Electrophysiological properties of maxillary trigeminal Aβ-afferent neurons of rats.
Yuya OkutsuAkihiro YamadaSotatsu TonomuraRyan J VadenJianguo G GuPublished in: Molecular pain (2022)
Aβ-afferents in maxillary or V2 trigeminal ganglion (TG) neurons are somatosensory neurons that may be involved in both non-nociceptive and nociceptive functions in orofacial regions. However, electrophysiological properties of these V2 trigeminal Aβ-afferent neurons have not been well characterized so far. Here, we used rat ex vivo trigeminal nerve preparations and applied patch-clamp recordings to large-sized V2 TG neurons to characterize their electrophysiological properties. All the cells recorded had afferent conduction velocities in the range of Aβ-afferent conduction speeds. However, these V2 trigeminal Aβ-afferent neurons displayed different action potential (AP) properties. APs showed fast kinetics in some cells but slow kinetics with shoulders in repolarization phases in other cells. Based on the derivatives of voltages in AP repolarization with time (dV/dt), we classified V2 trigeminal Aβ-afferent neurons into four types: type I, type II, type IIIa and type IIIb. Type I V2 trigeminal Aβ-afferent neurons had the largest dV/dt of repolarization, the fastest AP conduction velocities, the shortest AP and afterhyperpolarization (AHP) durations, and the highest AP success rates. In contrast, type IIIb V2 trigeminal Aβ-afferent neurons had the smallest dV/dt of AP repolarization, the slowest AP conduction velocities, the longest AP and AHP durations, and the lowest AP success rates. The type IIIb cells also had significantly lower voltage-activated K+ currents. For type II and type IIIa V2 trigeminal Aβ-afferent neurons, AP parameters were in the range between those of type I and type IIIb V2 trigeminal Aβ-afferent neurons. Our electrophysiological classification of V2 trigeminal Aβ-afferent neurons may be useful in future to study their non-nociceptive and nociceptive functions in orofacial regions.
Keyphrases
- neuropathic pain
- spinal cord
- transcription factor
- induced apoptosis
- spinal cord injury
- cell cycle arrest
- magnetic resonance
- machine learning
- computed tomography
- deep learning
- signaling pathway
- cell death
- endoplasmic reticulum stress
- current status
- transcranial direct current stimulation
- cone beam computed tomography