William A. Stoy

Ph.D. Proposal Presentation

Date: Thursday, April 20th 2017

Time: 12:00 PM

Location: Van Leer Building, Room C340

Advisor:

Craig R. Forest, Ph.D. (Georgia Institute of Technology, School of Mechanical Engineering and Dept. of BME)

Thesis Committee:

Dr. Garrett Stanley, PhD (Georgia Institute of Technology, BME)

Dr. Todd Sulchek, PhD (Georgia Institute of Technology, BME)

Dr. Jun Ueda, PhD (Georgia Institute of Technology, ME)

Dr. Albert Lee, PhD (Janelia Research Campus, HHMI)

Automated single-cell electroporation and subcortical whole-cell recording in vivo

The atomic unit of the brain is the neuron, a branching, tree-like cell that can perform computations with fluctuations in the electrical potentials across its membrane and communicate to its neighbors with chemical signals. In the early 20th century, Santiago Ramon y Cajal pioneered a histological procedure to reveal a sparse population of these neurons in the dense, webbed network of the brain. Not only did he draw the myriad of structures he saw, revealing cells of vastly different sizes and arborizations, but he connected them with arrows into networks decades before the first signals were recorded from individual neurons. Recently, scientists have developed techniques to reveal not only the fine structure of these neurons, but record the subtleties of their electrical activity. The simultaneous classification of these neurons into cell types by electrical activity, morphology, genetic expression, and connectivity throughout the brain is a major thrust of the Brain Research through Advancing Innovative Neurotechnologies (BRAIN) Initiative. Patch clamp recordings are a gold-standard technique for probing the electrophysiology and morphology of single cells in the living brain, and are therefore uniquely positioned for the classification of neuronal cell types. While valuable, the performance of these experiments in vivo is time consuming, highly manual, and suffers from low yield. In vivo patch clamping also presents unique challenges, including the identification, acquisition, and maintenance of single-cell contact, often without visual guidance in moving tissue.

To improve the throughput and reduce the barrier to entry for these high value experiments, our lab recently developed the Autopatcher, a robot that automatically performs whole-cell recordings in the living mouse brain by algorithmically detecting and recording from individual cells. Towards the goal of the BRAIN Initiative to develop tools to, “enable novel automated and scalable analyses of single neurons in situ,” and enable high-throughput cell typing throughout the living brain, I will (1) automate cell-attached recording and single cell labeling in vivo, (2) develop an algorithm to enable whole-cell electrophysiology far below the cortical surface, and (3) improve the yield of gigasealing in subcortical regions in vivo. The development of this system will open the door for a host of high-value experiments throughout the brain, including rapid cell type categorization along electrophysiological and morphological axes.

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Meeting ID: 922961741