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

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.

Keywords: automation; cell types; cortex; fluorescent object detection; fluorescent proteins; imaging; in vivo electrophysiology; mouse; patch clamp; two-photon microscopy.

Figure 1. Imagepatching: closed-loop real-time image-guided patch clamping in vivo.

(A) The closed-loop algorithm for continuous cell centroid localization and pipette position adjustment while approaching the targeted cell (for step-by-step flowchart, see Figure S2). Green, patch pipette filled with fluorescent dye; red, fluorescent cell targeted for patching; black x, target cell centroid; black arrows, pipette movement. (B) The six stages of the image-guided automated patching algorithm (for step-by-step flowchart, see Figure S2). ACSF, artificial cerebrospinal fluid; red, fluorescent cells; green, patch pipette filled with fluorescent dye; light red, laser for two-photon imaging; black solid arrows, pipette movements; black dotted arrow, cell movement; yellow, target cell filled with the fluorescent dye from the pipette. (C) Schematic of the imagepatcher hardware, composed of a conventional two-photon image-guided patch clamp rig and our previously developed autopatcher control box (Kodandaramaiah et al., 2012, 2016). Arrows indicate the direction of information flow. PMT, photomultiplier tube.

Figure 2. Key algorithms for closed-loop real-time image analysis

(A) Steps of the pipette tip detection algorithm. (i) A z-stack with 20 images and 2 μm step between consecutive images is acquired around a pipette filled with a dye (e.g., Alexa 488, green). (ii) Each image in the z-stack is analyzed to identify the cluster of bright pixels (area bounded by yellow outline, corresponding to the fluorescence from Alexa 488 inside the pipette) and the centroid of the cluster (x). The centroid in the topmost image of the z-stack ((ii.i), black x) is used as a reference location corresponding to the far end (i.e., end opposite to the pipette tip) of the pipette. Images 1 (ii.i), 10 (ii.ii), and 20 (ii.iii) of the z-stack, numbered from top to bottom, are shown as examples. (iii) The distance between the cluster centroid (x in (ii)) and the reference centroid (black x in (ii.i)) is calculated for each image in the z-stack. The image at which this distance is the largest is identified as the image focused on the pipette tip (magenta line). The z-coordinate of the focused image corresponds to that of the pipette tip (z_pipette). (iv) The image focused on the pipette tip is analyzed to yield the location of the pipette tip in the transverse plane (yellow star). For image analysis steps used to locate the pipette tip in the transverse plane, see Figure S3A. (B) Steps of the cell position detection algorithm. (i) A z-stack is acquired around a tdTomato-expressing cell (red), with N images and Δz step between consecutive images (N = 24, Δz = 3 μm for cell position detection in the brain penetration stage; N = 10, Δz = 2 μm for cell position detection in the closed-loop real-time image-guided pipette positioning stage). (ii) Each image in the z-stack is analyzed to detect the boundary of the cell body (red outline). Images 8 (ii.i), 12 (ii.ii), and 16 (ii.iii) of the z-stack, numbered from top to bottom, are shown as examples. (iii) The mean intensity of pixels representing the cell body (i.e., pixels surrounded by the detected boundary in (ii)) is calculated for each image in the z-stack. The image at which this mean intensity is the highest is identified as the image focused on the centroid of the cell body (magenta line). The z-coordinate of the focused image corresponds to that of the cell centroid (z_cell). (iv) The image corresponding to the z-coordinate of the cell centroid is analyzed to yield the centroid position in the transverse plane (red x). For image analysis steps used to detect the boundary and the centroid of the cell body, see Figure S3B.

Figure 3. Imagepatcher operation

(A) Two-photon images of a parvalbumin (PV)-positive neuron acquired at three different time points (indicated by Roman numerals for reference in (B) and (C)) during closed-loop real-time image-guided pipette positioning. White, sketch of pipette tip; green, Alexa 488; red, tdTomato; x, target cell centroid; numbers in the upper right, vector (x, y, z) from the pipette tip to the target cell centroid (in μm). (B) Pipette current traces in response to 10 mV voltage pulses, with Roman numerals indicating the corresponding images in (A). (C) Pipette resistance during imagepatching over time, with Roman numerals corresponding to time points referenced in (A) and (B): (i) pipette is 12 μm above the target cell centroid; (ii) pipette is 6 μm above the centroid; (iii) pipette is 0 μm above the centroid (i.e., in contact with the cell); (iv) gigaohm seal is established; (v) cell is broken-into. (D) Maximum intensity projection (MIP) of a z-stack (48 images, 2 μm step size) of an imagepatched PV-positive neuron, showing tdTomato (left, red), Alexa 488 (middle, green) and overlay (right).

Figure 4. Imagepatching of multiple cell types in mouse cortex

(A) Maximum intensity projection of a z-stack (20 images, 5 μm step size) of tdTomato (red)-expressing PV-positive cells in layer 2/3 somatosensory cortex of a PV-Cre x Ai14 mouse. (B) Maximum intensity projection of a z-stack (20 images, 5 μm step size) of tdTomato (red)-expressing CaMKIIα-positive cells in layer 2/3 somatosensory cortex of a CaMKIIα-Cre x Ai14 mouse. (C) Cell-attached current recording from an imagepatched PV-positive neuron. (D) Cell-attached current recording from an imagepatched CaMKIIα-positive neuron. (E) Whole-cell voltage recordings from an imagepatched PV-positive neuron under current injection (left, −100 and +200 pA), and at rest (right). (F) Whole-cell voltage recordings from an imagepatched CaMKIIα-positive neuron under current injection (left, −100 and +200 pA), and at rest (right). (G–J) Recording quality of imagepatched PV-positive neurons (white symbols; n = 24 cells from 14 PV-Cre x Ai14 mice for G–I; n = 9 cells from 5 PV-Cre x Ai14 mice for J) and imagepatched CaMKIIα-positive neurons (gray symbols; n = 13 cells from 7 CaMKIIα-Cre x Ai14 mice) in somatosensory and motor cortices of isoflurane-anesthetized mice. Square and error bars are mean ± standard deviation. (G) Access resistance. (H) Resting potential. (I) Holding current. (J) Recording duration.