| Title |
Lessons from single-cell transcriptome analysis of oxygen-sensing cells
|
|---|---|
| Published in |
Cell and Tissue Research, September 2017
|
| DOI | 10.1007/s00441-017-2682-0 |
| Pubmed ID | |
| Authors |
Ting Zhou, Hiroaki Matsunami |
| Abstract |
The advent of single-cell RNA-sequencing (RNA-Seq) technology has enabled transcriptome profiling of individual cells. Comprehensive gene expression analysis at the single-cell level has proven to be effective in characterizing the most fundamental aspects of cellular function and identity. This unbiased approach is revolutionary for small and/or heterogeneous tissues like oxygen-sensing cells in identifying key molecules. Here, we review the major methods of current single-cell RNA-Seq technology. We discuss how this technology has advanced the understanding of oxygen-sensing glomus cells in the carotid body and helped uncover novel oxygen-sensing cells and mechanisms in the mice olfactory system. We conclude by providing our perspective on future single-cell RNA-Seq research directed at oxygen-sensing cells. |
X Demographics
The data shown below were collected from the profiles of 3 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| France | 1 | 33% |
| Unknown | 2 | 67% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 2 | 67% |
| Scientists | 1 | 33% |
Mendeley readers
The data shown below were compiled from readership statistics for 36 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 36 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 11 | 31% |
| Researcher | 4 | 11% |
| Student > Doctoral Student | 3 | 8% |
| Student > Bachelor | 2 | 6% |
| Professor | 2 | 6% |
| Other | 5 | 14% |
| Unknown | 9 | 25% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 7 | 19% |
| Biochemistry, Genetics and Molecular Biology | 6 | 17% |
| Medicine and Dentistry | 4 | 11% |
| Neuroscience | 4 | 11% |
| Social Sciences | 1 | 3% |
| Other | 3 | 8% |
| Unknown | 11 | 31% |
Attention Score in Context
This research output has an Altmetric Attention Score of 1. 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 10 September 2017.
All research outputs
#16,061,913
of 23,839,820 outputs
Outputs from Cell and Tissue Research
#1,468
of 2,279 outputs
Outputs of similar age
#200,833
of 317,794 outputs
Outputs of similar age from Cell and Tissue Research
#7
of 29 outputs
Altmetric has tracked 23,839,820 research outputs across all sources so far. This one is in the 22nd percentile – i.e., 22% of other outputs scored the same or lower than it.
So far Altmetric has tracked 2,279 research outputs from this source. They receive a mean Attention Score of 3.3. This one is in the 27th percentile – i.e., 27% of its peers scored the same or lower than it.
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We're also able to compare this research output to 29 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 72% of its contemporaries.