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Gaasdal-Bech, K.

Publications and source records attributed to Gaasdal-Bech, K..

2 recordsLinked to original sources

Multiregional blood-brain barrier phenotyping identifies the prefrontal cortex as the most vulnerable region to ageing in mice

Age-associated vascular alterations make the brain more vulnerable to neuropathologies. Research in humans and rodents have demonstrated structural, molecular, and functional alterations of the aged brain vasculature that suggest blood-brain barrier (BBB) dysfunction. However, these studies focused on particular features of the BBB and specific brain regions. Thus, it remains unclear if and which BBB age-associated phenotypes are conserved across brain areas. Moreover, there is very limited information about how BBB dysfunction and cell-specific phenotypes relate to each other. In this manuscript, we use immunofluorescence, transmission electron microscopy (TEM), and permeability assays to assess how age-associated BBB molecular, structural, and functional phenotypes correlate between the BBB cell types at three brain regions (prefrontal cortex, hippocampus, and corpus callosum) during mouse early ageing. We discovered that at 18-20 months of age, the mouse prefrontal cortex BBB is the most affected region, with alterations in brain endothelial cell protein expression, BBB permeability, basement membrane thickness, and astrocyte endfoot size when compared to young mice. Here, we deliver a detailed multicellular characterisation of region-dependent BBB changes at early stages of ageing. Our data paves the way for future studies to investigate how region-specific BBB dysfunction may contribute to disease-associated regional vulnerability.

neuroscience↗

3BTRON: A Blood-Brain Barrier Recognition Network

The blood-brain barrier (BBB) plays a crucial role in maintaining brain homeostasis. During ageing, the BBB undergoes structural alterations. Electron microscopy (EM) is the gold standard for studying the structural alterations of the brain vasculature. However, analysis of EM images is time-intensive and can be prone to selection bias, limiting our understanding of the structural effect of ageing on the BBB. Here, we introduce 3BTRON, a deep learning framework for the automated analysis of electron microscopy images of the BBB. Using age as a readout, we trained and validated our model on a unique dataset (n = 359). We show that the proposed model could confidently identify the BBB of aged mouse brains from young mouse brains across three different brain regions, achieving a sensitivity of 77.8% and specificity of 80.0% post-stratification when predicting on unseen data. Additionally, feature importance methods revealed the spatial features of each image that contributed most to the predictions. These findings demonstrate a new data-driven approach to analysing age-related changes in the architecture of the BBB.

neuroscience↗