# Ultrafast membrane trafficking at synapses

> **NIH NIH R35** · JOHNS HOPKINS UNIVERSITY · 2024 · $973,214

## Abstract

Project Summary
Neuronal signaling events at synapses determine circuit responses and result in specific behavioral outputs.
This signaling is dynamic - modulated by the history of synaptic activity and perceived stimuli. Defects in
neurotransmission also play a causative role in neurological disorders. However, our understanding of this
process is limited by two factors: size and speed. Over the years, we have developed several techniques in
electron microscopy to visualize changes in protein localization and synaptic ultrastructure associated with
neuronal activity with millisecond temporal precision. Our experiments have revealed key insights into
mechanisms of the synaptic vesicle cycle as well as new parameters controlling synaptic plasticity. However,
many mysteries remain. How do exocytic and endocytic proteins coordinate to mediate membrane remodeling
at synapses on a millisecond time scale? How do these proteins interact with each other and membrane lipids
to perform their functions? How heterogenous are these processes? To address these questions, we will
develop novel approaches in electron microscopy and leverage these tools to dissect the molecular
mechanisms underlying synaptic vesicle exocytosis and endocytosis.

## Key facts

- **NIH application ID:** 10840343
- **Project number:** 5R35NS132153-02
- **Recipient organization:** JOHNS HOPKINS UNIVERSITY
- **Principal Investigator:** Shigeki Watanabe
- **Activity code:** R35 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $973,214
- **Award type:** 5
- **Project period:** 2023-05-15 → 2031-04-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10840343

## Citation

> US National Institutes of Health, RePORTER application 10840343, Ultrafast membrane trafficking at synapses (5R35NS132153-02). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10840343. Licensed CC0.

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