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Updated: Sep 18, 2025

A High-content Assay for Monitoring AMPA Receptor Trafficking
Published on: January 28, 2019
Single-Particle Tracking of AMPA Receptor-Containing Vesicles
Victor C Wong1, Deepika Walpita1, Zhe J Liu1
1Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA.
None:
AMPA-type receptors are transported large distances to support synaptic plasticity at distal dendritic locations. Studying the motion of AMPA receptor+ vesicles can improve our understanding of the mechanisms that underlie learning and memory. Nevertheless, technical challenges that prevent the visualization of AMPA receptor+ vesicles limit our ability to study how these vesicles are trafficked. Existing methods rely on the overexpression of fluorescent protein-tagged AMPA receptors from plasmids, resulting in a saturated signal that obscures vesicles. Photobleaching must be applied to detect individual AMPA receptor+ vesicles, which may eliminate important vesicle populations from analysis. Here, we present a protocol to study AMPA receptor+ vesicles that addresses these challenges by 1) tagging AMPA receptors expressed from native loci with HaloTag and 2) employing a block-and-chase strategy with Janelia Fluor-conjugated HaloTag ligand to achieve sparse AMPA receptor labeling that obviates the need for photobleaching. After timelapse imaging is performed, AMPA receptor+ vesicles can be identified during image analysis, and their motion can be characterized using a single-particle tracking pipeline. Key features • Track and characterize the motion of AMPAR GluA1+ vesicles in cultured rat hippocampal neurons. • GluA1 tagged with HaloTag (GluA1-HT) is expressed from native Gria1 loci to avoid overexpression. • Sparse GluA1-HT labeling densities can be achieved without photobleaching via a block-and-chase strategy that utilizes Janelia Fluor (JF) dyes conjugated to HaloTag ligand (HTL). • GluA1-HT+ vesicles are identified during image analysis, and their motion is characterized using single-particle tracking (SPT) and hidden Markov modeling with Bayesian model selection (HMM-Bayes).

