Multiphoton imaging of neural structure and activity in Drosophila through the intact cuticle

  1. Max Jameson Aragon
  2. Aaron T Mok
  3. Jamien Shea
  4. Mengran Wang
  5. Haein Kim
  6. Nathan Barkdull
  7. Chris Xu
  8. Nilay Yapici  Is a corresponding author
  1. Princeton University, United States
  2. Cornell University, United States
  3. University of Florida, United States

Abstract

We developed a multiphoton imaging method to capture neural structure and activity in behaving flies through the intact cuticles. Our measurements show that the fly head cuticle has surprisingly high transmission at wavelengths > 900 nm, and the difficulty of through-cuticle imaging is due to the air sacs and/or fat tissue underneath the head cuticle. By compressing or removing the air sacs, we performed multiphoton imaging of the fly brain through the intact cuticle. Our anatomical and functional imaging results show that 2- and 3-photon imaging are comparable in superficial regions such as the mushroom body, but 3-photon imaging is superior in deeper regions such as the central complex and beyond. We further demonstrated 2-photon through-cuticle functional imaging of odor-evoked calcium responses from the mushroom body g-lobes in behaving flies short-term and long-term. The through-cuticle imaging method developed here extends the time limits of in vivo imaging in flies and opens new ways to capture neural structure and activity from the fly brain.

Data availability

All data generated or analyzed during this study are included in the manuscript and supporting file; Source Data files have been provided.

Article and author information

Author details

  1. Max Jameson Aragon

    Princeton Neuroscience Institute, Princeton University, Princeton, United States
    Competing interests
    The authors declare that no competing interests exist.
  2. Aaron T Mok

    School of Applied and Engineering Physics, Cornell University, Ithaca, United States
    Competing interests
    The authors declare that no competing interests exist.
  3. Jamien Shea

    Department of Neurobiology and Behavior, Cornell University, Ithaca, United States
    Competing interests
    The authors declare that no competing interests exist.
  4. Mengran Wang

    School of Applied and Engineering Physics, Cornell University, Ithaca, United States
    Competing interests
    The authors declare that no competing interests exist.
  5. Haein Kim

    Department of Neurobiology and Behavior, Cornell University, Ithaca, United States
    Competing interests
    The authors declare that no competing interests exist.
  6. Nathan Barkdull

    Department of Physics, University of Florida, Gainesville, United States
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-1174-5046
  7. Chris Xu

    School of Applied and Engineering Physics, Cornell University, Ithaca, United States
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-3493-6427
  8. Nilay Yapici

    Department of Neurobiology and Behavior, Cornell University, Ithaca, United States
    For correspondence
    ny96@cornell.edu
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-1130-5083

Funding

National Science Foundation (DBI-1707312)

  • Nilay Yapici

National Institute of General Medical Sciences (R35 GM133698)

  • Nilay Yapici

Pew Charitable Trusts (Scholars Award)

  • Nilay Yapici

Alfred P. Sloan Foundation (Scholars Award)

  • Nilay Yapici

American Federation for Aging Research (Grants for Junior Faculty)

  • Nilay Yapici

National Science Foundation (DBI-1707312)

  • Chris Xu

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

Reviewing Editor

  1. Upinder Singh Bhalla, Tata Institute of Fundamental Research, India

Publication history

  1. Preprint posted: October 9, 2019 (view preprint)
  2. Received: April 4, 2021
  3. Accepted: January 23, 2022
  4. Accepted Manuscript published: January 24, 2022 (version 1)
  5. Accepted Manuscript updated: January 25, 2022 (version 2)
  6. Version of Record published: February 15, 2022 (version 3)

Copyright

© 2022, Aragon 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

  • 3,026
    Page views
  • 452
    Downloads
  • 2
    Citations

Article citation count generated by polling the highest count across the following sources: Crossref, PubMed Central, Scopus.

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. Max Jameson Aragon
  2. Aaron T Mok
  3. Jamien Shea
  4. Mengran Wang
  5. Haein Kim
  6. Nathan Barkdull
  7. Chris Xu
  8. Nilay Yapici
(2022)
Multiphoton imaging of neural structure and activity in Drosophila through the intact cuticle
eLife 11:e69094.
https://doi.org/10.7554/eLife.69094
  1. Further reading

Further reading

    1. Neuroscience
    Jonathan Nicholas, Nathaniel D Daw, Daphna Shohamy
    Research Article

    A key question in decision making is how humans arbitrate between competing learning and memory systems to maximize reward. We address this question by probing the balance between the effects, on choice, of incremental trial-and-error learning versus episodic memories of individual events. Although a rich literature has studied incremental learning in isolation, the role of episodic memory in decision making has only recently drawn focus, and little research disentangles their separate contributions. We hypothesized that the brain arbitrates rationally between these two systems, relying on each in circumstances to which it is most suited, as indicated by uncertainty. We tested this hypothesis by directly contrasting contributions of episodic and incremental influence to decisions, while manipulating the relative uncertainty of incremental learning using a well-established manipulation of reward volatility. Across two large, independent samples of young adults, participants traded these influences off rationally, depending more on episodic information when incremental summaries were more uncertain. These results support the proposal that the brain optimizes the balance between different forms of learning and memory according to their relative uncertainties and elucidate the circumstances under which episodic memory informs decisions.

    1. Neuroscience
    Andrew P Davison, Shailesh Appukuttan
    Insight

    Artificial neural networks could pave the way for efficiently simulating large-scale models of neuronal networks in the nervous system.