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Protocol for fine casting, imaging, and analysis of murine vascular networks with VALID
The majority of fluorescent vessel labeling techniques currently available are limited by their expense, incomplete labeling, or complexity. Here, we present VALID (vessel labeling via gelatin-based lipophilic dye solution)—a protocol for complete labeling of different vascular networks. We describe...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10425940/ https://www.ncbi.nlm.nih.gov/pubmed/37543943 http://dx.doi.org/10.1016/j.xpro.2023.102441 |
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author | Zhu, Jingtan Liu, Xiaomei Xu, Jianyi Liu, Zhang Deng, Yating Dai, Junyao Yu, Tingting Zhu, Dan |
author_facet | Zhu, Jingtan Liu, Xiaomei Xu, Jianyi Liu, Zhang Deng, Yating Dai, Junyao Yu, Tingting Zhu, Dan |
author_sort | Zhu, Jingtan |
collection | PubMed |
description | The majority of fluorescent vessel labeling techniques currently available are limited by their expense, incomplete labeling, or complexity. Here, we present VALID (vessel labeling via gelatin-based lipophilic dye solution)—a protocol for complete labeling of different vascular networks. We describe steps for preparing different dye hydrogels, murine vascular casting and tissue harvesting, immunolabeling, tissue clearing, and imaging, as well as detailed analysis of the vascular networks. This protocol is helpful for evaluating vascular lesions in studying different vessel-associated diseases. For complete details on the use and execution of this protocol, please refer to Zhu et al.(1) |
format | Online Article Text |
id | pubmed-10425940 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Elsevier |
record_format | MEDLINE/PubMed |
spelling | pubmed-104259402023-08-16 Protocol for fine casting, imaging, and analysis of murine vascular networks with VALID Zhu, Jingtan Liu, Xiaomei Xu, Jianyi Liu, Zhang Deng, Yating Dai, Junyao Yu, Tingting Zhu, Dan STAR Protoc Protocol The majority of fluorescent vessel labeling techniques currently available are limited by their expense, incomplete labeling, or complexity. Here, we present VALID (vessel labeling via gelatin-based lipophilic dye solution)—a protocol for complete labeling of different vascular networks. We describe steps for preparing different dye hydrogels, murine vascular casting and tissue harvesting, immunolabeling, tissue clearing, and imaging, as well as detailed analysis of the vascular networks. This protocol is helpful for evaluating vascular lesions in studying different vessel-associated diseases. For complete details on the use and execution of this protocol, please refer to Zhu et al.(1) Elsevier 2023-08-06 /pmc/articles/PMC10425940/ /pubmed/37543943 http://dx.doi.org/10.1016/j.xpro.2023.102441 Text en © 2023 The Author(s) https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). |
spellingShingle | Protocol Zhu, Jingtan Liu, Xiaomei Xu, Jianyi Liu, Zhang Deng, Yating Dai, Junyao Yu, Tingting Zhu, Dan Protocol for fine casting, imaging, and analysis of murine vascular networks with VALID |
title | Protocol for fine casting, imaging, and analysis of murine vascular networks with VALID |
title_full | Protocol for fine casting, imaging, and analysis of murine vascular networks with VALID |
title_fullStr | Protocol for fine casting, imaging, and analysis of murine vascular networks with VALID |
title_full_unstemmed | Protocol for fine casting, imaging, and analysis of murine vascular networks with VALID |
title_short | Protocol for fine casting, imaging, and analysis of murine vascular networks with VALID |
title_sort | protocol for fine casting, imaging, and analysis of murine vascular networks with valid |
topic | Protocol |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10425940/ https://www.ncbi.nlm.nih.gov/pubmed/37543943 http://dx.doi.org/10.1016/j.xpro.2023.102441 |
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