Integration of autopatching with automated pipette and cell detection in vitro

Qiuyu Wu, Ilya Kolb, Brendan M. Callahan, Zhaolun Su, William Stoy, Suhasa B. Kodandaramaiah, Rachael Neve, Hongkui Zeng, Edward S. Boyden, Craig R. Forest, Alexander A. Chubykin

Research output: Contribution to journalArticlepeer-review

20 Scopus citations

Abstract

Patch clamp is the main technique for measuring electrical properties of individual cells. Since its discovery in 1976 by Neher and Sakmann, patch clamp has been instrumental in broadening our understanding of the fundamental properties of ion channels and synapses in neurons. The conventional patch-clamp method requires manual, precise positioning of a glass micropipette against the cell membrane of a visually identified target neuron. Subsequently, a tight "gigaseal" connection between the pipette and the cell membrane is established, and suction is applied to establish the whole cell patch configuration to perform electrophysiological recordings. This procedure is repeated manually for each individual cell, making it labor intensive and time consuming. In this article we describe the development of a new automatic patch-clamp system for brain slices, which integrates all steps of the patch-clamp process: image acquisition through a microscope, computer visionbased identification of a patch pipette and fluorescently labeled neurons, micromanipulator control, and automated patching. We validated our system in brain slices from wild-type and transgenic mice expressing channelrhodopsin 2 under the Thy1 promoter (line 18) or injected with a herpes simplex virus-expressing archaerhodopsin, ArchT. Our computer vision-based algorithm makes the fluorescent cell detection and targeting user independent. Compared with manual patching, our system is superior in both success rate and average trial duration. It provides more reliable trial-to-trial control of the patching process and improves reproducibility of experiments.

Original languageEnglish (US)
Pages (from-to)1564-1578
Number of pages15
JournalJournal of neurophysiology
Volume116
Issue number4
DOIs
StatePublished - Oct 2016

Bibliographical note

Funding Information:
We are grateful for financial support from National Institutes of Health (NIH) Computational Neuroscience Training Grant DA032466-02 and NIH BRAIN Initiative Grant 1-U01-MH106027-01, the Simons Center for the Social Brain, and the Whitehall Foundation.

Keywords

  • Computer vision
  • Fluorescent cell detection
  • In vitro slice electrophysiology
  • Patch-clamp

Fingerprint Dive into the research topics of 'Integration of autopatching with automated pipette and cell detection in vitro'. Together they form a unique fingerprint.

Cite this