Semi-automated protocol to quantify and characterize fluorescent three-dimensional vascular images

被引:0
|
作者
Xie, Danny F. [1 ,2 ]
Crouzet, Christian [1 ,2 ]
Lopresti, Krystal [1 ,2 ]
Wang, Yuke [1 ,2 ]
Robinson, Christopher [1 ,2 ]
Jones, William [1 ]
Muqolli, Fjolla [1 ]
Fang, Chuo [3 ]
Cribbs, David H. [4 ]
Fisher, Mark [1 ,3 ,4 ,5 ]
Choi, Bernard [1 ,2 ]
机构
[1] Univ Calif Irvine, Beckman Laser Inst & Med Clin, Irvine, CA 92612 USA
[2] Univ Calif Irvine, Dept Biomed Engn, Irvine, CA 92612 USA
[3] Univ Calif Irvine, Dept Neurol, Irvine, CA USA
[4] Univ Calif Irvine, Inst Memory Impairments & Neurol Disorders, Irvine, CA USA
[5] Univ Calif Irvine, Dept Pathol & Lab Med, Irvine, CA USA
来源
PLOS ONE | 2024年 / 19卷 / 05期
基金
美国国家卫生研究院;
关键词
3D VISUALIZATION; TISSUE; IDISCO; MICROBLEEDS;
D O I
10.1371/journal.pone.0289109
中图分类号
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号
07 ; 0710 ; 09 ;
摘要
The microvasculature facilitates gas exchange, provides nutrients to cells, and regulates blood flow in response to stimuli. Vascular abnormalities are an indicator of pathology for various conditions, such as compromised vessel integrity in small vessel disease and angiogenesis in tumors. Traditional immunohistochemistry enables the visualization of tissue cross-sections containing exogenously labeled vasculature. Although this approach can be utilized to quantify vascular changes within small fields of view, it is not a practical way to study the vasculature on the scale of whole organs. Three-dimensional (3D) imaging presents a more appropriate method to visualize the vascular architecture in tissue. Here we describe the complete protocol that we use to characterize the vasculature of different organs in mice encompassing the methods to fluorescently label vessels, optically clear tissue, collect 3D vascular images, and quantify these vascular images with a semi-automated approach. To validate the automated segmentation of vascular images, one user manually segmented one hundred random regions of interest across different vascular images. The automated segmentation results had an average sensitivity of 83 +/- 11% and an average specificity of 91 +/- 6% when compared to manual segmentation. Applying this procedure of image analysis presents a method to reliably quantify and characterize vascular networks in a timely fashion. This procedure is also applicable to other methods of tissue clearing and vascular labels that generate 3D images of microvasculature.
引用
收藏
页数:14
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