识别可复制的转录调节器同表达模式与单细胞转录组学
Alexander Morin1,2,3, Ching Pan Chu1,2,3, Paul Pavlidis1,2
1Michael Smith Laboratories, University of British Columbia, Vancouver, British Columbia, Canada.
PLoS computational biology
|April 21, 2025
概括
这项研究在数百万个细胞中绘制基因协同表达网络,以确定人类和小鼠转录调节器 (TRs) 的基因伙伴. 这些发现揭示了可复制的调节模式,有助于理解基因转录动态.
科学领域:
- 基因组学就是基因组学.
- 计算生物学 计算生物学
- 分子生物学分子生物学
背景情况:
- 单细胞转录组学能够对基因转录调节进行详细的分析.
- 了解转录调节器 (TR) 网络对于破译细胞过程至关重要.
研究的目的:
- 使用大规模单细胞RNA-seq数据,与人类和小鼠的转录调节器 (TRs) 协调识别基因合作伙伴.
- 评估TR共同表达特征在各种生物环境和物种中的一致性.
主要方法:
- 组装了120个人类和103个小鼠单细胞RNA-seq数据集 (>2800万个细胞).
- 为人类和小鼠TRs构建的单细胞同表达网络.
- 在细胞类型和物种中优先重复可重复的共同表达模式.
主要成果:
- 为每个TR生成单细胞同表达排名,与文献策划的目标进行验证.
- 在识别TR目标方面实现了与ChIP-seq数据相比较的性能.
- 集成的协同表达和ChIP-seq数据,以确定强大的候选监管相互作用.
结论:
- 这项研究为了解转录监管网络提供了全面的资源.
- 确定了跨物种和细胞类型的可复制的基因协同表达模式.
- 强调综合数据对于发现新型监管相互作用的有用性,以ASCL1.1为例.
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