Single-cell RNA-seq reveals dynamic transcriptome profiling in human early neural differentiation

Background: Investigating cell fate decision and subpopulation specification in the context of the neural lineage is fundamental to understanding neurogenesis and neurodegenerative diseases. The differentiation process of neural-tube-like rosettes in vitro is representative of neural tube structures, which are composed of radially organized, columnar epithelial cells and give rise to functional neural cells. However, the underlying regulatory network of cell fate commitment during early neural differentiation remains elusive.

Results: In this study, we investigated the genome-wide transcriptome profile of single cells from six consecutive reprogramming and neural differentiation time points and identified cellular subpopulations present at each differentiation stage. Based on the inferred reconstructed trajectory and the characteristics of subpopulations contributing the most toward commitment to the central nervous system lineage at each stage during differentiation, we identified putative novel transcription factors in regulating neural differentiation. In addition, we dissected the dynamics of chromatin accessibility at the neural differentiation stages and revealed active cis-regulatory elements for transcription factors known to have a key role in neural differentiation as well as for those that we suggest are also involved. Further, communication network analysis demonstrated that cellular interactions most frequently occurred in the embryoid body stage and that each cell subpopulation possessed a distinctive spectrum of ligands and receptors associated with neural differentiation that could reflect the identity of each subpopulation.

Conclusions: Our study provides a comprehensive and integrative study of the transcriptomics and epigenetics of human early neural differentiation, which paves the way for a deeper understanding of the regulatory mechanisms driving the differentiation of the neural lineage.

Medienart:

E-Artikel

Erscheinungsjahr:

2018

Erschienen:

2018

Enthalten in:

Zur Gesamtaufnahme - volume:7

Enthalten in:

GigaScience - 7(2018), 11 vom: 01. Nov.

Sprache:

Englisch

Beteiligte Personen:

Shang, Zhouchun [VerfasserIn]
Chen, Dongsheng [VerfasserIn]
Wang, Quanlei [VerfasserIn]
Wang, Shengpeng [VerfasserIn]
Deng, Qiuting [VerfasserIn]
Wu, Liang [VerfasserIn]
Liu, Chuanyu [VerfasserIn]
Ding, Xiangning [VerfasserIn]
Wang, Shiyou [VerfasserIn]
Zhong, Jixing [VerfasserIn]
Zhang, Doudou [VerfasserIn]
Cai, Xiaodong [VerfasserIn]
Zhu, Shida [VerfasserIn]
Yang, Huanming [VerfasserIn]
Liu, Longqi [VerfasserIn]
Fink, J Lynn [VerfasserIn]
Chen, Fang [VerfasserIn]
Liu, Xiaoqing [VerfasserIn]
Gao, Zhengliang [VerfasserIn]
Xu, Xun [VerfasserIn]

Links:

Volltext

Themen:

Biomarkers
Journal Article
Research Support, Non-U.S. Gov't

Anmerkungen:

Date Completed 18.07.2019

Date Revised 09.01.2021

published: Electronic

Citation Status MEDLINE

doi:

10.1093/gigascience/giy117

funding:

Förderinstitution / Projekttitel:

PPN (Katalog-ID):

NLM288762037