Integrated and scalable molecular brain mapping
Thesis: Ph. D., Massachusetts Institute of Technology, Department of Chemical Engineering, 2017.
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| Format: | Thesis |
| Language: | eng |
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Massachusetts Institute of Technology
2017
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| Online Access: | http://hdl.handle.net/1721.1/111262 |
| _version_ | 1811092116142555136 |
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| author | Cho, Jae H. (Jae Hun) |
| author2 | Kwanghun Chung. |
| author_facet | Kwanghun Chung. Cho, Jae H. (Jae Hun) |
| author_sort | Cho, Jae H. (Jae Hun) |
| collection | MIT |
| description | Thesis: Ph. D., Massachusetts Institute of Technology, Department of Chemical Engineering, 2017. |
| first_indexed | 2024-09-23T15:13:14Z |
| format | Thesis |
| id | mit-1721.1/111262 |
| institution | Massachusetts Institute of Technology |
| language | eng |
| last_indexed | 2024-09-23T15:13:14Z |
| publishDate | 2017 |
| publisher | Massachusetts Institute of Technology |
| record_format | dspace |
| spelling | mit-1721.1/1112622019-04-11T03:02:58Z Integrated and scalable molecular brain mapping Cho, Jae H. (Jae Hun) Kwanghun Chung. Massachusetts Institute of Technology. Department of Chemical Engineering. Massachusetts Institute of Technology. Department of Chemical Engineering. Chemical Engineering. Thesis: Ph. D., Massachusetts Institute of Technology, Department of Chemical Engineering, 2017. This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections. Cataloged from student-submitted PDF version of thesis. Includes bibliographical references (pages 143-158). Understanding the brain requires integrative knowledge of its cellular-, network-, and system-level architectures. Existing volume imaging techniques have proven the potential to provide such information, but the lack of technology to label large volumes for visualization has limited their utility. Here, we address this challenge by developing technologies -- stochastic electrotransport and SWITCH -- to extend multiplexed labeling methods to larger volumes. Stochastic electrotransport selectively expedites transport of molecular probes into the tissue without damaging it. SWITCH synchronizes the labeling reaction to achieve consistent and uniform labeling. These technologies are demonstrated by successfully visualizing several molecular markers in adult mouse brain tissues, which have been previously infeasible in time and cost. Although our focus is on neuroscience, the concepts and methods described in this thesis are quite general. Stochastic electrotransport will be applicable to any nonlinear transport problems, and SWITCH will be applicable to any problem requiring synchronization of reaction kinetics across long distances.. by Jae H. Cho. Ph. D. 2017-09-15T14:21:50Z 2017-09-15T14:21:50Z 2017 2017 Thesis http://hdl.handle.net/1721.1/111262 1003292047 eng MIT theses are protected by copyright. They may be viewed, downloaded, or printed from this source but further reproduction or distribution in any format is prohibited without written permission. http://dspace.mit.edu/handle/1721.1/7582 158 pages application/pdf Massachusetts Institute of Technology |
| spellingShingle | Chemical Engineering. Cho, Jae H. (Jae Hun) Integrated and scalable molecular brain mapping |
| title | Integrated and scalable molecular brain mapping |
| title_full | Integrated and scalable molecular brain mapping |
| title_fullStr | Integrated and scalable molecular brain mapping |
| title_full_unstemmed | Integrated and scalable molecular brain mapping |
| title_short | Integrated and scalable molecular brain mapping |
| title_sort | integrated and scalable molecular brain mapping |
| topic | Chemical Engineering. |
| url | http://hdl.handle.net/1721.1/111262 |
| work_keys_str_mv | AT chojaehjaehun integratedandscalablemolecularbrainmapping |