From the project’s outset, we have developed and applied advanced molecular, imaging, and genetic tools to visualize, manipulate, and track AMPARs and associated proteins in brain tissue. This has involved the close collaboration of neurobiologists, chemists, and computer scientists. In the first phase, we established new high-resolution imaging techniques and biosensors, as well as novel mouse models that allow us to control receptor movement in brain slices and in living animals. We performed the first multicolor superresolution visualization of AMPA, NMDA, and mGluR receptors at synapses, offering an unprecedented look at their nanoscale organization.
As the project progressed, we refined these tools and began to exploit them to answer core questions in synaptic biology. We published key discoveries in leading journals, including the demonstration that AMPARs are mobile in intact brain tissue and that blocking their movement prevents both long-term potentiation (LTP)—a cellular basis for memory—and long-term memory formation itself. We also showed that the mobility of AMPARs regulates short-term synaptic plasticity, providing evidence that both rapid and long-lasting changes at synapses depend on the dynamic behavior of these receptors. Additional achievements include the development of new biosensors to monitor and label endogenous proteins in live neurons, and pioneering studies on synaptic cleft adhesion proteins.
Most recently, we have used these tools to dissect the precise contributions of presynaptic and postsynaptic mechanisms in rapid, activity-dependent synaptic adaptation. We found that different synapses rely on distinct strategies: some emphasize AMPAR desensitization, others on the trapping and release of AMPARs, to fine-tune responses to high-frequency neural activity. Furthermore, we discovered that signaling pathways activated during long-term synaptic changes (such as those involving CaMKII) can rapidly regulate AMPAR mobility, acting as a gain control for short-term synaptic responses and influencing how signals are integrated and transmitted within neural networks. These results have been shared widely through publications and presentations, and our new tools are now being used by other researchers to investigate synaptic function in various brain regions and disease models.
