机器学习推断了神经细胞分化和融合期间连续的单细胞状态过渡的推断
Amit Shakarchy1, Giulia Zarfati2, Adi Hazak2
1Department of Software and Information Systems Engineering, Ben-Gurion University of the Negev, Beer-Sheva, 84105, Israel.
Molecular systems biology
|January 18, 2024
概括
研究人员开发了一种机器学习模型,可以实时跟踪细胞分化. 这种工具揭示了肌肉细胞分化和融合的关键时间,识别了p38.
科学领域:
- 细胞生物学 细胞生物学
- 生物物理学的生物物理.
- 系统生物学 系统生物学
背景情况:
- 细胞为分裂和分化等关键功能动态重组其内部状态.
- 现有的工具很难测量在连续过渡过程中单个细胞的生理状态.
- 了解这些动态转变对于各种生物过程至关重要.
研究的目的:
- 开发和验证一种基于机器学习的方法,用于监测细胞转变中的动态生理状态.
- 精确追踪小鼠骨肌前体细胞中的实时分化和融合过程.
- 研究ERK1/2和p38信号通路在肌肉形成中的作用.
主要方法:
- 结合活细胞成像与单细胞跟踪的机器学习算法.
- 利用了从跟踪数据到训练预测模型的运动性和行为强度特征.
- 采用质谱法验证in silico预测并识别新型调节剂.
主要成果:
- 开发的模型准确地追踪了持续的细胞分化,发生在诱导后7.514.5小时.
- 细胞融合在分化后大约3小时后被观察到.
- 同时抑制ERK1/2和p38导致没有融合的分化,这表明p38对于过渡到融合至关重要.
结论:
- 这种新的机器学习方法有效地实时监控动态单细胞转换.
- 对于从终端分化到细胞融合的过渡,p38信号是特别需要的.
- 这种可适应的方法可以揭示不同生物系统中动态细胞状态和功能结果之间的新联系.
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