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Publication Detail
Classes and continua of hippocampal CA1 inhibitory neurons revealed by single-cell transcriptomics.
  • Publication Type:
    Journal article
  • Publication Sub Type:
    Article
  • Authors:
    Harris KD, Hochgerner H, Skene NG, Magno L, Katona L, Bengtsson Gonzales C, Somogyi P, Kessaris N, Linnarsson S, Hjerling-Leffler J
  • Publication date:
    18/06/2018
  • Pagination:
    e2006387
  • Journal:
    PLoS biology
  • Volume:
    16
  • Issue:
    6
  • Medium:
    Print-Electronic
  • Status:
    Published
  • Print ISSN:
    1544-9173
  • Language:
    eng
  • Addresses:
    University College London Institute of Neurology, London, United Kingdom.
Abstract
Understanding any brain circuit will require a categorization of its constituent neurons. In hippocampal area CA1, at least 23 classes of GABAergic neuron have been proposed to date. However, this list may be incomplete; additionally, it is unclear whether discrete classes are sufficient to describe the diversity of cortical inhibitory neurons or whether continuous modes of variability are also required. We studied the transcriptomes of 3,663 CA1 inhibitory cells, revealing 10 major GABAergic groups that divided into 49 fine-scale clusters. All previously described and several novel cell classes were identified, with three previously described classes unexpectedly found to be identical. A division into discrete classes, however, was not sufficient to describe the diversity of these cells, as continuous variation also occurred between and within classes. Latent factor analysis revealed that a single continuous variable could predict the expression levels of several genes, which correlated similarly with it across multiple cell types. Analysis of the genes correlating with this variable suggested it reflects a range from metabolically highly active faster-spiking cells that proximally target pyramidal cells to slower-spiking cells targeting distal dendrites or interneurons. These results elucidate the complexity of inhibitory neurons in one of the simplest cortical structures and show that characterizing these cells requires continuous modes of variation as well as discrete cell classes.
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