Software system that improves the measurement of ionic currents in electrophysiological recordings.
- Software functions offline within Matlab.
- Corrects for artifact in recordings, improving resolution in short duration ionic events.
- Potentially improves drug screening and performance study results in excitable tissues.
The patch clamp technique uses microelectrode pipettes to record electrical current of cells. The recordings allow individuals to study how compounds and pharmaceuticals affect the ionic properties of different cells. In order to conduct these studies, amplifiers, current generators, and monitors to inject current into the cell must be used. This information is very useful for drug studies and drug screening, but the use of electrical devices introduces current-voltage errors into measurements, especially when currents are of short duration. When expanded to whole cell recording techniques, artificial capacitive currents introduce measurement error into recordings. Approaches to compensate for such errors have been developed, however, they often use applied current techniques that lead to additional problems including feedback and cell death.
Emory University investigator, Cengiz Günay, has developed software that corrects for measurement errors introduced through the use of the patch clamp technique. The software does not introduce feedback and eliminates the chance of cell death, unlike other solutions. This program reduces artifact currents (from 4 to 1 nA) from small whole cell recordings on a very rapid timescale (approximately 1 msec). These levels of correction are particularly useful for improving measurements of rapid ionic currents, such as voltage-gated sodium currents from excitable tissues including nerve or muscle cells. In addition to applications for academic studies of rapid ionic currents in small cells, this technique could improve screening and performance assays of drugs affecting excitable tissues of pharmacological interest, including neurons and cardiac cells.
Software has been developed and tested on datasets.
Publication: Günay and Prinz. BMC Neuroscience. 2011; 12(Suppl 1): P259. doi: 10.1186/1471-2202-12-S1-P259