PatcherBot: a single-cell electrophysiology robot for adherent cells and brain slices

Abstract

Objective: Intracellular patch-clamp electrophysiology, one of the most ubiquitous, high-fidelity techniques in biophysics, remains laborious and low-throughput. While previous efforts have succeeded at automating some steps of the technique, here we demonstrate a robotic 'PatcherBot' system that can perform many patch-clamp recordings sequentially, fully unattended.

Approach: Comprehensive automation is accomplished by outfitting the robot with machine vision, and cleaning pipettes instead of manually exchanging them.

Main results: the PatcherBot can obtain data at a rate of 16 cells per hour and work with no human intervention for up to 3 h. We demonstrate the broad applicability and scalability of this system by performing hundreds of recordings in tissue culture cells and mouse brain slices with no human supervision. Using the PatcherBot, we also discovered that pipette cleaning can be improved by a factor of three.

Significance: The system is potentially transformative for applications that depend on many high-quality measurements of single cells, such as drug screening, protein functional characterization, and multimodal cell type investigations.

Figure 1.

PatcherBot system and operation. (a) Experimental setup: PatcherBot is built on a conventional Scientifica SliceScope electrophysiology system. Software performs unattended single-cell electrophysiology. Recording indicator lights up upon establishing a whole-cell configuration. (b) Simplified workflow of patch-clamp experiments. In manual experiments, only the electrophysiology (ephys) component is automated in some types of experiments. In the PatcherBot, a calibration state is added to enable unattended operation. See detailed block diagram (supplementary figure 2). (c) Comparison of approximate unattended operation time of the PatcherBot and conventional manual experiments as well as previous patch-clamp automation techniques. Recordings are assumed to be short (1 min) for all three modalities. Manual and previous automation timing information was taken from [21] (supplementary table 1). (d) Representative whole-cell recordings in brain slices obtained using the PatcherBot. Green neuron symbols represent successful whole-cell recordings; red symbols represent failed attempts. Cells are shown in a coordinate system that depicts their centroid location in the slice. The (0, 0, 0) point corresponds to the location where manual calibration was performed. Numbers represent order in which cells were chosen and patched (1, 2, 3, …). Cell 1 had low spike amplitude, likely due to an unhealthy cell or incomplete break-in.

Figure 2.

PatcherBot performance in cultured HEK cells. (a) Whole-cell success rate of pipettes over ten trials (nine reuses) in HEK cells. Reuses did not significantly decrease likelihood of whole-cell recording (odds ratio, OR = 1.17, CI: 0.13–1.18, P = 0.12). Numbers above columns indicate number of trials performed at each reuse number. (b) Seal resistances attained with and without cleaning. When cleaning is enabled (left), a pipette can achieve successive gigaseals (R_GS ⩾ 1GΩ) on multiple cells. When cleaning is disabled (right) by draining Alconox from the clean bath, a gigaseal is obtained with a fresh pipette (0) but not on any other attempt (1–5). (c) Cumulative success rate in long-hold experiments. Increased unattended operation time contributed to slight diminishment in success rate (OR = 1.02, CI: 1.01–1.03, P = 0.0019, n = 74 attempts). Alconox was used as the cleaning agent.

Figure 3.

Machine vision elements in brain slices. (a) Sequence of events for cell detection. After the user initially picks the cell (top panels), the system comes back to patch-clamp it after 5–60 min with some inaccuracy (middle panel). The cell detection procedure re-centers the cell (bottom panel). Images on the left show raw screen capture frames, images on the right are annotated for clarity. Red circle and crosshair show the expected cell location; white outline shows actual cell boundary. Circle diameter: 10 µm. (b) Before (top) and after (bottom) the pipette detection state. The algorithm successfully refocused on the pipette after it entered the field of view off-center. (c) Sample cell tracking results. See supplementary video 3 (cell tracker ON) for real-time video of this trial. Cell boundaries (green outline) are automatically tracked and the centroid (green circle) is computed. Pipette tip location on the screen is estimated from the manipulator position (blue dot). Left: cell boundary before pipette descent. Center: cell position and pipette position after pipette descent. Right: cell position and pipette position after trajectory adjustment. Red circle on top left of images is a different cell. (d) Cell centroid position (green) and pipette position (blue) during the ‘approach cell’ state. The pipette moves laterally towards the tracked cell centroid. Same attempt as in (c) and supplementary video 3.

Figure 4.

Benchmarking machine vision performance in brain slices. (a) Top: scatterplot of cell displacement (in XY plane) after pipette descent. Blue dots denote successful cell detection (algorithm reaching the ‘establish seal’ state but not necessarily whole-cell), red circles denote failed detection (pipette missing the cell). ‘P1’ shows the approximate direction of pipette 1 entering tissue. The 1σ and 2σ dotted ellipses represent 1 and 2 standard deviations of the displacements, respectively. Bottom: distribution of cell displacement. Most (87%) cells exhibited displacements of ⩽4 µm. Larger cell displacements in tissue significantly decreased the likelihood of successful cell detection (OR = 1.3, CI: 1.05–1.72, P = 0.0187). (b) Top: sample cell images and outlines of cells (white) used to calculate cell area. Bottom: distribution of cell areas. The size of the target cell did not significantly affect cell detection (OR = 0.99, CI: 0.97–1.00, P = 0.11). (c) Top: sample cell images showing poor (left) and good (right) image quality. Image quality was defined as the number of unique pixel values in the image. Bottom: distribution of contrast values. Image contrast did not significantly affect cell detection (OR = 1.06, CI: 0.99–1.13, P = 0.07, n = 126 attempts).

Figure 5.

PatcherBot performance in brain slices. (a) Success rates of cell detection and whole-cell recordings as a function of cell depth in slices. Data from cortical and sub-cortical experiments were combined. The depth of the target cell did not significantly impact cell detection likelihood (OR = 1.00, CI: 0.98–1.03, P = 0.8, n = 126 attempts) and whole-cell recording likelihood (OR = 1.00, CI: 0.98–1.03, P = 0.65, n = 126 attempts). (b) Whole-cell success rate in brain slices. Reuses did not significantly decrease likelihood of whole-cell recording (OR = 1.07, CI: 0.97–1.17, P = 0.19, n = 245 attempts). (c) Success rate of cell detection and whole-cell recordings with and without machine vision (machine vision off: n = 18 attempts; machine vision on: n = 161 attempts). Cell detection (top): P = 2.4 × 10⁻⁵, whole-cell (bottom): P = 1.7 × 10⁻⁵, Fisher’s exact test. Alconox was used as the cleaning agent.

Figure 6.

Multi-patch electrophysiology. (a) Representative two-manipulator PatcherBot experiment in cultured HEK cells. Left dashed line encircles cells patch-clamped by pipette 1 (P1); right dashed line encircles cells patch-clamped by pipette 2 (P2). Cells are shown in an XY coordinate system because they are effectively in the same z plane on the coverslip. Green cell symbols represent successful whole-cell recordings; red symbols represent failed attempts. Pipettes not to scale. (b) Experimental and simulated results of multi-patch system scalability. From simulations, the largest increases in whole-cell recordings/hour are predicted in going from 1 to 2 manipulators (5.7 cells h⁻¹), 2 to 3 (4.1 cells h⁻¹) and 3 to 4 (3.1 cells h⁻¹).

Figure 7.

Using the PatcherBot to validate Tergazyme as an improved cleaning solution for pipettes. (a) Cumulative success rate of Alconox and Tergazyme over 31 pipette uses in cultured HEK cells. Tergazyme overall success rate: 71.7% (n = 132 whole-cell recordings/184 attempts, 6 pipettes), Alconox overall success rate: 39.0% (n = 29 whole-cell recordings/75 attempts, 3 pipettes), saline overall success rate: 6.4% (n = 3 whole-cell recordings/47 attempts, 3 pipettes). *P = 2.6 × 10⁻⁵ (Kolmogorov–Smirnov test). (b) Whole-cell success rate over 31 uses with Tergazyme as the cleaning agent using Tergazyme data from (a). Reuses did not significantly decrease likelihood of whole-cell recording (OR = 1.00, CI: 0.97–1.04, p = 0.89, n = 184 attempts).