| dc.contributor.advisor | Manolis Kellis and Myriam Heiman. | en_US |
| dc.contributor.author | Pineda, Sergio Sebastian. | en_US |
| dc.contributor.other | Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science. | en_US |
| dc.date.accessioned | 2021-01-06T18:32:47Z | |
| dc.date.available | 2021-01-06T18:32:47Z | |
| dc.date.copyright | 2020 | en_US |
| dc.date.issued | 2020 | en_US |
| dc.identifier.uri | https://hdl.handle.net/1721.1/129176 | |
| dc.description | Thesis: S.M., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, September, 2020 | en_US |
| dc.description | Cataloged from student-submitted PDF version of thesis. | en_US |
| dc.description | Includes bibliographical references (pages 45-48). | en_US |
| dc.description.abstract | Uncovering the mechanisms that drive cell type-specific vulnerability differences in Huntington's disease (HD) is an imperative prerequisite to finding a reliable therapeutic target. The most affected regions and cell types likely possess vulnerability factors or lack protective factors possessed by other cell types that lead to their early dysfunction and enhanced loss, but such factors remain elusive. In order to characterize the cell type-specific responses induced by mutant huntingtin (mHTT), we applied single-nucleus RNA sequencing to pro- file gene expression changes in human HD and two commonly used mouse models of HD. In the process, we produced the first molecular atlas of the human neostriatum, identified new and previously unobserved cell types, and examined the molecular conservation of this brain region across species. We also developed and documented new computational methods and techniques for curating and analyzing single-cell data from post-mortem human brain samples. | en_US |
| dc.description.statementofresponsibility | by Sergio Sebastian Pineda. | en_US |
| dc.format.extent | 48 pages | en_US |
| dc.language.iso | eng | en_US |
| dc.publisher | Massachusetts Institute of Technology | en_US |
| dc.rights | MIT theses may be protected by copyright. Please reuse MIT thesis content according to the MIT Libraries Permissions Policy, which is available through the URL provided. | en_US |
| dc.rights.uri | http://dspace.mit.edu/handle/1721.1/7582 | en_US |
| dc.subject | Electrical Engineering and Computer Science. | en_US |
| dc.title | Single-cell transcriptional profiling of Huntington's disease in human and mouse models | en_US |
| dc.title.alternative | Single-cell transcriptional profiling of HD in human and mouse models | en_US |
| dc.type | Thesis | en_US |
| dc.description.degree | S.M. | en_US |
| dc.contributor.department | Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science | en_US |
| dc.identifier.oclc | 1227278096 | en_US |
| dc.description.collection | S.M. Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science | en_US |
| dspace.imported | 2021-01-06T18:32:46Z | en_US |
| mit.thesis.degree | Master | en_US |
| mit.thesis.department | EECS | en_US |