bioRxiv Science⌕ Search

Biology subjects

Bravo-Ferrer, I.

Publications and source records attributed to Bravo-Ferrer, I..

3 recordsLinked to original sources

Macrophage chemotaxis steered by complex self-generated gradients of complement C5a

Macrophages rely on efficient chemotaxis to locate sites of infection and tissue damage. One general strategy that enhances chemotactic accuracy is the use of self-generated gradients, where cells locally deplete attractants to create or sharpen guidance cues. Here we show that mouse bone marrow-derived macrophages (BMDMs) migrate toward the complement component C5a using this strategy. Cells actively deplete C5a from their surroundings, establishing local gradients as fresh attractant diffuses inward. We visualized this process in real time with fluorescent C5a and reproduced its dynamics using computational models. C5a depletion is mediated primarily by C5aR1-dependent endocytosis. This mechanism produces complex responses, with different C5a concentrations inducing temporally distinct waves of migration, and maximal chemotaxis occurring below 10 nM C5a. As expected, increasing C5a concentrations recruit more cells. In contrast, human macrophages inactivate C5a mainly through carboxypeptidase-mediated enzymatic degradation, yielding a higher optimal concentration ([~]30 nM) and distinct migratory dynamics. Both species also deplete externally imposed C5a gradients, sharpening them and enhancing guidance. These findings identify C5a degradation as a critical mechanism by which macrophages extract directional information from their environment. Self-generated gradient formation, despite different mechanisms across species, emerges as a conserved and versatile strategy for immune navigation.

immunology↗

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↗