| Chapter title |
Directed Myogenic Differentiation of Human Induced Pluripotent Stem Cells.
|
|---|---|
| Chapter number | 257 |
| Book title |
Patient-Specific Induced Pluripotent Stem Cell Models
|
| Published in |
Methods in molecular biology, May 2015
|
| DOI | 10.1007/7651_2015_257 |
| Pubmed ID | |
| Book ISBNs |
978-1-4939-3033-3, 978-1-4939-3034-0
|
| Authors |
Shoji, Emi, Woltjen, Knut, Sakurai, Hidetoshi, Emi Shoji, Knut Woltjen, Hidetoshi Sakurai |
| Editors |
Andras Nagy, Kursad Turksen |
| Abstract |
Patient-derived induced pluripotent stem cells (iPSCs) have opened the door to recreating pathological conditions in vitro using differentiation into diseased cells corresponding to each target tissue. Yet for muscular diseases, a method for reproducible and efficient myogenic differentiation from human iPSCs is required for in vitro modeling. Here, we introduce a myogenic differentiation protocol mediated by inducible transcription factor expression that reproducibly and efficiently drives human iPSCs into myocytes. Delivering a tetracycline-inducible,myogenic differentiation 1 (MYOD1) piggyBac (PB) vector to human iPSCs enables the derivation of iPSCs that undergo uniform myogenic differentiation in a short period of time. This differentiation protocol yields a homogenous skeletal muscle cell population, reproducibly reaching efficiencies as high as 70-90 %. MYOD1-induced myocytes demonstrate characteristics of mature myocytes such as cell fusion and cell twitching in response to electric stimulation within 14 days of differentiation. This differentiation protocol can be applied widely in various types of patient-derived human iPSCs and has great prospects in disease modeling particularly with inherited diseases that require studies of early pathogenesis and drug screening. |
X Demographics
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 1 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 1 | 100% |
Mendeley readers
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 1 | 2% |
| Unknown | 56 | 98% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 13 | 23% |
| Researcher | 11 | 19% |
| Student > Bachelor | 6 | 11% |
| Student > Master | 4 | 7% |
| Student > Doctoral Student | 2 | 4% |
| Other | 5 | 9% |
| Unknown | 16 | 28% |
| Readers by discipline | Count | As % |
|---|---|---|
| Biochemistry, Genetics and Molecular Biology | 15 | 26% |
| Agricultural and Biological Sciences | 10 | 18% |
| Neuroscience | 6 | 11% |
| Chemical Engineering | 3 | 5% |
| Engineering | 3 | 5% |
| Other | 4 | 7% |
| Unknown | 16 | 28% |