Cortical ChAT+ neurons co-transmit acetylcholine and GABA in a target-and brain-region specific manner

Abstract

The mouse cerebral cortex contains neurons that express choline acetyltransferase (ChAT) and are a potential local source of acetylcholine. However, the neurotransmitters released by cortical ChAT+ neurons and their synaptic connectivity are unknown. We show that the nearly all cortical ChAT+ neurons in mice are specialized VIP+ interneurons that release GABA strongly onto other inhibitory interneurons and acetylcholine sparsely onto layer 1 interneurons and other VIP+/ChAT+ interneurons. This differential transmission of ACh and GABA based on the postsynaptic target neuron is reflected in VIP+/ChAT+ interneuron pre-synaptic terminals, as quantitative molecular analysis shows that only a subset of these are specialized to release acetylcholine. In addition, we identify a separate, sparse population of non-VIP ChAT+ neurons in the medial prefrontal cortex with a distinct developmental origin that robustly release acetylcholine in layer 1. These results demonstrate both cortex-region heterogeneity in cortical ChAT+ interneurons and target-specific co-release of acetylcholine and GABA.

Data availability

All data generated during this study are summarized in the figures and supporting files of this manuscript. Source data files from which the figures were generated are available at: https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/AIUTNJ

The following data sets were generated

Article and author information

Author details

  1. Adam J Granger

    Department of Neurobiology, Howard Hughes Medical Institute, Harvard Medical School, Boston, United States
    Competing interests
    The authors declare that no competing interests exist.
  2. Wengang Wang

    Department of Neurobiology, Howard Hughes Medical Institute, Harvard Medical School, Boston, United States
    Competing interests
    The authors declare that no competing interests exist.
  3. Keiramarie Robertson

    Department of Neurobiology, Harvard Medical School, Boston, United States
    Competing interests
    The authors declare that no competing interests exist.
  4. Mahmoud El-Rifai

    Department of Neurobiology, Harvard Medical School, Boston, United States
    Competing interests
    The authors declare that no competing interests exist.
  5. Andrea F Zanello

    Department of Neurobiology, Harvard Medical School, Boston, United States
    Competing interests
    The authors declare that no competing interests exist.
  6. Karina Bistrong

    Department of Neurobiology, Harvard Medical School, Boston, United States
    Competing interests
    The authors declare that no competing interests exist.
  7. Arpiar Saunders

    Department of Genetics, Harvard Medical School, Boston, United States
    Competing interests
    The authors declare that no competing interests exist.
  8. Brian W Chow

    Department of Neurobiology, Harvard Medical School, Boston, United States
    Competing interests
    The authors declare that no competing interests exist.
  9. Vicente Nuñez

    Department of Neurobiology, Harvard Medical School, Boston, United States
    Competing interests
    The authors declare that no competing interests exist.
  10. Miguel Turrero García

    Department of Neurobiology, Harvard Medical School, Boston, United States
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-7294-169X
  11. Corey C Harwell

    Department of Neurobiology, Harvard Medical School, Boston, United States
    Competing interests
    The authors declare that no competing interests exist.
  12. Chenghua Gu

    Department of Neurobiology, Harvard Medical School, Boston, United States
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-4212-7232
  13. Bernardo L Sabatini

    Department of Neurobiology, Howard Hughes Medical Institute, Harvard Medical School, Boston, United States
    For correspondence
    bsabatini@hms.harvard.edu
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-0095-9177

Funding

National Institute of Neurological Disorders and Stroke (R37 NS046579)

  • Bernardo L Sabatini

National Institute of Neurological Disorders and Stroke (K99 NS102429)

  • Adam J Granger

National Institute of Neurological Disorders and Stroke (P30Ns072030)

  • Mahmoud El-Rifai

Jane Coffin Childs Memorial Fund for Medical Research

  • Adam J Granger

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

Reviewing Editor

  1. Solange P Brown, Johns Hopkins University, United States

Ethics

Animal experimentation: This study was performed in accordance with the recommendations in the Guide for the Care and Use of Laboratory Animals of the NIH, and according to strict adherence to the protocols approved by the Institutional Animal Care and Use Committee (IACUC) of Harvard Medical School (protocol #IS00000571). Routine examination, veterinary care, disease surveillance, and animal use compliance were all carried out by certified veterinary staff of the Harvard Center for Comparative Medicine (HCCM) in addition to full daily animal husbandry provided by trained animal technicians.

Version history

  1. Received: April 10, 2020
  2. Accepted: July 1, 2020
  3. Accepted Manuscript published: July 2, 2020 (version 1)
  4. Version of Record published: July 14, 2020 (version 2)

Copyright

© 2020, Granger 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

  • 7,791
    views
  • 992
    downloads
  • 63
    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. Adam J Granger
  2. Wengang Wang
  3. Keiramarie Robertson
  4. Mahmoud El-Rifai
  5. Andrea F Zanello
  6. Karina Bistrong
  7. Arpiar Saunders
  8. Brian W Chow
  9. Vicente Nuñez
  10. Miguel Turrero García
  11. Corey C Harwell
  12. Chenghua Gu
  13. Bernardo L Sabatini
(2020)
Cortical ChAT+ neurons co-transmit acetylcholine and GABA in a target-and brain-region specific manner
eLife 9:e57749.
https://doi.org/10.7554/eLife.57749

Share this article

https://doi.org/10.7554/eLife.57749

Further reading

    1. Neuroscience
    Olivier Codol, Jonathan A Michaels ... Paul L Gribble
    Tools and Resources

    Artificial neural networks (ANNs) are a powerful class of computational models for unravelling neural mechanisms of brain function. However, for neural control of movement, they currently must be integrated with software simulating biomechanical effectors, leading to limiting impracticalities: (1) researchers must rely on two different platforms and (2) biomechanical effectors are not generally differentiable, constraining researchers to reinforcement learning algorithms despite the existence and potential biological relevance of faster training methods. To address these limitations, we developed MotorNet, an open-source Python toolbox for creating arbitrarily complex, differentiable, and biomechanically realistic effectors that can be trained on user-defined motor tasks using ANNs. MotorNet is designed to meet several goals: ease of installation, ease of use, a high-level user-friendly application programming interface, and a modular architecture to allow for flexibility in model building. MotorNet requires no dependencies outside Python, making it easy to get started with. For instance, it allows training ANNs on typically used motor control models such as a two joint, six muscle, planar arm within minutes on a typical desktop computer. MotorNet is built on PyTorch and therefore can implement any network architecture that is possible using the PyTorch framework. Consequently, it will immediately benefit from advances in artificial intelligence through PyTorch updates. Finally, it is open source, enabling users to create and share their own improvements, such as new effector and network architectures or custom task designs. MotorNet’s focus on higher-order model and task design will alleviate overhead cost to initiate computational projects for new researchers by providing a standalone, ready-to-go framework, and speed up efforts of established computational teams by enabling a focus on concepts and ideas over implementation.

    1. Neuroscience
    Meike E van der Heijden, Amanda M Brown ... Roy V Sillitoe
    Research Article

    The cerebellum contributes to a diverse array of motor conditions, including ataxia, dystonia, and tremor. The neural substrates that encode this diversity are unclear. Here, we tested whether the neural spike activity of cerebellar output neurons is distinct between movement disorders with different impairments, generalizable across movement disorders with similar impairments, and capable of causing distinct movement impairments. Using in vivo awake recordings as input data, we trained a supervised classifier model to differentiate the spike parameters between mouse models for ataxia, dystonia, and tremor. The classifier model correctly assigned mouse phenotypes based on single-neuron signatures. Spike signatures were shared across etiologically distinct but phenotypically similar disease models. Mimicking these pathophysiological spike signatures with optogenetics induced the predicted motor impairments in otherwise healthy mice. These data show that distinct spike signatures promote the behavioral presentation of cerebellar diseases.