{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"institution":[{"name":"bioRxiv"}],"indexed":{"date-parts":[[2025,12,11]],"date-time":"2025-12-11T14:23:58Z","timestamp":1765463038473,"version":"3.48.0"},"posted":{"date-parts":[[2025,12,9]]},"group-title":"Neuroscience","reference-count":68,"publisher":"openRxiv","license":[{"start":{"date-parts":[[2025,12,9]],"date-time":"2025-12-09T00:00:00Z","timestamp":1765238400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.biorxiv.org\/about\/FAQ#license"}],"funder":[{"name":"NIH","award":["R01DC015512"],"award-info":[{"award-number":["R01DC015512"]}]},{"name":"NIH","award":["T32 DC009975"],"award-info":[{"award-number":["T32 DC009975"]}]},{"name":"Caruso Department of Otolaryngology, Keck School of Medicine, Los Angeles"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"accepted":{"date-parts":[[2025,12,9]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                <jats:p>Spiral ganglion neurons (SGNs) transmit auditory signals from the cochlea to the brain and are divided into two main types: type I and type II, distinguished by their anatomy and connectivity. However, the function of type II SGNs remains poorly understood due to their scarcity and lack of clear physiological markers. In this study, we use two Cre-dependent fluorescent reporter mouse lines to enhance the identification and targeting of type II SGNs for whole-cell patch-clamp recordings. We reveal a set of distinguishing biophysical features, most notably, the presence of an inactivating potassium current and weaker voltage-gated sodium currents, that clearly separate type II SGNs from their type I counterparts. Additionally, we uncover greater-than-expected heterogeneity among type II SGNs, including variation in size, excitability, and ion channel expression. These features suggest the existence of distinct subtypes of type II SGNs, with potential differences in function. We find that most type II SGNs are relatively unexcitable and incapable of repetitive firing. Instead, they appear to be better suited to integrating sustained signals, potentially supporting roles in detecting cochlear damage or modulating efferent feedback. Additionally, through computational modeling, we demonstrate that removing the inactivation component of the inactivating potassium current specific to type II SGNs allowed repetitive spiking to similar levels seen in type I SGNs, suggesting a crucial role for the current in stifling type II SGN activity. Together, our findings define biophysical signatures that distinguish SGN types and subtypes, offering new insight into their contributions to normal hearing and cochlear pathology.<\/jats:p>\n                <jats:sec>\n                  <jats:title>Significance<\/jats:title>\n                  <jats:p>The sensory neurons of the cochlea are divided into type I and type II spiral ganglion neurons. Type I spiral ganglion neurons convey the main features of sound information. The rarer type II spiral ganglion neurons appear to be putative auditory nociceptors, responding to cochlear damage. By combining genetic tools, electrical activity recordings, and computational models, we demonstrate that type I and type II spiral ganglion neurons have distinctive ion channel profiles and firing properties. Furthermore, we report previously undescribed ion channel diversity within the type II spiral ganglion neuron population, suggesting varied functions. Our results highlight the parallels between type II spiral ganglion neurons and somatosensory nociceptors and provide a framework for selectively targeting distinct auditory neuron populations.<\/jats:p>\n                <\/jats:sec>","DOI":"10.64898\/2025.12.05.692182","type":"posted-content","created":{"date-parts":[[2025,12,10]],"date-time":"2025-12-10T08:00:11Z","timestamp":1765353611000},"source":"Crossref","is-referenced-by-count":0,"title":["Genetic Targeting and Conductance-Based Modeling Reveal Novel Diversity Within Mouse Type II Spiral Ganglion Neurons"],"prefix":"10.64898","author":[{"given":"Nathaniel","family":"Nowak","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0360-8965","authenticated-orcid":false,"given":"Radha","family":"Kalluri","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"54368","reference":[{"key":"2025121106200529000_2025.12.05.692182v1.1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuron.2013.07.051"},{"key":"2025121106200529000_2025.12.05.692182v1.2","doi-asserted-by":"publisher","DOI":"10.1002\/cne.10244"},{"key":"2025121106200529000_2025.12.05.692182v1.3","doi-asserted-by":"publisher","DOI":"10.1523\/JNEUROSCI.1210-22.2022"},{"key":"2025121106200529000_2025.12.05.692182v1.4","doi-asserted-by":"crossref","unstructured":"Arzt M , Deschamps J , Schmied C , Pietzsch T , Schmidt D , Tomancak P , Haase R , Jug F (2022) LABKIT: Labeling and Segmentation Toolkit for Big Image Data. 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