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Generation of 3D Skin Equivalents Fully Reconstituted from Human Induced Pluripotent Stem Cells (iPSCs)

Overview of attention for article published in PLOS ONE, October 2013
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Title
Generation of 3D Skin Equivalents Fully Reconstituted from Human Induced Pluripotent Stem Cells (iPSCs)
Published in
PLOS ONE, October 2013
DOI 10.1371/journal.pone.0077673
Pubmed ID
Authors

Munenari Itoh, Noriko Umegaki-Arao, Zongyou Guo, Liang Liu, Claire A. Higgins, Angela M. Christiano

Abstract

Recent generation of patient-specific induced pluripotent stem cells (PS-iPSCs) provides significant advantages for cell- and gene-based therapy. Establishment of iPSC-based therapy for skin diseases requires efficient methodology for differentiating iPSCs into both keratinocytes and fibroblasts, the major cellular components of the skin, as well as the reconstruction of skin structures using these iPSC-derived skin components. We previously reported generation of keratinocytes from human iPSCs for use in the treatment of recessive dystrophic epidermolysis bullosa (RDEB) caused by mutations in the COL7A1 gene. Here, we developed a protocol for differentiating iPSCs into dermal fibroblasts, which also produce type VII collagen and therefore also have the potential to treat RDEB. Moreover, we generated in vitro 3D skin equivalents composed exclusively human iPSC-derived keratinocytes and fibroblasts for disease models and regenerative therapies for skin diseases, first demonstrating that iPSCs can provide the basis for modeling a human organ derived entirely from two different types of iPSC-derived cells.

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The data shown below were compiled from readership statistics for 316 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Netherlands 2 <1%
Canada 1 <1%
Unknown 313 99%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 61 19%
Researcher 55 17%
Student > Master 44 14%
Student > Bachelor 37 12%
Other 16 5%
Other 37 12%
Unknown 66 21%
Readers by discipline Count As %
Agricultural and Biological Sciences 72 23%
Biochemistry, Genetics and Molecular Biology 70 22%
Medicine and Dentistry 30 9%
Engineering 21 7%
Immunology and Microbiology 13 4%
Other 34 11%
Unknown 76 24%