Abstract

The advancement of single cell RNA-sequencing technologies has led to an explosion of cell type definitions across multiple organs and organisms. While standards for data and metadata intake are arising, organization of cell types has largely been left to individual investigators, resulting in widely varying nomenclature and limited alignment between taxonomies. To facilitate cross-dataset comparison, the Allen Institute created the Common Cell type Nomenclature (CCN) for matching and tracking cell types across studies that is qualitatively similar to gene transcript management across different genome builds. The CCN can be readily applied to new or established taxonomies and was applied herein to diverse cell type datasets derived from multiple quantifiable modalities. The CCN facilitates assigning accurate yet flexible cell type names in the mammalian cortex as a step towards community-wide efforts to organize multi-source, data-driven information related to cell type taxonomies from any organism.

Data availability

This work describes the creation of a convention that will, with adoption by the community, become a standard. The data cited is open data though the Allen Institute open web portal, https://brain-map.orgAn open Forum is available to engage the community in further development, at https://portal.brain-map.org/explore/classes/nomenclatureData referenced in this study is also made available according the terms of NIH's Brain Research through Advancing Innovative Neurotechnologies (BRAIN) Initiative - Cell Census Network (BICCN), through the Brain Cell Data Center portal, https://biccn.org/ and https://biccn.org/data

The following previously published data sets were used

Article and author information

Author details

  1. Jeremy A Miller

    Allen Institute for Brain Science, Seattle, United States
    For correspondence
    jeremym@alleninstitute.org
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-4549-588X
  2. Nathan W Gouwens

    Allen Institute for Brain Science, Seattle, United States
    Competing interests
    The authors declare that no competing interests exist.
  3. Bosiljka Tasic

    Allen Institute for Brain Science, Seattle, United States
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-6861-4506
  4. Forrest Collman

    Allen Institute for Brain Science, Seattle, United States
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-0280-7022
  5. Cindy TJ van Velthoven

    Allen Institute for Brain Science, Seattle, United States
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-5120-4546
  6. Trygve E Bakken

    Allen Institute for Brain Science, Seattle, United States
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-3373-7386
  7. Michael J Hawrylycz

    Allen Institute for Brain Science, Seattle, United States
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-5741-8024
  8. Hongkui Zeng

    Allen Institute for Brain Science, Seattle, United States
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-0326-5878
  9. Ed S Lein

    Allen Institute for Brain Science, Seattle, United States
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-9012-6552
  10. Amy Bernard

    Allen Institute for Brain Science, Seattle, United States
    For correspondence
    amyb@alleninstitute.org
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-2540-1153

Funding

Allen Institute

  • Jeremy A Miller
  • Nathan W Gouwens
  • Bosiljka Tasic
  • Forrest Collman
  • Cindy TJ van Velthoven
  • Trygve E Bakken
  • Michael J Hawrylycz
  • Hongkui Zeng
  • Ed S Lein
  • Amy Bernard

National Institute of Mental Health (U19MH114830)

  • Hongkui Zeng

National Institute of Mental Health (U01MH114812)

  • Ed S Lein

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

Reviewing Editor

  1. Genevieve Konopka, University of Texas Southwestern Medical Center, United States

Version history

  1. Received: June 12, 2020
  2. Accepted: December 28, 2020
  3. Accepted Manuscript published: December 29, 2020 (version 1)
  4. Version of Record published: January 7, 2021 (version 2)

Copyright

© 2020, Miller 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

  • 6,295
    views
  • 674
    downloads
  • 57
    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. Jeremy A Miller
  2. Nathan W Gouwens
  3. Bosiljka Tasic
  4. Forrest Collman
  5. Cindy TJ van Velthoven
  6. Trygve E Bakken
  7. Michael J Hawrylycz
  8. Hongkui Zeng
  9. Ed S Lein
  10. Amy Bernard
(2020)
Common cell type nomenclature for the mammalian brain
eLife 9:e59928.
https://doi.org/10.7554/eLife.59928

Share this article

https://doi.org/10.7554/eLife.59928

Further reading

    1. Neuroscience
    Elissavet Chartampila, Karim S Elayouby ... Helen E Scharfman
    Research Article

    Maternal choline supplementation (MCS) improves cognition in Alzheimer’s disease (AD) models. However, the effects of MCS on neuronal hyperexcitability in AD are unknown. We investigated the effects of MCS in a well-established mouse model of AD with hyperexcitability, the Tg2576 mouse. The most common type of hyperexcitability in Tg2576 mice are generalized EEG spikes (interictal spikes [IIS]). IIS also are common in other mouse models and occur in AD patients. In mouse models, hyperexcitability is also reflected by elevated expression of the transcription factor ∆FosB in the granule cells (GCs) of the dentate gyrus (DG), which are the principal cell type. Therefore, we studied ΔFosB expression in GCs. We also studied the neuronal marker NeuN within hilar neurons of the DG because reduced NeuN protein expression is a sign of oxidative stress or other pathology. This is potentially important because hilar neurons regulate GC excitability. Tg2576 breeding pairs received a diet with a relatively low, intermediate, or high concentration of choline. After weaning, all mice received the intermediate diet. In offspring of mice fed the high choline diet, IIS frequency declined, GC ∆FosB expression was reduced, and hilar NeuN expression was restored. Using the novel object location task, spatial memory improved. In contrast, offspring exposed to the relatively low choline diet had several adverse effects, such as increased mortality. They had the weakest hilar NeuN immunoreactivity and greatest GC ΔFosB protein expression. However, their IIS frequency was low, which was surprising. The results provide new evidence that a diet high in choline in early life can improve outcomes in a mouse model of AD, and relatively low choline can have mixed effects. This is the first study showing that dietary choline can regulate hyperexcitability, hilar neurons, ΔFosB, and spatial memory in an animal model of AD.

    1. Neuroscience
    Guozheng Feng, Yiwen Wang ... Ni Shu
    Research Article

    Brain structural circuitry shapes a richly patterned functional synchronization, supporting for complex cognitive and behavioural abilities. However, how coupling of structural connectome (SC) and functional connectome (FC) develops and its relationships with cognitive functions and transcriptomic architecture remain unclear. We used multimodal magnetic resonance imaging data from 439 participants aged 5.7–21.9 years to predict functional connectivity by incorporating intracortical and extracortical structural connectivity, characterizing SC–FC coupling. Our findings revealed that SC–FC coupling was strongest in the visual and somatomotor networks, consistent with evolutionary expansion, myelin content, and functional principal gradient. As development progressed, SC–FC coupling exhibited heterogeneous alterations dominated by an increase in cortical regions, broadly distributed across the somatomotor, frontoparietal, dorsal attention, and default mode networks. Moreover, we discovered that SC–FC coupling significantly predicted individual variability in general intelligence, mainly influencing frontoparietal and default mode networks. Finally, our results demonstrated that the heterogeneous development of SC–FC coupling is positively associated with genes in oligodendrocyte-related pathways and negatively associated with astrocyte-related genes. This study offers insight into the maturational principles of SC–FC coupling in typical development.