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Nat Commun. 2014 Mar 24;5:3512. doi: 10.1038/ncomms4512.

A genetic and computational approach to structurally classify neuronal types.

Author information

1
1] Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, USA [2] Department of Ophthalmology, Harvard Medical School, Boston, Massachusetts 02114, USA [3].
2
1] Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, USA [2] Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing 100084, China [3].
3
1] Department of Ophthalmology, Harvard Medical School, Boston, Massachusetts 02114, USA [2] Department of Ecology and Evolutionary Biology, University of California at Irvine, Irvine, California 92697, USA.
4
Department of Ophthalmology, Harvard Medical School, Boston, Massachusetts 02114, USA.
5
1] Department of Ophthalmology, Harvard Medical School, Boston, Massachusetts 02114, USA [2] Department of Anatomy, University of Hong Kong, Hong Kong, Hong Kong.
6
Center for Brain Science, Department of Molecular and Cellular Biology, Harvard University, Cambridge, Massachusetts 02138, USA.
7
1] Department of Ophthalmology, Harvard Medical School, Boston, Massachusetts 02114, USA [2] Department of Neurobiology, Harvard Medical School, Boston, Massachusetts 02114, USA.
8
1] Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, USA [2].

Erratum in

  • Nat Commun. 2014;5:4690.

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.

PMID:
24662602
PMCID:
PMC4164236
DOI:
10.1038/ncomms4512
[Indexed for MEDLINE]
Free PMC Article
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