Populations of touch-sensitive afferents in the skin transduce mechanical stimuli into neural responses that
inform the brain about our natural environment. There is a need to mechanistically understand how superficial
and deep tissues, as well as mechanosensitive and nociceptive neurons, are engaged during touch. We currently
have little quantitative understanding of how innocuous stimuli elicit pain after tissue injury, how touch-based
manipulations relieve pain, or their exact impact, in terms of change in tissue stiffness or extensibility. The
overarching goal of this exploratory project is to develop a new, multiscale in vivo imaging platform for
monitoring the spatiotemporal dynamics of skin deformation and mechanosensory neuron activity. If successful,
the project will break technical barriers and enable mechanistic studies of persistent pain and its relief by manual
therapies in mouse models amenable to genetic manipulations.
Recent studies that combine transgenic mouse models with calcium imaging or electrophysiology have
identified genetically distinct populations of sensory neurons that respond preferentially to innocuous (e.g., brush,
vibration) or noxious mechanical stimuli (e.g., hair pull). Currently, however, single point measurements of
stimulus force or displacement are typical. To understand sensory encoding, we must instead ask – how does
the skin move during touch, and how does these skin deformations lead to activation of sensory neurons? Such
mechanical quantities ultimately recruit a population of sensory afferents to encode different qualities of touch.
To address this technological gap, these studies will develop 3D computer vision and digital image
correlation to directly quantify the distribution of stresses and strains over the entire surface of the skin,
simultaneously with stimulus movement, and while recording from populations of sensory neurons in vivo. Aim
1 focuses on a non-invasive, imaging approach in mice to evaluate localized skin surface deformation, strain
fields, and lateral stretch and motion, at high spatial (5 µm) and temporal resolution (1,000 frames/s), and
computational modeling to estimate mechanical stress in four dimensions (x/y/z/time). Aim 2 will demonstrate
the utility of these newly validated methods in contexts relevant for mechanistic studies of 1) mechanical pain
and 2) manual therapies. To do so, the methods for estimating skin mechanics will be used during in vivo calcium
imaging of DRG neurons and well-validated mouse models in two biological contexts, a well-established model
of inflammatory pain in glabrous paw skin, as well as hair-bearing skin areas. The latter is an essential step in
creating relevant mouse models for mechanistic studies of touch-based manual therapies such as massage.
This project is innovative because it will reveal how dynamic changes in the stress and strain in skin drive
the recruitment of distinct neural complements. Understanding their coupling is relevant to addressing key
questions in the context of heightened mechanical pain, such as in inflammation, as well as creating a proof-of-
concept, physiologically compatible approach for use in studying interventions used in manual therapy.