School of Computer Engineering and Science, Shanghai University, Shanghai, China.
School of Life Sciences, Shanghai University, Shanghai, China.
Interdiscip Sci. 2021 Mar;13(1):91-102. doi: 10.1007/s12539-020-00415-2. Epub 2021 Jan 13.
Deciphering regulatory patterns of neural stem cell (NSC) differentiation with multiple stages is essential to understand NSC differentiation mechanisms. Recent single-cell transcriptome datasets became available at individual differentiation. However, a systematic and integrative analysis of multiple datasets at multiple temporal stages of NSC differentiation is lacking. In this study, we propose a new method integrating prior information to construct three gene regulatory networks at pair-wise stages of transcriptome and apply this method to investigate five NSC differentiation paths on four different single-cell transcriptome datasets. By constructing gene regulatory networks for each path, we delineate their regulatory patterns via differential topology and network diffusion analyses. We find 12 common differentially expressed genes among the five NSC differentiation paths, with one common regulatory pattern (Gsk3b_App_Cdk5) shared by all paths. The identified regulatory pattern, partly supported by previous experimental evidence, is essential to all differentiation paths, but it plays a different role in each path when regulating other genes. Together, our integrative analysis provides both common and specific regulatory mechanisms for each of the five NSC differentiation paths.
解析神经干细胞 (NSC) 多阶段分化的调控模式对于理解 NSC 分化机制至关重要。最近，单个分化的单细胞转录组数据集已经可用。然而，缺乏对 NSC 分化多个时间阶段的多个数据集的系统和综合分析。在这项研究中，我们提出了一种新的方法，该方法集成了先验信息，以构建转录组的两两阶段的三个基因调控网络，并将该方法应用于四个不同的单细胞转录组数据集上的五个 NSC 分化路径的研究。通过为每条路径构建基因调控网络，我们通过差异拓扑和网络扩散分析来描绘它们的调控模式。我们发现五个 NSC 分化路径之间有 12 个共同的差异表达基因，其中所有路径都有一个共同的调控模式（Gsk3b_App_Cdk5）。所识别的调控模式部分得到了先前实验证据的支持，对于所有分化途径都是必不可少的，但在调节其他基因时，在每个途径中都起着不同的作用。总的来说，我们的综合分析为五个 NSC 分化路径中的每一个提供了共同和特定的调控机制。
