细胞极星:转移学习用于基因调控网络的建设,以指导细胞状态过渡
Guihai Feng1,2,3, Xin Qin4,5, Jiahao Zhang5,6
1State Key Laboratory of Organ Regeneration and Reconstruction, Institute of Zoology, Chinese Academy of Sciences, Beijing, China.
Advanced science (Weinheim, Baden-Wurttemberg, Germany)
|January 7, 2026
概括
细胞极星通过识别对细胞命运至关重要的转录主因子 (TFs) 来解码基因调控网络 (GRNs). 这种计算框架有助于理解发育过程和模拟TF扰动.
科学领域:
- 计算生物学是一种计算生物学.
- 发育生物学是发展生物学.
- 系统生物学 系统生物学
背景情况:
- 基因调控网络 (GRNs) 通过精确的时间空间基因表达来控制细胞命运的决定.
- 准确地捕捉特定环境的基因调节,特别是使用多omics数据,仍然是一个挑战.
研究的目的:
- 介绍CellPolaris,一个统一的计算框架来解码转录因子 (TF) 在开发中的角色.
- 为了实现以TF为中心的GRN构建,主TF识别和TF扰动模拟.
主要方法:
- CellPolaris利用转移学习,从先前存在的高可信度GRNs中构建组织或细胞类型特定的GRNs.
- 该框架只需要转录组数据用于GRN生成.
- 它识别主TF驱动细胞命运过渡,并模拟TF扰动效应.
主要成果:
- 在GRN建设中,CellPolaris表现出强的性能.
- 预测的主调节器与用于细胞命运转换的实验验证TF组合有显著的重叠.
- 该框架准确地模拟了精子分化过程中的Rfx2淘汰效应.
结论:
- 塞尔Polaris为GRN构建,主TF识别和扰动模拟提供了一个全面的框架.
- 该工具增强了对发育过程和细胞状态转变中的调节机制的阐明.
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