Detection of SK2 Channels on Hippocampal Neurons

<p>Calcium-activated small conductance potassium channels (SK) are crucial for synaptic plasticity, sleep, and learning and memory (Hammond, Bond et al. 2006; Cueni, Canepari et al. 2008; Lin, Lujan et al. 2008). Despite the recent progress on SK channel physiology, the precise spatial organization of SK channels in neurons has remained unknown. Such knowledge is critical as the subcellular distribution of SK channels is an important determinant of neuronal excitability. Currently, there are no techniques to image ion channel distribution quantitatively at the nanometer scale in living cells. Here, it is demonstrated that integration of natural toxins with single molecule atomic force microscopy (AFM) allows for the mapping of native SK channels in living cells. By measuring the adhesive forces between cell surface expressed SK channels and apamin, a toxin that specifically binds to SK channels, it was found that SK channels are spatially organized in nanodomains of one to three channels. It is also shown that SK channel distribution in pyramidal neurons is polarized, increasing by 40 fold between the soma and dendrites. Additionally, the SK channel dendritic maps are dynamic under the control of the cAMP second messenger cascade. Together, our study demonstrates that integration of pharmacology with single molecule AFM allows to quantitatively reveal ion channel distribution in living cells thus providing a new tool for the study of ion channels and receptors in cell physiology.</p>

Name thesis:degree_name

Master of Science

Discipline thesis:degree_discipline

Biomedical Engineering

Year dc:date.available

2012

Author dc:creator

• Maciaszek, Jamie L

Contributors dc:contributor

• Anastasios V. Tzingounis, Wei Sun
• George Lykotrafitis

• calcium-activated potassium channels
• SK channels
• atomic force microscopy
• apamin
• hippocampus

Repository record dc:identifier

https://digitalcommons.lib.uconn.edu/gs_theses/237

OAI identifier oai:identifier

oai:digitalcommons.lib.uconn.edu:gs_theses-1270