Title |
Mapping human pluripotent stem cell differentiation pathways using high throughput single-cell RNA-sequencing
|
---|---|
Published in |
Genome Biology, April 2018
|
DOI | 10.1186/s13059-018-1426-0 |
Pubmed ID | |
Authors |
Xiaoping Han, Haide Chen, Daosheng Huang, Huidong Chen, Lijiang Fei, Chen Cheng, He Huang, Guo-Cheng Yuan, Guoji Guo |
Abstract |
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. 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. 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. |
Twitter Demographics
Geographical breakdown
Country | Count | As % |
---|---|---|
United States | 5 | 17% |
United Kingdom | 4 | 13% |
Japan | 2 | 7% |
France | 2 | 7% |
Austria | 1 | 3% |
Denmark | 1 | 3% |
Singapore | 1 | 3% |
Uganda | 1 | 3% |
Canada | 1 | 3% |
Other | 1 | 3% |
Unknown | 11 | 37% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Members of the public | 16 | 53% |
Scientists | 11 | 37% |
Science communicators (journalists, bloggers, editors) | 3 | 10% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
Unknown | 258 | 100% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Student > Ph. D. Student | 61 | 24% |
Researcher | 50 | 19% |
Student > Bachelor | 33 | 13% |
Student > Master | 20 | 8% |
Student > Doctoral Student | 17 | 7% |
Other | 26 | 10% |
Unknown | 51 | 20% |
Readers by discipline | Count | As % |
---|---|---|
Biochemistry, Genetics and Molecular Biology | 94 | 36% |
Agricultural and Biological Sciences | 47 | 18% |
Medicine and Dentistry | 17 | 7% |
Engineering | 14 | 5% |
Neuroscience | 9 | 3% |
Other | 17 | 7% |
Unknown | 60 | 23% |