通过细胞状态来揭示因果调节机制
Alexander P Wu1, Rohit Singh2,3,4, Christopher A Walsh5,6,7
1Computer Science and Artificial Intelligence Laboratory, MIT, Cambridge, MA, USA.
Nature communications
|August 29, 2025
概括
我们开发了一种新型图形神经网络, 以揭示非编码基因变异与单细胞基因调节之间的因果关系. 这种方法以"细胞状态"为灵感, 提高了对精神分裂症等复杂疾病的理解.
科学领域:
- 基因组学
- 计算生物学
- 神经科学
背景情况:
- 全基因组关联研究 (GWAS) 在非编码区域中确定与疾病相关的遗传变异.
- 了解这些变体的组织特异性调节作用对于治疗发展至关重要.
- 目前的计算方法在单细胞数据中的变量级精度和因果推理方面存在困难.
研究的目的:
- 引入Grid-Net,一个图形神经网络方法,从单细胞多模式数据推断因果基因关联.
- 利用"细胞状态"的概念,从静态单细胞快照中推断出因果机制.
- 确定精神分裂症 (SCZ) 的非编码调节机制.
主要方法:
- 开发了Grid-Net,一个图形神经网络,将单细胞轨迹的格兰杰因果推理概括.
- 应用 GrID-Net 对单细胞染色体可访问性和基因表达的数据.
- 使用一个
- 细胞状态偏移
- 从时间滞后的表观遗传和转录状态推断因果关系的方法.
主要成果:
- 在SCZ基因变异中,Grid-Net的变异覆盖率增加了36%.
- 确定了破坏132个基因的非编码机制,包括KCNG2和SLC12A6的载体.
- 发现神经转录因子结合干扰在SCZ病因中的重要作用.
结论:
- GrID-Net提供了一种策略,用于阐明非编码变体的组织特异性影响.
- "细胞状态"概念为发现单细胞多组学中的基因调节机制提供了突破.
- 这种方法促进了对复杂疾病的遗传贡献的理解.
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