TRACE: An Unbiased Method to Permanently Tag Transiently Activated Inputs

A fundamental interest in circuit analysis is to parse out the synaptic inputs underlying a behavioral experience. Toward this aim, we have devised an unbiased strategy that specifically labels the afferent inputs that are activated by a defined stimulus in an activity-dependent manner. We validated...

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Detalles Bibliográficos
Autores principales: Krauth, Nathalie, Khalil, Valentina, Jariwala, Meet, Mermet-Joret, Noemie, Vestergaard, Anne-Katrine, Capogna, Marco, Nabavi, Sadegh
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2020
Materias:
Cellular Neuroscience
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7243865/
https://www.ncbi.nlm.nih.gov/pubmed/32499680
http://dx.doi.org/10.3389/fncel.2020.00114
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author Krauth, Nathalie
Khalil, Valentina
Jariwala, Meet
Mermet-Joret, Noemie
Vestergaard, Anne-Katrine
Capogna, Marco
Nabavi, Sadegh
author_facet Krauth, Nathalie
Khalil, Valentina
Jariwala, Meet
Mermet-Joret, Noemie
Vestergaard, Anne-Katrine
Capogna, Marco
Nabavi, Sadegh
author_sort Krauth, Nathalie
collection PubMed
description A fundamental interest in circuit analysis is to parse out the synaptic inputs underlying a behavioral experience. Toward this aim, we have devised an unbiased strategy that specifically labels the afferent inputs that are activated by a defined stimulus in an activity-dependent manner. We validated this strategy in four brain circuits receiving known sensory inputs. This strategy, as demonstrated here, accurately identifies these inputs.
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spelling pubmed-72438652020-06-03 TRACE: An Unbiased Method to Permanently Tag Transiently Activated Inputs Krauth, Nathalie Khalil, Valentina Jariwala, Meet Mermet-Joret, Noemie Vestergaard, Anne-Katrine Capogna, Marco Nabavi, Sadegh Front Cell Neurosci Cellular Neuroscience A fundamental interest in circuit analysis is to parse out the synaptic inputs underlying a behavioral experience. Toward this aim, we have devised an unbiased strategy that specifically labels the afferent inputs that are activated by a defined stimulus in an activity-dependent manner. We validated this strategy in four brain circuits receiving known sensory inputs. This strategy, as demonstrated here, accurately identifies these inputs. Frontiers Media S.A. 2020-05-12 /pmc/articles/PMC7243865/ /pubmed/32499680 http://dx.doi.org/10.3389/fncel.2020.00114 Text en Copyright © 2020 Krauth, Khalil, Jariwala, Mermet-Joret, Vestergaard, Capogna and Nabavi. 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) and the copyright owner(s) 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 Cellular Neuroscience
Krauth, Nathalie
Khalil, Valentina
Jariwala, Meet
Mermet-Joret, Noemie
Vestergaard, Anne-Katrine
Capogna, Marco
Nabavi, Sadegh
TRACE: An Unbiased Method to Permanently Tag Transiently Activated Inputs
title TRACE: An Unbiased Method to Permanently Tag Transiently Activated Inputs
title_full TRACE: An Unbiased Method to Permanently Tag Transiently Activated Inputs
title_fullStr TRACE: An Unbiased Method to Permanently Tag Transiently Activated Inputs
title_full_unstemmed TRACE: An Unbiased Method to Permanently Tag Transiently Activated Inputs
title_short TRACE: An Unbiased Method to Permanently Tag Transiently Activated Inputs
title_sort trace: an unbiased method to permanently tag transiently activated inputs
topic Cellular Neuroscience
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7243865/
https://www.ncbi.nlm.nih.gov/pubmed/32499680
http://dx.doi.org/10.3389/fncel.2020.00114
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