Candidate cells are identified automatically within one second.
A unique and customizable machine-vision and image-data-processing technique has been developed for use in automated identification of cells that are optimal for patch clamping. [Patch clamping (in which patch electrodes are pressed against cell membranes) is an electrophysiological technique widely applied for the study of ion channels, and of membrane proteins that regulate the flow of ions across the membranes. Patch clamping is used in many biological research fields such as neurobiology, pharmacology, and molecular biology.] While there exist several hardware techniques for automated patch clamping of cells, very few of those techniques incorporate machine vision for locating cells that are ideal subjects for patch clamping. In contrast, the present technique is embodied in a machine-vision algorithm that, in practical application, enables the user to identify “good” and “bad” cells for patch clamping in an image captured by a charge-coupled-device (CCD) camera attached to a microscope, within a processing time of one second. Hence, the present technique can save time, thereby increasing efficiency and reducing cost.
The present technique involves the utilization of cell-feature metrics to accurately make decisions on the degree to which individual cells are “good” or “bad” candidates for patch clamping. These metrics include position coordinates (x,y) in the image plane, major-axis length, minor-axis length, area, elongation, roundness, smoothness, angle of orientation, and degree of inclusion in the field of view.
The present technique does not require any special hardware beyond commercially available, off-the-shelf patch-clamping hardware: A standard patch-clamping microscope system with an attached CCD camera, a personal computer with an image-data-processing board, and some experience in utilizing image-data-processing software are all that are needed. A cell image is first captured by the microscope CCD camera and image- data-processing board, then the image data are analyzed by software that implements the present machine-vision technique. This analysis results in the identification of cells that are “good” candidates for patch clamping (see figure). Once a “good” cell is identified, a patch clamp can be effected by an automated patch-clamping apparatus or by a human operator.
This technique has been shown to enable reliable identification of “good” and “bad” candidate cells for patch clamping. The ultimate goal in further development of this technique is to combine artificial-intelligence processing with instrumentation and controls in order to produce a complete “turnkey” automated patch-clamping system capable of accurately and reliably patch clamping cells with a minimum intervention by a human operator. Moreover, this technique can be adapted to virtually any cellular-analysis procedure that includes repetitive operation of microscope hardware by a human.
This work was done by Mark McDowell of Glenn Research Center and Elizabeth Gray of Scientific Consulting, Inc.
Inquiries concerning rights for the commercial use of this invention should be addressed to NASA Glenn Research Center, Innovative Partnerships Office, Attn: Steve Fedor, Mail Stop 4–8, 21000 Brookpark Road, Cleveland, Ohio 44135. Refer to LEW-17902-1.