Affiliation:
1. Institute of Neurobiology Eberhard Karls University of Tübingen Tübingen Germany
2. Werner Reichardt Centre for Integrative Neuroscience Tübingen Germany
3. Graduate Training Centre of Neuroscience International Max‐Planck Research School (IMPRS) Tübingen Germany
4. Hertie Institute for AI in Brain Health University of Tübingen Tübingen Germany
5. Tübingen AI Center University of Tübingen Tübingen Germany
Abstract
AbstractThe locus coeruleus (LC) is the primary source of noradrenergic transmission in the mammalian central nervous system. This small pontine nucleus consists of a densely packed nuclear core—which contains the highest density of noradrenergic neurons—embedded within a heterogeneous surround of non‐noradrenergic cells. This local heterogeneity, together with the small size of the LC, has made it particularly difficult to infer noradrenergic cell identity based on extracellular sampling of in vivo spiking activity. Moreover, the relatively high cell density, background activity and synchronicity of LC neurons have made spike identification and unit isolation notoriously challenging. In this study, we aimed at bridging these gaps by performing juxtacellular recordings from single identified neurons within the mouse LC complex. We found that noradrenergic neurons (identified by tyrosine hydroxylase, TH, expression; TH‐positive) and intermingled putatively non‐noradrenergic (TH‐negative) cells displayed similar morphologies and responded to foot shock stimuli with excitatory responses; however, on average, TH‐positive neurons exhibited more prominent foot shock responses and post‐activation firing suppression. The two cell classes also displayed different spontaneous firing rates, spike waveforms and temporal spiking properties. A logistic regression classifier trained on spontaneous electrophysiological features could separate the two cell classes with 76% accuracy. Altogether, our results reveal in vivo electrophysiological correlates of TH‐positive neurons, which can be useful for refining current approaches for the classification of LC unit activity.
Funder
Gemeinnützige Hertie-Stiftung
Deutsche Forschungsgemeinschaft
Werner Reichardt Centre for Neuroscience
Eberhard Karls Universität Tübingen