Biology subjects

Ertürk, A.

Publications and source records attributed to Ertürk, A..

4 recordsLinked to original sources

bioRxiv2026

Reversible hypervascularization drives cognitive decline and blood-brain barrier damage during aging

Cerebrovascular dysfunction emerges early in neurodegeneration, yet how vascular structure, blood-brain barrier (BBB) failure, and cognition are linked remains undefined. Using VesselPro, a whole-brain pipeline integrating perfusion-resolved 3D imaging with spatial proteomics, we mapped vascular aging across the mouse lifespan. We identify two discrete trajectories: a hypovascular state and a previously unrecognized hyper-vascular, BBB-compromised state, defined relative to a young-adult vascular baseline. The hyper-vascular trajectory, concentrated in the cortex and hippocampus, was associated with marked spatial memory impairment and pervasive BBB leakage. Spatial proteomics revealed a coordinated program involving angiogenic activation, endothelial stress, cytoskeletal remodeling, and inflammatory signaling. Cross-species comparison with human proteomic biomarkers from the UK Biobank showed strong alignment between the mouse hypervascular signature and vascular dementia risk, but minimal concordance with Alzheimers disease, defining a vascular-specific dementia endotype. Transient Tie2 activation with AKB-9778 attenuated this trajectory, improving vessel organization, BBB integrity, and memory performance. Our findings show that endothelial instability is a key mechanistic driver of this heterogeneous vascular aging state. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=95 SRC="FIGDIR/small/720441v1_ufig1.gif" ALT="Figure 1"> View larger version (33K): org.highwire.dtl.DTLVardef@39494aorg.highwire.dtl.DTLVardef@faa4acorg.highwire.dtl.DTLVardef@1511dd5org.highwire.dtl.DTLVardef@7db29b_HPS_FORMAT_FIGEXP M_FIG C_FIG HighlightsO_LIVesselPro: we developed a pipeline for capturing perfused blood vessels in the brain: unexpected bifurcation in vascular aging: traditional hypovascular state and a novel, hypervascular, BBB-compromised state. C_LIO_LIHypervascular pathology: Linked the hypervascular profile in the cortex and hippocampus to spatial memory impairment, pervasive BBB leakage, and angiogenic inflammatory signaling. C_LIO_LIClinical endotype: Cross-species analysis via the UK Biobank confirms this hypervascular signature aligns with vascular dementia risk rather than Alzheimers disease. C_LIO_LIPharmacological stabilization: Demonstrated that Tie2 signaling activation (AKB-9778) improves the vessel organization, BBB integrity, and memory performance. C_LI

neuroscience↗

bioRxiv2023

The Benchtop mesoSPIM: a next-generation open-source light-sheet microscope for large cleared samples

In 2015, we launched the mesoSPIM initiative (www.mesospim.org), an open-source project for making light-sheet microscopy of large cleared tissues more accessible. Meanwhile, the demand for imaging larger samples at higher speed and resolution has increased, requiring major improvements in the capabilities of light-sheet microscopy. Here, we introduce the next-generation mesoSPIM ("Benchtop") with significantly increased field of view, improved resolution, higher throughput, more affordable cost and simpler assembly compared to the original version. We developed a new method for testing objectives, enabling us to select detection objectives optimal for light-sheet imaging with large-sensor sCMOS cameras. The new mesoSPIM achieves high spatial resolution (1.5 {micro}m laterally, 3.3 {micro}m axially) across the entire field of view, a magnification up to 20x, and supports sample sizes ranging from sub-mm up to several centimetres, while being compatible with multiple clearing techniques. The new microscope serves a broad range of applications in neuroscience, developmental biology, and even physics.

neuroscience↗

bioRxiv2023

Including biotic interactions in species distribution models improves the understanding of species niche: a case of study with the brown bear in Europe

Biotic interactions are expected to influence species responses to climate change, but they are usually not included when predicting future range shifts. We assessed the importance of biotic interactions to understand future consequences of climate and land use change for biodiversity using as a model system the brown bear (Ursus arctos) in Europe. By including biotic interactions using the spatial variation of energy contribution and habitat models of each food species, we showed that the use of biotic factors considerably improves our understanding of the distribution of brown bears. Predicted future range shifts, which included changes in the distribution of food species, varied greatly when considering various scenarios of change in biotic factors, warning about future indirect climate change effects. Our study confirmed that advancing our understanding of ecological networks of species interactions will improve future scenarios of biodiversity change, which is key for conserving biodiversity and ecosystem services.

ecology↗

bioRxiv2022

Graph neural networks learn emergent tissue properties from spatial molecular profiles

Tissue phenotypes such as metabolic states, inflammation, and tumor properties are functions of molecular states of cells that constitute the tissue. Recent spatial molecular profiling assays measure tissue architecture motifs in a molecular and often unbiased way and thus can explain some aspects of emergence of these phenotypes. Here, we characterize the ability of graph neural networks to model tissue-level emergent phenotypes based on spatial data by evaluating phenotype prediction across model complexities. First, we show that immune cell dispersion in colorectal tumors, which is known to be predictive of disease outcome, can be captured by graph neural networks. Second, we show that breast cancer tumor classes can be predicted from gene expression alone without spatial information and are thus too simplistic a phenotype to require a complex model of emergence. Third, we show that representation learning approaches for spatial graphs of molecular profiles are limited by overfitting in the prevalent regime of up to 100s of images per study. We address overfitting with within-graph self-supervision and illustrate its promise for tissue representation learning as a constraint for node representations.

bioinformatics↗