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Classes and continua of hippocampal CA1 inhibitory neurons revealed by single-cell transcriptomics

Author

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  • Kenneth D Harris
  • Hannah Hochgerner
  • Nathan G Skene
  • Lorenza Magno
  • Linda Katona
  • Carolina Bengtsson Gonzales
  • Peter Somogyi
  • Nicoletta Kessaris
  • Sten Linnarsson
  • Jens Hjerling-Leffler

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.Author summary: Single-cell RNA sequencing allows scientists to count the number of copies of each gene expressed in multiple individually isolated cells. Because different cell types express genes in different amounts, “clusters” of cells with similar expression patterns are likely to correspond to different cell types. As well as discrete classes, however, cells also show continuous variation in gene expression. To study the relationship between cell classes and continua in a well-understood brain system, we applied new analysis methods to a dataset of inhibitory interneurons from area CA1 of the mouse hippocampus. Thanks to decades of intensive work, at least 23 classes of CA1 interneurons have been previously defined. We were able to identify them all with our transcriptomic clusters but unexpectedly found three to be identical. Because the connectivity of these cells has already been established, we were also able to identify the primary mode of continuous variation in these cells, which related to their axon target location. This in-depth understanding of the relatively simple cortical circuit of CA1 not only clarifies the cellular composition of this important brain structure but also will form a solid foundation for understanding more complex structures, such as the isocortex.

Suggested Citation

  • Kenneth D Harris & Hannah Hochgerner & Nathan G Skene & Lorenza Magno & Linda Katona & Carolina Bengtsson Gonzales & Peter Somogyi & Nicoletta Kessaris & Sten Linnarsson & Jens Hjerling-Leffler, 2018. "Classes and continua of hippocampal CA1 inhibitory neurons revealed by single-cell transcriptomics," PLOS Biology, Public Library of Science, vol. 16(6), pages 1-37, June.
  • Handle: RePEc:plo:pbio00:2006387
    DOI: 10.1371/journal.pbio.2006387
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    References listed on IDEAS

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    1. Yina Wei & Anirban Nandi & Xiaoxuan Jia & Joshua H. Siegle & Daniel Denman & Soo Yeun Lee & Anatoly Buchin & Werner Geit & Clayton P. Mosher & Shawn Olsen & Costas A. Anastassiou, 2023. "Associations between in vitro, in vivo and in silico cell classes in mouse primary visual cortex," Nature Communications, Nature, vol. 14(1), pages 1-20, December.
    2. Ian Covert & Rohan Gala & Tim Wang & Karel Svoboda & Uygar Sümbül & Su-In Lee, 2023. "Predictive and robust gene selection for spatial transcriptomics," Nature Communications, Nature, vol. 14(1), pages 1-14, December.
    3. Hansol Lim & Yue Zhang & Christian Peters & Tobias Straub & Johanna Luise Mayer & Rüdiger Klein, 2024. "Genetically- and spatially-defined basolateral amygdala neurons control food consumption and social interaction," Nature Communications, Nature, vol. 15(1), pages 1-22, December.
    4. Olga Gliko & Matt Mallory & Rachel Dalley & Rohan Gala & James Gornet & Hongkui Zeng & Staci A. Sorensen & Uygar Sümbül, 2024. "High-throughput analysis of dendrite and axonal arbors reveals transcriptomic correlates of neuroanatomy," Nature Communications, Nature, vol. 15(1), pages 1-13, December.
    5. François G. C. Blot & Joshua J. White & Amy van Hattem & Licia Scotti & Vaishnavi Balaji & Youri Adolfs & R. Jeroen Pasterkamp & Chris I. De Zeeuw & Martijn Schonewille, 2023. "Purkinje cell microzones mediate distinct kinematics of a single movement," Nature Communications, Nature, vol. 14(1), pages 1-15, December.
    6. Alicia Hernández-Vivanco & Nuria Cano-Adamuz & Alberto Sánchez-Aguilera & Alba González-Alonso & Alberto Rodríguez-Fernández & Íñigo Azcoitia & Liset Menendez de la Prida & Pablo Méndez, 2022. "Sex-specific regulation of inhibition and network activity by local aromatase in the mouse hippocampus," Nature Communications, Nature, vol. 13(1), pages 1-14, December.
    7. Chong Guo & Vincent Huson & Evan Z. Macosko & Wade G. Regehr, 2021. "Graded heterogeneity of metabotropic signaling underlies a continuum of cell-intrinsic temporal responses in unipolar brush cells," Nature Communications, Nature, vol. 12(1), pages 1-12, December.
    8. Wendy Xueyi Wang & Julie L. Lefebvre, 2022. "Morphological pseudotime ordering and fate mapping reveal diversification of cerebellar inhibitory interneurons," Nature Communications, Nature, vol. 13(1), pages 1-21, December.

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