Astrocytes exhibit regional heterogeneity in morphology, function and molecular composition to support and modulate neuronal function and signaling in a region-specific manner. To characterize regional heterogeneity of astrocytic proteomes of different brain regions we established an inducible Aldh1l1-methionyl-tRNA-synthetase (MetRS ) mouse line that allows astrocyte-specific metabolic labeling of newly synthesized proteins by azidonorleucine (ANL) in vivo and subsequent isolation of tagged proteins by click chemistry. We analyzed astrocytic proteins from four different brain regions by mass spectrometry. The induced expression of MetRS is restricted to astrocytes and identified proteins show a high overlap with proteins compiled in "AstroProt," a newly established database for astrocytic proteins. Gene enrichment analysis reveals a high similarity among brain regions with subtle differences in enriched biological processes and in abundances of key astrocytic proteins for hippocampus, cortex and striatum. However, the cerebellar proteome stands out with proteins being highly associated with the calcium signaling pathway or with bipolar disorder. Subregional analysis of single astrocyte TAMRA intensities in hippocampal layers indicates distinct subregional heterogeneity of astrocytes and highlights the applicability of our toolbox to study differences of astrocytic proteomes in vivo.
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http://dx.doi.org/10.1002/glia.24304 | DOI Listing |
Europace
March 2025
Clinical Cardiac Academic Group, Genetic and Cardiovascular Sciences Institute, City-St George's University of London, London, UK.
Atrial fibrillation (AF) is one of the most common cardiac diseases and a complicating comorbidity for multiple associated diseases. Many clinical decisions regarding AF are currently based on the binary recognition of AF being present or absent with the categorical appraisal of AF as continued or intermittent. Assessment of AF in clinical trials is largely limited to the time to (first) detection of an AF episode.
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March 2025
Center of Functionally Integrative Neuroscience (CFIN), Department of Clinical Medicine, Aarhus University, Aarhus, Denmark.
The human brain has a remarkable ability to learn and update its beliefs about the world. Here, we investigate how thermosensory learning shapes our subjective experience of temperature and the misperception of pain in response to harmless thermal stimuli. Through computational modeling, we demonstrate that the brain uses a probabilistic predictive coding scheme to update beliefs about temperature changes based on their uncertainty.
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March 2025
College of Computer Science and Technology, Zhejiang University, Hangzhou, China.
Brain age gap (BAG), the deviation between estimated brain age and chronological age, is a promising marker of brain health. However, the genetic architecture and reliable targets for brain aging remains poorly understood. In this study, we estimate magnetic resonance imaging (MRI)-based brain age using deep learning models trained on the UK Biobank and validated with three external datasets.
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March 2025
Functional Neuroimaging Laboratory, Istituto Italiano di Tecnologia, Center for Neuroscience and Cognitive Systems @UniTn, Rovereto, Italy.
Chromosome 22q11.2 deletion increases the risk of neuropsychiatric disorders like autism and schizophrenia. Disruption of large-scale functional connectivity in 22q11 deletion syndrome (22q11DS) has been widely reported, but the biological factors driving these changes remain unclear.
View Article and Find Full Text PDFCereb Cortex
March 2025
Neuropsychiatry, Department of Neurosciences, Leuven Brain Institute, KU Leuven, Herestraat 49, B-3000 Leuven, Belgium.
This study investigates the relationship between resting-state functional magnetic resonance imaging (rs-fMRI) topological properties and synaptic vesicle glycoprotein 2A (SV2A) positron emission tomography (PET) synaptic density (SD) in late-life depression (LLD). 18 LLD patients and 33 healthy controls underwent rs-fMRI, 3D T1-weighted MRI, and 11C-UCB-J PET scans to assess SD. The rs-fMRI data were utilized to construct weighted networks for calculating four global topological metrics, including clustering coefficient, characteristic path length, global efficiency, and small-worldness, and six nodal metrics, including nodal clustering coefficient, nodal characteristic path length, nodal degree, nodal strength, local efficiency, and betweenness centrality.
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