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

Abstract

SUMMARY 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.

High-throughput microcircuit analysis of individual human brains through next-generation multineuron patch-clamp

“…While fully automated approaches have been introduced for in vitro and in vivo patch-clamp experiments (Annecchino et al, 2017; Desai et al, 2015; Kodandaramaiah et al, 2018; Kodandaramaiah et al, 2012; Kolb et al, 2019; Suk et al, 2017), the advantage for fully automated multipatch is less clear. We found that manual adjustments during the visually guided approach of the cell and the fine-tuned application of pressure to obtain a whole-cell configuration yielded higher success rates (88%/85 % / 79% on the 6-/8-/10-manipulator setup) compared to a fully automated system with pipette cleaning and machine vision which reported a success rate of 43% to 51% (Wu et al, 2016; Kolb et al, 2019).…”

Accurate signal-source localization in brain slices by means of high-density microelectrode arrays

“…The localization method can be applied to control moving micropipettes for advanced electrophysiology experiments by combining HD-MEA recordings with single-cell-targeted experiments, such as local puffing of compounds 52–54 , electrically-guided automated intracellular recordings 30–32,55 , virus-stamping 56 , single-cell electroporation 44–46 , and other single-cell-based methods. Other advanced single-cell experiments, such as Patch-seq, can be easily implemented together with HD-MEA extracellular recording.…”

Robotic platform for microinjection into single cells in brain tissue

“…This is followed by a calibration step, where displacement of the injection micropipette in three dimensions is projected onto the corresponding displacement in the twodimensional microscope image (Appendix Fig S2A, Appendix Note S1). Once the calibration step is completed, the Autoinjector can guide the injection micropipette to specific locations in the FOV using the micromanipulator, similar to previous algorithms [10,11]. The user then draws a line along the desired path of microinjection on the microscope image using the graphical user interface (GUI; Fig 2B bottom, 2C.iii; see also User Manual).…”

“…Robotic systems have enabled the automation of difficult laboratory techniques that require precise micromanipulation such as in vivo patch clamping of single [53][54][55] as well as multiple neurons in vivo [56]. Additionally, previous work relied on camera images to guide automated patch clamping systems to specific locations in tissue [11,57]. These applications resulted in significant improvement in the success of patch clamping and enabled neuroscientists to perform complex experiments previously limited by technical difficulties.…”