Abstract
Research on the cerebrovasculature may provide insights into brain health and disease. Immunohistochemical staining is one way to visualize blood vessels, and digital pathology has the potential to revolutionize the measurement of blood vessel parameters. These tools provide opportunities for translational mouse model research. However, mouse brain tissue presents a formidable set of technical challenges, including potentially high background staining and crossreactivity of endogenous IgG. Formalin-fixed paraffin-embedded (FFPE) and fixed frozen sections, both of which are widely used, may require different methods. In this study, we optimized blood vessel staining in mouse brain tissue, testing both FFPE and frozen fixed sections. A panel of immunohistochemical blood vessel markers were tested (including CD31, CD34, collagen IV, DP71, and VWF), to evaluate their suitability for digital pathological analysis. Collagen IV provided the best immunostaining results in both FFPE and frozen fixed murine brain sections, with highly-specific staining of large and small blood vessels and low background staining. Subsequent analysis of collagen IV-stained sections showed region and sex-specific differences in vessel density and vessel wall thickness. We conclude that digital pathology provides a useful tool for relatively unbiased analysis of the murine cerebrovasculature, provided proper protein markers are used.
Document Type
Article
Publication Date
2024
Digital Object Identifier (DOI)
https://doi.org/10.1177/0271678X231216142
Funding Information
The author(s) declared the following potential conflicts of interest with respect to the research, authorship, and/or publication of this article: This work was funded by NIH grants P01 AG078116 and R01 AG057187.
Repository Citation
Niedowicz, Dana M.; Gollihue, Jenna L.; Weekman, Erica M.; Phe, Panhavuth; Wilcock, Donna M.; Norris, Christopher M.; and Nelson, Peter T., "Using digital pathology to analyze the murine cerebrovasculature" (2024). Neurology Faculty Publications. 109.
https://uknowledge.uky.edu/neurology_facpub/109
Notes/Citation Information
© The Author(s) 2023