Towards dissecting neural ensembles : development of genetic profiling and targeting approaches

The recent development of genetic neural modulation technologies has brought about a renaissance in systems neurobiology, where manipulation of specific neural ensembles coupled with measurements of the resulting behavioral changes have begun to chart the functional organization of the brain. However, the scope of the application of this emerging paradigm depends critically on the ability to systematically identify and manipulate specific neural ensembles, which is currently lacking. In this thesis, we focus on the development of technologies towards systematic identification and targeting of specific neural ensembles. First, we present the development of single nuclei RNA sequencing methods and the application of the methods to chart the cellular diversity and its signature gene expression patterns in the adult mouse hippocampus. Second, we introduce rational design and predictable implementation of ultrasensitive synthetic gene circuits that can sense expression levels of cell-type specific marker genes and compute complex Boolean logic to label these cells specifically. As a proof of principle, we demonstrate that the synthetic circuit achieves classification of two cell lines based on their gene expression profiles with high accuracy. Third, we provide a design of gene circuits that can compute the strength of correlation of neural activity to an external stimulus using immediate early genes. As a proof of principle, we demonstrate that the synthetic gene circuit can be used to label cells that respond to exogenously controlled chemical stimuli as an analog to neural activity. Finally, we describe a streamlined framework for modular construction of large synthetic gene circuits on single vectors and their targeted delivery into mammalian cells.

Department dc:contributor.department

Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science.

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2016

Author dc:creator

• Li, Yinqing, Ph. D. Massachusetts Institute of Technology

Advisor dc:contributor.advisor

• Feng Zhang.

• Electrical Engineering and Computer Science.

Statement dc:rights

• M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission.

Licence dc:rights.uri

• http://dspace.mit.edu/handle/1721.1/7582

Language dc:language.iso

eng

Handle dc:identifier.uri

http://hdl.handle.net/1721.1/106081

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/106081