title: Automated computation of arbor densities: a step toward identifying neuronal cell types.
abstract: The shape and position of a neuron convey information regarding its molecular and functional identity. The identification of cell types from structure, a classic method, relies on the time-consuming step of arbor tracing. However, as genetic tools and imaging methods make data-driven approaches to neuronal circuit analysis feasible, the need for automated processing increases. Here, we first establish that mouse retinal ganglion cell types can be as precise about distributing their arbor volumes across the inner plexiform layer as they are about distributing the skeletons of the arbors. Then, we describe an automated approach to computing the spatial distribution of the dendritic arbors, or arbor density, with respect to a global depth coordinate based on this observation. Our method involves three-dimensional reconstruction of neuronal arbors by a supervised machine learning algorithm, post-processing of the enhanced stacks to remove somata and isolate the neuron of interest, and registration of neurons to each other using automatically detected arbors of the starburst amacrine interneurons as fiducial markers. In principle, this method could be generalizable to other structures of the CNS, provided that they allow sparse labeling of the cells and contain a reliable axis of spatial reference.
authors.affiliation: Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology Cambridge, MA, USA ; Department of Ophthalmology, Harvard Medical School Boston, MA, USA.
authors.forename: Uygar
authors.initials: U
authors.lastname: Sümbül
authors.affiliation: Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology Cambridge, MA, USA.
authors.forename: Aleksandar
authors.initials: A
authors.lastname: Zlateski
authors.affiliation: Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology Cambridge, MA, USA.
authors.forename: Ashwin
authors.initials: A
authors.lastname: Vishwanathan
authors.affiliation: Department of Ophthalmology, Harvard Medical School Boston, MA, USA ; Department of Neurobiology, Harvard Medical School Boston, MA, USA.
authors.forename: Richard H
authors.initials: RH
authors.lastname: Masland
authors.affiliation: Princeton Neuroscience Institute and Computer Science Department, Princeton University Princeton, NJ, USA.
authors.forename: H Sebastian
authors.initials: HS
authors.lastname: Seung
keywords: cell types; classification; laminar structures; reconstruction; retinal ganglion cells; stratification