Cell segmentation and pipette identification for automated patch clamp recording
© Yang et al.; licensee Springer. 2014
Received: 15 October 2014
Accepted: 5 November 2014
Published: 5 December 2014
A visual-based approach for identifying living cells and performing the automated patch clamp recording was reported. Based on the image processing and blob detection algorithm, the vision-based method was developed for the detection and identification of biological cells and micropipette. The method was implemented in a micromanipulation system that enabled the identification of the boundary and the center of the target cell and separation from its neighboring cells. The method successfully identified a batch of neuroblastoma cells with the highest yield of 90%. The results demonstrated that the visual-based approach can be integrated to the micromanipulation system to automatically manipulate the patch pipette tip to the center of the target cell, and as a result, the whole-cell recording can be performed precisely and effectively.
Patch clamp technique is one of the promising intracellular bio-electrical signal recording methods for measuring the electric current flowing through ion channels in a cell membrane . It is well understood that the ion channels played important roles in regulating the flow of ions across the cell membrane, which are involved critically in various physiological processes . Therefore, patch clamp recording was widely used for studying the electrophysiological properties of cells  and applied for investigating the effect of different drugs on the cell . In addition, patch clamp was specially suitable for high-resolution current measurements under low noise by using the patch clamp pipette and applying a known voltage across the membrane . Therefore, the adoption of the patch clamp technique has led to significant advances in the life sciences and biomedical studies, including the characterization of the properties of ion channels in various types of cells and the pharmacological treatment of diseases implicated with ion channels such as diabetes and heart disease  and Parkinson's disease .
However, the current patch clamp method required a skilled technician to manually manipulate the pipette, apply the appropriate pressure, and observe the electrical resistance . This complicated operation normally requires several months or even 1 year of training before the technician is able to reliably record from the cells  because of the difficulty in obtaining successful recording. This also significantly increases the cost of the whole patch clamp recording process. Due to these challenging issues, several groups started incorporating automated systems to current patch clamping so as to reduce the operation time, complexity, and cost. The automated patch clamp technology was previously reported for the integrative analysis of molecular, anatomical, and electrophysiological properties of a single cell . The planar patch clamp is another kind of patch clamp systems that provides the automatic recording of cells . More recently, the lateral patch clamp device was demonstrated to provide the automatic whole-cell recording . The micro glass pipette has also been used for automated patch clamp ,. Compared to the methods of planar and lateral patch clamp, the micro glass pipette provided better seal formation .
The current automated patch clamp system normally applies a negative pressure to catch the suspended cell in a buffer medium. This method causes some problems, for example, the tip of the pipette is blocked by the contaminant in the buffer. Moreover, the automated patch clamp system is currently difficult to fully determine the condition of the cell before catching and selecting the cell for patch clamp recording. Additionally, most of the current automated patch clamps were performed on the suspended cells, and the cells were fixed in the fluid medium by either mechanical method, e.g., cell scrapers or chemical method using trypsin , which would change the property of the cells ,. An automated patch clamp is still difficult to be performed on those adherent cells that are grown directly on the substrate. Therefore, it is necessary to develop an automated micromanipulation system for manipulating the glass pipette to perform patch clamp recording on adherent cells.
Several methods based on computer vision were reported which have potential to be used for automated patch clamp. The method based on template matching was used to successfully identify sperm cells  and other suspended cells . Its concept is to find the sub-region in the input image that best matches the pre-defined template. However, the template of adherent cells is hardly described because the pattern of adherent cells is quite different with each other. It is difficult to use the template-matching method to identify adherent cells with high yield. Feature matching is another widely used matching algorithm to identify cells by matching the nuclei of the cells because the nuclei of same type of cells have similar features . However, the limitation of this method is that the height of the nuclei of the cells should be much higher than other regions of the cells. The yield of identification is strongly affected by the condition of the cells. In this paper, the blob detection will be used as the main method to detect cells and pipettes for patch clamp. Blobs are binary objects or points which are in the same state . Blobs can be classified as different objects based on color, shape, area, perimeter, etc. . This method can thus be applied to identify the target cell based on its own shape and feature and separate the cell from other neighboring cells in the same image. Suspended cells such as red blood cells and white blood cells have been successfully detected and distinguished by using the blob detection method . For detecting adherent cells, the situation is more complicated than the suspended cells because the pattern of adherent cells is varied. Moreover, the adherent cells after several days of culture are grown quite close to each other. Therefore, it is necessary to separate the neighboring cells during the identification process so as to avoid the wrong identification. For pipette detection, some methods were used to identify the pipettes for cell injection  or micromanipulation . However, these methods cannot be directly used for the identification of the patch pipette in the patch clamp recording. It is because the pipette and cells are located closely.
To overcome these challenges, a method for detection and identification of a single cell and pipette for automated patch clamp is developed in this paper. More importantly, we report the novel use of the visual-based approach for automatically identifying the target adhered cells and pipettes. Based on the position and feature of the cells and pipette, the condition of the cell can be revealed more clearly, and the cell can be selected for automated patch clamp recording. The method is further combined with the automatic control techniques for performing automated patch clamp on adherent cells, which is different with the current non-visual-based automated patch clamp system.
Neuroblastoma cells were employed as the sample cells for the identification and patch clamp recording. The neuroblastoma cells were cultured in Dulbecco's modified eagle medium containing nutrient mixture F-12 (Gibco, Thermo Fisher Scientific Inc., Waltham, MA, USA) with 10% fetal bovine serum (Gibco, USA) and 1% penicillin-streptomycin-neomycin antibiotic mixture (Gibco, USA) and maintained at 37°C in 5% CO2 humidified air. Stock cultures were passaged at 1:6 weekly and fed twice weekly. The cells were subcultured with the cell density of 100,000 in a culture flask. During the process of subculture, the cells were detached from the culture flask by the treatment of 1 ml trypsin-EDTA (Gibco, USA). Around 5,000 cells were then counted and seeded onto the cover-slip coated with Poly-D-lysine hydrobromide (Sigma-Aldrich, St. Louis, MO, USA) and cultured for 6 days before patch clamp recording. These cells were induced to differentiate by the treatment with 10 μM retinoic acid. All the experiments were conducted at 37°C.
Experimental setup and control strategy
To achieve the automated patch clamp, the patch pipette was mounted to a computer-controllable micromanipulator (Sutter MP-285; Sutter Instrument Company, Novato, CA, USA) connected to a controller (Sutter MPC-200, USA). The micromanipulator was controlled to move along the x-, y-, or z-axis with the range from 0 to 25,000 μm. The micromanipulator was then moved to the desired position based on the command received from the controller. The pipette was connected to the manipulator via a pipette holder, and the position of pipette tip was then controlled by the manipulator. The amplifier was used to perform the patch clamp recording. The signal ground of the patch clamp amplifier was connected to the extracellular buffer in the dish. The patch clamp amplifier applied a voltage pulse to the cell and the corresponding current between the electrode inside the pipette and the signal ground was then recorded. By monitoring the current, the condition of the pipette and the cell membrane was determined.
The automatic control strategy can be divided into four steps. In the first step, a digital image containing cells and pipette is captured by the CCD camera, and the system transforms the digital image to the grayscale image. In the second step, the dilation, erosion, and threshold operation are applied to the grayscale image, and the binary image is then automatically obtained. In the third step, the boundary of the cell is identified, and the center of the cell is calculated from the binary image. Besides, the tip of the pipette is also displayed and identified in the binary image. Finally, the coordinate of the center of the cell and the tip of the pipette are obtained. The movement from the tip of the pipette to the center of the cell is calculated based on the updated coordinates, and the command is sent to the controller for moving the manipulator and the pipette tip to the center of the cell. The movement of the pipette tip can be adjusted based on the updated coordinates of the cell and the pipette.
Identification of cells
where dst(x, y) represents the intensity value of each pixel in the output image, src(x, y) represents the intensity value of each pixel in the input image, and threshold is the threshold value.
where C x is the x-coordinate of the center of the cell, C y is the y-coordinate of the center of the cell. Finally, the membrane and the center of the cells were identified as shown in Figure 5c.
Segmentation of neighboring cells by using distance transform and watershed transformation
Threshold operations were then applied to the distance transform image. After the distance transform image was obtained (shown in Figure 6e), another binary image can be generated by the threshold operations. The pixels having intensity value equal or lower than the threshold were changed to 0. Therefore, the area of the cells in the binary image generated by the threshold operations (shown in Figure 6f) was smaller than the area of the cells in the distance transform image (shown in Figure 6e). These smaller foreground regions represent the markers of the cells as shown in Figure 6f. Moreover, after the binary image was generated by the threshold operations, markers can be generated from the different blobs in the binary image. After obtaining the markers of the cells and the background as well as the gradient image of the cells as shown in Figure 6f, the watershed transformation  was used, and the markers were colored with different gray levels, i.e. white color for background, lighter gray color for the upper cell, and darker gray color for the bottom cell. In the watershed transformation, the lighter gray maker spreads over all pixels of the upper cell while the darker gray marker spreads over all pixels of the bottom cell. When different markers merged together, they can be recognized from different sources, and thus watershed lines were drawn to separate them as shown in Figure 6g. The cells were finally segmented and identified as shown in Figure 6h.
Identification of pipettes
Results and discussion
Identification of different shapes of cells
Yield of cell identification
Identification of pipettes
Comparison of the gigaseal formation in manual and automated patch clamp process
Conditions of identification of the cells
Yield of gigaseal formation (%)
One cell in the image can be identified at a time
Ninety percent of the cells in the image can be identified at a time
The identification method based on blob detection for the detection of cells and pipette was developed and applied for the automated patch clamp. The capability of identifying different shapes of adherent cells and pipettes was demonstrated. By combining the blob detection to distance transform and watershed algorithm, the automatic cell segmentation was realized. The automated patch clamp was implemented with the identification approach successfully. The method presented here provides a visual-based approach to the current automated patch clamp system that further facilitates the patch clamp recording.
The project described is supported in part by GRF grant from the Research Grant Council of the Hong Kong Special Administrative Region Government (CityU139313 and CityU124213).
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