ArticlePloS one2024
Semi-automated protocol to quantify and characterize fluorescent three-dimensional vascular images.
Article in PloS one, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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5 citing papers in PubMed.
- Accelerated immunostaining of thick biological tissues using bidirectional electric fields with reversible deformation.Biomedical optics express · 2026Article
- Development of cerebral microhemorrhages in a mouse model of hypertension.Journal of neuroinflammation · 2025Article
- Cerebrovascular alterations in a mouse model of late-onset Alzheimer's disease.Neurophotonics · 2025Article
- Rat perivascular adipose tissue microvasculature revealed by tissue clearing.Frontiers in physiology · 2024Article
- Chronic kidney disease promotes cerebral microhemorrhage formation.Journal of neuroinflammation · 2023Article
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Authors and funding
11 authors.
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Abstract
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.
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