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Simple Derivation of Spinal Motor Neurons from ESCs/iPSCs Using Sendai Virus Vectors

Overview of attention for article published in Molecular Therapy - Methods & Clinical Development, January 2017
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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 (92nd percentile)
  • High Attention Score compared to outputs of the same age and source (90th percentile)

Mentioned by

news
1 news outlet
blogs
1 blog
twitter
4 X users
patent
2 patents

Citations

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33 Dimensions

Readers on

mendeley
103 Mendeley
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Title
Simple Derivation of Spinal Motor Neurons from ESCs/iPSCs Using Sendai Virus Vectors
Published in
Molecular Therapy - Methods & Clinical Development, January 2017
DOI 10.1016/j.omtm.2016.12.007
Pubmed ID
Authors

Kazuya Goto, Keiko Imamura, Kenichi Komatsu, Kohnosuke Mitani, Kazuhiro Aiba, Norio Nakatsuji, Makoto Inoue, Akihiro Kawata, Hirofumi Yamashita, Ryosuke Takahashi, Haruhisa Inoue

Abstract

Amyotrophic lateral sclerosis (ALS) is a progressive and fatal degenerative disorder of motor neurons (MNs). Embryonic stem cells (ESCs)/induced pluripotent stem cells (iPSCs) now help us to understand the pathomechanisms of ALS via disease modeling. Various methods to differentiate ESCs/iPSCs into MNs by the addition of signaling molecules have been reported. However, classical methods require multiple steps, and newer simple methods using the transduction of transcription factors run the risk of genomic integration of the vector genes. Heterogeneity of the expression levels of the transcription factors also remains an issue. Here we describe a novel approach for differentiating human and mouse ESCs/iPSCs into MNs using a single Sendai virus vector encoding three transcription factors, LIM/homeobox protein 3, neurogenin 2, and islet-1, which are integration free. This single-vector method, generating HB9-positive cells on day 2 from human iPSCs, increases the ratio of MNs to neurons compared to the use of three separate Sendai virus vectors. In addition, the MNs derived via this method from iPSCs of ALS patients and model mice display disease phenotypes. This simple approach significantly reduces the efforts required to generate MNs, and it provides a useful tool for disease modeling.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 103 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 103 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 20 19%
Researcher 17 17%
Student > Bachelor 13 13%
Student > Master 13 13%
Student > Postgraduate 5 5%
Other 12 12%
Unknown 23 22%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 25 24%
Neuroscience 21 20%
Agricultural and Biological Sciences 16 16%
Medicine and Dentistry 9 9%
Engineering 4 4%
Other 6 6%
Unknown 22 21%
Attention Score in Context

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 11 August 2021.
All research outputs
#1,575,358
of 26,151,587 outputs
Outputs from Molecular Therapy - Methods & Clinical Development
#65
of 1,237 outputs
Outputs of similar age
#31,268
of 428,250 outputs
Outputs of similar age from Molecular Therapy - Methods & Clinical Development
#3
of 32 outputs
Altmetric has tracked 26,151,587 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 93rd percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 1,237 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 9.2. This one has done particularly well, scoring higher than 94% 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 428,250 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 92% of its contemporaries.
We're also able to compare this research output to 32 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 90% of its contemporaries.