Developing A Quantitative, Multiscale Imaging Approach to Identify Peripheral Mechanisms of Noxious and Innocuous Force Encoding in Mouse Models

皮肤中大量的触敏传入将机械刺激转化为神经反应， 让大脑了解我们的自然环境。有必要机械地理解这种肤浅 而深层组织，以及机械敏感和伤害感受性神经元，在触摸过程中都会被激活。我们目前 对于无害的刺激如何在组织损伤后引发疼痛，如何基于触摸，我几乎没有定量的了解 根据组织僵硬或延展性的变化，手法可以减轻疼痛或其确切影响。这个 这一探索性项目的总体目标是开发一种新的、多尺度的活体成像平台 监测皮肤变形和机械感觉神经元活动的时空动态。如果成功， 该项目将打破技术障碍，实现对持续性疼痛及其手动缓解的机械性研究 适用于基因操作的小鼠模型的疗法。 最近将转基因小鼠模型与钙成像或电生理学相结合的研究已经 确定了遗传上不同的感觉神经元群体，它们优先对无害的(例如，刷子， 振动)或有害的机械刺激(例如，拔头发)。然而，目前，单点测量 刺激力或位移是典型的。要理解感觉编码，我们必须反问--如何 皮肤在触摸过程中移动，这些皮肤变形是如何导致感觉神经元激活的？是这样的 机械量最终招募了一群感官传入来编码不同的触摸质量。 为了解决这一技术差距，这些研究将开发3D计算机视觉和数字图像 相关性以直接量化整个皮肤表面上的应力和应变分布， 与刺激运动同时进行，同时从活体中的感觉神经元群体进行记录。目标 1重点介绍了一种非侵入性的成像方法，以评估小鼠局部皮肤表面的变形、应变 在高空间(5微米)和时间分辨率(1,000帧/S)下的场以及横向拉伸和运动 用于估计四个维度(x/y/z/时间)的机械应力的计算模型。Aim 2将演示 这些新验证的方法在与机械性疼痛的机制研究相关的上下文中的实用性 2)手法治疗。要做到这一点，在体内钙化过程中将使用估计皮肤力学的方法 在两种生物学背景下对DRG神经元和经过充分验证的小鼠模型进行成像，这是一个公认的模型 无毛的爪子皮肤以及有毛的皮肤区域的炎症性疼痛。后者是以下方面的关键一步 创建相关的鼠标模型，用于按摩等基于触摸的手动疗法的机械研究。 这个项目是创新的，因为它将揭示皮肤驱动中应力和应变的动态变化 不同的神经补体的招募。了解它们的耦合关系与解决关键问题相关 在机械性疼痛加剧的背景下，如炎症，以及创建一个证据- 概念，生理兼容的方法，用于研究手动治疗中使用的干预措施。 1

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. 1