# Mechanism of touch sensitivity in rapidly-adapting mechanosensory corpuscles

> **NIH NIH R01** · YALE UNIVERSITY · 2023 · $461,897

## Abstract

The ability to sense the world though physical contact is essential for all living organisms. In vertebrates, transient
touch and vibration are detected by Pacinian corpuscles, but the mechanism of their function is poorly
understood. Pacinian corpuscles are innervated by rapidly-adapting mechanoreceptor afferents, which detect
touch due to expression of mechanically activated ion channels. The mechanoreceptor is surrounded by inner
core cells, which are thought to provide auxiliary non-sensory support for the mechanoreceptor. Here, we will
functionally test the hypothesis that inner core cells of Pacinian corpuscles are active touch sensors. We seek
to reveal the molecular mechanism of touch sensitivity and touch-evoked excitability in inner core cells, and
determine the effect of inner core cell activation on mechanoreceptor function. To do this, we will use bill skin of
tactile specialist ducks, which contains a high density of Pacinian corpuscles accessible for electrophysiological
manipulations. Because Pacinian corpuscles are present in the skin or internal organs of most vertebrates,
including all mammals, our results will further our understanding of general molecular and cellular mechanisms
of touch detection.

## Key facts

- **NIH application ID:** 10557111
- **Project number:** 5R01NS097547-07
- **Recipient organization:** YALE UNIVERSITY
- **Principal Investigator:** Sviatoslav Bagriantsev
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2023
- **Award amount:** $461,897
- **Award type:** 5
- **Project period:** 2016-12-01 → 2027-01-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10557111

## Citation

> US National Institutes of Health, RePORTER application 10557111, Mechanism of touch sensitivity in rapidly-adapting mechanosensory corpuscles (5R01NS097547-07). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10557111. Licensed CC0.

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