Whole-cell patch clamp electrophysiology of neurons in vivo enables the recording of electrical events in cells with great precision, and supports a wide diversity of morphological and molecular analysis experiments important for the understanding of single-cell and network functions in the intact brain. However, high levels of skill are required in order to perform in vivo patching, and the process is time-consuming and painstaking. Robotic systems for in vivo patching would not only empower a great number of neuroscientists to perform such experiments, but would also open up fundamentally new kinds of experiment enabled by the resultant high throughput and scalability. We discovered that in vivo blind whole cell patch clamp electrophysiology could be implemented as a straightforward algorithm and developed an automated robotic system that was capable of performing this algorithm. We validated the performance of the robot in both the cortex and hippocampus of anesthetized mice. The robot achieves yields, cell recording qualities, and operational speeds that are comparable to, or exceed, those of experienced human investigators. Building upon this framework, we developed a multichannel version of “autopatcher” robot capable establishing whole cell patch clamp recordings from pairs and triplets of neurons in the cortex simultaneously. These algorithms can be generalized to control arbitrarily large number of electrodes and the high yield, throughput and automation of complex set of tasks results in a practical solution for conducting patch clamp recordings in potentially dozens of interconnected neurons in vivo.
| Identifer | oai:union.ndltd.org:GATECH/oai:smartech.gatech.edu:1853/51932 |
| Date | 10 January 2012 |
| Creators | Kodandaramaiah, Suhasa Bangalore |
| Contributors | Forest, Craig Richard, Boyden, Edward S. |
| Publisher | Georgia Institute of Technology |
| Source Sets | Georgia Tech Electronic Thesis and Dissertation Archive |
| Language | en_US |
| Detected Language | English |
| Type | Dissertation |
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