Cellular and Circuit Organization of the Locus Coeruleus of Adult Mice

Abstract

The locus coeruleus (LC) houses the vast majority of noradrenergic neurons in the brain and regulates many fundamental functions. While efferent projections of the LC have been extensively investigated, little is known about its local circuit organization. Here, we performed large-scale multi-patch recordings of LC noradrenergic neurons to profile their morpho-electric properties while simultaneously examining their interactions. LC neurons were diverse and could be classified into two major morpho-electric types. While chemical transmission was not detected, these neurons electrically connected at a rate comparable to many brain regions. Most connections form between dendrites and were restricted to narrowly spaced pairs or small clusters of neurons of the same type. Furthermore, more than two coupled pairs were often identified from individual recording sets that follow a train-like organizational pattern. The assembly of LC neurons follows a spatial and cell type-specific wiring principle that may be imposed by a unique train-like rule.

Two types of locus coeruleus norepinephrine neurons drive reinforcement learning

“…Type I neurons also showed higher correlations in firing rates among simultaneously recorded pairs than type II neurons (Figures 2i and A2d). The latter observation agrees with recent in vivo (Totah et al, 2018) and ex vivo (McKinney et al, 2022) experiments, indicating that LC-NE neurons are embedded in subtype-specific networks.…”

“…It will be important to determine whether biased inputs to subtypes of LC-NE neurons are involved in the responses we observed. Alternatively, afferents to LC-NE neurons may be mixed, and intra-LC networks (Totah et al, 2018; Breton-Provencher and Sur, 2019; McKinney et al, 2022) may produce the distinct outputs we measured here (that is, RPE and excitation to lack of reward).…”

“…There is growing knowledge of heterogeneity among LC-NE neurons (Schwarz et al, 2015; Uematsu et al, 2017; Chandler et al, 2019; Poe et al, 2020; Breton-Provencher et al, 2022; Luskin et al, 2022). It remains to be determined whether our two discrete subtypes of LC-NE neurons correspond to known morphological and molecular differences (Swanson, 1976; Loughlin et al, 1986; Luskin et al, 2022; McKinney et al, 2022) or differences in how LC affects different targets (Hirschberg et al, 2017; Uematsu et al, 2017; Breton-Provencher et al, 2022). Mapping structure and function in subtypes of LC-NE neurons will help refine theories of NE in the nervous system, including ideas about exploration, attention, and uncertainty (Aston-Jones and Cohen, 2005; Bouret and Sara, 2005; Dayan and Yu, 2006; Sara, 2009).…”

The locus coeruleus broadcasts prediction errors across the cortex to promote sensorimotor plasticity

“…We know that we are likely antidromically activating the LC with our optogenetic stimulation owing to the resulting pupil dilation ( Figures 3C and 4B ). Given that LC neurons are electrically coupled (McKinney et al, 2022) and have widely divergent axons, our stimulation likely has effects on other LC output targets, such as other neuromodulatory systems, and other areas of the cortex. Thus, the plasticity effect that we measure may not entirely result from LC output in V1 alone.…”

PatchView: A Python Package for Patch-clamp Data Analysis and Visualization

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PatchView is a Python package for visualizing and analyzing multi channel patch-clamp (multipatch) data (Neher & Sakmann, 1992), including intrinsic membrane properties and firing pattern analysis (Komendantov et al, 2019;McKinney et al, 2022), mini-event analysis (Clements & Bekkers, 1997), synaptic connection detection (Jiang et al, 2015), morphological analysis (Palacios et al, 2022) and more.PatchView integrates multiple open-source tools and provides an intuitive graphic user interface (GUI) for navigating through the data files and analyses pipeline. It is aimed to enable users to perform most analysis quickly for the data collected in a typical patch-clamp experiment without managing a Python environment or writing any Python scripts.Please refer to the full documentation and the source code for detailed information.

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