Zille, M. | bioRxiv Science

bioRxiv2025

Aging of the blood-brain barrier: Metabolic signatures of mouse brain endothelial cell senescence

The blood-brain barrier (BBB) declines with age, with endothelial senescence implicated as a contributor, yet its metabolic features remain incompletely defined. We induced senescence in mouse brain endothelial cells (BECs) using the chemotherapeutic doxorubicin and the oxidative stress mediator hydrogen peroxide and performed untargeted mass spectrometry of cell lysates and supernatants. Senescence was confirmed by increased senescence-associated {beta}-galactosidase activity and elevated cyclin-dependent kinase inhibitors p16 and p21, along with increased Il-6 mRNA as senescence-associated secretory phenotype marker. At the BBB structural level, occludin was consistently reduced, ZO-1 decreased with H2O2 (with a similar trend under doxorubicin), and claudin-5 showed a variable, non-significant reduction, indicating target- and inducer-specific junctional remodeling. Pathway mapping highlighted prominent remodeling of lipid metabolism (including glycerophospholipids, sphingolipids, acylcarnitines) and amino-acid pathways, with both shared and inducer-specific shifts. These discovery-stage in vitro results recapitulate several metabolites previously associated with senescence across tissues and nominate additional candidates for hypothesis testing. To increase mechanistic and translational relevance, we outline a staged roadmap: validation in advanced multicellular and flow-based BBB models followed by in vivo studies, with attention to route-specific BBB readouts. Overall, this work provides a curated snapshot of metabolic remodeling in brain endothelial senescence that co-occurs with selective tight-junction alterations relevant to BBB aging. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=75 SRC="FIGDIR/small/670255v2_ufig1.gif" ALT="Figure 1"> View larger version (25K): org.highwire.dtl.DTLVardef@1644442org.highwire.dtl.DTLVardef@1bd5821org.highwire.dtl.DTLVardef@1e4efd5org.highwire.dtl.DTLVardef@13d942d_HPS_FORMAT_FIGEXP M_FIG C_FIG Mouse brain endothelial cells exposed to doxorubicin (Dox) or hydrogen peroxide (H2O2) underwent senescence ({uparrow}SA-{beta}-gal activity, p16/p21 expression), accompanied by selective reductions in tight-junction proteins. Untargeted mass spectrometry profiled metabolites in cell lysates and supernatants. Lipids, followed by amino acid metabolites, comprised the largest share of alterations, highlighting these pathways as major components of metabolic reprogramming associated in endothelial senescence.

neuroscience↗

bioRxiv2020

EntireAxon: Deep learning deciphers four distinct patterns of axonal degeneration

BackgroundAxonal degeneration (AxD) is a pathological hallmark of many neurodegenerative diseases. Deciphering the morphological patterns of AxD will help to understand the underlying mechanisms and to develop effective therapeutic interventions. Here, we evaluated the progression of AxD in cortical neurons using a novel microfluidic device in combination with a deep learning tool, the EntireAxon, that we developed for the enhanced-throughput analysis of AxD on microscopic images. ResultsThe EntireAxon convolutional neural network sensitively and specifically segmented the features of AxD, including axons, axonal swellings, and axonal fragments, and its performance exceeded that of human expert raters. In an in vitro model of AxD in hemorrhagic stroke induced by the hemolysis product hemin, we detected the concentration- and time-dependent degeneration of axons leading to a decrease in axon area, while the axonal swelling and axonal fragment area increased. Time course analysis revealed that axonal swellings preceded axon fragmentation, suggesting that swellings may be reliable predictors of AxD. Using a recurrent neural network, we further identified four morphological patterns of AxD (granular, retraction, swelling, and transport degeneration) in cortical axons subjected to hemin. ConclusionsThese findings indicate a morphological heterogeneity of AxD under pathophysiologic conditions. The combination of the microfluidic device with the EntireAxon deep learning tool enable the systematic analysis of AxD but also unravel a so far unknown intricacy in which AxD can occur in a disease context.

neuroscience↗