编程细胞死亡 (PCD) 异质性.
科学领域:
- 细胞生物学 细胞生物学
- 病毒学 病毒学
- 计算生物学是一种计算生物学.
背景情况:
- 细胞命运决定是关键的生物过程.
- 从历史上看,细胞形态变化被用来识别细胞命运过渡.
- 现代分子分析提供了精确的量化,但面临着含糊信号的挑战,特别是在涉及复杂编程细胞死亡 (PCD) 途径的病毒感染期间.
研究的目的:
- 通过模两可的分子信号,解决在病毒感染期间分配细胞命运的挑战.
- 通过使用基于深度学习的图像分类来分析单个简单疹病毒-1 (HSV-1) 感染细胞中的编程细胞死亡 (PCD).
- 调查PCD期间异质分子信号和细胞形态之间的关系.
主要方法:
- 采用基于深度学习的图像分类来分析垂死的细胞.
- 专注于单个感染简单疹病毒-1 (HSV-1) 的细胞.
- 与观察到的细胞形态学相关的分子信号.
主要成果:
- 尽管分子信号异质,但个体感染细胞主要采用原型的死亡形态.
- 编程细胞死亡 (PCD) 在病毒感染细胞的统一群体内异质地执行.
- 细胞死亡的执行随着时间的推移而变化.
结论:
- 基于图像的表型化为细胞命运决定提供了宝贵的见解,补充了分子分析.
- 深度学习分析可以解决病毒感染中复杂的PCD事件.
- 细胞形态仍然是细胞命运的关键指标,即使是复杂的潜在分子事件.
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