A single-cell transcriptomic dataset of pluripotent stem cell-derived astrocytes via NFIB/SOX9 overexpression.

IF 5.8 2区 综合性期刊 Q1 MULTIDISCIPLINARY SCIENCES Scientific Data Pub Date : 2024-09-10 DOI:10.1038/s41597-024-03823-x
Ran Yi, Shuai Chen, Mingfeng Guan, Chunyan Liao, Yao Zhu, Jacque Pak Kan Ip, Tao Ye, Yu Chen
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Abstract

Astrocytes, the predominant glial cells in the central nervous system, play essential roles in maintaining brain function. Reprogramming induced pluripotent stem cells (iPSCs) to become astrocytes through overexpression of the transcription factors, NFIB and SOX9, is a rapid and efficient approach for studying human neurological diseases and identifying therapeutic targets. However, the precise differentiation path and molecular signatures of induced astrocytes remain incompletely understood. Accordingly, we performed single-cell RNA sequencing analysis on 64,736 cells to establish a comprehensive atlas of NFIB/SOX9-directed astrocyte differentiation from human iPSCs. Our dataset provides detailed information about the path of astrocyte differentiation, highlighting the stepwise molecular changes that occur throughout the differentiation process. This dataset serves as a valuable reference for dissecting uncharacterized transcriptomic features of NFIB/SOX9-induced astrocytes and investigating lineage progression during astrocyte differentiation. Moreover, these findings pave the way for future studies on neurological diseases using the NFIB/SOX9-induced astrocyte model.

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通过 NFIB/SOX9 过度表达多能干细胞衍生星形胶质细胞的单细胞转录组数据集。
星形胶质细胞是中枢神经系统中最主要的胶质细胞,在维持大脑功能方面发挥着重要作用。通过过量表达转录因子 NFIB 和 SOX9,将诱导多能干细胞(iPSC)重编程为星形胶质细胞,是研究人类神经系统疾病和确定治疗靶点的一种快速有效的方法。然而,人们对诱导星形胶质细胞的精确分化路径和分子特征仍不甚了解。因此,我们对 64736 个细胞进行了单细胞 RNA 测序分析,建立了人类 iPSCs NFIB/SOX9 引导星形胶质细胞分化的综合图谱。我们的数据集提供了有关星形胶质细胞分化路径的详细信息,突出显示了整个分化过程中发生的逐步分子变化。该数据集为剖析 NFIB/SOX9 诱导的星形胶质细胞未表征的转录组特征和研究星形胶质细胞分化过程中的谱系进展提供了有价值的参考。此外,这些发现还为今后利用 NFIB/SOX9 诱导的星形胶质细胞模型研究神经系统疾病铺平了道路。
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来源期刊
Scientific Data
Scientific Data Social Sciences-Education
CiteScore
11.20
自引率
4.10%
发文量
689
审稿时长
16 weeks
期刊介绍: Scientific Data is an open-access journal focused on data, publishing descriptions of research datasets and articles on data sharing across natural sciences, medicine, engineering, and social sciences. Its goal is to enhance the sharing and reuse of scientific data, encourage broader data sharing, and acknowledge those who share their data. The journal primarily publishes Data Descriptors, which offer detailed descriptions of research datasets, including data collection methods and technical analyses validating data quality. These descriptors aim to facilitate data reuse rather than testing hypotheses or presenting new interpretations, methods, or in-depth analyses.
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