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Visual Search of Neuropil-Enriched RNAs from Brain In Situ Hybridization Data through the Image Analysis Pipeline Hippo-ATESC

MOTIVATION: RNA molecules specifically enriched in the neuropil of neuronal cells and in particular in dendritic spines are of great interest for neurobiology in virtue of their involvement in synaptic structure and plasticity. The systematic recognition of such molecules is therefore a very importa...

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Autores principales: Ugolotti, Roberto, Mesejo, Pablo, Zongaro, Samantha, Bardoni, Barbara, Berto, Gaia, Bianchi, Federico, Molineris, Ivan, Giacobini, Mario, Cagnoni, Stefano, Di Cunto, Ferdinando
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2013
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3767670/
https://www.ncbi.nlm.nih.gov/pubmed/24040258
http://dx.doi.org/10.1371/journal.pone.0074481
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author Ugolotti, Roberto
Mesejo, Pablo
Zongaro, Samantha
Bardoni, Barbara
Berto, Gaia
Bianchi, Federico
Molineris, Ivan
Giacobini, Mario
Cagnoni, Stefano
Di Cunto, Ferdinando
author_facet Ugolotti, Roberto
Mesejo, Pablo
Zongaro, Samantha
Bardoni, Barbara
Berto, Gaia
Bianchi, Federico
Molineris, Ivan
Giacobini, Mario
Cagnoni, Stefano
Di Cunto, Ferdinando
author_sort Ugolotti, Roberto
collection PubMed
description MOTIVATION: RNA molecules specifically enriched in the neuropil of neuronal cells and in particular in dendritic spines are of great interest for neurobiology in virtue of their involvement in synaptic structure and plasticity. The systematic recognition of such molecules is therefore a very important task. High resolution images of RNA in situ hybridization experiments contained in the Allen Brain Atlas (ABA) represent a very rich resource to identify them and have been so far exploited for this task through human-expert analysis. However, software tools that may automatically address the same objective are not very well developed. RESULTS: In this study we describe an automatic method for exploring in situ hybridization data and discover neuropil-enriched RNAs in the mouse hippocampus. We called it Hippo-ATESC (Automatic Texture Extraction from the Hippocampal region using Soft Computing). Bioinformatic validation showed that the Hippo-ATESC is very efficient in the recognition of RNAs which are manually identified by expert curators as neuropil-enriched on the same image series. Moreover, we show that our method can also highlight genes revealed by microdissection-based methods but missed by human visual inspection. We experimentally validated our approach by identifying a non-coding transcript enriched in mouse synaptosomes. The code is freely available on the web at http://ibislab.ce.unipr.it/software/hippo/.
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spelling pubmed-37676702013-09-13 Visual Search of Neuropil-Enriched RNAs from Brain In Situ Hybridization Data through the Image Analysis Pipeline Hippo-ATESC Ugolotti, Roberto Mesejo, Pablo Zongaro, Samantha Bardoni, Barbara Berto, Gaia Bianchi, Federico Molineris, Ivan Giacobini, Mario Cagnoni, Stefano Di Cunto, Ferdinando PLoS One Research Article MOTIVATION: RNA molecules specifically enriched in the neuropil of neuronal cells and in particular in dendritic spines are of great interest for neurobiology in virtue of their involvement in synaptic structure and plasticity. The systematic recognition of such molecules is therefore a very important task. High resolution images of RNA in situ hybridization experiments contained in the Allen Brain Atlas (ABA) represent a very rich resource to identify them and have been so far exploited for this task through human-expert analysis. However, software tools that may automatically address the same objective are not very well developed. RESULTS: In this study we describe an automatic method for exploring in situ hybridization data and discover neuropil-enriched RNAs in the mouse hippocampus. We called it Hippo-ATESC (Automatic Texture Extraction from the Hippocampal region using Soft Computing). Bioinformatic validation showed that the Hippo-ATESC is very efficient in the recognition of RNAs which are manually identified by expert curators as neuropil-enriched on the same image series. Moreover, we show that our method can also highlight genes revealed by microdissection-based methods but missed by human visual inspection. We experimentally validated our approach by identifying a non-coding transcript enriched in mouse synaptosomes. The code is freely available on the web at http://ibislab.ce.unipr.it/software/hippo/. Public Library of Science 2013-09-09 /pmc/articles/PMC3767670/ /pubmed/24040258 http://dx.doi.org/10.1371/journal.pone.0074481 Text en © 2013 Ugolotti et al http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.
spellingShingle Research Article
Ugolotti, Roberto
Mesejo, Pablo
Zongaro, Samantha
Bardoni, Barbara
Berto, Gaia
Bianchi, Federico
Molineris, Ivan
Giacobini, Mario
Cagnoni, Stefano
Di Cunto, Ferdinando
Visual Search of Neuropil-Enriched RNAs from Brain In Situ Hybridization Data through the Image Analysis Pipeline Hippo-ATESC
title Visual Search of Neuropil-Enriched RNAs from Brain In Situ Hybridization Data through the Image Analysis Pipeline Hippo-ATESC
title_full Visual Search of Neuropil-Enriched RNAs from Brain In Situ Hybridization Data through the Image Analysis Pipeline Hippo-ATESC
title_fullStr Visual Search of Neuropil-Enriched RNAs from Brain In Situ Hybridization Data through the Image Analysis Pipeline Hippo-ATESC
title_full_unstemmed Visual Search of Neuropil-Enriched RNAs from Brain In Situ Hybridization Data through the Image Analysis Pipeline Hippo-ATESC
title_short Visual Search of Neuropil-Enriched RNAs from Brain In Situ Hybridization Data through the Image Analysis Pipeline Hippo-ATESC
title_sort visual search of neuropil-enriched rnas from brain in situ hybridization data through the image analysis pipeline hippo-atesc
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3767670/
https://www.ncbi.nlm.nih.gov/pubmed/24040258
http://dx.doi.org/10.1371/journal.pone.0074481
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