Bayesian Image Analysis to Quantify the Perturbation of Cell Membrane by Exogenous AC Electric Field

We recently showed that an exogenous electric field (EF) can electrostatically perturb membrane protein initiated intracellular signaling pathways. But, the molecular mechanism of EF modulation of enzymatic activities at the cell membrane is unclear. Here we try to quantify the overall coupling strength between EF and cell membrane by identifying dynamic membrane shifts under different EF stimulations at millisecond time scale. Sequential cell images are taken under EF stimulation synchronized with the exposure. A Bayesian inference method is used to find the most likely position of the membrane using a Gaussian model of the intensity profile across the membrane. Analysis was tested on simulated data and then applied on real cell images. Comparing results to a previously reported differential image analysis method, we showed that the Bayesian method has better signal-to-noise ratio beyond optical resolution of the images. We hope to develop this method into a framework for quantifying mechanical motions of cell membrane and study the correlation between different EF stimulations and cell response at a fast timescale, for quantification and modeling of the EF coupling of membrane and effect on enzymatic activities of membrane proteins.