In the peripheral nervous system, ligand-receptor interactions between cells and neurons shape sensory experience, including pain. We set out to identify the potential interactions between sensory neurons and peripheral cell types implicated in disease-associated pain. Using mouse and human RNA sequencing datasets and computational analysis, we created interactome maps between dorsal root ganglion (DRG) sensory neurons and an array of normal cell types, as well as colitis-associated glial cells, rheumatoid arthritis–associated synovial macrophages, and pancreatic tumor tissue. These maps revealed a common correlation between the abundance of heparin-binding EGF-like growth factor (HBEGF) in peripheral cells with that of its receptor EGFR (a member of the ErbB family of receptors) in DRG neurons. Subsequently, we confirmed that increased abundance of HBEGF enhanced nociception in mice, likely acting on DRG neurons through ErbB family receptors. Collectively, these interactomes highlight ligand-receptor interactions that may lead to treatments for disease-associated pain and, furthermore, reflect the complexity of cell-to-neuron signaling in chronic pain states.

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Smith for help with the colonic single neuron sequencing data, Dr. Brian Gulbransen and lab for help with the enteric glia TRAP data and Dr. Zhenyu Xuan for clarifying TCGA metadata formats. We thank all the authors of the papers from which we used their sequencing data for their exemplary transparency in sharing the details of their work with us. AbstractCells communicate with each other through ligand and receptor interactions. In the case of the peripheral nervous system, these ligand-receptor interactions shape sensory experience. In disease states, such as chronic pain, these ligand-receptor interactions can change the excitability of target neurons augmenting nociceptive input to the CNS. While the importance of these cell to neuron interactions are widely acknowledged, they have not been thoroughly characterized. We sought to address this by cataloging how peripheral cell types interact with sensory neurons in the dorsal root ganglion (DRG) using RNA sequencing datasets. Using single cell sequencing datasets from mouse we created a comprehensive interactome map for how mammalian sensory neurons interact with 42 peripheral cell types. We used this knowledge base to understand how specific cell types and sensory neurons interact in disease states. In mouse datasets, we created an interactome of colonic enteric glial cells in the naïve and inflamed state with sensory neurons that specifically innervate this tissue. In human datasets, we created interactomes of knee joint macrophages from rheumatoid arthritis patients and pancreatic cancer samples with human DRG. Collectively, these interactomes highlight ligandreceptor interactions in mouse models and human disease states that reflect the complexity of cell to neuron signaling in chronic pain states. These interactomes also highlight therapeutic targets, such as the epidermal growth factor receptor (EGFR), which was a common interaction point emerging from our studies.In this formula stands for distance, and stands for gene expression level across all cells in the first condition and second condition respectively, stands for standard deviation and stands for mean.

Background Automated infrared pupillometry (AIP) and the Neurological Pupil index (NPi) provide an objective means of assessing and trending the pupillary light reflex (PLR) across a broad spectrum of neurological diseases. NPi quantifies the PLR and ranges from 0 to 5; in healthy individuals, the NPi of both eyes is expected to be ≥ 3.0 and symmetric. AIP values demonstrate emerging value as a prognostic tool with predictive properties that could allow practitioners to anticipate neurological deterioration and recovery. The presence of an NPi differential (a difference ≥ 0.7 between the left and right eye) is a potential sign of neurological abnormality. Methods We explored NPi differential by considering the modified Rankin Score at discharge (DC mRS) among patients admitted to neuroscience intensive care units (NSICU) of 4 U.S. and 1 Japanese hospitals and for two cohorts of brain injuries: stroke (including subarachnoid hemorrhage, intracerebral hemorrhage, acute ischemic stroke, and aneurysm, 1,200 total patients) and 185 traumatic brain injury (TBI) patients for a total of more than 54,000 pupillary measurements. Results Stroke patients with at least 1 occurrence of an NPi differential during their NSICU stay have higher DC mRS scores (3.9) compared to those without an NPi differential (2.7; P < .001). Patients with TBI and at least 1 occurrence of an NPi differential during their NSICU stay have higher discharge modified Rankin Scale scores (4.1) compared to those without an NPi differential (2.9; P < .001). When patients experience both abnormalities, abnormal (NPi < 3.0) and an NPi differential, the latter has an anticipatory relationship with respect to the former (P < .001 for z-score skewness analysis). Finally, our analysis confirmed ≥ 0.7 as the optimal cutoff value for the NPi differential (AUC = 0.71, P < .001). Conclusion The NPi differential is an important factor that clinicians should consider when managing critically ill neurological injured patients admitted to the neurocritical care units. Trial registration NCT02804438, Date of Registration: June 17, 2016.

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