| Chapter title |
Capturing Three-Dimensional Genome Organization in Individual Cells by Single-Cell Hi-C
|
|---|---|
| Chapter number | 6 |
| Book title |
Functional Genomics
|
| Published in |
Methods in molecular biology, January 2017
|
| DOI | 10.1007/978-1-4939-7231-9_6 |
| Pubmed ID | |
| Book ISBNs |
978-1-4939-7230-2, 978-1-4939-7231-9
|
| Authors |
Takashi Nagano, Steven W. Wingett, Peter Fraser |
| Abstract |
Hi-C is a powerful method to investigate genome-wide, higher-order chromatin and chromosome conformations averaged from a population of cells. To expand the potential of Hi-C for single-cell analysis, we developed single-cell Hi-C. Similar to the existing "ensemble" Hi-C method, single-cell Hi-C detects proximity-dependent ligation events between cross-linked and restriction-digested chromatin fragments in cells. A major difference between the single-cell Hi-C and ensemble Hi-C protocol is that the proximity-dependent ligation is carried out in the nucleus. This allows the isolation of individual cells in which nearly the entire Hi-C procedure has been carried out, enabling the production of a Hi-C library and data from individual cells. With this new method, we studied genome conformations and found evidence for conserved topological domain organization from cell to cell, but highly variable interdomain contacts and chromosome folding genome wide. In addition, we found that the single-cell Hi-C protocol provided cleaner results with less technical noise suggesting it could be used to improve the ensemble Hi-C technique. |
X Demographics
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Canada | 1 | 13% |
| Japan | 1 | 13% |
| Hong Kong | 1 | 13% |
| France | 1 | 13% |
| Unknown | 4 | 50% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Scientists | 4 | 50% |
| Members of the public | 3 | 38% |
| Science communicators (journalists, bloggers, editors) | 1 | 13% |
Mendeley readers
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 29 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 8 | 28% |
| Student > Ph. D. Student | 6 | 21% |
| Professor | 3 | 10% |
| Other | 3 | 10% |
| Student > Doctoral Student | 2 | 7% |
| Other | 4 | 14% |
| Unknown | 3 | 10% |
| Readers by discipline | Count | As % |
|---|---|---|
| Biochemistry, Genetics and Molecular Biology | 12 | 41% |
| Agricultural and Biological Sciences | 9 | 31% |
| Physics and Astronomy | 2 | 7% |
| Computer Science | 1 | 3% |
| Immunology and Microbiology | 1 | 3% |
| Other | 1 | 3% |
| Unknown | 3 | 10% |