To gain insight into the role of ion channels via non-invasive sensor technology, we are currently developing ion-selective microoelectrodes (ISMs) with improved response time (<1ms) and signal-to-noise ratio. We are addressing whether these ISMs can resolve rapid flux events, attributable to ion channels, within the diffusive boundary layer of cells (see Data Acquisition). In light of this, we are developing novel analytical tools and approaches that allow us to see the signal of interest while modeling of channel ion fluxes will improve our understanding of the dynamics of ion diffusion within the 3D extracellular domain.

Results
Simulating The Diffusive Boundary Layer in 3D

Ion channel models are being developed in collaboration with Joel Stiles at the Center for Quantitative Biological Simulation at the Pittsburgh Supercomputing Center. Diffusion of ions from a 270pS channel (calcium-activated potassium channel) has been modeled using MCell; a Monte-Carlo simulation of random-walk of particles through space. Sampling of ion concentration away from the point source was performed over millisecond time periods. An ion-selective microelectrode (ISM) was designed using the 3D design software, Blender and integrated into the model environment. The speed of the simulation was optimized by partitioning the simulation space and by increasing the time step for the random walk of particles. The simulation was visually rendered using DReAMM (Fig. 1).

This model has revealed that the tip of the electrode is an effective ion-trap, artificially increasing the local ion concentration at the sensing surface of the ISM. The ion-trapping effect is greatest when the ISM was positioned either perpendicular to or at 45 degrees to the plane of the membrane and ion source (Fig. 1B). This would prove to be beneficial for fast electrochemical recording since would effectively increase signal:noise. The diffusion profile for a single channel event (Fig. 1B), that includes the ion-trap component, was a good fit for rapid flux events recorded from cells. The model will be expanded to include a background concentration of ions plus multiple ion channels at varying distances from the point source. This will determine the efficacy with which an ISM can resolve a single channel event within a population of active channels.

In order to extract meaningful information concerning ion transport through channels or transporters we are currently exploring a number of signal processing tools that enable the removal of  noise from physiological recordings. These include spectral analysis, deconvolution and autocorrelation functions.

2A & B

The BRC is also considering automated, high throughput approaches to signal analysis that will be performed in real-time during data acquisition. We have generated distribution plots of flux event data that will analyze populations of channels during physiological changes in the cell, providing possible information on the distribution and recycling of channel proteins. The example below (Fig. 3) shows the slope of binned flux events (as seen in Data Acquisition) plotted against their corresponding amplitude.

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