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Automated computation of arbor densities: a step toward identifying neuronal cell types
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 n...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2014
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4243570/ https://www.ncbi.nlm.nih.gov/pubmed/25505389 http://dx.doi.org/10.3389/fnana.2014.00139 |
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author | Sümbül, Uygar Zlateski, Aleksandar Vishwanathan, Ashwin Masland, Richard H. Seung, H. Sebastian |
author_facet | Sümbül, Uygar Zlateski, Aleksandar Vishwanathan, Ashwin Masland, Richard H. Seung, H. Sebastian |
author_sort | Sümbül, Uygar |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-4243570 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2014 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-42435702014-12-10 Automated computation of arbor densities: a step toward identifying neuronal cell types Sümbül, Uygar Zlateski, Aleksandar Vishwanathan, Ashwin Masland, Richard H. Seung, H. Sebastian Front Neuroanat Neuroscience 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. Frontiers Media S.A. 2014-11-25 /pmc/articles/PMC4243570/ /pubmed/25505389 http://dx.doi.org/10.3389/fnana.2014.00139 Text en Copyright © 2014 Sümbül, Zlateski, Vishwanathan, Masland and Seung. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Neuroscience Sümbül, Uygar Zlateski, Aleksandar Vishwanathan, Ashwin Masland, Richard H. Seung, H. Sebastian Automated computation of arbor densities: a step toward identifying neuronal cell types |
title | Automated computation of arbor densities: a step toward identifying neuronal cell types |
title_full | Automated computation of arbor densities: a step toward identifying neuronal cell types |
title_fullStr | Automated computation of arbor densities: a step toward identifying neuronal cell types |
title_full_unstemmed | Automated computation of arbor densities: a step toward identifying neuronal cell types |
title_short | Automated computation of arbor densities: a step toward identifying neuronal cell types |
title_sort | automated computation of arbor densities: a step toward identifying neuronal cell types |
topic | Neuroscience |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4243570/ https://www.ncbi.nlm.nih.gov/pubmed/25505389 http://dx.doi.org/10.3389/fnana.2014.00139 |
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