Closed-Loop Real-Time Imaging Enables Fully Automated Cell-Targeted Patch-Clamp Neural Recording In Vivo

Ho Jun Suk, Ingrid van Welie, Suhasa B. Kodandaramaiah, Brian Allen, Craig R. Forest, Edward S. Boyden

Research output: Contribution to journalArticle

12 Scopus citations

Abstract

Targeted patch-clamp recording is a powerful method for characterizing visually identified cells in intact neural circuits, but it requires skill to perform. We previously developed an algorithm that automates “blind” patching in vivo, but full automation of visually guided, targeted in vivo patching has not been demonstrated, with currently available approaches requiring human intervention to compensate for cell movement as a patch pipette approaches a targeted neuron. Here we present a closed-loop real-time imaging strategy that automatically compensates for cell movement by tracking cell position and adjusting pipette motion while approaching a target. We demonstrate our system's ability to adaptively patch, under continuous two-photon imaging and real-time analysis, fluorophore-expressing neurons of multiple types in the living mouse cortex, without human intervention, with yields comparable to skilled human experimenters. Our “imagepatching” robot is easy to implement and will help enable scalable characterization of identified cell types in intact neural circuits.

Original languageEnglish (US)
Pages (from-to)1037-1047.e11
JournalNeuron
Volume95
Issue number5
DOIs
StatePublished - Aug 30 2017

Keywords

  • automation
  • cell types
  • cortex
  • fluorescent object detection
  • fluorescent proteins
  • imaging
  • in vivo electrophysiology
  • mouse
  • patch clamp
  • two-photon microscopy

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