Ran Yi, Shuai Chen, Mingfeng Guan, Chunyan Liao, Yao Zhu, Jacque Pak Kan Ip, Tao Ye, Yu Chen
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A single-cell transcriptomic dataset of pluripotent stem cell-derived astrocytes via NFIB/SOX9 overexpression.
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.
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.