Mechanisms and functional roles of glutamatergic synapse diversity in a cerebellar circuit
Abstract
Synaptic currents display a large degree of heterogeneity of their temporal characteristics, but the functional role of such heterogeneities remains unknown. We investigated in rat cerebellar slices synaptic currents in Unipolar Brush Cells (UBCs), which generate intrinsic mossy fibers relaying vestibular inputs to the cerebellar cortex. We show that UBCs respond to sinusoidal modulations of their sensory input with heterogeneous amplitudes and phase shifts. Experiments and modeling indicate that this variability results both from the kinetics of synaptic glutamate transients and from the diversity of postsynaptic receptors. While phase inversion is produced by an mGluR2-activated outward conductance in OFF-UBCs, the phase delay of ON UBCs is caused by a late rebound current resulting from AMPAR recovery from desensitization. Granular layer network modeling indicates that phase dispersion of UBC responses generates diverse phase coding in the granule cell population, allowing climbing-fiber-driven Purkinje cell learning at arbitrary phases of the vestibular input.
Article and author information
Author details
Funding
Centre National de la Recherche Scientifique
- Marco A. Diana
- Nicolas Brunel
- Stéphane Dieudonné
Institut National de la Santé et de la Recherche Médicale
- Stéphane Dieudonné
Agence Nationale de la Recherche (ANR-BBSRC grant VESTICODE)
- Valeria Zampini
- Jian K Liu
- Marco A. Diana
- Paloma P Maldonado
- Nicolas Brunel
- Stéphane Dieudonné
Agence Nationale de la Recherche (ANR-10-LABX-54 MEMO LIFE)
- Stéphane Dieudonné
Agence Nationale de la Recherche (ANR-11- 4 IDEX-0001-02 PSL*)
- Stéphane Dieudonné
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 animal manipulations were made in accordance with guidelines of the Centre national de la recherche scientifique. Protocols were approved under number 02235.02 of the general agreement C750520
Copyright
© 2016, Zampini 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
-
- 2,196
- views
-
- 543
- downloads
-
- 55
- citations
Views, downloads and citations are aggregated across all versions of this paper published by eLife.
Download links
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)
Further reading
-
- Neuroscience
Techniques that enable precise manipulations of subsets of neurons in the fly central nervous system (CNS) have greatly facilitated our understanding of the neural basis of behavior. Split-GAL4 driver lines allow specific targeting of cell types in Drosophila melanogaster and other species. We describe here a collection of 3060 lines targeting a range of cell types in the adult Drosophila CNS and 1373 lines characterized in third-instar larvae. These tools enable functional, transcriptomic, and proteomic studies based on precise anatomical targeting. NeuronBridge and other search tools relate light microscopy images of these split-GAL4 lines to connectomes reconstructed from electron microscopy images. The collections are the result of screening over 77,000 split hemidriver combinations. Previously published and new lines are included, all validated for driver expression and curated for optimal cell-type specificity across diverse cell types. In addition to images and fly stocks for these well-characterized lines, we make available 300,000 new 3D images of other split-GAL4 lines.
-
- Neuroscience
Cell identification is an important yet difficult process in data analysis of biological images. Previously, we developed an automated cell identification method called CRF_ID and demonstrated its high performance in Caenorhabditis elegans whole-brain images (Chaudhary et al., 2021). However, because the method was optimized for whole-brain imaging, comparable performance could not be guaranteed for application in commonly used C. elegans multi-cell images that display a subpopulation of cells. Here, we present an advancement, CRF_ID 2.0, that expands the generalizability of the method to multi-cell imaging beyond whole-brain imaging. To illustrate the application of the advance, we show the characterization of CRF_ID 2.0 in multi-cell imaging and cell-specific gene expression analysis in C. elegans. This work demonstrates that high-accuracy automated cell annotation in multi-cell imaging can expedite cell identification and reduce its subjectivity in C. elegans and potentially other biological images of various origins.