Abstract

Seeking new insights into the homeostasis, modulation and plasticity of cortical synaptic networks, we have analyzed results from a single-cell RNA-seq study of 22,439 mouse neocortical neurons. Our analysis exposes transcriptomic evidence for dozens of molecularly distinct neuropeptidergic modulatory networks that directly interconnect all cortical neurons. This evidence begins with a discovery that transcripts of one or more neuropeptide precursor (NPP) and one or more neuropeptide-selective G-protein-coupled receptor (NP-GPCR) genes are highly abundant in all, or very nearly all, cortical neurons. Individual neurons express diverse subsets of NP signaling genes from palettes encoding 18 NPPs and 29 NP-GPCRs. These 47 genes comprise 37 cognate NPP/NP-GPCR pairs, implying the likelihood of local neuropeptide signaling. Here we use neuron-type-specific patterns of NP gene expression to offer specific, testable predictions regarding 37 peptidergic neuromodulatory networks that may play prominent roles in cortical homeostasis and plasticity.

Data availability

The present study is an analysis of a large transcriptomic dataset that is now freely available for download in its entirety athttp://celltypes.brain-map.org/rnaseq/ and is described fully in a rigorously peer-reviewed publication (Tasic, et al., Nature 563:72-78, 2018). All code and intermediate data products involved in preparing this manuscript are freely available from a well-documented GitHub repository: https://github.com/AllenInstitute/PeptidergicNetworks

The following previously published data sets were used

Article and author information

Author details

  1. Stephen J Smith

    Allen Institute for Brain Science, Seattle, United States
    For correspondence
    stephens@alleninstitute.org
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-2290-8701
  2. Uygar Sümbül

    Allen Institute for Brain Science, Seattle, United States
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-7134-8897
  3. Lucas T Graybuck

    Allen Institute for Brain Science, Seattle, United States
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-8814-6818
  4. Forrest Collman

    Allen Institute for Brain Science, Seattle, United States
    Competing interests
    The authors declare that no competing interests exist.
  5. Shamishtaa Seshamani

    Allen Institute for Brain Science, Seattle, United States
    Competing interests
    The authors declare that no competing interests exist.
  6. Rohan Gala

    Allen Institute for Brain Science, Seattle, United States
    Competing interests
    The authors declare that no competing interests exist.
  7. Olga Gliko

    Allen Institute for Brain Science, Seattle, United States
    Competing interests
    The authors declare that no competing interests exist.
  8. Leila Elabbady

    Allen Institute for Brain Science, Seattle, United States
    Competing interests
    The authors declare that no competing interests exist.
  9. Jeremy A Miller

    Allen Institute for Brain Science, Seattle, United States
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-4549-588X
  10. Trygve E Bakken

    Allen Institute for Brain Science, Seattle, United States
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-3373-7386
  11. Jean Rossier

    Neuroscience Paris Seine, Sorbonne Université, Paris, France
    Competing interests
    The authors declare that no competing interests exist.
  12. Zizhen Yao

    Allen Institute for Brain Science, Seattle, United States
    Competing interests
    The authors declare that no competing interests exist.
  13. Ed Lein

    Allen Institute for Brain Science, Seattle, United States
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-9012-6552
  14. Hongkui Zeng

    Allen Institute for Brain Science, Seattle, United States
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-0326-5878
  15. Bosiljka Tasic

    Allen Institute for Brain Science, Seattle, United States
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-6861-4506
  16. Michael Hawrylycz

    Allen Institute for Brain Science, Seattle, United States
    For correspondence
    MikeH@alleninstitute.org
    Competing interests
    The authors declare that no competing interests exist.

Funding

National Institutes of Health (R01NS092474)

  • Stephen J Smith

National Institutes of Health (R01MH104227)

  • Stephen J Smith

National Institutes of Health (1U24NS109113)

  • Stephen J Smith

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

Copyright

© 2019, Smith 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

  • 6,343
    views
  • 936
    downloads
  • 116
    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. Stephen J Smith
  2. Uygar Sümbül
  3. Lucas T Graybuck
  4. Forrest Collman
  5. Shamishtaa Seshamani
  6. Rohan Gala
  7. Olga Gliko
  8. Leila Elabbady
  9. Jeremy A Miller
  10. Trygve E Bakken
  11. Jean Rossier
  12. Zizhen Yao
  13. Ed Lein
  14. Hongkui Zeng
  15. Bosiljka Tasic
  16. Michael Hawrylycz
(2019)
Single-cell transcriptomic evidence for dense intracortical neuropeptide networks
eLife 8:e47889.
https://doi.org/10.7554/eLife.47889

Share this article

https://doi.org/10.7554/eLife.47889

Further reading

    1. Cell Biology
    2. Neuroscience
    Luting Yang, Chunqing Hu ... Yaping Yan
    Research Article

    Reactive astrocytes play critical roles in the occurrence of various neurological diseases such as multiple sclerosis. Activation of astrocytes is often accompanied by a glycolysis-dominant metabolic switch. However, the role and molecular mechanism of metabolic reprogramming in activation of astrocytes have not been clarified. Here, we found that PKM2, a rate-limiting enzyme of glycolysis, displayed nuclear translocation in astrocytes of EAE (experimental autoimmune encephalomyelitis) mice, an animal model of multiple sclerosis. Prevention of PKM2 nuclear import by DASA-58 significantly reduced the activation of mice primary astrocytes, which was observed by decreased proliferation, glycolysis and secretion of inflammatory cytokines. Most importantly, we identified the ubiquitination-mediated regulation of PKM2 nuclear import by ubiquitin ligase TRIM21. TRIM21 interacted with PKM2, promoted its nuclear translocation and stimulated its nuclear activity to phosphorylate STAT3, NF-κB and interact with c-myc. Further single-cell RNA sequencing and immunofluorescence staining demonstrated that TRIM21 expression was upregulated in astrocytes of EAE. TRIM21 overexpressing in mice primary astrocytes enhanced PKM2-dependent glycolysis and proliferation, which could be reversed by DASA-58. Moreover, intracerebroventricular injection of a lentiviral vector to knockdown TRIM21 in astrocytes or intraperitoneal injection of TEPP-46, which inhibit the nuclear translocation of PKM2, effectively decreased disease severity, CNS inflammation and demyelination in EAE. Collectively, our study provides novel insights into the pathological function of nuclear glycolytic enzyme PKM2 and ubiquitination-mediated regulatory mechanism that are involved in astrocyte activation. Targeting this axis may be a potential therapeutic strategy for the treatment of astrocyte-involved neurological disease.

    1. Neuroscience
    Felix Michaud, Ruggiero Francavilla ... Lisa Topolnik
    Research Article

    Alzheimer’s disease (AD) leads to progressive memory decline, and alterations in hippocampal function are among the earliest pathological features observed in human and animal studies. GABAergic interneurons (INs) within the hippocampus coordinate network activity, among which type 3 interneuron-specific (I-S3) cells expressing vasoactive intestinal polypeptide and calretinin play a crucial role. These cells provide primarily disinhibition to principal excitatory cells (PCs) in the hippocampal CA1 region, regulating incoming inputs and memory formation. However, it remains unclear whether AD pathology induces changes in the activity of I-S3 cells, impacting the hippocampal network motifs. Here, using young adult 3xTg-AD mice, we found that while the density and morphology of I-S3 cells remain unaffected, there were significant changes in their firing output. Specifically, I-S3 cells displayed elongated action potentials and decreased firing rates, which was associated with a reduced inhibition of CA1 INs and their higher recruitment during spatial decision-making and object exploration tasks. Furthermore, the activation of CA1 PCs was also impacted, signifying early disruptions in CA1 network functionality. These findings suggest that altered firing patterns of I-S3 cells might initiate early-stage dysfunction in hippocampal CA1 circuits, potentially influencing the progression of AD pathology.