| Chapter title |
A Review of the Methods for Human iPSC Derivation
|
|---|---|
| Chapter number | 3 |
| Book title |
Pluripotent Stem Cells
|
| Published in |
Methods in molecular biology, January 2013
|
| DOI | 10.1007/978-1-62703-348-0_3 |
| Pubmed ID | |
| Book ISBNs |
978-1-62703-347-3, 978-1-62703-348-0
|
| Authors |
Nasir Malik, Mahendra S. Rao, Malik, Nasir, Rao, Mahendra S. |
| Abstract |
The ability to reprogram somatic cells to induced pluripotent stem cells (iPSCs) offers an opportunity to generate pluripotent patient-specific cell lines that can help model human diseases. These iPSC lines could also be powerful tools for drug discovery and the development of cellular transplantation therapies. Many methods exist for generating iPSC lines but those best suited for use in studying human diseases and developing therapies must be of adequate efficiency to produce iPSCs from samples that may be of limited abundance, capable of reprogramming cells from both skin fibroblasts and blood, and footprint-free. Several reprogramming techniques meet these criteria and can be utilized to derive iPSCs in projects with both basic scientific and therapeutic goals. Combining these reprogramming methods with small molecule modulators of signaling pathways can lead to successful generation of iPSCs from even the most recalcitrant patient-derived somatic cells. |
X Demographics
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 1 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Scientists | 1 | 100% |
Mendeley readers
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Netherlands | 1 | <1% |
| Sweden | 1 | <1% |
| Finland | 1 | <1% |
| United Kingdom | 1 | <1% |
| Spain | 1 | <1% |
| United States | 1 | <1% |
| Unknown | 709 | 99% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Bachelor | 148 | 21% |
| Student > Ph. D. Student | 130 | 18% |
| Student > Master | 112 | 16% |
| Researcher | 50 | 7% |
| Student > Doctoral Student | 27 | 4% |
| Other | 44 | 6% |
| Unknown | 204 | 29% |
| Readers by discipline | Count | As % |
|---|---|---|
| Biochemistry, Genetics and Molecular Biology | 197 | 28% |
| Agricultural and Biological Sciences | 96 | 13% |
| Neuroscience | 49 | 7% |
| Medicine and Dentistry | 46 | 6% |
| Engineering | 27 | 4% |
| Other | 75 | 10% |
| Unknown | 225 | 31% |