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Borggrewe, M.

Publications and source records attributed to Borggrewe, M..

2 recordsLinked to original sources

Short-term microglia depletion via CSF-1R inhibition promotes functional network reorganization and motor recovery after cortical ischemia

BACKGROUNDPharmacological options to promote long-term rehabilitation after stroke remain limited. Microglia play a complex role in post-stroke pathology, contributing both to repair and secondary injury. How short-term depletion during the subacute phase affects functional recovery remains unknown. METHODSWild-type mice were trained in a skilled reaching task and underwent permanent distal medial cerebral artery occlusion or sham intervention. Mice received either a colony-stimulating factor 1 receptor inhibitor or vehicle treatment between days 3 and 7 post-stroke to deplete microglia. Fine motor performance was assessed behaviorally while bilateral cortical activity was recorded longitudinally through epidural electrocorticography. RESULTSMicroglia depletion did not affect infarct size but resulted in near-complete restoration of fine motor function by day 7, coinciding with maximal microglial depletion. Recovery of fine motor function was accompanied by significant functional connectivity changes in bilateral sensorimotor networks, including increased beta-band connectivity in the ipsilesional motor cortex, which correlated with contralateral fine motor improvement. After microglial repopulation, cells showed altered morphology and gene expression profiles. CONCLUSIONSTransient microglial modulation during the subacute phase after stroke promotes cortical network reorganization and motor recovery, highlighting a potential time window for future translational studies.

neuroscience↗

Seumetry: a versatile and comprehensive R toolkit to accelerate high-dimensional flow and mass cytometry data analysis

Recent progress in flow and mass cytometry technologies enables the simultaneous measurement of over 50 parameters for an individual cell. The resulting increase in data volume and complexity present challenges, as conventional analysis methods based on manual gating are time-consuming and fail to capture unknown or minor cell populations. Advances in single-cell RNA sequencing (scRNAseq) technologies have prompted the development of sophisticated computational analysis tools specifically designed to process and analyze high-dimensional biological data, some of which could significantly improve certain aspects of cytometry data analysis. Building on these advances, we here present Seumetry, a framework that combines flow and mass cytometry data-specific analysis methods with the capabilities of Seurat, a powerful tool for the analysis of scRNAseq data. Seumetry offers advanced quality control, data visualizations, and differential population abundance and protein expression analysis. We tested Seumetry on an in-house generated complex dataset of immune cells from different layers of human intestines, demonstrating that Seumetry accurately identifies distinct immune cell populations. Furthermore, using a publicly available mass cytometry dataset, Seumetry recapitulates previously published results, further validating its use for high-dimensional flow and mass cytometry data. In summary, Seumetry provides a new scalable framework for the comprehensive analysis of high-dimensional cytometry data with seamless integration into commonly used scRNAseq analysis tools, enabling in-depth analysis methods to facilitate biological interpretations.

bioinformatics↗