1. Chromosomes and Gene Expression
  2. Neuroscience

Conditional protein tagging methods reveal highly specific subcellular distribution of ion channels in motion-sensing neurons

  1. Sandra Fendl  Is a corresponding author
  2. Renee Marie Vieira
  3. Alexander Borst  Is a corresponding author
  1. Max Planck Institute of Neurobiology, Germany
  2. Max-Planck-Institute of Neurobiology, Germany
https://doi.org/10.7554/eLife.62953
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  1. Sandra Fendl
  2. Renee Marie Vieira
  3. Alexander Borst
(2020)
Conditional protein tagging methods reveal highly specific subcellular distribution of ion channels in motion-sensing neurons
eLife 9:e62953.
https://doi.org/10.7554/eLife.62953
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Abstract

Neurotransmitter receptors and ion channels shape the biophysical properties of neurons, from the sign of the response mediated by neurotransmitter receptors to the dynamics shaped by voltage-gated ion channels. Therefore, knowing the localizations and types of receptors and channels present in neurons is fundamental to our understanding of neural computation. Here, we developed two approaches to visualize the subcellular localization of specific proteins in Drosophila: The flippase-dependent expression of GFP-tagged receptor subunits in single neurons and 'FlpTag', a versatile new tool for the conditional labelling of endogenous proteins. Using these methods, we investigated the subcellular distribution of the receptors GluClα, Rdl, and Dα7 and the ion channels para and Ih in motion-sensing T4/T5 neurons of the Drosophila visual system. We discovered a strictly segregated subcellular distribution of these proteins and a sequential spatial arrangement of glutamate, acetylcholine, and GABA receptors along the dendrite that matched the previously reported EM-reconstructed synapse distributions.

Data availability

All data generated or analysed during this study are included in the manuscript and supporting files. Source data files have been provided for Figures 2, 3 and 4. Previously Published Datasets: Seven medulla column connectome: Kazunori Shinomiya, 2019, http://emdata.janelia.org/#/repo/medulla7column, #3b548

The following previously published data sets were used

Article and author information

Author details

  1. Sandra Fendl

    Circuits - Computation - Models, Max Planck Institute of Neurobiology, Martinsried, Germany
    For correspondence
    sfendl@neuro.mpg.de
    Competing interests
    No competing interests declared.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-6442-2542

  2. Renee Marie Vieira

    Circuits - Computation - Models, Max Planck Institute of Neurobiology, Martinsried, Germany
    Competing interests
    No competing interests declared.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-8520-7382

  3. Alexander Borst

    Circuits - Computation - Models, Max-Planck-Institute of Neurobiology, Martinsried, Germany
    For correspondence
    aborst@neuro.mpg.de
    Competing interests
    Alexander Borst, Reviewing editor, eLife.

Funding

Max-Planck-Gesellschaft

  • Sandra Fendl
  • Renee Marie Vieira
  • Alexander Borst

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

Reviewing Editor

  1. Claude Desplan, New York University, United States

Version history

  1. Received: September 10, 2020
  2. Accepted: October 14, 2020
  3. Accepted Manuscript published: October 20, 2020 (version 1)
  4. Accepted Manuscript updated: October 21, 2020 (version 2)
  5. Version of Record published: November 10, 2020 (version 3)

Copyright

© 2020, Fendl 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.

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  1. Sandra Fendl
  2. Renee Marie Vieira
  3. Alexander Borst
(2020)
Conditional protein tagging methods reveal highly specific subcellular distribution of ion channels in motion-sensing neurons
eLife 9:e62953.
https://doi.org/10.7554/eLife.62953

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