Integration of autopatching with automated pipette and cell detection in vitro

Abstract

Patch clamp is the main technique for measuring electrical properties of individual cells. Since its discovery in 1976 by Neher and Sakmann, patch clamp has been instrumental in broadening our understanding of the fundamental properties of ion channels and synapses in neurons. The conventional patch-clamp method requires manual, precise positioning of a glass micropipette against the cell membrane of a visually identified target neuron. Subsequently, a tight "gigaseal" connection between the pipette and the cell membrane is established, and suction is applied to establish the whole cell patch configuration to perform electrophysiological recordings. This procedure is repeated manually for each individual cell, making it labor intensive and time consuming. In this article we describe the development of a new automatic patch-clamp system for brain slices, which integrates all steps of the patch-clamp process: image acquisition through a microscope, computer vision-based identification of a patch pipette and fluorescently labeled neurons, micromanipulator control, and automated patching. We validated our system in brain slices from wild-type and transgenic mice expressing channelrhodopsin 2 under the Thy1 promoter (line 18) or injected with a herpes simplex virus-expressing archaerhodopsin, ArchT. Our computer vision-based algorithm makes the fluorescent cell detection and targeting user independent. Compared with manual patching, our system is superior in both success rate and average trial duration. It provides more reliable trial-to-trial control of the patching process and improves reproducibility of experiments.

Keywords: computer vision; fluorescent cell detection; in vitro slice electrophysiology; patch-clamp.

Fig. 1.

Automated image-guided in vitro patch-clamp workflow. A: steps in an automated in vitro patch-clamp experiment. 1, Primary calibration is done automatically through computer vision (also see B). 2, Target cell selection is then done using either mouse clicks (bottom) or automatic fluorescent cell detection (top; algorithm explained in detail in C). 3, Selected cell coordinates are stored for further patching (subscripts indicate the cell identification no.). 4, This is followed by a pipette calibration step that determines the coordinates of the patch pipette with micrometer-scale accuracy and resolution (indicated by red crosshairs). 5, With the coordinates of the pipette tip and target neuron determined, a pipette guidance algorithm determines the trajectory to be taken by the pipette and automatically guides the pipette to the targeted cells. 6–8, The patch algorithm (also see Fig. 6 for detailed algorithm flowchart) is then initiated, which uses pipette impedance measurements to detect contact with the neuron (6), form a gigaseal (7), and break in (8). 9, After successful break-in, a whole cell recording is performed. A fully automatic patching process is defined as the successful automatic execution of all steps from loading a new pipette to obtaining a whole cell patch (marked by dark green lines). If adjustments are to be made at any point to this automatic process, it is defined as a semiautomatic patching trial (marked by light green lines). Such adjustments are mainly manipulator mechanical error correction, caused by mechanical errors in manipulator positioning, and touch cell error correction, caused by incorrect cell contact detection. Dark green borders indicate fully automatic procedure; light green borders indicate a semiautomatic trial, involving at least some human interference. DIC, differential interference contrast. B: computer vision algorithm is used to determine the coordinates of the pipette tip during automatic calibration. A series of images along the optical z-axis are acquired under bright-field illumination to determine if the pipette tip is in focus using local contrast detection. Gaussian blur, Canny edge detection, and Hough transform are then applied to identify the pipette tip (indicated by red dot), and the tip coordinates are identified (x_p, y_p, z_p; also see Fig. 4A). C: computer vision algorithm used to detect and log coordinates of fluorescent cells. A series of images are acquired under epifluorescence illumination along the optical z-axis of the microscope (left), with the step size and the depth defined by an experimenter. Each acquired image at depth z_n is analyzed using a series of thresholds to detect cell contours. The centroids of the identified cell contours for each threshold are superimposed and clustered along the x and y dimensions. Final cell coordinates are computed as the average of the corresponding x, y, z cluster coordinates.

Fig. 2.

Experimental setup. A: standard patch-clamp electrophysiology equipment is used in conjunction with a pneumatic pressure control unit (also see Fig. 3) and our custom-written software. B: image of the pipette pneumatic pressure control unit prototype. Two solenoid valves (white circles, center) and an air pressure sensor (black square, top left) are connected to control the pipette internal pressure. The air pump is not shown. C: 3 different valve configurations resulting in no pressure (top) or brief pulses of positive (middle) or negative pressure (bottom) applied to the back end of the pipette when the pump is activated by a transistor-transistor logic (TTL) signal. The pressure sensor provides feedback information to control the minimum and maximum pressure during patching. D: block diagram of the hardware setup. A central computer controls all components of the Autopatcher IG. The primary data acquisition system provides an interface to the patch-clamp amplifier and allows the user to perform a standard electrophysiology experiment. The secondary data acquisition board provides an interface to the pressure control unit and to the external electronics hardware, which can communicate via TTL signals. On the sensor side are signals from the patch pipette, microscope camera, and internal pipette pressure sensor. The custom graphical user interface (GUI; see Fig. 3) allows the user to control the manipulator, camera setting, microscope stage, pressure control unit, and patch-clamp amplifier [via software development kits (SDK) for digital amplifiers (Axon MultiClamp 700B)].

Fig. 3.

GUIs of the Python-based software featuring image acquisition, manipulator, and patch control. A: camera view of a brain slice with target cells (i) selected at a low magnification of ×4 (top) and at a high magnification of ×40 (bottom left). Yellow labels indicate the cell no.; coordinates of the cells are stored as the corresponding sequence of memory positions and indicated in the GUI (bottom right) B: main GUI providing settings for image acquisition, microscope stage, and micromanipulator control. ii, Microscope stage: controls include settings for stage coordinates, magnification, pixel-μm calibration; iii, micromanipulators: user can initiate automatic calibration and control individual micromanipulators; additional micromanipulator units are automatically recognized and added; iv, controls for camera exposure (in ms), image brightness, and contrast; v, automatic pipette calibration; vi, calibration save and load. C: patch control GUI during an ongoing patching experiment. Top trace indicates pressure (in mmHg); bottom trace indicates current measurements from the patch amplifier (letters denote key events in the patch-clamping process: S denotes the touch cell surface event, G denotes the time point at which a gigaseal is obtained, and B denotes when break-in is achieved). vii, Automatic patch algorithm; viii, independent valve configuration control: allows user to override the patch algorithm and manually apply user-required positive or suction pressure; ix, independent pump control: allows user to override the patch algorithm and control the pump; x, real-time pressure; xi, real-time resistance.

Fig. 4.

LabVIEW GUI, representative patch, and cell property statistics. A: LabVIEW-based GUI. B: a representative patched mouse neuron was filled with Alexa Fluor 594 and imaged at ×40 magnification. C: current-clamp recording of the same neuron in B shows response to a series of step current injections (−120 to +180 pA in 20-pA increments). D: measured properties of whole cell patches (n = 39 from 12 animals) are similar to properties determined by manual patching or automatic patching using a Python-based package.

Fig. 5.

Computer vision-aided identification of the pipette tip coordinates. A: image acquisition and pipette tip detection. i, Original pipette image acquired by the microscope; ii, image after application of Gaussian blur; iii, Canny edge detection algorithm applied to the image in ii defines the contours of the pipette tip; iv, Hough transform performs feature extraction to fit the pipette contours with lines; v, color inversion and intensity calculation are used to detect the lines' point of intersection; vi, pipette tip detected by the algorithm as indicated by red dot. B: automatic pipette calibration achieves high precision. To test the precision of automatic pipette calibration, a predefined calibration grid was used and the pipette tip was then targeted to the centroids of four quadrants and the screen center. Top row shows the relative location of the pipette on the screen at ×4 magnification; bottom row shows the precision of the pipette placement at ×40 magnification. Red dots are 1 pixel in size and are the target locations.

Fig. 6.

Automatic patch function algorithm logic. Nine distinct stages are defined by a series of resistance (R_p, pipette resistance; R_m, membrane resistance; R_a, access resistance), positive and negative pressure [P(+/−)], and time (t) thresholds. Thresholds used in actual experiments are shown in Table 1.

Fig. 7.

Automatic image-guided patch clamp yields high-quality whole cell recordings comparable with manual patching. A: example patch log of a successful patching trial with a history of current (I), resistance (R), and internal pipette pressure (P) parameters. Top, raw voltage input from the data acquisition board (light green) and the membrane test current (dark green). Middle, membrane resistance. Bottom, internal pipette pressure (letters denote key events in the patch-clamping process: S denotes the touch cell surface event, G denotes the time point at which a gigaseal is obtained, and B denotes when break-in is achieved). The “saw tooth” pressure pattern is caused by the on-off feedback pressure controller switching between pump-on and pump-off states. B: representative images show an automatically patched cell at ×4 magnification (left) and ×40 magnification DIC optics (middle) in a mouse visual cortex brain slice. Right, the same neuron filled with Lucifer yellow, postfixed, and visualized with ×40 magnification epifluorescence optics. C: electrophysiological responses of an automatically patch-clamped neuron to hyperpolarizing and depolarizing current injections. D: representative image of 2 simultaneously patched cells in a slice. E: confocal image of the 2 cells in D filled with Alexa 568 hydrazide and fixed after patching. F: electrophysiological responses of these 2 patched cells to hyperpolarizing and depolarizing current injections. Top, cell on the left (L); bottom, cell on the right (R). G: simultaneous recordings of excitatory postsynaptic potentials (EPSPs) from these neurons evoked by white matter stimulation. H: automatic patching (top; n = 30 from 3 mice) generates high-quality patches that are comparable to those obtained using conventional manual patching (bottom; n = 30 from 6 mice). There was no significant difference between the 2 groups in the distribution of membrane capacitance (P = 0.06), holding potential (P = 0.70), access resistance (P = 0.70), membrane resistance (P = 0.97), and seal resistance (P = 0.33, 2-tailed Student's t-test).

Fig. 8.

Automatic patching algorithm significantly improves patch clamp efficiency. A: average time spent during pipette placement, gigaseal formation, and establishment of whole cell configuration (break in) in both automatic patching (dark green) and semiautomatic patching (light green) is significantly shorter than in manual patching (light blue) in successful trials. The time from the end of pipette placement to termination of a failed trial (gray) is not significantly different between the 2 methods (*P < 0.05; ***P < 0.001; 2-tailed Student's t-test). Error bars represent SE. B: success rate for automatic (n = 44 from 3 animals) and manual patching (n = 85 from 6 animals). C: distribution of times spent during the 3 patching steps. The automatic patching steps are faster and more reproducible compared with the manual patching steps. Data points are the times for pipette placement in all successful trials vs. gigaseal time (top) and break-in time (bottom). D: schematic illustration showing the average time and success/failure rates of automatic and manual patching.

Fig. 9.

Automatic identification and patch clamp of fluorescent neurons in brain slices. A: computer vision processing of images acquired with epifluorescence optics detects fluorescent neurons and identifies their x, y, z coordinates. Three representative z sections are shown from a complete experiment (20 total z sections) using brain slices prepared from a mouse expressing channelrhodopsin-2-EYFP in layer 5 pyramidal cells (Thy1-ChR2-EYFP mouse line 18). i, Original image after histogram equalization; ii, pseudo-colored image after thresholding; iii, superimposed cell-like contours detected after a series of varying thresholds; iv, centroids of detected contours are accumulated from z sections; v, centroids from the complete z scan (20 z sections) are clustered and the final coordinates calculated. B: representative patched fluorescent neuron (green) filled with Alexa Fluor 568 dye (red) in layer 5 mouse neocortex. An acute brain slice was postfixed and immunolabeled with the anti-GFP antibody. Image acquisition was performed using confocal microscopy. C: current-clamp recordings of a patched cell responding to hyperpolarizing and depolarizing current injection. Firing pattern shows intrinsic bursting, which is characteristic of a layer 5 intrinsically bursting pyramidal neuron. D: the same neuron as in C reacts to light (480 nm) activation with bursts of action potentials. Blue arrows show the light on epochs that are 2 ms each and 150 ms apart. E: patched cell properties measured from each successful trial (n = 20 from 3 animals). No significant differences in holding current, access resistance, membrane resistance, and seal resistance were observed compared with those for nonfluorescent cells, shown in Fig. 7. Membrane capacitance distribution was significantly different from that in nonfluorescent cells, which can be explained by the larger size of the layer 5 pyramidal cells (P < 0.05, 2-tailed Student's t-test).

Fig. 10.

Computer vision-aided automatic cell detection of ArchT-EYFP-positive cells. A: computer vision processing of images acquired with epifluorescence optics detects fluorescent neurons and identifies their x, y, z coordinates. Three representative z sections are shown from a complete experiment (20 total z sections) using brain slices prepared from a mouse injected with HSV-ArchT-EYFP virus. i, Original image after histogram equalization; ii, pseudo-colored image after thresholding; iii, superimposed cell-like contours detected after a series of varying thresholds; iv, centroids of detected contours are accumulated from z sections; v, centroids from the complete z scan (20 z sections) are clustered and the final coordinates calculated. B: representative ArchT-EYFP-positive cell (green) filled with Alexa Fluor 568, postfixed, and immunolabeled with the anti-GFP antibody. Images are acquired using confocal microscopy. C: representative current-clamp recording trace of a layer 5 bursting cell hyperpolarized in response to light (550 nm) activation (green bar).