Doctoral Thesis: Towards Dissecting Neural Ensembles: Development of Genetic Profiling and Targeting Approaches

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Event Speaker: 

Yinqing Li

Event Location: 

32-D463 (Star Room)

Event Date/Time: 

Wednesday, February 24, 2016 - 10:00am

Abstract
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.
 
Thesis Supervisor: Feng Zhang
Broad Institute