期刊
JOURNAL OF NEUROPHYSIOLOGY
卷 121, 期 6, 页码 2341-2357出版社
AMER PHYSIOLOGICAL SOC
DOI: 10.1152/jn.00738.2018
关键词
automated; layer 5; in vivo; patch clamp; robotic; visual cortex
资金
- NSF Integrative Graduate Education Research Traineeship [0965945]
- Georgia Institute of Technology Presidential Fellowship
- NSF Graduate Research Fellowship
- NIH Computational Neuroscience Training grant [DA032466-02]
- Georgia Tech Neural Engineering Center Seed Grant
- NIH [NIH R01NS102727, 1-U01-MH106027-01, 1-R01-EY023173, 5-R44-NS083108-03]
- Georgia Tech Fund for Innovation in Research and Education (GT-FIRE)
- Georgia Tech Institute for Bioengineering and Biosciences Junior Faculty Award
- Georgia Tech Technology Fee Fund
- Georgia Tech Invention Studio
- George W. Woodruff School of Mechanical Engineering
- Division Of Graduate Education
- Direct For Education and Human Resources [0965945] Funding Source: National Science Foundation
Patch clamping is the gold standard measurement technique for cell-type characterization in vivo, but it has low throughput, is difficult to scale, and requires highly skilled operation. We developed an autonomous robot that can acquire multiple consecutive patch-clamp recordings in vivo. In practice, 40 pipettes loaded into a carousel are sequentially filled and inserted into the brain, localized to a cell, used for patch clamping, and disposed. Automated visual stimulation and electrophysiology software enables functional cell-type classification of whole cell-patched cells, as we show for 37 cells in the anesthetized mouse in visual cortex (V1) layer 5. We achieved 9% yield, with 5.3 min per attempt over hundreds of trials. The highly variable and low-yield nature of in vivo patch-clamp recordings will benefit from such a standardized, automated, quantitative approach, allowing development of optimal algorithms and enabling scaling required for large-scale studies and integration with complementary techniques. NEW & NOTEWORTHY In vivo patch-clamp is the gold standard for intracellular recordings, but it is a very manual and highly skilled technique. The robot in this work demonstrates the most automated in vivo patch-clamp experiment to date, by enabling production of multiple, serial intracellular recordings without human intervention. The robot automates pipette tilling, wire threading. pipette positioning, neuron hunting, break-in, delivering sensory stimulus, and recording quality control, enabling in vivo cell-type characterization.
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