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

Overview of attention for article published in Genome Biology (Online Edition), April 2018
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (90th percentile)

Mentioned by

blogs
1 blog
twitter
30 tweeters
wikipedia
1 Wikipedia page

Citations

dimensions_citation
72 Dimensions

Readers on

mendeley
249 Mendeley
citeulike
1 CiteULike
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Title
Mapping human pluripotent stem cell differentiation pathways using high throughput single-cell RNA-sequencing
Published in
Genome Biology (Online Edition), 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

The data shown below were collected from the profiles of 30 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 249 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 249 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 58 23%
Researcher 51 20%
Student > Bachelor 32 13%
Student > Master 19 8%
Student > Doctoral Student 17 7%
Other 28 11%
Unknown 44 18%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 93 37%
Agricultural and Biological Sciences 46 18%
Medicine and Dentistry 18 7%
Engineering 13 5%
Neuroscience 8 3%
Other 17 7%
Unknown 54 22%

Attention Score in Context

This research output has an Altmetric Attention Score of 24. 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
#1,237,940
of 21,431,229 outputs
Outputs from Genome Biology (Online Edition)
#1,112
of 3,969 outputs
Outputs of similar age
#29,642
of 298,223 outputs
Outputs of similar age from Genome Biology (Online Edition)
#1
of 1 outputs
Altmetric has tracked 21,431,229 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 3,969 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 27.5. This one has gotten more attention than average, scoring higher than 71% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 298,223 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 90% of its contemporaries.
We're also able to compare this research output to 1 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them