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

  • 3,530
    views
  • 564
    downloads
  • 14
    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. Microbiology and Infectious Disease
    Gaetan De Waele, Gerben Menschaert, Willem Waegeman
    Research Article

    Timely and effective use of antimicrobial drugs can improve patient outcomes, as well as help safeguard against resistance development. Matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) is currently routinely used in clinical diagnostics for rapid species identification. Mining additional data from said spectra in the form of antimicrobial resistance (AMR) profiles is, therefore, highly promising. Such AMR profiles could serve as a drop-in solution for drastically improving treatment efficiency, effectiveness, and costs. This study endeavors to develop the first machine learning models capable of predicting AMR profiles for the whole repertoire of species and drugs encountered in clinical microbiology. The resulting models can be interpreted as drug recommender systems for infectious diseases. We find that our dual-branch method delivers considerably higher performance compared to previous approaches. In addition, experiments show that the models can be efficiently fine-tuned to data from other clinical laboratories. MALDI-TOF-based AMR recommender systems can, hence, greatly extend the value of MALDI-TOF MS for clinical diagnostics. All code supporting this study is distributed on PyPI and is packaged at https://github.com/gdewael/maldi-nn.

    1. Computational and Systems Biology
    2. Genetics and Genomics
    Sanjarbek Hudaiberdiev, Ivan Ovcharenko
    Research Article

    Enhancers and promoters are classically considered to be bound by a small set of transcription factors (TFs) in a sequence-specific manner. This assumption has come under increasing skepticism as the datasets of ChIP-seq assays of TFs have expanded. In particular, high-occupancy target (HOT) loci attract hundreds of TFs with often no detectable correlation between ChIP-seq peaks and DNA-binding motif presence. Here, we used a set of 1003 TF ChIP-seq datasets (HepG2, K562, H1) to analyze the patterns of ChIP-seq peak co-occurrence in combination with functional genomics datasets. We identified 43,891 HOT loci forming at the promoter (53%) and enhancer (47%) regions. HOT promoters regulate housekeeping genes, whereas HOT enhancers are involved in tissue-specific process regulation. HOT loci form the foundation of human super-enhancers and evolve under strong negative selection, with some of these loci being located in ultraconserved regions. Sequence-based classification analysis of HOT loci suggested that their formation is driven by the sequence features, and the density of mapped ChIP-seq peaks across TF-bound loci correlates with sequence features and the expression level of flanking genes. Based on the affinities to bind to promoters and enhancers we detected five distinct clusters of TFs that form the core of the HOT loci. We report an abundance of HOT loci in the human genome and a commitment of 51% of all TF ChIP-seq binding events to HOT locus formation thus challenging the classical model of enhancer activity and propose a model of HOT locus formation based on the existence of large transcriptional condensates.