Mapping the transcriptional diversity of genetically and anatomically defined cell populations in the mouse brain

  1. Ken Sugino  Is a corresponding author
  2. Erin Clark
  3. Anton Schulmann
  4. Yasuyuki Shima
  5. Lihua Wang
  6. David L Hunt
  7. Bryan M Hooks
  8. Dimitri Traenkner
  9. Jayaram Chandrashekar
  10. Serge Picard
  11. Andrew L Lemire
  12. Nelson Spruston
  13. Adam W Hantman
  14. Sacha B Nelson  Is a corresponding author
  1. Janelia Research Campus, Howard Hughes Medical Institute, United States
  2. Brandeis University, United States

Abstract

Understanding the principles governing neuronal diversity is a fundamental goal for neuroscience. Here we provide an anatomical and transcriptomic database of nearly 200 genetically identified cell populations. By separately analyzing the robustness and pattern of expression differences across these cell populations, we identify two gene classes contributing distinctly to neuronal diversity. Short homeobox transcription factors distinguish neuronal populations combinatorially, and exhibit extremely low transcriptional noise, enabling highly robust expression differences. Long neuronal effector genes, such as channels and cell adhesion molecules, contribute disproportionately to neuronal diversity, based on their patterns rather than robustness of expression differences. By linking transcriptional identity to genetic strains and anatomical atlases we provide an extensive resource for further investigation of mouse neuronal cell types.

Data availability

Sequencing data have been deposited in NCBI GEO under accession number GSE79238.

The following data sets were generated
The following previously published data sets were used
    1. Tasic B et al
    (2018) Cell Diversity in the Mouse Cortex
    Available at http://celltypes.brain-map.org/rnaseq.

Article and author information

Author details

  1. Ken Sugino

    Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, United States
    For correspondence
    ken.sugino@gmail.com
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-5795-0635
  2. Erin Clark

    Brandeis University, Waltham, United States
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-4013-325X
  3. Anton Schulmann

    Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, United States
    Competing interests
    The authors declare that no competing interests exist.
  4. Yasuyuki Shima

    Brandeis University, Waltham, United States
    Competing interests
    The authors declare that no competing interests exist.
  5. Lihua Wang

    Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, United States
    Competing interests
    The authors declare that no competing interests exist.
  6. David L Hunt

    Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, United States
    Competing interests
    The authors declare that no competing interests exist.
  7. Bryan M Hooks

    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:" 0000-0003-0135-4284
  8. Dimitri Traenkner

    Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, United States
    Competing interests
    The authors declare that no competing interests exist.
  9. Jayaram Chandrashekar

    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:" 0000-0001-6412-0114
  10. Serge Picard

    Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, United States
    Competing interests
    The authors declare that no competing interests exist.
  11. Andrew L Lemire

    Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, United States
    Competing interests
    The authors declare that no competing interests exist.
  12. Nelson Spruston

    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:" 0000-0003-3118-1636
  13. Adam W Hantman

    Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, United States
    Competing interests
    The authors declare that no competing interests exist.
  14. Sacha B Nelson

    Brandeis University, Waltham, United States
    For correspondence
    nelson@brandeis.edu
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-0108-8599

Funding

Howard Hughes Medical Institute

  • Ken Sugino
  • Anton Schulmann
  • Lihua Wang
  • David L Hunt
  • Bryan M Hooks
  • Dimitri Traenkner
  • Jayaram Chandrashekar
  • Andrew L Lemire
  • Nelson Spruston
  • Adam W Hantman
  • Sacha B Nelson

National Eye Institute (EY022360)

  • Erin Clark
  • Yasuyuki Shima
  • Sacha B Nelson

National Institute of Mental Health (MH105949)

  • Erin Clark
  • Yasuyuki Shima
  • Sacha B Nelson

National Institute of Neurological Disorders and Stroke (NS075007)

  • Erin Clark
  • Yasuyuki Shima
  • Sacha B Nelson

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 experiments were conducted in accordance with the requirements of theInstitutional Animal Care and Use Committees at Janelia Research Campus (protocol# not available) and Brandeis University (protocol#17001).

Reviewing Editor

  1. Chris P Ponting, University of Edinburgh, United Kingdom

Publication history

  1. Received: July 20, 2018
  2. Accepted: April 11, 2019
  3. Accepted Manuscript published: April 12, 2019 (version 1)
  4. Version of Record published: May 3, 2019 (version 2)

Copyright

© 2019, Sugino 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

  • 7,017
    Page views
  • 1,036
    Downloads
  • 33
    Citations

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

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. Ken Sugino
  2. Erin Clark
  3. Anton Schulmann
  4. Yasuyuki Shima
  5. Lihua Wang
  6. David L Hunt
  7. Bryan M Hooks
  8. Dimitri Traenkner
  9. Jayaram Chandrashekar
  10. Serge Picard
  11. Andrew L Lemire
  12. Nelson Spruston
  13. Adam W Hantman
  14. Sacha B Nelson
(2019)
Mapping the transcriptional diversity of genetically and anatomically defined cell populations in the mouse brain
eLife 8:e38619.
https://doi.org/10.7554/eLife.38619

Further reading

    1. Neuroscience
    Narges Doostani, Gholam-Ali Hossein-Zadeh, Maryam Vaziri-Pashkam
    Research Article Updated

    Divisive normalization of the neural responses by the activity of the neighboring neurons has been proposed as a fundamental operation in the nervous system based on its success in predicting neural responses recorded in primate electrophysiology studies. Nevertheless, experimental evidence for the existence of this operation in the human brain is still scant. Here, using functional MRI, we examined the role of normalization across the visual hierarchy in the human visual cortex. Using stimuli form the two categories of human bodies and houses, we presented objects in isolation or in clutter and asked participants to attend or ignore the stimuli. Focusing on the primary visual area V1, the object-selective regions LO and pFs, the body-selective region EBA, and the scene-selective region PPA, we first modeled single-voxel responses using a weighted sum, a weighted average, and a normalization model and demonstrated that although the weighted sum and weighted average models also made acceptable predictions in some conditions, the response to multiple stimuli could generally be better described by a model that takes normalization into account. We then determined the observed effects of attention on cortical responses and demonstrated that these effects were predicted by the normalization model, but not by the weighted sum or the weighted average models. Our results thus provide evidence that the normalization model can predict responses to objects across shifts of visual attention, suggesting the role of normalization as a fundamental operation in the human brain.

    1. Neuroscience
    Godber M Godbersen, Sebastian Klug ... Andreas Hahn
    Research Article Updated

    External tasks evoke characteristic fMRI BOLD signal deactivations in the default mode network (DMN). However, for the corresponding metabolic glucose demands both decreases and increases have been reported. To resolve this discrepancy, functional PET/MRI data from 50 healthy subjects performing Tetris were combined with previously published data sets of working memory, visual and motor stimulation. We show that the glucose metabolism of the posteromedial DMN is dependent on the metabolic demands of the correspondingly engaged task-positive networks. Specifically, the dorsal attention and frontoparietal network shape the glucose metabolism of the posteromedial DMN in opposing directions. While tasks that mainly require an external focus of attention lead to a consistent downregulation of both metabolism and the BOLD signal in the posteromedial DMN, cognitive control during working memory requires a metabolically expensive BOLD suppression. This indicates that two types of BOLD deactivations with different oxygen-to-glucose index may occur in this region. We further speculate that consistent downregulation of the two signals is mediated by decreased glutamate signaling, while divergence may be subject to active GABAergic inhibition. The results demonstrate that the DMN relates to cognitive processing in a flexible manner and does not always act as a cohesive task-negative network in isolation.