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Direct reprogramming of urine-derived cells with inducible MyoD for modeling human muscle disease

Overview of attention for article published in Skeletal Muscle, September 2016
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About this Attention Score

  • Good Attention Score compared to outputs of the same age (68th percentile)

Mentioned by

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8 tweeters

Citations

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27 Dimensions

Readers on

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59 Mendeley
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Title
Direct reprogramming of urine-derived cells with inducible MyoD for modeling human muscle disease
Published in
Skeletal Muscle, September 2016
DOI 10.1186/s13395-016-0103-9
Pubmed ID
Authors

Ellis Y. Kim, Patrick Page, Lisa M. Dellefave-Castillo, Elizabeth M. McNally, Eugene J. Wyatt

Abstract

Cellular models of muscle disease are taking on increasing importance with the large number of genes and mutations implicated in causing myopathies and the concomitant need to test personalized therapies. Developing cell models relies on having an easily obtained source of cells, and if the cells are not derived from muscle itself, a robust reprogramming process is needed. Fibroblasts are a human cell source that works well for the generation of induced pluripotent stem cells, which can then be differentiated into cardiomyocyte lineages, and with less efficiency, skeletal muscle-like lineages. Alternatively, direct reprogramming with the transcription factor MyoD has been used to generate myotubes from cultured human fibroblasts. Although useful, fibroblasts require a skin biopsy to obtain and this can limit their access, especially from pediatric populations. We now demonstrate that direct reprogramming of urine-derived cells is a highly efficient and reproducible process that can be used to establish human myogenic cells. We show that this method can be applied to urine cells derived from normal individuals as well as those with muscle diseases. Furthermore, we show that urine-derived cells can be edited using CRISPR/Cas9 technology. With progress in understanding the molecular etiology of human muscle diseases, having a readily available, noninvasive source of cells from which to generate muscle-like cells is highly useful.

Twitter Demographics

The data shown below were collected from the profiles of 8 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 59 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 1 2%
Unknown 58 98%

Demographic breakdown

Readers by professional status Count As %
Researcher 11 19%
Student > Ph. D. Student 11 19%
Student > Bachelor 6 10%
Student > Postgraduate 5 8%
Student > Doctoral Student 4 7%
Other 11 19%
Unknown 11 19%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 23 39%
Agricultural and Biological Sciences 12 20%
Medicine and Dentistry 6 10%
Neuroscience 4 7%
Immunology and Microbiology 1 2%
Other 1 2%
Unknown 12 20%

Attention Score in Context

This research output has an Altmetric Attention Score of 4. 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 26 October 2016.
All research outputs
#3,888,742
of 13,845,567 outputs
Outputs from Skeletal Muscle
#137
of 260 outputs
Outputs of similar age
#82,835
of 263,885 outputs
Outputs of similar age from Skeletal Muscle
#1
of 1 outputs
Altmetric has tracked 13,845,567 research outputs across all sources so far. This one has received more attention than most of these and is in the 71st percentile.
So far Altmetric has tracked 260 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 7.6. This one is in the 44th percentile – i.e., 44% 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 263,885 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 68% 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