Cellular and circuit organization of the locus coeruleus of adult mice

  1. Andrew McKinney
  2. Ming Hu
  3. Amber Hoskins
  4. Arian Mohammadyar
  5. Nabeeha Naeem
  6. Junzhan Jing
  7. Saumil S Patel
  8. Bhavin R Sheth  Is a corresponding author
  9. Xiaolong Jiang  Is a corresponding author
  1. Baylor College of Medicine, United States
  2. University of Houston, United States

Abstract

The locus coeruleus (LC) houses the vast majority of noradrenergic neurons in the brain and regulates many fundamental functions including fight and flight response, attention control, and sleep/wake cycles. 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 noradrenergic neurons in adult mouse LC to profile their morpho-electric properties while simultaneously examining their interactions. LC noradrenergic neurons are diverse and could be classified into two major morpho-electric types. While fast excitatory synaptic transmission among LC noradrenergic neurons was not observed in our preparation, these mature LC neurons connected via gap junction at a rate similar to their early developmental stage and comparable to other brain regions. Most electrical connections form between dendrites and are restricted to narrowly spaced pairs or small clusters of neurons of the same type. In addition, more than two electrically coupled cell pairs were often identified across a cohort of neurons from individual multi-cell recording sets that followed a chain-like organizational pattern. The assembly of LC noradrenergic neurons thus follows a spatial and cell type-specific wiring principle that may be imposed by a unique chain-like rule.

Data availability

All data generated or analyzed during this study are included in the manuscript and supporting files. The data and custom codes supporting the findings are being deposited in Dryad (doi:10.5061/dryad.kh1893283)

The following data sets were generated

Article and author information

Author details

  1. Andrew McKinney

    Neuroscience Graduate Program, Baylor College of Medicine, Houston, United States
    Competing interests
    The authors declare that no competing interests exist.
  2. Ming Hu

    Department of Neuroscience, Baylor College of Medicine, Houston, United States
    Competing interests
    The authors declare that no competing interests exist.
  3. Amber Hoskins

    Department of Electrical and Computer Engineering, University of Houston, Houston, United States
    Competing interests
    The authors declare that no competing interests exist.
  4. Arian Mohammadyar

    Department of Electrical and Computer Engineering, University of Houston, Houston, United States
    Competing interests
    The authors declare that no competing interests exist.
  5. Nabeeha Naeem

    Department of Electrical and Computer Engineering, University of Houston, Houston, United States
    Competing interests
    The authors declare that no competing interests exist.
  6. Junzhan Jing

    Department of Neuroscience, Baylor College of Medicine, Houston, United States
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-4647-0932
  7. Saumil S Patel

    Department of Neuroscience, Baylor College of Medicine, Houston, United States
    Competing interests
    The authors declare that no competing interests exist.
  8. Bhavin R Sheth

    Department of Electrical and Computer Engineering, University of Houston, Houston, United States
    For correspondence
    brsheth@uh.edu
    Competing interests
    The authors declare that no competing interests exist.
  9. Xiaolong Jiang

    Department of Neuroscience, Baylor College of Medicine, Houston, United States
    For correspondence
    xiaolonj@bcm.edu
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-8066-1383

Funding

Eunice Kennedy Shriver National Institute of Child Health and Human Development (P50HD103555)

  • Andrew McKinney
  • Ming Hu
  • Junzhan Jing
  • Xiaolong Jiang

National Eye Institute (T32 EY07001)

  • Andrew McKinney

National Institute of Mental Health (MH109556)

  • Ming Hu
  • Junzhan Jing
  • Xiaolong Jiang

National Institute of Neurological Disorders and Stroke (NS101596)

  • Andrew McKinney
  • Ming Hu
  • Xiaolong Jiang

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Copyright

© 2023, McKinney et al.

This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.

Metrics

  • 4,122
    views
  • 616
    downloads
  • 22
    citations

Views, downloads and citations are aggregated across all versions of this paper published by eLife.

Download links

A two-part list of links to download the article, or parts of the article, in various formats.

Downloads (link to download the article as PDF)

Open citations (links to open the citations from this article in various online reference manager services)

Cite this article (links to download the citations from this article in formats compatible with various reference manager tools)

  1. Andrew McKinney
  2. Ming Hu
  3. Amber Hoskins
  4. Arian Mohammadyar
  5. Nabeeha Naeem
  6. Junzhan Jing
  7. Saumil S Patel
  8. Bhavin R Sheth
  9. Xiaolong Jiang
(2023)
Cellular and circuit organization of the locus coeruleus of adult mice
eLife 12:e80100.
https://doi.org/10.7554/eLife.80100

Share this article

https://doi.org/10.7554/eLife.80100

Further reading

    1. Computational and Systems Biology
    2. Genetics and Genomics
    Fangluo Chen, Dylan C Sarver ... G William Wong
    Research Article

    Obesity is a major risk factor for type 2 diabetes, dyslipidemia, cardiovascular disease, and hypertension. Intriguingly, there is a subset of metabolically healthy obese (MHO) individuals who are seemingly able to maintain a healthy metabolic profile free of metabolic syndrome. The molecular underpinnings of MHO, however, are not well understood. Here, we report that CTRP10/C1QL2-deficient mice represent a unique female model of MHO. CTRP10 modulates weight gain in a striking and sexually dimorphic manner. Female, but not male, mice lacking CTRP10 develop obesity with age on a low-fat diet while maintaining an otherwise healthy metabolic profile. When fed an obesogenic diet, female Ctrp10 knockout (KO) mice show rapid weight gain. Despite pronounced obesity, Ctrp10 KO female mice do not develop steatosis, dyslipidemia, glucose intolerance, insulin resistance, oxidative stress, or low-grade inflammation. Obesity is largely uncoupled from metabolic dysregulation in female KO mice. Multi-tissue transcriptomic analyses highlighted gene expression changes and pathways associated with insulin-sensitive obesity. Transcriptional correlation of the differentially expressed gene (DEG) orthologs in humans also shows sex differences in gene connectivity within and across metabolic tissues, underscoring the conserved sex-dependent function of CTRP10. Collectively, our findings suggest that CTRP10 negatively regulates body weight in females, and that loss of CTRP10 results in benign obesity with largely preserved insulin sensitivity and metabolic health. This female MHO mouse model is valuable for understanding sex-biased mechanisms that uncouple obesity from metabolic dysfunction.

    1. Computational and Systems Biology
    Huiyong Cheng, Dawson Miller ... Qiuying Chen
    Research Article

    Mass spectrometry imaging (MSI) is a powerful technology used to define the spatial distribution and relative abundance of metabolites across tissue cryosections. While software packages exist for pixel-by-pixel individual metabolite and limited target pairs of ratio imaging, the research community lacks an easy computing and application tool that images any metabolite abundance ratio pairs. Importantly, recognition of correlated metabolite pairs may contribute to the discovery of unanticipated molecules in shared metabolic pathways. Here, we describe the development and implementation of an untargeted R package workflow for pixel-by-pixel ratio imaging of all metabolites detected in an MSI experiment. Considering untargeted MSI studies of murine brain and embryogenesis, we demonstrate that ratio imaging minimizes systematic data variation introduced by sample handling, markedly enhances spatial image contrast, and reveals previously unrecognized metabotype-distinct tissue regions. Furthermore, ratio imaging facilitates identification of novel regional biomarkers and provides anatomical information regarding spatial distribution of metabolite-linked biochemical pathways. The algorithm described herein is applicable to any MSI dataset containing spatial information for metabolites, peptides or proteins, offering a potent hypothesis generation tool to enhance knowledge obtained from current spatial metabolite profiling technologies.