Visualizing synaptic plasticity in vivo by large-scale imaging of endogenous AMPA receptors

Abstract

Elucidating how synaptic molecules such as AMPA receptors mediate neuronal communication and tracking their dynamic expression during behavior is crucial to understand cognition and disease, but current technological barriers preclude large-scale exploration of molecular dynamics in vivo. We have developed a suite of innovative methodologies that break through these barriers: a new knockin mouse line with fluorescently tagged endogenous AMPA receptors, two-photon imaging of hundreds of thousands of labeled synapses in behaving mice, and computer-vision-based automatic synapse detection. Using these tools, we can longitudinally track how the strength of populations of synapses changes during behavior. We used this approach to generate an unprecedentedly detailed spatiotemporal map of synapses undergoing changes in strength following sensory experience. More generally, these tools can be used as an optical probe capable of measuring functional synapse strength across entire brain areas during any behavioral paradigm, describing complex system-wide changes with molecular precision.

Data availability

We have provided Source data for our Figures and deposited the remaining data to Dryad with doi:10.5061/dryad.ttdz08m0b. All code used to analyze and process data is freely available on GitHub, with links specified in the manuscript.

The following data sets were generated
    1. Huganir RL
    2. Graves A
    (2021) Raw data for Graves et. al 2021 eLife
    Dryad Digital Repository, doi:10.5061/dryad.ttdz08m0b.

Article and author information

Author details

  1. Austin R Graves

    Department of Neuroscience, Johns Hopkins University School of Medicine, Baltimore, United States
    Competing interests
    No competing interests declared.
  2. Richard H Roth

    Department of Neuroscience, Johns Hopkins University School of Medicine, Baltimore, United States
    Competing interests
    No competing interests declared.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-6855-999X
  3. Han L Tan

    Solomon H Snyder Department of Neuroscience, Johns Hopkins University School of Medicine, Baltimore, United States
    Competing interests
    No competing interests declared.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-5163-7720
  4. Qianwen Zhu

    Department of Neuroscience, Johns Hopkins University School of Medicine, Baltimore, United States
    Competing interests
    No competing interests declared.
  5. Alexei M Bygrave

    Solomon H Snyder Department of Neuroscience, Johns Hopkins University School of Medicine, Baltimore, United States
    Competing interests
    No competing interests declared.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-2291-923X
  6. Elena Lopez-Ortega

    Department of Neuroscience, Johns Hopkins University School of Medicine, Baltimore, United States
    Competing interests
    No competing interests declared.
  7. Ingie Hong

    Solomon H Snyder Department of Neuroscience, Johns Hopkins University School of Medicine, Baltimore, United States
    Competing interests
    No competing interests declared.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-7246-9233
  8. Alina C Spiegel

    Department of Neuroscience, Johns Hopkins University School of Medicine, Baltimore, United States
    Competing interests
    No competing interests declared.
  9. Richard C Johnson

    Department of Neuroscience, Johns Hopkins University School of Medicine, Baltimore, United States
    Competing interests
    No competing interests declared.
  10. Joshua T Vogelstein

    Biomedical Engineering, Johns Hopkins University, Baltimore, United States
    Competing interests
    No competing interests declared.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-2487-6237
  11. Daniel J Tward

    Center for Imaging Science, Johns Hopkins University, Baltimore, United States
    Competing interests
    No competing interests declared.
  12. Michael I Miller

    Center for Imaging Science, Johns Hopkins University, Baltimore, United States
    Competing interests
    Michael I Miller, Dr. Miller is a joint owner of AnatomyWorks. Dr. Miller's relationship with Anatomy-Works is being handled under full disclosure by the Johns Hopkins University..
  13. Richard L Huganir

    Solomon H Snyder Department of Neuroscience, Johns Hopkins University School of Medicine, Baltimore, United States
    For correspondence
    rhuganir@jhmi.edu
    Competing interests
    No competing interests declared.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-9783-5183

Funding

National Institutes of Health (R21 AG063193)

  • Austin R Graves
  • Richard L Huganir

Kavli Foundation

  • Austin R Graves
  • Alina C Spiegel
  • Daniel J Tward

National Institutes of Health (R01 MH123212)

  • Austin R Graves
  • Alexei M Bygrave
  • Michael I Miller
  • Richard L Huganir

Schmidt Science Nascent Innovation Grant (1)

  • Austin R Graves
  • Joshua T Vogelstein
  • Richard L Huganir

National Institutes of Health (K99 MH124920)

  • Alexei M Bygrave

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

Ethics

Animal experimentation: These studies were conducted in accordance with US Public Health Service on Human Care and Use of Laboratory Animals (PHS Policy) and all procedures involving animals were approved by the Johns Hopkins Animal Care and Use Committee (ACUC) protocols (MO19M274, MO20M372, MO20M92, MO20M336). All surgeries were performed under isoflourane anesthesia. Every effort was made to reduce or eliminate pain and suffering during all surgical procedures, in vivo imaging sessions, and behavioral experiments.

Copyright

© 2021, Graves 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

  • 8,451
    views
  • 1,090
    downloads
  • 47
    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. Austin R Graves
  2. Richard H Roth
  3. Han L Tan
  4. Qianwen Zhu
  5. Alexei M Bygrave
  6. Elena Lopez-Ortega
  7. Ingie Hong
  8. Alina C Spiegel
  9. Richard C Johnson
  10. Joshua T Vogelstein
  11. Daniel J Tward
  12. Michael I Miller
  13. Richard L Huganir
(2021)
Visualizing synaptic plasticity in vivo by large-scale imaging of endogenous AMPA receptors
eLife 10:e66809.
https://doi.org/10.7554/eLife.66809

Share this article

https://doi.org/10.7554/eLife.66809

Further reading

    1. Neuroscience
    Tai-Ying Lee, Yves Weissenberger ... Johannes C Dahmen
    Research Article

    Hearing involves analyzing the physical attributes of sounds and integrating the results of this analysis with other sensory, cognitive, and motor variables in order to guide adaptive behavior. The auditory cortex is considered crucial for the integration of acoustic and contextual information and is thought to share the resulting representations with subcortical auditory structures via its vast descending projections. By imaging cellular activity in the corticorecipient shell of the inferior colliculus of mice engaged in a sound detection task, we show that the majority of neurons encode information beyond the physical attributes of the stimulus and that the animals’ behavior can be decoded from the activity of those neurons with a high degree of accuracy. Surprisingly, this was also the case in mice in which auditory cortical input to the midbrain had been removed by bilateral cortical lesions. This illustrates that subcortical auditory structures have access to a wealth of non-acoustic information and can, independently of the auditory cortex, carry much richer neural representations than previously thought.

    1. Genetics and Genomics
    2. Neuroscience
    Thomas P Spargo, Lachlan Gilchrist ... Alfredo Iacoangeli
    Research Article

    Continued methodological advances have enabled numerous statistical approaches for the analysis of summary statistics from genome-wide association studies. Genetic correlation analysis within specific regions enables a new strategy for identifying pleiotropy. Genomic regions with significant ‘local’ genetic correlations can be investigated further using state-of-the-art methodologies for statistical fine-mapping and variant colocalisation. We explored the utility of a genome-wide local genetic correlation analysis approach for identifying genetic overlaps between the candidate neuropsychiatric disorders, Alzheimer’s disease (AD), amyotrophic lateral sclerosis (ALS), frontotemporal dementia, Parkinson’s disease, and schizophrenia. The correlation analysis identified several associations between traits, the majority of which were loci in the human leukocyte antigen region. Colocalisation analysis suggested that disease-implicated variants in these loci often differ between traits and, in one locus, indicated a shared causal variant between ALS and AD. Our study identified candidate loci that might play a role in multiple neuropsychiatric diseases and suggested the role of distinct mechanisms across diseases despite shared loci. The fine-mapping and colocalisation analysis protocol designed for this study has been implemented in a flexible analysis pipeline that produces HTML reports and is available at: https://github.com/ThomasPSpargo/COLOC-reporter.