Single Molecule Kv2.1 Channel Dynamics in Live Mammalian Cells

Abstract

Neuronal Kv2.1 potassium channels localize into micron-sized clusters which are regulated by extracellular glutamate and intracellular Ca2+ levels. The physical mechanisms underlying the formation and maintenance of these unique structures are largely unknown. We are investigating the dynamics of clustered Kv2.1 channels using high resolution total internal reflection fluorescence microscopy (TIRFM) to track single molecules with 8 nm accuracy.Transfected human embryonic kidney (HEK) cells expressing biotinylated and GFP-tagged Kv2.1 channels are detected with streptavidin-conjugated red quantum dots (QD). While the red QDs enable tracking of individual channels, GFP fluorescence provides characteristics of clusters as an ensemble. The channel dynamics inside Kv2.1 clusters are analyzed in the membrane of live cells in terms of their mean square displacement (MSD) and cumulative distribution function (CDF).In our current model, the actin cytoskeleton plays a dominant role in Kv2.1 cluster formation and maintenance. To test this model we are studying the effects of depolymerization agents such as Cytochalasin and Swinholide A on the individual channel dynamics.Clustered channels remain confined within the cluster perimeter throughout the entire imaging time, up to 25 minutes. MSD analysis indicates similar diffusion constants for clustered and non-clustered channels, D = 0.013 ± 0.017 μm2/s and 0.014 ± 0.011 μm2/s respectively. The CDF of all analyzed trajectories (n=900) deviates from a monoexponential, indicating a discrepancy with Brownian diffusion. Instead, the data can be accurately fit to a double exponential.Our results show a bimodal distribution of channels (clustered and non-clustered) and indicate that both populations experience anomalous subdiffusion. The double exponential term of the CDF suggests two stochastic processes which have slow and fast mobility respectively. Single molecule tracking with simultaneous channel cluster imaging is shown to be an effective way to study the mechanisms underlying clustering phenomena.