Optimal combinations of fluorescent vessel labeling and tissue clearing methods for three-dimensional visualization of vasculature.
Neurophotonics
; 9(4): 045008, 2022 Oct.
Article
in En
| MEDLINE
| ID: mdl-36466188
Significance: Visualization of intact vasculatures is crucial to understanding the pathogeneses of different neurological and vascular diseases. Although various fluorescent vessel labeling methods have been used in combination with tissue clearing for three-dimensional (3D) visualization of different vascular networks, little has been done to quantify the labeling effect of each vessel labeling routine, as well as their applicability alongside various clearing protocols, making it difficult to select an optimal combination for finely constructing different vasculatures. Therefore, it is necessary to systematically assess the overall performance of these common vessel labeling methods combined with different tissue-clearing protocols. Aim: A comprehensive evaluation of the labeling quality of various vessel labeling routines in different organs, as well as their applicability alongside various clearing protocols, were performed to find the optimal combinations for 3D reconstruction of vascular networks with high quality. Approach: Four commonly-used vessel labeling techniques and six typical tissue optical clearing approaches were selected as candidates for the systematic evaluation. Results: The vessel labeling efficiency, vessel labeling patterns, and compatibility of each vessel labeling method with different tissue-clearing protocols were quantitatively evaluated and compared. Based on the comprehensive evaluation results, the optimal combinations were selected for 3D reconstructions of vascular networks in several organs, including mouse brain, liver, and kidney. Conclusions: This study provides valuable insight on selecting the proper pipelines for 3D visualization of vascular networks, which may facilitate understanding of the underlying mechanisms of various neurovascular diseases.
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01-internacional
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MEDLINE
Language:
En
Journal:
Neurophotonics
Year:
2022
Document type:
Article
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