title: MATLAB-based automated patch-clamp system for awake behaving mice.
abstract: Automation has been an important part of biomedical research for decades, and the use of automated and robotic systems is now standard for such tasks as DNA sequencing, microfluidics, and high-throughput screening. Recently, Kodandaramaiah and colleagues (Nat Methods 9: 585-587, 2012) demonstrated, using anesthetized animals, the feasibility of automating blind patch-clamp recordings in vivo. Blind patch is a good target for automation because it is a complex yet highly stereotyped process that revolves around analysis of a single signal (electrode impedance) and movement along a single axis. Here, we introduce an automated system for blind patch-clamp recordings from awake, head-fixed mice running on a wheel. In its design, we were guided by 3 requirements: easy-to-use and easy-to-modify software; seamless integration of behavioral equipment; and efficient use of time. The resulting system employs equipment that is standard for patch recording rigs, moderately priced, or simple to make. It is written entirely in MATLAB, a programming environment that has an enormous user base in the neuroscience community and many available resources for analysis and instrument control. Using this system, we obtained 19 whole cell patch recordings from neurons in the prefrontal cortex of awake mice, aged 8-9 wk. Successful recordings had series resistances that averaged 52 ± 4 MΩ and required 5.7 ± 0.6 attempts to obtain. These numbers are comparable with those of experienced electrophysiologists working manually, and this system, written in a simple and familiar language, will be useful to many cellular electrophysiologists who wish to study awake behaving mice.
authors.affiliation: Center for Learning and Memory, Department of Neuroscience, The University of Texas at Austin, Austin, Texas desai@utexas.edu.
authors.forename: Niraj S
authors.initials: NS
authors.lastname: Desai
authors.affiliation: Center for Learning and Memory, Department of Neuroscience, The University of Texas at Austin, Austin, Texas.
authors.forename: Jennifer J
authors.initials: JJ
authors.lastname: Siegel
authors.affiliation: Center for Learning and Memory, Department of Neuroscience, The University of Texas at Austin, Austin, Texas.
authors.forename: William
authors.initials: W
authors.lastname: Taylor
authors.affiliation: Center for Learning and Memory, Department of Neuroscience, The University of Texas at Austin, Austin, Texas.
authors.forename: Raymond A
authors.initials: RA
authors.lastname: Chitwood
authors.affiliation: Center for Learning and Memory, Department of Neuroscience, The University of Texas at Austin, Austin, Texas.
authors.forename: Daniel
authors.initials: D
authors.lastname: Johnston
mesh_terms: D000818:Animals; D001522:Behavior, Animal / Q000502:physiology; D004867:Equipment Design; D008297:Male; D051379:Mice; D008810:Mice, Inbred C57BL; D009474:Neurons / Q000502:physiology*; D018408:Patch-Clamp Techniques / Q000295:instrumentation / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D017397:Prefrontal Cortex / Q000502:physiology*; D012420:Running / Q000502:physiology*; D012984:Software; D013997:Time Factors; D014851:Wakefulness / Q000502:physiology
keywords: automation; awake; behaving; mouse; patch clamp