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Single-cell transcriptome in the identification of disease biomarkers: opportunities and challenges

Overview of attention for article published in Journal of Translational Medicine, August 2014
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1 tweeter

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40 Mendeley
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Title
Single-cell transcriptome in the identification of disease biomarkers: opportunities and challenges
Published in
Journal of Translational Medicine, August 2014
DOI 10.1186/s12967-014-0212-3
Pubmed ID
Authors

Zhitu Zhu, Diane C Wang, Laurenţiu M Popescu, Xiangdong Wang

Abstract

Single cell transcriptome defined as the entire RNA or polyadenylated products of RNA polymerase II on a cell can describe the gene regulation networks responsible for physiological functions, behaviours, and phenotypes in response to signals and microenvironmental changes. Single cell transcriptome/sequencing has the special power to investigate small groups of differentiating cells, circulating tumour cells, or tissue stem cells. A large number of factors may influence the extent of single-cell heterogeneity within a system. It is the opportunity that the single-cell sequencing can be used for the identification of genetic changes in rare cells, e.g. cancer and tissue stem cells, in clinical samples. The methodologies of single-cell sequencing have been improved and developed with the increase of the understanding and attention. The clinical research and application of the single cell sequencing analysis are expected to identify and validate disease-specific biomarkers, network biomarkers, dynamic network biomarkers. The single cell research and value will be also dependent upon the understanding of genomic heterogeneity, planning and design of study protocol, representative of selected and targeted cells, and sensitivity and repeatability of the methodology. The single cell sequencing can be used to develop new diagnostics, monitor disease progresses, measure responses to therapies, and predict the prognosis of patients, although there are still a large number of challenges and difficulties to be faced. It would be more values and specificities of the single cell sequencing to integrate with the function of cells, organs, and systems of the body, the clinical phenotypes of patients, and the description of clinical bioinformatics.

Twitter Demographics

The data shown below were collected from the profile of 1 tweeter 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 40 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 40 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 10 25%
Student > Master 5 13%
Student > Bachelor 5 13%
Student > Postgraduate 4 10%
Other 4 10%
Other 6 15%
Unknown 6 15%
Readers by discipline Count As %
Agricultural and Biological Sciences 11 28%
Biochemistry, Genetics and Molecular Biology 9 23%
Medicine and Dentistry 5 13%
Engineering 3 8%
Psychology 2 5%
Other 4 10%
Unknown 6 15%

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 13 August 2014.
All research outputs
#2,278,850
of 4,508,238 outputs
Outputs from Journal of Translational Medicine
#566
of 1,265 outputs
Outputs of similar age
#54,730
of 111,075 outputs
Outputs of similar age from Journal of Translational Medicine
#45
of 73 outputs
Altmetric has tracked 4,508,238 research outputs across all sources so far. This one is in the 36th percentile – i.e., 36% of other outputs scored the same or lower than it.
So far Altmetric has tracked 1,265 research outputs from this source. They receive a mean Attention Score of 2.9. This one is in the 29th percentile – i.e., 29% of its peers scored the same or lower than it.
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 111,075 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 39th percentile – i.e., 39% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 73 others from the same source and published within six weeks on either side of this one. This one is in the 2nd percentile – i.e., 2% of its contemporaries scored the same or lower than it.