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A genetic and computational approach to structurally classify neuronal types

Author

Listed:
  • Uygar Sümbül

    (Massachusetts Institute of Technology
    Harvard Medical School)

  • Sen Song

    (Massachusetts Institute of Technology
    School of Medicine, Tsinghua University)

  • Kyle McCulloch

    (Harvard Medical School
    University of California at Irvine)

  • Michael Becker

    (Harvard Medical School)

  • Bin Lin

    (Harvard Medical School
    University of Hong Kong)

  • Joshua R. Sanes

    (Center for Brain Science, Harvard University)

  • Richard H. Masland

    (Harvard Medical School
    Harvard Medical School)

  • H. Sebastian Seung

    (Massachusetts Institute of Technology
    Present address: Princeton Neuroscience Institute, Princeton, New Jersey 08544, USA)

Abstract

The importance of cell types in understanding brain function is widely appreciated but only a tiny fraction of neuronal diversity has been catalogued. Here we exploit recent progress in genetic definition of cell types in an objective structural approach to neuronal classification. The approach is based on highly accurate quantification of dendritic arbor position relative to neurites of other cells. We test the method on a population of 363 mouse retinal ganglion cells. For each cell, we determine the spatial distribution of the dendritic arbors, or arbor density, with reference to arbors of an abundant, well-defined interneuronal type. The arbor densities are sorted into a number of clusters that is set by comparison with several molecularly defined cell types. The algorithm reproduces the genetic classes that are pure types, and detects six newly clustered cell types that await genetic definition.

Suggested Citation

  • Uygar Sümbül & Sen Song & Kyle McCulloch & Michael Becker & Bin Lin & Joshua R. Sanes & Richard H. Masland & H. Sebastian Seung, 2014. "A genetic and computational approach to structurally classify neuronal types," Nature Communications, Nature, vol. 5(1), pages 1-12, May.
    • Handle: RePEc:nat:natcom:v:5:y:2014:i:1:d:10.1038_ncomms4512
    DOI: 10.1038/ncomms4512
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