单细胞形态动力学轨迹可以预测伴随细胞状态变化的基因表达
Jeremy Copperman1, Ian C Mclean2, Sean M Gross2,3,1
1Cancer Early Detection Advanced Research Center, Oregon Health and Science University, Portland OR 97239, U.S.A.
bioRxiv : the preprint server for biology
|January 31, 2024
概括
我们开发了一种计算方法,MMIST,将细胞行为动态与基因表达联系起来. 这种方法揭示了细胞外信号是如何驱动细胞状态转换的,例如上皮细胞-介质细胞转换 (EMT).
科学领域:
- 细胞动力学和分子编程
- 定量生物学的定量生物学.
- 系统生物学 系统生物学
背景情况:
- 细胞外信号影响细胞表型 (增殖,运动,分化).
- 动态细胞状态和潜在的分子程序之间的联系是不太了解的.
- 当前的方法往往缺乏时间分辨率来捕捉动态的表型变化.
研究的目的:
- 开发一种数据驱动的计算方法,将单细胞基因转录与可观测的细胞动力学 (形态动力学) 联系起来.
- 模拟由细胞外刺激驱动的细胞状态过渡.
- 从活细胞成像数据推断分子变化的框架.
主要方法:
- 开发了MMIST (分子和形态动力学集成单细胞轨迹),一个计算框架.
- 集成的时间间隔成像数据 (形态动力学) 与快照基因转录水平.
- 根据共享的形态动力学反应,将单细胞轨迹分组为状态.
- 分析了细胞状态景观和状态之间的过渡率.
主要成果:
- 确定了一个细胞状态景观,具有上皮和介质细胞终点和中间状态.
- 描述了表皮细胞-介质细胞过渡 (EMT) 和介质细胞-表皮细胞过渡 (MET) 的不同的序列.
- 预测了数千个涉及EMT和MET的RNA转录,具有近连续的时间分辨率.
- 与精选的EMT基因组相对应的验证预测.
结论:
- MMIST有效地将活细胞动态映射到分子配置文件中,为研究细胞状态过渡提供了一种新的方法.
- 细胞外信号通过调节转变速率来动态改变细胞状态分布.
- 该框架提供了对EMT和MET等动态细胞过程的高分辨率分子洞察力.
- MMIST广泛适用于其他生物系统和数据类型,包括时间间隔成像和分子快照.
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