Mapping human pluripotent stem cell differentiation pathways using high throughput single-cell RNA-sequencing

Genome Biology

Haide Chen¹  Daosheng Huang¹  Guoji Guo¹  Xiaoping Han¹  Lijiang Fei¹  Chen Cheng²  Huidong Chen³  Guo-Cheng Yuan³  He Huang⁴ 

[1] Center for Stem Cell and Regenerative Medicine, Zhejiang University School of Medicine;College of Life Sciences, Zhejiang University;Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, Harvard Chan School of Public Health;Institute of Hematology, The 1st Affiliated Hospital, Zhejiang University School of Medicine;

关键词: Single-cell RNA-sequencing;    Primed human pluripotent stem cell;    Embryoid body;    Naïve human pluripotent stem cell;   

DOI  :  10.1186/s13059-018-1426-0

来源: DOAJ

【 摘 要 】

Abstract Background Human pluripotent stem cells (hPSCs) provide powerful models for studying cellular differentiations and unlimited sources of cells for regenerative medicine. However, a comprehensive single-cell level differentiation roadmap for hPSCs has not been achieved. Results We use high throughput single-cell RNA-sequencing (scRNA-seq), based on optimized microfluidic circuits, to profile early differentiation lineages in the human embryoid body system. We present a cellular-state landscape for hPSC early differentiation that covers multiple cellular lineages, including neural, muscle, endothelial, stromal, liver, and epithelial cells. Through pseudotime analysis, we construct the developmental trajectories of these progenitor cells and reveal the gene expression dynamics in the process of cell differentiation. We further reprogram primed H9 cells into naïve-like H9 cells to study the cellular-state transition process. We find that genes related to hemogenic endothelium development are enriched in naïve-like H9. Functionally, naïve-like H9 show higher potency for differentiation into hematopoietic lineages than primed cells. Conclusions Our single-cell analysis reveals the cellular-state landscape of hPSC early differentiation, offering new insights that can be harnessed for optimization of differentiation protocols.

【 授权许可】

Unknown