| Chapter title |
Isolation and Expansion of Muscle Precursor Cells from Human Skeletal Muscle Biopsies
|
|---|---|
| Chapter number | 321 |
| Book title |
Stem Cell Heterogeneity
|
| Published in |
Methods in molecular biology, April 2016
|
| DOI | 10.1007/7651_2016_321 |
| Pubmed ID | |
| Book ISBNs |
978-1-4939-6549-6, 978-1-4939-6550-2
|
| Authors |
Franzin, Chiara, Piccoli, Martina, Urbani, Luca, Biz, Carlo, Gamba, Piergiorgio, De Coppi, Paolo, Pozzobon, Michela, Franzin, C, Piccoli, M, Urbani, L, Biz, C, Gamba, P, De Coppi, P, Pozzobon, M, Chiara Franzin, Martina Piccoli, Luca Urbani, Carlo Biz, Piergiorgio Gamba, Paolo De Coppi, Michela Pozzobon, Coppi, Paolo De, Coppi, Paolo |
| Abstract |
One of the major issues concerning human skeletal muscle progenitor cells is represented by the efficient isolation and in vitro expansion of cells retaining the ability to proliferate, migrate and differentiate once transplanted. Here we describe a method (1) effective in obtaining human muscle precursor cells both from fresh and frozen biopsies coming from different muscles, (2) selective to yield cells uniformly positive for CD56 and negative for CD34 without FACS sorting, (3) reliable in maintaining proliferative and in vitro differentiative capacity up to passage 10. |
X Demographics
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 4 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 3 | 75% |
| Scientists | 1 | 25% |
Mendeley readers
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 30 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 5 | 17% |
| Other | 4 | 13% |
| Student > Master | 4 | 13% |
| Student > Ph. D. Student | 3 | 10% |
| Student > Postgraduate | 2 | 7% |
| Other | 3 | 10% |
| Unknown | 9 | 30% |
| Readers by discipline | Count | As % |
|---|---|---|
| Biochemistry, Genetics and Molecular Biology | 5 | 17% |
| Agricultural and Biological Sciences | 5 | 17% |
| Medicine and Dentistry | 4 | 13% |
| Engineering | 2 | 7% |
| Chemistry | 1 | 3% |
| Other | 0 | 0% |
| Unknown | 13 | 43% |