*********************************
There is now a CONTENT FREEZE for Mercury while we switch to a new platform. It began on Friday, March 10 at 6pm and will end on Wednesday, March 15 at noon. No new content can be created during this time, but all material in the system as of the beginning of the freeze will be migrated to the new platform, including users and groups. Functionally the new site is identical to the old one. webteam@gatech.edu
*********************************
Ilya Kolb
Ph.D. Proposal Presentation
Tuesday, Nov. 10th, 2015, 1 PM
Suddath Room (1128 IBB)
Advisor: Craig R. Forest, Ph.D. (Georgia Institute of Technology)
Committee:
Christopher Rozell, Ph.D. (Georgia Institute of Technology)
Annabelle Singer, Ph.D. (Georgia Institute of Technology)
Andrew Jenkins, Ph.D. (Emory University)
Tim Jarsky, Ph.D. (Allen Institute for Brain Science)
Autonomous image-guided patch-clamp electrophysiology in vitro
Neurons in the brain are highly diverse in their function, shape, and inter-connectivity patterns, leading to incredible complexity of brain circuits in even the simplest organisms. Classifying neurons into unambiguous cell types based on these features would facilitate management and dissemination of neuroscience knowledge and is thus considered a “holy grail” of neuroscience. Whole-cell patch-clamp recording in vitro is a gold-standard technique for probing the electrophysiology, morphology and connectivity properties of single neurons, making it ideally suited for classifying neuronal cell types. However, the highly manual and time-consuming nature of patch-clamp experiments limits the number of neurons that can be sampled to only a handful per day. In contrast, initiatives aimed at creating unified taxonomies of cell types and their inter-connectivities will require data from thousands of patch-clamped cells to achieve their goal.
To reduce the manual labor associated with gathering large sets of patch-clamp data, this work focuses on the development of a system for patch-clamp recording that can operate autonomously. Specifically, the aims of this proposal are to (1) integrate manipulator and pipette pressure control into an open-source patch-clamp automation system, (2) develop and optimize a method for patch-clamp pipette re-use, (3) use image processing to enable automatic cell recognition and targeting, and finally, (4) enable sequential autonomous image-guided patch-clamp recordings in vitro. The development of this system will transform in-vitro patch-clamp recording from a highly manual and time-consuming experiment into a “walk-away” autonomous system. This will enable vast parallelization of patch-clamp experiments, a necessity for gathering large datasets.
