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Metadata

Name
A myogenic double reporter human pluripotent stem cell line allows prospective isolation of skeletal muscle progenitors
Repository
Gene Expression Omnibus
Identifier
geo.series:GSE121154
Description
Myogenic differentiation of iPSCs has been done by gene–overexpression or directed differentiation. However, viral integration, long-term culture and the presence of unwanted cells are the main obstacles. By using CRISPR/Cas9n, a novel double reporter hESC line was generated for PAX7/MYF5, allowing prospective readout. This strategy allowed pathway screen to define efficient myogenic induction in hESC/iPSCs. Next, surface marker screen allowed identification of CD10 and CD24 for purification of myogenic progenitors. CD10 expression was also confirmed on human satellite cells and muscle progenitors. In vivo studies using transgene/reporter-free hESC/iPSCs further validated myogenic potential of the cells by dystrophin restoration and satellite cell engraftment in NSG-mdx4cv mice. In addition, side-by-side in vitro and in vivo comparison proved superior specificity of CD10/CD24 compared to recently reported markers (ERBB3/NGFR). This study provides new biological insights for myogenic differentiation using a new cell resource and identifies CD10 as a potential myogenic marker.
Data or Study Types
Expression profiling by high throughput sequencing
Source Organization
National Center for Biotechnology Information
Access Conditions
available
Year
2018
Access Hyperlink
http://www.ncbi.nlm.nih.gov/sites/GDSbrowser?acc=GSE121154

Distributions

  • Encoding Format: Bioproject ; URL: https://www.ncbi.nlm.nih.gov/bioproject/PRJNA495854
  • Encoding Format: TXT ; URL: ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE1nnn/GSE121154/matrix/
  • Encoding Format: MINiML ; URL: ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE1nnn/GSE121154/miniml/
  • Encoding Format: SOFT ; URL: ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE1nnn/GSE121154/soft/
This project was funded in part by grant U24AI117966 from the NIH National Institute of Allergy and Infectious Diseases as part of the Big Data to Knowledge program. We thank all members of the bioCADDIE community for their valuable input on the overall project.