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Closed-Loop Real-Time Imaging Enables Fully Automated Cell-Targeted Patch-Clamp Neural Recording In Vivo
Name
nihms901269.pdf
Description
Accepted version
Size
1.6 MB
Format
Adobe PDF
Checksum (MD5)
6c13dd8c7cb48532e71c1bd2e07264ac
Author(s)
Suk, Ho-Jun
van Welie, Ingrid
Kodandaramaiah, Suhasa B
Allen, Brian
Forest, Craig R
Boyden, Edward S
Date Issued
2017
Journal
Neuron
Publisher
Elsevier BV
Version
Author's final manuscript
Abstract
© 2017 Elsevier Inc. 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.
Terms of Use
Creative Commons Attribution-NonCommercial-NoDerivs License
Persistent DSpace Link
DOI of Published Version
10.1016/J.NEURON.2017.08.011
