@article{db987f3e4e034e9d891a73644eb740b9,
title = "A genetic and computational approach to structurally classify neuronal types",
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.",
author = "Uygar S{\"u}mb{\"u}l and Sen Song and Kyle McCulloch and Michael Becker and Bin Lin and Sanes, {Joshua R.} and Masland, {Richard H.} and Seung, {Hyunjune Sebastian}",
note = "Funding Information: We are grateful for financial support from the Harvard NeuroDiscovery Center, the Howard Hughes Medical Institute, the Gatsby Charitable Foundation and the Human Frontier Science Program. S.S. is supported by National Science Foundation of China Grant 91332122 and National Key Laboratory of Cognitive Neuroscience and Learning at Beijing Normal University open grant CNKOPZD1004. J.R.S. is supported by NIH grant NS29169. We thank Mu Qiao for helping with transgenic animals, Ashwin Vishwanathan for helpful discussions and Justin Zhang for helping with the pipeline software. We thank the image analysts Alexandra Saali, Ryan Berry and Tim Baloda at Massachusetts Eye and Ear Infirmary, Seleeke Flingai at Massachusetts Institute of Technology, and Li Xuesong, Zhang Yanjuan, Chen Chenggang, He Jingyu, He Mingming, Liu Yuanyuan, Wang Shuo, Xu Mingjie and Xue Xiaowei at Tsinghua University. Publisher Copyright: {\textcopyright} 2014 Macmillan Publishers Limited. All rights reserved.",
year = "2014",
month = mar,
day = "24",
doi = "10.1038/ncomms4512",
language = "English (US)",
volume = "5",
journal = "Nature Communications",
issn = "2041-1723",
publisher = "Nature Publishing Group",
}