Information flow, cell types and stereotypy in a full olfactory connectome

  1. Philipp Schlegel
  2. Alexander Shakeel Bates
  3. Tomke Stürner
  4. Sridhar R Jagannathan
  5. Nikolas Drummond
  6. Joseph Hsu
  7. Laia Serratosa Capdevila
  8. Alexandre Javier
  9. Elizabeth C Marin
  10. Asa Barth-Maron
  11. Imaan FM Tamimi
  12. Feng Li
  13. Gerald M Rubin
  14. Stephen M Plaza
  15. Marta Costa
  16. Gregory SXE Jefferis  Is a corresponding author
  1. MRC Laboratory of Molecular Biology, United Kingdom
  2. University of Cambridge, United Kingdom
  3. Howard Hughes Medical Institute, United States
  4. Harvard Medical School, United States
  5. Janelia Research Campus, Howard Hughes Medical Institute, United States

Abstract

The hemibrain connectome provides large scale connectivity and morphology information for the majority of the central brain of Drosophila melanogaster. Using this data set, we provide a complete description of the Drosophila olfactory system, covering all first, second and lateral horn-associated third-order neurons. We develop a generally applicable strategy to extract information flow and layered organisation from connectome graphs, mapping olfactory input to descending interneurons. This identifies a range of motifs including highly lateralised circuits in the antennal lobe and patterns of convergence downstream of the mushroom body and lateral horn. Leveraging a second data set we provide a first quantitative assessment of inter- versus intra-individual stereotypy. Comparing neurons across two brains (three hemispheres) reveals striking similarity in neuronal morphology across brains. Connectivity correlates with morphology and neurons of the same morphological type show similar connection variability within the same brain as across two brains.

Data availability

The hemibrain connectome including our annotations is hosted via neuPrint at https://neuprint.janelia.orgPublished data (neuronal reconstructions and connectivity) from the FAFB EM data set is hosted by Virtual Fly Brain (VFB) at https://catmaid.virtualflybrain.org. A snapshot of the FAFB data used in this study will be shared with VFB prior to publication.Meta data (e.g. neuron classifications, axon-dendrite splits, glomeruli meshes, etc) are included in the manuscript and supporting files.In addition, we maintain Github repositories with meta data (https://github.com/flyconnectome/hemibrain_olf_data) and code examples (https://github.com/flyconnectome/2020hemibrain_examples).

Article and author information

Author details

  1. Philipp Schlegel

    Division of Neurobiology, MRC Laboratory of Molecular Biology, Cambridge, United Kingdom
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-5633-1314
  2. Alexander Shakeel Bates

    Neurobiology Division, MRC Laboratory of Molecular Biology, Cambridge, United Kingdom
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-1195-0445
  3. Tomke Stürner

    Department of Zoology, University of Cambridge, Cambridge, United Kingdom
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-4054-0784
  4. Sridhar R Jagannathan

    Department of Zoology, University of Cambridge, Cambridge, United Kingdom
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-2078-1145
  5. Nikolas Drummond

    Department of Zoology, University of Cambridge, Cambridge, United Kingdom
    Competing interests
    The authors declare that no competing interests exist.
  6. Joseph Hsu

    Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, United States
    Competing interests
    The authors declare that no competing interests exist.
  7. Laia Serratosa Capdevila

    Department of Zoology, University of Cambridge, Cambridge, United Kingdom
    Competing interests
    The authors declare that no competing interests exist.
  8. Alexandre Javier

    Department of Zoology, University of Cambridge, Cambridge, United Kingdom
    Competing interests
    The authors declare that no competing interests exist.
  9. Elizabeth C Marin

    Drosophila Connectomics Group, Department of Zoology, University of Cambridge, Cambridge, United Kingdom
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-6333-0072
  10. Asa Barth-Maron

    Neurobiology, Harvard Medical School, Boston, United States
    Competing interests
    The authors declare that no competing interests exist.
  11. Imaan FM Tamimi

    Department of Zoology, University of Cambridge, Cambridge, United Kingdom
    Competing interests
    The authors declare that no competing interests exist.
  12. Feng Li

    Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, United States
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-6658-9175
  13. Gerald M Rubin

    Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, United States
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-8762-8703
  14. Stephen M Plaza

    Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, United States
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-7425-8555
  15. Marta Costa

    Department of Zoology, University of Cambridge, Cambridge, United Kingdom
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-5948-3092
  16. Gregory SXE Jefferis

    Neurobiology Division, MRC Laboratory of Molecular Biology, Cambridge, United Kingdom
    For correspondence
    jefferis@mrc-lmb.cam.ac.uk
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-0587-9355

Funding

Wellcome Trust (Collaborative Award,203261/Z/16/Z)

  • Philipp Schlegel
  • Tomke Stürner
  • Sridhar R Jagannathan
  • Nikolas Drummond
  • Joseph Hsu
  • Laia Serratosa Capdevila
  • Alexandre Javier
  • Elizabeth C Marin
  • Imaan FM Tamimi
  • Feng Li
  • Gerald M Rubin
  • Marta Costa
  • Gregory SXE Jefferis

European Research Council (Consolidator grant,649111)

  • Laia Serratosa Capdevila
  • Alexandre Javier
  • Gregory SXE Jefferis

Medical Research Council (Core support,MC-U105188491)

  • Alexander Shakeel Bates
  • Gregory SXE Jefferis

National Institutes of Health (BRAIN Initiative grant,1RF1MH120679-01)

  • Philipp Schlegel
  • Tomke Stürner
  • Gregory SXE Jefferis

National Institutes of Health (F31 fellowship,DC016196)

  • Asa Barth-Maron

Boehringer Ingelheim Fonds (PhD Fellowship)

  • Alexander Shakeel Bates

Herchel Smith (Studentship)

  • Alexander Shakeel Bates

National Institutes of Health (R01DC008174)

  • Asa Barth-Maron

Howard Hughes Medical Institute

  • Gerald M Rubin
  • Stephen M Plaza

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

Copyright

© 2021, Schlegel 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

  • 6,491
    views
  • 830
    downloads
  • 123
    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. Philipp Schlegel
  2. Alexander Shakeel Bates
  3. Tomke Stürner
  4. Sridhar R Jagannathan
  5. Nikolas Drummond
  6. Joseph Hsu
  7. Laia Serratosa Capdevila
  8. Alexandre Javier
  9. Elizabeth C Marin
  10. Asa Barth-Maron
  11. Imaan FM Tamimi
  12. Feng Li
  13. Gerald M Rubin
  14. Stephen M Plaza
  15. Marta Costa
  16. Gregory SXE Jefferis
(2021)
Information flow, cell types and stereotypy in a full olfactory connectome
eLife 10:e66018.
https://doi.org/10.7554/eLife.66018

Share this article

https://doi.org/10.7554/eLife.66018

Further reading

    1. Computational and Systems Biology
    2. Neuroscience
    Louis K Scheffer, C Shan Xu ... Stephen M Plaza
    Research Article Updated

    The neural circuits responsible for animal behavior remain largely unknown. We summarize new methods and present the circuitry of a large fraction of the brain of the fruit fly Drosophila melanogaster. Improved methods include new procedures to prepare, image, align, segment, find synapses in, and proofread such large data sets. We define cell types, refine computational compartments, and provide an exhaustive atlas of cell examples and types, many of them novel. We provide detailed circuits consisting of neurons and their chemical synapses for most of the central brain. We make the data public and simplify access, reducing the effort needed to answer circuit questions, and provide procedures linking the neurons defined by our analysis with genetic reagents. Biologically, we examine distributions of connection strengths, neural motifs on different scales, electrical consequences of compartmentalization, and evidence that maximizing packing density is an important criterion in the evolution of the fly’s brain.

    1. Neuroscience
    Christine Ahrends, Mark W Woolrich, Diego Vidaurre
    Tools and Resources

    Predicting an individual’s cognitive traits or clinical condition using brain signals is a central goal in modern neuroscience. This is commonly done using either structural aspects, such as structural connectivity or cortical thickness, or aggregated measures of brain activity that average over time. But these approaches are missing a central aspect of brain function: the unique ways in which an individual’s brain activity unfolds over time. One reason why these dynamic patterns are not usually considered is that they have to be described by complex, high-dimensional models; and it is unclear how best to use these models for prediction. We here propose an approach that describes dynamic functional connectivity and amplitude patterns using a Hidden Markov model (HMM) and combines it with the Fisher kernel, which can be used to predict individual traits. The Fisher kernel is constructed from the HMM in a mathematically principled manner, thereby preserving the structure of the underlying model. We show here, in fMRI data, that the HMM-Fisher kernel approach is accurate and reliable. We compare the Fisher kernel to other prediction methods, both time-varying and time-averaged functional connectivity-based models. Our approach leverages information about an individual’s time-varying amplitude and functional connectivity for prediction and has broad applications in cognitive neuroscience and personalised medicine.