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

皮肤中的触敏传入神经群将机械刺激转换为神经反应， 让大脑了解我们的自然环境。有必要机械地理解如何肤浅 和深部组织以及机械敏感和伤害感受神经元在触摸期间参与。我们目前 对于组织损伤后无害的刺激如何引起疼痛， 就组织硬度或延展性的变化而言，操作减轻疼痛或其确切影响。的 该探索性项目的首要目标是开发一种新的多尺度体内成像平台， 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，使持续性疼痛及其人工缓解机制研究成为可能 在适合基因操作的小鼠模型中进行治疗。 最近的研究将联合收割机转基因小鼠模型与钙成像或电生理学相结合， 鉴定了优先响应无害物质的遗传上不同的感觉神经元群体（例如，刷子， 振动）或有害的机械刺激（例如，头发拉）。然而，目前， 刺激力或位移是典型。为了理解感官编码，我们必须问-- 皮肤在触摸过程中会移动，这些皮肤变形是如何导致感觉神经元激活的？等 机械量最终招募了一群感觉传入神经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 相关性以直接量化皮肤的整个表面上的应力和应变的分布， 与刺激运动同时，并且同时从体内感觉神经元群体记录。目的 1侧重于一种非侵入性的成像方法，在小鼠中评估局部皮肤表面变形，应变 场，横向拉伸和运动，在高空间（5微米）和时间分辨率（1,000帧/秒），和 计算建模以估计四维（x/y/z/时间）中的机械应力。目标2将展示 这些新验证的方法在与1）机械性疼痛的机制研究相关的背景下的效用 2）手法治疗。为此，将在体内钙离子注入期间使用估计皮肤力学的方法。 在两种生物学背景下，DRG神经元成像和经过充分验证的小鼠模型， 无毛爪皮肤以及有毛皮肤区域的炎性疼痛。后者是一个重要步骤， 建立相关的小鼠模型，用于基于触摸的手动治疗（如按摩）的机理研究。 这个项目是创新的，因为它将揭示如何动态变化的压力和应变在皮肤驱动器 不同神经补充物的募集。了解它们的耦合关系有助于解决 在机械性疼痛加剧的背景下，如炎症，以及创建一个证明- 概念，生理上兼容的方法，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