Fully-automated in vivo single cell electrophysiology

In this work, we report progress in developing a device that allows fully autonomous sequential patch clamp experimentation. The machine works by integrating a storage magazine of pre-filled pipettes that can be accessed, and swapped, by the headstage at the conclusion of each experiment. In operation, following each neuron measurement, the program enters “swap” state where a set of programmed actuator movements take place. First, the headstage translates towards the pipette storage assembly and deposits its used pipette. The storage assembly rotates to index a fresh pipette, its is grasped, and finally, the headstage returns to its previously designated home position in preparation of subsequent experiments

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

© 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