FluoEM, virtual labeling of axons in 3-dimensional electron microscopy data for long-range connectomics

  1. Florian Drawitsch
  2. Ali Karimi
  3. Kevin M Boergens
  4. Moritz Helmstaedter  Is a corresponding author
  1. Max Planck Institute for Brain Research, Germany

Abstract

The labeling and identification of long-range axonal inputs from multiple sources within densely reconstructed EM datasets from mammalian brains has been notoriously difficult because of the limited color label space of EM. Here, we report FluoEM for the identification of multi-color fluorescently labeled axons in dense EM data without the need for artificial fiducial marks or chemical label conversion. The approach is based on correlated tissue imaging and computational matching of neurite reconstructions, amounting to a virtual color labeling of axons in dense EM circuit data. We show that the identification of fluorescent light- microscopically (LM) imaged axons in 3D EM data from mouse cortex is faithfully possible as soon as the EM dataset is about 40-50 µm in extent, relying on the unique trajectories of axons in dense mammalian neuropil. The method is exemplified for the identification of long-distance axonal input into layer 1 of the mouse cerebral cortex.

Data availability

All imaging data is available for online browsing and annotation at demo.webknossos.org as detailed in the data availability section of the Methods.

The following data sets were generated
    1. Drawitsch F
    2. Helmstaedter M
    (2018) FluoEM low-res EM dataset
    FluoEM_2016-05-23_FD0144-2_st001_v1, openly accessible via webknossos.org.
    1. Drawitsch F
    2. Helmstaedter M
    (2018) FluoEM high-res EM dataset
    FluoEM_2016-05-26_FD0144-2_v2s2s, openly accessible via webknossos.org.
    1. Drawitsch F
    2. Helmstaedter M
    (2018) FluoEM LM dataset
    FluoEM_2016-06-02-FD0144_2_Confocal , openly accessible via webknossos.org.

Article and author information

Author details

  1. Florian Drawitsch

    Department of Connectomics, Max Planck Institute for Brain Research, Frankfurt, Germany
    Competing interests
    No competing interests declared.
  2. Ali Karimi

    Department of Connectomics, Max Planck Institute for Brain Research, Frankfurt, Germany
    Competing interests
    No competing interests declared.
  3. Kevin M Boergens

    Department of Connectomics, Max Planck Institute for Brain Research, Frankfurt, Germany
    Competing interests
    No competing interests declared.
  4. Moritz Helmstaedter

    Department of Connectomics, Max Planck Institute for Brain Research, Frankfurt, Germany
    For correspondence
    mh@brain.mpg.de
    Competing interests
    Moritz Helmstaedter, Reviewing editor, eLife.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-7973-0767

Funding

Max-Planck-Gesellschaft (Open-access funding)

  • Florian Drawitsch
  • Ali Karimi
  • Kevin M Boergens
  • Moritz Helmstaedter

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

Ethics

Animal experimentation: All experimental procedures were performed according to the law of animal experimentation issued by the German Federal Government under the supervision of local ethics committees and according to the guidelines of the Max Planck Society. The experimental procedures were approved by Regierungspräsidium Darmstadt, V54 - 19c20/15 - F126/1015.

Copyright

© 2018, Drawitsch 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

  • 3,267
    views
  • 532
    downloads
  • 25
    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. Florian Drawitsch
  2. Ali Karimi
  3. Kevin M Boergens
  4. Moritz Helmstaedter
(2018)
FluoEM, virtual labeling of axons in 3-dimensional electron microscopy data for long-range connectomics
eLife 7:e38976.
https://doi.org/10.7554/eLife.38976

Share this article

https://doi.org/10.7554/eLife.38976

Further reading

    1. Neuroscience
    Claire Meissner-Bernard, Friedemann Zenke, Rainer W Friedrich
    Research Article

    Biological memory networks are thought to store information by experience-dependent changes in the synaptic connectivity between assemblies of neurons. Recent models suggest that these assemblies contain both excitatory and inhibitory neurons (E/I assemblies), resulting in co-tuning and precise balance of excitation and inhibition. To understand computational consequences of E/I assemblies under biologically realistic constraints we built a spiking network model based on experimental data from telencephalic area Dp of adult zebrafish, a precisely balanced recurrent network homologous to piriform cortex. We found that E/I assemblies stabilized firing rate distributions compared to networks with excitatory assemblies and global inhibition. Unlike classical memory models, networks with E/I assemblies did not show discrete attractor dynamics. Rather, responses to learned inputs were locally constrained onto manifolds that ‘focused’ activity into neuronal subspaces. The covariance structure of these manifolds supported pattern classification when information was retrieved from selected neuronal subsets. Networks with E/I assemblies therefore transformed the geometry of neuronal coding space, resulting in continuous representations that reflected both relatedness of inputs and an individual’s experience. Such continuous representations enable fast pattern classification, can support continual learning, and may provide a basis for higher-order learning and cognitive computations.

    1. Neuroscience
    Raven Star Wallace, Bronte Mckeown ... Jonathan Smallwood
    Research Article

    Movie-watching is a central aspect of our lives and an important paradigm for understanding the brain mechanisms behind cognition as it occurs in daily life. Contemporary views of ongoing thought argue that the ability to make sense of events in the ‘here and now’ depend on the neural processing of incoming sensory information by auditory and visual cortex, which are kept in check by systems in association cortex. However, we currently lack an understanding of how patterns of ongoing thoughts map onto the different brain systems when we watch a film, partly because methods of sampling experience disrupt the dynamics of brain activity and the experience of movie-watching. Our study established a novel method for mapping thought patterns onto the brain activity that occurs at different moments of a film, which does not disrupt the time course of brain activity or the movie-watching experience. We found moments when experience sampling highlighted engagement with multi-sensory features of the film or highlighted thoughts with episodic features, regions of sensory cortex were more active and subsequent memory for events in the movie was better—on the other hand, periods of intrusive distraction emerged when activity in regions of association cortex within the frontoparietal system was reduced. These results highlight the critical role sensory systems play in the multi-modal experience of movie-watching and provide evidence for the role of association cortex in reducing distraction when we watch films.