| 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. |
X Demographics
The data shown below were collected from the profiles of 30 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United Kingdom | 4 | 13% |
| United States | 4 | 13% |
| Japan | 2 | 7% |
| France | 2 | 7% |
| Austria | 1 | 3% |
| Singapore | 1 | 3% |
| Uganda | 1 | 3% |
| Canada | 1 | 3% |
| Portugal | 1 | 3% |
| Other | 0 | 0% |
| Unknown | 13 | 43% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 16 | 53% |
| Scientists | 11 | 37% |
| Science communicators (journalists, bloggers, editors) | 3 | 10% |
Mendeley readers
The data shown below were compiled from readership statistics for 266 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 266 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 62 | 23% |
| Researcher | 53 | 20% |
| Student > Bachelor | 33 | 12% |
| Student > Master | 20 | 8% |
| Student > Doctoral Student | 17 | 6% |
| Other | 28 | 11% |
| Unknown | 53 | 20% |
| Readers by discipline | Count | As % |
|---|---|---|
| Biochemistry, Genetics and Molecular Biology | 97 | 36% |
| Agricultural and Biological Sciences | 47 | 18% |
| Medicine and Dentistry | 17 | 6% |
| Engineering | 15 | 6% |
| Neuroscience | 9 | 3% |
| Other | 18 | 7% |
| Unknown | 63 | 24% |
Attention Score in Context
This research output has an Altmetric Attention Score of 25. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 17 February 2020.
All research outputs
#1,510,095
of 25,382,440 outputs
Outputs from Genome Biology
#1,214
of 4,468 outputs
Outputs of similar age
#32,678
of 343,387 outputs
Outputs of similar age from Genome Biology
#13
of 34 outputs
Altmetric has tracked 25,382,440 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 94th percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 4,468 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 27.6. This one has gotten more attention than average, scoring higher than 72% of its peers.
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We're also able to compare this research output to 34 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 61% of its contemporaries.