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Fluorescence microscopy tensor imaging representations for large-scale dataset analysis

Understanding complex biological systems requires the system-wide characterization of cellular and molecular features. Recent advances in optical imaging technologies and chemical tissue clearing have facilitated the acquisition of whole-organ imaging datasets, but automated tools for their quantita...

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Autores principales: Vinegoni, Claudio, Fumene Feruglio, Paolo, Courties, Gabriel, Schmidt, Stephen, Hulsmans, Maarten, Lee, Sungon, Wang, Rui, Sosnovik, David, Nahrendorf, Matthias, Weissleder, Ralph
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7101442/
https://www.ncbi.nlm.nih.gov/pubmed/32221334
http://dx.doi.org/10.1038/s41598-020-62233-2
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author Vinegoni, Claudio
Fumene Feruglio, Paolo
Courties, Gabriel
Schmidt, Stephen
Hulsmans, Maarten
Lee, Sungon
Wang, Rui
Sosnovik, David
Nahrendorf, Matthias
Weissleder, Ralph
author_facet Vinegoni, Claudio
Fumene Feruglio, Paolo
Courties, Gabriel
Schmidt, Stephen
Hulsmans, Maarten
Lee, Sungon
Wang, Rui
Sosnovik, David
Nahrendorf, Matthias
Weissleder, Ralph
author_sort Vinegoni, Claudio
collection PubMed
description Understanding complex biological systems requires the system-wide characterization of cellular and molecular features. Recent advances in optical imaging technologies and chemical tissue clearing have facilitated the acquisition of whole-organ imaging datasets, but automated tools for their quantitative analysis and visualization are still lacking. We have here developed a visualization technique capable of providing whole-organ tensor imaging representations of local regional descriptors based on fluorescence data acquisition. This method enables rapid, multiscale, analysis and virtualization of large-volume, high-resolution complex biological data while generating 3D tractographic representations. Using the murine heart as a model, our method allowed us to analyze and interrogate the cardiac microvasculature and the tissue resident macrophage distribution and better infer and delineate the underlying structural network in unprecedented detail.
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spelling pubmed-71014422020-03-31 Fluorescence microscopy tensor imaging representations for large-scale dataset analysis Vinegoni, Claudio Fumene Feruglio, Paolo Courties, Gabriel Schmidt, Stephen Hulsmans, Maarten Lee, Sungon Wang, Rui Sosnovik, David Nahrendorf, Matthias Weissleder, Ralph Sci Rep Article Understanding complex biological systems requires the system-wide characterization of cellular and molecular features. Recent advances in optical imaging technologies and chemical tissue clearing have facilitated the acquisition of whole-organ imaging datasets, but automated tools for their quantitative analysis and visualization are still lacking. We have here developed a visualization technique capable of providing whole-organ tensor imaging representations of local regional descriptors based on fluorescence data acquisition. This method enables rapid, multiscale, analysis and virtualization of large-volume, high-resolution complex biological data while generating 3D tractographic representations. Using the murine heart as a model, our method allowed us to analyze and interrogate the cardiac microvasculature and the tissue resident macrophage distribution and better infer and delineate the underlying structural network in unprecedented detail. Nature Publishing Group UK 2020-03-27 /pmc/articles/PMC7101442/ /pubmed/32221334 http://dx.doi.org/10.1038/s41598-020-62233-2 Text en © The Author(s) 2020 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.
spellingShingle Article
Vinegoni, Claudio
Fumene Feruglio, Paolo
Courties, Gabriel
Schmidt, Stephen
Hulsmans, Maarten
Lee, Sungon
Wang, Rui
Sosnovik, David
Nahrendorf, Matthias
Weissleder, Ralph
Fluorescence microscopy tensor imaging representations for large-scale dataset analysis
title Fluorescence microscopy tensor imaging representations for large-scale dataset analysis
title_full Fluorescence microscopy tensor imaging representations for large-scale dataset analysis
title_fullStr Fluorescence microscopy tensor imaging representations for large-scale dataset analysis
title_full_unstemmed Fluorescence microscopy tensor imaging representations for large-scale dataset analysis
title_short Fluorescence microscopy tensor imaging representations for large-scale dataset analysis
title_sort fluorescence microscopy tensor imaging representations for large-scale dataset analysis
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7101442/
https://www.ncbi.nlm.nih.gov/pubmed/32221334
http://dx.doi.org/10.1038/s41598-020-62233-2
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