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Volume 14 Supplement 1

Abstracts from the Twenty Second Annual Computational Neuroscience Meeting: CNS*2013

  • Poster presentation
  • Open Access

Automated, in-vivo, whole-cell electrophysiology using an integrated patch-clamp amplifier

  • †1,
  • †2,
  • 3,
  • 4,
  • 4, 5,
  • 6 and
  • †2
†Contributed equally
BMC Neuroscience201314 (Suppl 1) :P131

https://doi.org/10.1186/1471-2202-14-S1-P131

©  Kolb et al; licensee BioMed Central Ltd. 2013

  • Published:

Keywords

  • Patch Clamp
  • Software Suite
  • Commercial Software Package
  • Current Clamp
  • Data Acquisition Board

Whole-cell patch clamp recordings of neuronal activity in vivo exhibit signal quality sufficient to report synaptic and ion channel-mediated subthreshold events of importance for understanding neural processing during normal as well as drug-mediated behavior. Recently, we developed a prototype "autopatching" robot [1] that enables automated whole-cell patch clamp recording of neurons in a living mouse brain. While automation of the in-vivo patch clamping process opens up the possibility of multi-channel in vivo electrophysiology, scaling up the current system would dramatically increase system cost and complexity. Specifically, the current autopatching algorithm relies on several discrete pieces of hardware such as an amplifier, headstage and digitizer--as well as commercial software to communicate with the equipment and perform electrophysiology.

We will present an automated, in-vivo, whole cell electrophysiology suite that consists of a four-channel microchip-based patch clamp amplifier [2] and software that dramatically simplifies the setup necessary for traditional automated patch clamping. This system (Figure 1A) eliminates the need for the conventional amplifier, digitizer, headstage, and data acquisition board to effectively combine the high-throughput nature of the autopatcher [1] with the voltage and current clamp toolsets available in commercial software packages. Un-automated in vivo whole-cell recordings have already been demonstrated using a single-channel microchip-based patch clamp amplifier with conventional pCLAMP software (Molecular Devices, Sunnyvale, CA; Figure 1B). Preliminary results indicate that our simpler, multi-channel electrophysiology suite can perform computations required for automatic neuron detection, leading to successful whole-cell patches. We will report progress toward total automated integration and incorporation of electrophysiology tools into the software suite. The total cost required for the patch chip and the electrophysiology software suite should be several orders of magnitude less than that of conventional systems, and far simpler to handle, integrate with existing rigs, and use.
Figure 1
Figure 1

(A) Single-channel patch chip amplifier in the patch clamping setup. (B) Representative in vivo whole-cell recordings in current clamp mode using conventional pCLAMP software and the patch chip.

Notes

Authors’ Affiliations

(1)
Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology, Atlanta, GA 30332, USA
(2)
George W. Woodruff School of Mechanical Engineering, Georgia Institute of Technology, Atlanta, GA 30332, USA
(3)
Department of Electrical Engineering, Yale University, New Haven, CT 06520, USA
(4)
Media Lab, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
(5)
McGovern Institute, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
(6)
Weldon School of Biomedical Engineering, Purdue University, West Lafayette, IN 47907, USA

References

  1. Kodandaramaiah SB, Franzesi GT, Chow BY, Boyden ES, Forest CR: Automated whole-cell patch-clamp electrophysiology of neurons in vivo. Nature Methods. 2012, 9: 585-587. 10.1038/nmeth.1993.PubMed CentralView ArticlePubMedGoogle Scholar
  2. Goldstein B, Choe K, Sigworth FJ, Culurciello E: A four-channel integrated patch-clamp amplifier with current-clamp capability. 2011 IEEE 54th International Midwest Symposium on Circuits and Systems (MWSCAS). 2011, 1-4.View ArticleGoogle Scholar

Copyright

© Kolb et al; licensee BioMed Central Ltd. 2013

This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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BMC Neuroscience

ISSN: 1471-2202

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