Biology subjects

Ronan, E. A.

Publications and source records attributed to Ronan, E. A..

5 recordsLinked to original sources

bioRxiv2026

Deep-tissue mechanosensation emerges from the interaction of external force and internal tissue state

Muscle sensation is often considered in terms of proprioception, but muscle pain illustrates that other types of sensory neurons are also involved. Here, we show that trigeminal neurons innervating the masseter muscle fall into three classes: A{beta} low-threshold mechanoreceptors, A{delta} high-threshold mechanoreceptors (A{delta}-HTMRs), and peptidergic neurons (PEP). All three types are recruited by mechanical stimulation, with massage being a particularly effective stimulus. Chemogenetic activation of masseter A{delta}-HTMRs and PEP neurons results in pain-like symptoms. Moreover, acute and chronic inflammation sensitize nociceptors, altering behavioral tolerance in an animal model of massage. Taken together, our results provide a framework for understanding muscle somatosensation and how massage of sore muscles may be painful yet beneficial. SignificanceMuscle sensation is usually most prominent when something goes wrong: after overuse, injury, or inflammation, even ordinary pressure or movement can become painful. Yet how mechanical force is detected in muscle, and when a stimulus becomes painful, remain poorly understood. Here, we identify a simple cellular organization for muscle mechanosensation in which three sensory-neuron classes encode force through graded population recruitment. Massage effectively recruited all three classes, including the two nociceptive populations. We further show that these nociceptive neurons are sensitized by acute and chronic inflammation, leading to enhanced recruitment during massage. These findings show that the representation of mechanical force in muscle can be shaped by tissue state and provide a foundation for understanding muscle soreness, pain, and therapeutic touch.

neuroscience↗

bioRxiv2026

LabGrymace: Automated Analysis of Mouse Grimace for Quantitative Assessment of Pain Dynamics

Accurate assessment of pain in animal models is essential for understanding pain mechanisms, developing analgesics, and ensuring animal welfare. The Mouse Grimace Scale (MGS) provides a sensitive, non-invasive measure of spontaneous pain by quantifying pain-related facial expressions, but its utility is limited by labor-intensive manual scoring, observer variability, and reliance on static images that fail to capture the temporal dynamics of facial behavior. Existing automated approaches improve throughput but typically rely on highly standardized imaging conditions, selected viewing angles, and static facial appearance, while providing limited temporal resolution and little insight into the relative contributions of individual facial action units. Here, we introduce LabGrymace, an open-source, artificial intelligence-powered framework for automated, frame-by-frame analysis of pain-related facial dynamics in freely moving mice. Built on the LabGym behavioral analysis platform, LabGrymace uses deep-learning-based facial feature detection and tracking to quantify ear, eye, and nose movements continuously from video recordings. To generate a quantitative pain metric and facilitate reproducibility, we calibrated facial dynamics against graded chemogenetic activation of nociceptors and identified the kinematic features most strongly associated with pain intensity. These features were integrated into a weighted composite pain score that reflects the differential contributions of individual facial action units. LabGrymace accurately classified pain-related facial actions and generated continuous pain scores without manual frame selection or restrictive recording conditions. The resulting pain scale exhibited dose-dependent responses generalized across distinct pain modalities, including visceral pain induced by MgSO and somatic pain induced by capsaicin. By combining automated facial-feature analysis with quantitative temporal modeling, LabGrymace provides an objective, scalable, interpretable, and flexible tool for assessing spontaneous pain in laboratory mice.

neuroscience↗

bioRxiv2025

Cold sensing by a glutamate receptor drives avoidance behavior in Drosophila larvae

The ability to sense and avoid noxious environments is essential for animal survival; yet, how this is achieved at the behavioral, neuronal, and molecular levels is not well understood. Here, we use Drosophila larvae as a model to investigate how animals sense and avoid cold temperatures. By implementing custom-built thermoelectric devices capable of delivering rapid and precise thermal stimuli, we find that cold delivered to the larval head evokes robust escape behavioral responses. We identify a group of head-located cold-sensitive neurons as necessary and sufficient for such avoidance responses. We further demonstrate that the kainate-type glutamate receptor Clumsy acts as a novel cold sensor required for head cold sensitivity. Knockdown of Clumsy in head cold-sensing neurons suppresses their cold sensitivity. Heterologous expression of Clumsy confers cold sensitivity. Our results show that Drosophila larvae have evolved the capacity to detect and avoid cold temperatures through a previously uncharacterized cold-sensing mechanism.

neuroscience↗

bioRxiv2025

Automated analysis of C. elegans behavior by LabGym: an open-source, AI-powered platform

The genetic tractability, well-mapped circuitry, and diverse behavioral repertoire of the nematode C. elegans make it an ideal model for physiological and behavioral studies. A wide range of methods has been developed for analyzing C. elegans behaviors, evolving with advances in technology such as videography and computer-assisted analysis. Here, we introduce LabGym--an open-source, artificial intelligence (AI)-based platform we recently developed--to the C. elegans research community. We trained deep learning models in LabGym capable of automatically categorizing and quantifying multiple user-defined parameters of worm locomotion behavior in multi-worm videos with high accuracy. Furthermore, we demonstrated their efficacy in quantifying locomotion changes in aging worms. Our work offers a cost-effective, user-accessible approach to behavioral analysis in C. elegans.

animal behavior and cognition↗

bioRxiv2024

Intradental mechano-nociceptors serve as sentinels that prevent tooth damage

Pain is the anticipated output of the trigeminal sensory neurons that innervate the tooths vital interior1,2; however, the contribution of intradental neurons to healthy tooth sensation has yet to be defined. Here, we employ in vivo Ca2+ imaging to identify and define a population of myelinated high-threshold mechano-nociceptors (intradental HTMRs) that detect superficial structural damage of the tooth, produce pain, and initiate a jaw opening reflex. Intradental HTMRs remain inactive when direct forces are applied to the intact tooth but become responsive to forces when the structural integrity of the tooth is compromised, and the dentin or pulp is exposed. Their terminals collectively innervate the inner dentin through overlapping receptive fields, allowing them to monitor the superficial structures of the tooth. Indeed, intradental HTMRs detect superficial enamel damage and encode its degree, and their responses persist in the absence of either PIEZO2 or Nav1.83,4. As predicted, chemogenetic activation of intradental HTMRs results in a marked pain phenotype like that produced by systemic chemogenetic activation of nociceptors. Remarkably, optogenetic activation of intradental HTMRs triggers a rapid, jaw opening reflex via contraction of the digastric muscle. Taken together, our data indicate that intradental HTMRs serve as sentinels that guard against mechanical threats to the tooth; their activation not only triggers pain, but also results in physical tooth separation, which would prevent damage during mastication. Our work provides a new perspective of intradental neurons, highlighting their protective role, and illustrates the functional diversity of interoreceptors.

neuroscience↗