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

The robotic system was based on a conventional electrophysiology setup (SliceScope Pro 3000, Scientifica Ltd), comprising two motorized PatchStar micromanipulators mounted on a motorized stage. Samples (cultured cells and brain slices) were imaged using a 40  ×  objective (LUMPLFL40XW/IR, NA 0.8, Olympus) on a motorized focus drive, illuminated under differential interference contrast (DIC) with an infrared light-emitting diode (Scientifica), and captured with a Rolera Bolt camera (QImaging). Köhler illumination was set up and routinely checked to ensure consistent illumination. A peristaltic pump (120S/DV, Watson-Marlow) was used to perfuse cells and slices with buffer solution. Recordings were acquired using the Multiclamp 700b amplifier (Molecular Devices) and digitized to a USB-6221 OEM data acquisition board (National Instruments). We performed two main hardware modifications to the conventional Scientifica electrophysiology workstation to enable full automation. First, we built a custom two-channel pipette pressure controller. For each pipette, pressure was controlled by a  ±10 psi regulator (QPV1TBNEEN10P10PSGAXL, ProportionAir) using an analog (0–10 V) control signal. The control signal for each regulator was generated by a microcontroller (Arduino Uno, Arduino) via a digital-to-analog converter (MAX539, Maxim Integrated). Individual pressure regulators for each pipette were necessary to ensure that different pressures could be maintained on each pipette, e.g. if one pipette is forming a seal (atmospheric or negative pressure), and another is approaching a neuron (positive pressure). The second modification was a custom-machined electrophysiology chamber (supplementary figure 1 (stacks.iop.org/JNE/16/046003/mmedia)) with small side chambers for cleaning and rinsing solutions. The chamber could be used for up to four manipulators.

A finite state machine architecture was implemented to repeatedly patch-clamp user-selected cells (supplementary figure 2). The overall steps in the block diagram were unchanged between HEK cell preparations and brain slice preparations; however, specific details of the 'calibration', 'descend pipette', 'approach cell' and 'establish seal' states necessitated adjustments. The software (written in LabVIEW, National Instruments) interfaces with the MATLAB-based cell tracking algorithm [29], communicates with the stage, manipulators, and pipette pressure controller with a serial interface, and communicates with the amplifier using an ActiveX interface.

Upon software initialization, the user is asked to load the pipette and perform an initial calibration (calibrate, supplementary figure 2). To perform the calibration, the user first centers the pipette under the objective and the software zeros the coordinate systems of the manipulator and stage. At this time, it also stores an image of the pipette to be used for machine vision. Then, the user defines a 'HOME' position for the pipette. It serves as an intermediate position for the pipette between the sample chamber and the clean/rinse baths. The position must be sufficiently far from the sample to avoid pipette collisions with the objective. The user then identifies the positions of the clean/rinse baths. These steps are necessary for every pipette due to inter-pipette differences in taper and tip geometry. The pipette is subsequently returned to the 'HOME' position.

The user then chooses a 'parking plane', an XY plane normal to the slice surface (or cover slip for HEK cells) defined by $z=~{{z}_{park}}$ where ${{z}_{park}}$ is ideally 20–50 µm above the tissue surface (or cover slip). Pipettes will 'park' on this plane prior to patching in order to perform the pipette refocusing routine (supplementary figure 4(c)). Finally, the user picks any number of cells, up to 10 per pipette when using Alconox as the cleaning agent (pick cells, supplementary figure 2). Selected cells are outlined with a red circle and an index number (supplementary figure 3). When a cell is picked, the program stores the coordinates of each cell as well as an image of the cell (200  ×  200 pixels) for later machine vision use. Overall, the initialization procedure takes ~4 min for single-manipulator operation and ~8 min for dual-manipulator operation.

Once the PatcherBot is initialized and loaded with cell locations, the remaining procedure is fully automated. First, a user-picked cell is brought into focus (focus on cell i, supplementary figure 2) and a recalibration is performed (recalibrate, supplementary figures 2 and 4(a)). When the stage focuses on a target cell, there is error (0–10 µm) that is caused by one or more of the following factors: (1) drift of tissue slice or the cover slip, (2) mechanical drift in the optical system, (3) imprecision in the stage actuation and (4) inaccuracies in the manual calibration. The error is corrected by finding the true cell position using normalized 2D cross-correlation (NI Vision Development Module, IMAQ Find Pattern) and moving the stage to that position (supplementary figure 4(b)). If the cell is not found in the field of view, the focusing plane is changed in 2 µm step increments over 20 µm. When the cell is found, the coordinate system of the stage and every active pipette undergo a translation by $({{x}_{found}}-{{x}_{orig}},{{y}_{found}}-{{y}_{orig}},{{z}_{found}}-{{z}_{orig}})$ where $({{x}_{found}},{{y}_{found}},{{z}_{found}})$ are the coordinates of the detected cell and $({{x}_{orig}},{{y}_{orig}},{{z}_{orig}})$ are the original coordinates where the cell was expected to be. After the coordinate transform, the stage is focused on the true position of the cell. If the cell is not found in any plane, the stage returns to the original expected coordinates of the cell $({{x}_{orig}},{{y}_{orig}},{{z}_{orig}})$.

The stage subsequently focuses on $\left( {{x}_{found}},{{y}_{found}},{{z}_{park}} \right)$, directly above the cell, in the 'parking plane' the user selected earlier. The pipette moves to those coordinates with some error (typically, 0–20 µm away from true location). This is due to two major factors: (1) micromanipulator drift or low repeatability and (2) calibration-based errors. The calibration-based errors stem from unavoidable angular inaccuracies of the initial manual calibration step. To correct for the error, we use the same refocusing technique as in the cell refocusing step (supplementary figure 4(c)). Briefly, the pipette tip is found using machine vision, moved to the true $\left( {{x}_{found}},{{y}_{found}},{{z}_{park}} \right)$ location, and the coordinate system of the manipulator is adjusted to reflect the true pipette position. If the pipette is not found in any plane, it returns to the original coordinates and the software assumes that it is already centered.

In HEK cell experiments, typically performed on a monolayer of cells adherent to a glass cover slip, the pipette subsequently descends to 15 µm above the target cell (descend pipette, supplementary figure 2), at which point, a 'blind' cell approach algorithm is engaged (supplementary figure 5(a)). The pipette moves down in 1 µm steps until the targeted cell is encountered, based on a three-step resistance-based threshold, similar to the original Autopatcher algorithm [22]. In slice experiments, the pipette instead first moves in the XY plane above the slice such that it can approach the cell on a trajectory parallel to the pipette axis. The trajectory is planned such that the pipette arrives 15 µm above the target cell in the brain slice. As the pipette arrives, it deforms the surrounding brain tissue both due to mechanical insertion and due to pressurized intracellular solution being ejected from the pipette. In previous patch-clamp automation efforts, any such deformation would result in a failed attempt because the algorithm had no way of determining in which direction the cell moved. Here, the PatcherBot engages the cell tracker to observe cell boundaries in real time [29] (supplementary figure 5(b)). The cell tracker is comprised of a prefiltering stage to reduce interference due to the surrounding tissue, followed by deconvolution via a custom dynamic filtering algorithm and a thresholding step to accurately identify membrane boundaries. The cell tracker outputs the detected centroid of the neuron and moves the pipette towards the centroid (in x and y  only) in 1 µm steps until the pipette tip is found to be within 2 µm (in x and y ) of the cell centroid. Once the pipette is aligned with the cell in X and Y, it moves down in 1 µm steps until the neuron is detected with a resistance-based method. If the cell centroid moves at any time, the algorithm re-aligns the pipette with the cell centroid.

After the target cell is detected, sealing and break-in algorithms are engaged (establish seal, break in, supplementary figure 2). To establish a gigaseal, suction is applied with the pipette pressure controller based on the slope of the resistance trace over time (sampled at 2 Hz). Small resistance slopes (<2.5 MΩ s⁻¹ in HEK cells, <0.25 MΩ s⁻¹ in slices) indicate a slow or failing seal, thus more suction is applied in 5 mBar increments up to a maximum (−70 mBar). Large slopes (>40 MΩ s⁻¹) indicate successful seal formation and suction is reduced in 5 mBar increments. For slopes between these extremes, suction is maintained. Once the measured resistance reaches 1 GΩ, the algorithm waits (5 s) and proceeds to the break-in state (break in, supplementary figure 2). Break-in is accomplished by short pulses of suction (100–1000 ms, −400 mBar). A break-in is considered successful when the measured resistance drops to under 800 MΩ and the holding current remains low (>  −200 pA at  −50 mV in HEK cells, −70 mV in slices). The whole-cell electrophysiology state (ephys, supplementary figure 2) consists of a voltage clamp protocol where cell parameters (access resistance, membrane resistance, holding current) are measured as well as a current clamp protocol (0 pA for 1 s, −300 to  +300 pA step for 1 s, 0 pA for 1 s).

Pipette cleaning is performed as previously described [28] (clean pipette, supplementary figure 2) except due to the long unattended operation time, we needed a way to account for fluid evaporation (measured to be ~23 µl h⁻¹). To do this, we relied on the observation that pipette transient capacitive current increases consistently (10 s of pA) when it touches fluid. Thus, the algorithm would descend the pipette into the clean and rinse bath at the user-provided XY coordinates until detecting a sudden increase in the transient current.

We made two changes to the single-manipulator patcherBot algorithm to enable it to be used for dual patching. First, a 'pick cell' state was added in which the algorithm decides which cell to target and which manipulator to use. This is needed because in single-manipulator trials, cells are simply patch-clamped in the order in which they were picked by the user, but for two manipulators, this could cause the pipettes to collide. The 'pick cell' state ensures that each manipulator is assigned to the cell closest to its home position from an array of un-patched cells. The second addition is 'microscope reservation' feature which ensures that each manipulator can 'reserve' the microscope stage and imaging system for the pick cell, calibration, pipette descent, and cell approach states. It is essential that each manipulator has complete control over the microscope during these steps since they rely on camera output. If a manipulator is ready for the 'pick cell' stage, but the microscope is reserved by the other manipulator, it must wait until the microscope is unreserved.

Human embryonic kidney (HEK293T) cells (American Type Culture Collection, Manassas, VA) were cultured as previously described [28]. Briefly, cells were cultured and passaged regularly in accordance with the manufacturer's instructions. For patch-clamp recording, cells were grown on glass coverslips (12 mm diameter, No.2, VWR), and used within one week of splitting. Cells were not transfected.

All animal procedures were in accordance with the US National Institutes of Health Guide for the Care and Use of Laboratory Animals and were approved by the Institutional Animal Care and Use Committee at the Georgia Institute of Technology. For brain slice experiments, male mice (C57BL/6, P31–P46, Charles River) were anesthetized with isofluorane, and the brain was quickly removed and mounted in agar (2% w/v). Coronal sections (300 µm thick) were then sliced on a compresstome (VF-300, Precisionary Instruments) while the brain was submerged in ice-cold sucrose solution containing (in mM) 40 NaCl, 4 KCl, 1.25 NaH₂PO₄·H₂O, 7 MgCl₂, 25 NaHCO₃, 10 D-Gluocse, 0.5 CaCl₂·2H₂O, 150 Sucrose (pH 7.3–7.4, 300–310 mOsm). The slices were incubated at 37 °C for 1 h in neuronal artificial cerebro-spinal fluid (ACSF) consisting of (in mM) 124 NaCl, 2.5 KCl, 1.25 NaH₂PO₄·H₂O, 1.3 MgCl₂, 26 NaHCO₃, 10 D-Gluocse, 2 CaCl₂·2H₂O, 1 L-Ascorbate·H₂O (pH 7.3–7.4, 290–300 mOsm). Prior to recording, the slices were maintained at room temperature for at least 15 min (22 °C–25 °C). The sucrose solution and neuronal ACSF were bubbled with 95% O₂/5% CO₂. Recordings were performed in mouse primary area V1, hippocampal area CA3, and thalamus.

Borosilicate pipettes were pulled on the day of the experiment using a horizontal puller (P-97, Sutter Instruments) to a resistance of 4–8 MΩ. For HEK cells, the intracellular solution was composed of (in mM): 120 KCl, 2 MgCl₂, 10 EGTA, 10 HEPES (pH: 7.2–7.3, 290–300 mOsm) and recordings were performed at room temperature with constant superfusion of ACSF: (in mM) 161 NaCl, 10 HEPES, 6 D-Glucose, 3 KCl, 1 MgCl₂, 1.5 CaCl₂ (pH: 7.4). For neurons in brain slices, the intracellular solution was composed of (in mM) 135 K-Gluconate, 10 HEPES, 4 KCl, 1 EGTA, 0.3 Na-GTP, 4 Mg-ATP, 10 Na₂-phosphocreatine (pH: 7.2–7.3, 290–300 mOsm). Recordings were performed at room temperature with constant superfusion of oxygenated neuronal ACSF.

To evaluate PatcherBot performance, the user chose 10 cells for single-manipulator HEK cell studies, between 3 and 10 for brain slice studies, between 18 and 24 for dual-manipulator HEK cell studies, and up to 30 for single-manipulator HEK cell studies with Tergazyme. An attempt was considered successful upon the successful completion of the break-in state (see Unattended operation).

For machine vision benchmarking, a pipette or cell refocusing attempt was considered successful if there was sufficient similarity between one image of the pipette taken in focus in the beginning of the experiment and after refocusing. Image similarity was quantified using the IMAQ Find Pattern function in NI LabVIEW, which outputs a similarity score. The similarity score is the result of a normalized 2D cross-correlation algorithm, scaled to a value between 0 (no similarity) and 1000 (exact match). The similarity score threshold was empirically determined based on lighting conditions and set to between 700 and 900. In brain slices, cell area was measured manually after the experiment by outlining boundaries in saved cell images. Image contrast was found by calculating the range (min to max) of image histograms.

We created a computational model to extrapolate throughput for PatcherBot systems with more than two manipulators. Success rates and trial durations of pilot single-manipulaotr and two-manipulator PatcherBot experiments were used to set default model parameters. For HEK293 cells, the whole-cell success rate was set to 77% to be consistent with our empirical observations for a single-manipulator PatcherBot. In the simulations, unsuccessful attempts lasted 3.5 min while successful attempts lasted one minute longer to mimic running a whole-cell recording. Other time considerations included the 1 min microscope reservation time per manipulator and the 1 min of cleaning after each attempt. After ten cleans, the pipette would have to be replaced and re-calibrated which we modeled with a 4 min reloading time. Any setup time prior to starting the first attempt was not included in the model. We simulated 8 h of PatcherBot runtime, and computed an average hourly throughput rate. Monte Carlo simulations were run 100 000 times for each manipulator number using MATLAB (Mathworks) on a personal computer.

The success rate of the PatcherBot as a function of (1) number of performed cleans, (2) lateral displacement, (3) cell area, (4) image quality, and (5) cell depth was assessed using a logistic regression model (MATLAB function mnrfit). An odds ratio (OR) was calculated for each category. An odds ratio can be interpreted as the odds of success improving given a 1-unit increase in the category; for example, a cell area OR of 1.5 indicates that the likelihood of the formation of a successful gigaseal or whole-cell increases by 1.5 with every 1 µm² added to the cell area. Odds ratios are reported with 95% confidence intervals (CI). To assess the effect of the number of performed cleans on recording characteristics, we used a linear mixed effects model (MATLAB function fitlme). The response variables were R_a and T_GS and the predictor variable was the reuse number. Analyses and statistics were computed using MATLAB (Mathworks). Data are presented as mean  ±  standard deviation unless otherwise stated. Data normality was assessed using the Anderson–Darling test. Probability values P  <  0.05 were considered to be significant.

The PatcherBot (figure 1(a), supplementary figure 1, supplementary video 1) was built by augmenting standard patch-clamp electrophysiology system with a custom-made pipette cleaning chamber and pressure control hardware [30]. Pipette cleaning was performed automatically using Alconox (Alconox Inc) as the detergent, as previously demonstrated [28]. For robotic control, a software package was written to interface with three-axis manipulators, XY stage, Z focus drive, pressure control system, amplifier, and camera.

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PatcherBot software only requires user input for several minutes to perform basic calibration steps and choose cells (figure 1(b), supplementary figure 2). First, a manual calibration step is performed to align manipulator and stage coordinate systems. Second, the user selects cells of interest using the graphical user interface (supplementary figure 3). Subsequently, all steps of the patch-clamp procedure are done automatically, conferring a ~50-fold increase in unattended operation time over manual experiments and a ~10-fold increase over previous automation efforts (figure 1(c), supplementary table 1). At the end of the unattended experiment, the user can collect and process the data from all successfully recorded cells (figure 1(d)).

We first validated PatcherBot performance in HEK cells cultured on glass coverslips. After the user installed each pipette, performed the initial calibration, and selected cells, the PatcherBot was engaged with no further experimenter intervention. It achieved an overall success rate of 77% (n  =  54 whole-cell recordings/70 attempts, 7 pipettes). This is comparable to the 60%–70% manual success rates often reported in similar experiments [15, 31], although differences in preparations preclude direct comparisons. The number of performed cleans was not a significant factor in determining whether a particular trial will be successful, suggesting that there was no degradation in performance over the ten attempts per pipette (figure 2(a)). In a separate experiment, we confirmed that disabling pipette cleaning prevented successful recordings (figure 2(b)).

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We then demonstrated that the PatcherBot is suitable for drug discovery and characterization experiments. Drug studies often require long (5–30 min) patch-clamp recording experiments to allow for multiple drug application and washout steps and to study drug kinetics [32, 33]; we therefore programmed the PatcherBot to perform 20 min long recordings. It obtained whole-cell recordings for 98–182 min with an overall success rate of 43% (n  =  32 whole-cell recordings/74 attempts, 7 pipettes, figure 2(c), supplementary video 2). The success rate was slightly diminished over ~1.5 h, perhaps due to cell health deterioration outside the culture medium.

To demonstrate how the PatcherBot could be used as a tool for high-throughput screening, we ran it for four hours and measured the throughput. The overall success rate was 67% (n  =  4 four-hour experiments, n  =  136 whole-cell recordings/202 attempts, 27 pipettes, supplementary video 1). During the four-hour experiments, the only user intervention was replacing the pipette and performing a recalibration approximately once every hour. Cells were held for short periods of time (1 min) during which a brief electrophysiological survey of a cell was performed. Based on these experiments, a user can expect on average 34 whole-cell recordings in 51 attempts over a span of 4 h.

Brain slice recording tends to be more difficult than recordings in cultured cells, and thus could benefit more from full automation. Navigating pipettes to cells of interest in the 3D, heterogeneous environment of brain tissue is challenging even for trained operators. Computer-initiated navigation to target cells without feedback is error-prone due to the accumulation of mechanical errors in patch-clamp actuators. The PatcherBot corrects for these errors and cell motion in real time using machine vision (figure 3, [29]). Actuator errors (micromanipulator and stage) were mitigated by performing pipette auto-focus and cell auto-focus routines (99% and 79% success rate, respectively, with ~1 µm accuracy, n  =  161 attempts, supplementary figures 4(b) and (c)). Real-time cell tracking compensated for cell displacement during the final approach of the pipette (supplementary figure 5, supplementary video 3). The two auto-focus routines and cell tracking were subsequently benchmarked together.

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Brain tissue is deformed by pipette entry, which, if uncompensated, could lead to the PatcherBot missing the target cell. Machine vision successfully compensated for the motion in 133 cells/161 attempts (83%, supplementary figure 2) where a successful compensation was defined as the algorithm reaching the 'establish seal' state with the target cell. Overall, neurons in brain tissue moved by a magnitude of 2.5  ±  1.9 µm (n  =  126 trials) in the XY plane in response to pipette descent. Large cell displacements (⩾4 µm) could not be fully compensated with machine vision and led to decreased success rate (figure 4(a)). Cells tended to displace in the direction of pipette approach, likely due to the pressure applied by the pipette on the tissue during the descent state. Depth, but not cell size was correlated with the magnitude of the displacement (depth versus displacement: r  =  0.35, P  =  0.0001; cell size versus displacement: r  =  −0.11, P  =  0.20; Pearson's r), indicating that cells deeper in the tissue tended to be moved more by the pipette than shallower cells.

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We subsequently evaluated the ability of the PatcherBot to attain successful recordings in various regions of brain slices. The PatcherBot functioned unattended (15–48 min operation time, supplementary video 4) and achieved a success rate in cortical areas similar to the success rate in subcortical areas (cortical whole-cell success rate: 104 cells/205 attempts  =  51%; subcortical whole-cell success rate: 17 cells/40 attempts  =  43%, P  =  0.39, Fisher's Exact Test). Thalamic recordings exhibited expected characteristics of neurons associated with that region such as bursting, sag current, and after-hyperpolarization bursts [34–36] in response to current injections (supplementary figure 6). Success rate was not significantly affected by targeted cell size (figure 4(b)), imaging contrast (figure 4(c)), or depth (figure 5(a)), suggesting that our automation approach can be readily applied to cells in many brain regions. As in the HEK cell preparation, no deterioration of success rate over subsequent pipette reuses was observed (figure 5(b)). On the other hand, as expected, when all machine vision is disabled, the success rates of cell detection and whole-cell recording decrease (figure 5(c)).

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Unlike a human operator, the PatcherBot can command many manipulators, pressure channels, and electrophysiology controls simultaneously; thus, it can theoretically surpass the throughput of even the most experienced investigator. To showcase this, we selected up to 24 cells and programmed two manipulators to perform patch-clamp trials as quickly as possible to maximize throughput (figure 6(a), supplementary videos 5 and 6). The dual-manipulator PatcherBot operated unattended for 38–49 min, achieving a success rate of 62% (n  =  83 whole-cell recordings/134 attempts, 7 pipettes per manipulator). Thus, by adding a second manipulator, the average throughput increased from 13 attempts h⁻¹ to 25 attempts h⁻¹. The throughput increase was less than two-fold as would be expected from a perfectly parallelized process because (1) manipulator speed was decreased from 3.4 mm s⁻¹ to 0.4 mm s⁻¹ to reduce vibration and (2) microscope optics that are needed for machine vision had to be shared between the manipulators (supplementary figure 7).

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To determine the optimal number of manipulators that should be used to attain maximum throughput, we created a computational model. The model predicted that going from one manipulator to two will result in the most drastic increase in throughput, with progressively smaller increases as more manipulators are added (figure 6(b)). Two factors contributed to diminishing returns on throughput when adding more manipulators: (1) manipulators waiting for the microscope optics will contribute to a bottleneck and (2) a limit of ten cleans per pipette [28] means a significant amount of time is spent manually replacing pipettes, especially for many manipulators.

The PatcherBot enabled us to screen candidate detergents in an attempt to find one that can reliably clean pipettes better than Alconox. When cleaning pipettes with Alconox, the PatcherBot can only patch-clamp ~10 cells without recording quality degrading [28], which is the main bottleneck preventing even longer unsupervised operation. We suspected that residual proteins adsorbed to the pipette tip were responsible for recording quality degradation after many cleans. Tergazyme (Alconox Inc) is an inexpensive glassware detergent similar to Alconox with an additional protease enzyme Subtilisin Carlsberg (from the bacterium Bacillus licheniformis) which removes proteins adsorbed to glass [37]. Demonstrating the superiority of a candidate cleaning solution over Alconox requires hundreds of recordings to reach statistical significance so we used the PatcherBot for these experiments. In experiments where the user was blinded to the cleaning solution being used, Tergazyme-cleaned pipettes outperformed Alconox-cleaned pipettes after 30 cleaning cycles (31 attempted cells, figure 7(a)). We found no relationship between the number of cleans and the patch-clamp success rate or quality parameters in Tergazyme-cleaned pipettes (figure 7(b), supplementary figure 7). With Tergazyme as the cleaning agent, the PatcherBot ran unattended for 109–120 min.

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