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Review
. 2013 Dec;1305(1):63-71.
doi: 10.1111/nyas.12171. Epub 2013 Jul 10.

In vivo robotics: the automation of neuroscience and other intact-system biological fields

Suhasa B Kodandaramaiah  1 , Edward S Boyden, Craig R Forest
Affiliations
Review

In vivo robotics: the automation of neuroscience and other intact-system biological fields

Suhasa B Kodandaramaiah et al. Ann N Y Acad Sci. 2013 Dec.

Abstract

Robotic and automation technologies have played a huge role in in vitro biological science, having proved critical for scientific endeavors such as genome sequencing and high-throughput screening. Robotic and automation strategies are beginning to play a greater role in in vivo and in situ sciences, especially when it comes to the difficult in vivo experiments required for understanding the neural mechanisms of behavior and disease. In this perspective, we discuss the prospects for robotics and automation to influence neuroscientific and intact-system biology fields. We discuss how robotic innovations might be created to open up new frontiers in basic and applied neuroscience and present a concrete example with our recent automation of in vivo whole-cell patch clamp electrophysiology of neurons in the living mouse brain.

Keywords: neuroscience; patch clamping; robotics.

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Conflict of interest statement

Conflicts of interest

C.R.F., E.S.B., and S.B.K. are co-inventors on a patent owned by MIT and Georgia Institute of Technology. C.R.F. and S.B.K. are financially affiliated with Neuromatic Devices, which is seeking to manufacture and sell autopatching robots.

Figures

Figure 1
Figure 1
One of many rows of ABI 3730xl automated DNA Analyzers for shotgun sequencing of the human genome in months (30 billion bp/year) in 2005. (Courtesy: Steve Jurvetson)
Figure 2
Figure 2
The autopatcher: a robot for in vivo patch clamping. (Ai) The four stages of the automated in vivo patch algorithm, discovered through iterative exploration of the parameters governing successful patch clamping: regional pipette localization, in which the pipette is lowered to a target zone in the brain; neuron hunting, in which the pipette is advanced until a neuron is detected via a change in pipette resistance; gigaseal formation, in which a gigaseal state is achieved (if cell-attached patching is desired, the algorithm can end here); break-in, in which the whole cell state is achieved. (Aii) Yields and durations of each of the four stages, when executed by the robot shown in B, running the autopatching algorithm in the living mouse brain, aiming for targets in cortex and hippocampus. (B) Schematic of a simple robotic system capable of performing the autopatching algorithm. The system consists of a conventional in vivo patch setup (i.e., pipette, headstage, three axis linear actuator, patch amplifier plus computer interface board, and computer), equipped with a few additional modules: a programmable linear motor (to move the pipette up and down in a temporally precise fashion), a controllable bank of pneumatic valves for pressure control, and a secondary computer interface board to enable closed-loop control of the motor based upon sequences of pipette resistance measurements. (C) Photograph of the setup, focusing on three axis linear actuator (with additional programmable linear motor) and the holder for head fixing the mouse. (D) Current clamp traces during current injection for a cortical neuron for which whole-cell state was established via autopatcher. (E) Current clamp traces during current injection for a hippocampal neuron for which whole-cell state was established via autopatcher. Adapted, with permission, from Kodandaramaiah et al.
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