The long-term goal of this research is to determine how mammalian touch receptors transduce forces into neural signals that inform the brain about objects in our dynamic environment. The sense of touch is essential for behaviors that range from avoiding bodily harm to vital social interactions such as child rearing. The touch receptors that innervate the skin are likewise diverse in their peripheral morphologies and physiological outputs. Previous studies demonstrate that different classes of touch receptors produce distinctive firing patterns that encode spatial and temporal features of objects. Despite past progress, the principles that govern neural output in mammalian touch receptors have not been defined. The objective of this application is to elucidate cellular and systems-level mechanisms that generate neural signals in mouse Merkel cell-neurite complexes, which we use as a model for molecular, physiological and computational studies. These complexes mediate slowly adapting type I (SAI) touch responses, which resolve fine spatial details, such as Braille patterns. Our ability to extract edges and object curvature with high speed and fidelity may relate directly to the SAI afferent's distinctive biphasic firing pattern. The SAI afferent's morphology is also unique among touch receptors because it is synaptically coupled to sensory receptor cells. Each SAI afferent has a branching arbor that contacts ~10-40 Merkel cells. The evolutionary maxim 'form follows function'leads to our central hypothesis that the SAI afferent's unique architecture is fundamental to its distinctive firing properties. This new collaborative project will test this hypothesis by combining computational models, microscopy and neurophysiology. We will build novel computational models using solid mechanics, differential equations and statistics to define the key principles that dictate biphasic SAI firing patterns. To inform the modeling, we will elucidate the three dimensional architecture of mouse SAI afferents, including the quantity and arrangement of Merkel cells and action potential initiation zones. The resulting models will make specific predictions about biological mechanisms that underlie touch-evoked responses in mammals. These predictions will then be experimentally tested with neurophysiological recordings from transgenic mice that allow direct visualization of Merkel cells in receptive fields. The intellectual merit of the proposed research lies in our means of joining computational and experimental techniques to determine how touch-receptor anatomy governs physiology. The power of computation allows us to evaluate thousands of possibilities that would be virtually impossible to empirically test one by one. The power of experimental observation allows us to construct realistic models by visualizing specific anatomical structures and molecules, as well as by measuring neuronal outputs. This strategy fits into an emerging paradigm of biological exploration - that of building predictive models to first explore questions in a modeling space and to subsequently test predictions in empirical space. This project is a new venture between researchers in systems engineering and neurobiology whose careers are dedicated to understanding touch. This research proposal describes a new collaborative project that will benefit from infrastructure developed through our recent study of skin mechanics, which resulted in peer-review manuscripts and conference papers [1, 2, 3, 4]. The broader impacts resulting from the proposed research will be to advance the understanding of force transduction mechanisms in biological systems. This project will support teaching and graduate student training in systems engineering, neuroscience and physiology. The biological principles elucidated in this work may further the understanding of neural signaling in other sensory modalities including pain. We expect the models to be critical for engineering artificial touch sensors that can interface with the human nervous system to restore touch sensitivity (e.g., in burn victims and amputees), as well as for applications in human-robotic manipulation in medicine. We expect the experimental results to impact researchers in fields of sensor design, tissue modeling, neurobiology, psychophysics, haptics, and dermatology. Results will be disseminated in appropriate peer-reviewed journals and conference presentations.

National Institute of Health (NIH)
National Institute of Neurological Disorders and Stroke (NINDS)
Research Project (R01)
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Special Emphasis Panel (ZRG1-IFCN-B (51))
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Gnadt, James W
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Columbia University (N.Y.)
Schools of Medicine
New York
United States
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Wang, Yuxiang; Baba, Yoshichika; Lumpkin, Ellen A et al. (2016) Computational modeling indicates that surface pressure can be reliably conveyed to tactile receptors even amidst changes in skin mechanics. J Neurophysiol 116:218-28
Marshall, Kara L; Clary, Rachel C; Baba, Yoshichika et al. (2016) Touch Receptors Undergo Rapid Remodeling in Healthy Skin. Cell Rep 17:1719-1727
Hauser, Steven C; Gerling, Gregory J (2016) Measuring tactile cues at the fingerpad for object compliances harder and softer than the skin. IEEE Haptics Symp 2016:247-252
Marshall, Kara L; Chadha, Mohit; deSouza, Laura A et al. (2015) Somatosensory substrates of flight control in bats. Cell Rep 11:851-858
Nakatani, Masashi; Maksimovic, Srdjan; Baba, Yoshichika et al. (2015) Mechanotransduction in epidermal Merkel cells. Pflugers Arch 467:101-8
Walsh, Carolyn M; Bautista, Diana M; Lumpkin, Ellen A (2015) Mammalian touch catches up. Curr Opin Neurobiol 34:133-9
Wang, Yuxiang; Marshall, Kara L; Baba, Yoshichika et al. (2015) Compressive viscoelasticity of freshly excised mouse skin is dependent on specimen thickness, strain level and rate. PLoS One 10:e0120897
Wang, Yuxiang; Gerling, Gregory J (2014) Computational Modeling Reinforces that Proprioceptive Cues May Augment Compliance Discrimination When Elasticity Is Decoupled From Radius of Curvature. Haptics (2014) 2014:360-368
Lesniak, Daine R; Marshall, Kara L; Wellnitz, Scott A et al. (2014) Computation identifies structural features that govern neuronal firing properties in slowly adapting touch receptors. Elife 3:e01488
Gerling, Gregory J; Rivest, Isabelle I; Lesniak, Daine R et al. (2014) Validating a population model of tactile mechanotransduction of slowly adapting type I afferents at levels of skin mechanics, single-unit response and psychophysics. IEEE Trans Haptics 7:216-28

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