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 journalArticlepeer-review

31 Scopus citations


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
Issue number5
StatePublished - Aug 30 2017

Bibliographical note

Funding Information:
We thank S. Komai (Nara Institute of Science and Technology, Japan), L. Gentet (Lyon Neuroscience Research Center, France), A. Pala (Georgia Tech, USA),and I.-W. Chen (Université René Descartes, France) for advice and information on manual two-photon image-guided patch-clamp recordings in vivo . We would also like to acknowledge D. Park (MIT, USA) for assistance with manual image-guided patch-clamp recordings in acute brain slices. We thank L.-H. Tsai and C. Adaikkan (MIT, USA) for supplying CaMKIIɑ-Cre x Ai14 mice. We thank T. Diefenbach (Harvard University, USA) for advice and information on the Ultima IV two-photon microscope, and C. Deister (Brown University, USA) as well as C. Sun (MIT, USA) for assistance with operating the Ultima IV two-photon microscope with ScanImage 3.8. We also thank I. Wickersham (MIT, USA) for advice on the autopatcher control box assembly. H.-J.S. acknowledges the Samsung Scholarship. E.S.B. acknowledges support by Jeremy and Joyce Wertheimer, NIH 1R01NS102727, NIH 1R01EY023173, NIH 1R01MH103910, NIH Director’s Pioneer Award 1DP1NS087724, the MIT Synthetic Intelligence Project, the MIT Media Lab, the HHMI-Simons Faculty Scholars Program, and the New York Stem Cell Foundation–Robertson Award. All of the authors on the paper are inventors on a patent application describing this invention.

Publisher Copyright:
© 2017 Elsevier Inc.


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


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