Background: The intermediate filament nestin was first described in stem and progenitor cells of neural and mesenchymal origin. Additionally, it is expressed in endothelial cells during wound healing and tumorigenesis. Thus, nestin is widely regarded as a marker for proliferative endothelium. However, little is known about its role in lymphatic endothelium. Methods: Here, we analyzed the expression of nestin in the endothelium of ten human haemangiomas and ten lymphangiomas in situ by immunohistochemistry. This study aimed to investigate the expression of nestin in haemangiomas and lymphangiomas to determine its potential role as a vascular marker. Specifically, we aimed to assess whether nestin expression is restricted to proliferating endothelial cells or also present in non-proliferative blood vessels. Results: Immunohistochemically, haemangiomas were positive for CD31 but negative for D2-40. The endothelial cells within these lesions showed a homogeneous expression of nestin. In contrast, the endothelium of lymphangiomas reacted positively for D2-40 and CD31 but did not show any nestin expression. Additionally, only a few endothelial cells of capillary haemangiomas showed a Ki-67 positivity. Conclusions: The differential expression of nestin in haemangiomas and lymphangiomas indicates a specificity of nestin for the endothelium of blood vessels. The Ki-67 negativity in the majority of the endothelial cells reveals the proliferative quiescence of these cells. These findings indicate that nestin could be used as a marker to differentiate between blood and lymphatic vessels.
- Publikační typ
- časopisecké články MeSH
Angiogenesis is the process of new blood vessels growing from existing vasculature. Visualizing them as a three-dimensional (3D) model is a challenging, yet relevant, task as it would be of great help to researchers, pathologists, and medical doctors. A branching analysis on the 3D model would further facilitate research and diagnostic purposes. In this paper, a pipeline of vision algorithms is elaborated to visualize and analyze blood vessels in 3D from formalin-fixed paraffin-embedded (FFPE) granulation tissue sections with two different staining methods. First, a U-net neural network is used to segment blood vessels from the tissues. Second, image registration is used to align the consecutive images. Coarse registration using an image-intensity optimization technique, followed by finetuning using a neural network based on Spatial Transformers, results in an excellent alignment of images. Lastly, the corresponding segmented masks depicting the blood vessels are aligned and interpolated using the results of the image registration, resulting in a visualized 3D model. Additionally, a skeletonization algorithm is used to analyze the branching characteristics of the 3D vascular model. In summary, computer vision and deep learning is used to reconstruct, visualize and analyze a 3D vascular model from a set of parallel tissue samples. Our technique opens innovative perspectives in the pathophysiological understanding of vascular morphogenesis under different pathophysiological conditions and its potential diagnostic role.
