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推断细胞类型特定的基因调控网络从单细胞欧米数据集对细胞系的推断

Shilu Zhang¹, Saptarshi Pyne¹, Stefan Pietrzak^1,2

¹Wisconsin Institute for Discovery, University of Wisconsin-Madison, Madison, WI, USA.

Nature communications

|May 27, 2023

概括

我们开发了单细胞多任务网络推断 (scMTNI) 以跨细胞系绘制基因调节网络 (GRNs). 该方法整合了单细胞RNA和ATAC测序数据,以揭示细胞命运过渡期间的动态网络变化和关键调节器.

科学领域:

分子生物学分子生物学
基因组学就是基因组学.
计算生物学计算生物学

背景情况:

细胞类型特定的基因表达受复杂的转录基因调节网络 (GRNs) 的控制.
像scRNA-seq和scATAC-seq这样的单细胞技术为基因调节提供了高分辨率的洞察力.
现有的方法难以整合多式单细胞数据,并沿细胞系模拟动态GRN.

研究的目的:

开发一种新的计算框架,从多式单细胞数据中推断出细胞类型特定的GRNs.
模拟细胞系内GRNs的动态变化.
确定驱动细胞命运决策的关键调控因素.

主要方法:

引入了单细胞多任务网络推断 (scMTNI),一个多任务学习框架.
集成的scRNA-seq和scATAC-seq数据用于GRN推断.
将scMTNI应用于模拟和真实生物数据集,包括线性和分支血统.

主要成果:

scMTNI准确地推断了跨细胞系的GRN动态.
该框架成功地集成了scRNA-seq和scATAC-seq测量.
确定了驱动细胞命运过渡过程中的关键调节者,如分化和重编程.

结论:

scMTNI提供了一种广泛适用的方法来剖析细胞类型特定的GRN动态.
该框架增强了对细胞发育和可塑性过程中的基因调节的理解.
通过使用多式单细胞数据,可对细胞过程中的关键调节器进行可靠的识别.

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