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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...
Autores principales: | , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2020
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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. |
format | Online Article Text |
id | pubmed-7101442 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
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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