Proteomics of mouse brain endothelium uncovers dysregulation of vesicular transport pathways during aging.

Age-related decline in brain endothelial cell (BEC) function contributes critically to neurological disease. Comprehensive atlases of the BEC transcriptome have become available, but results from proteomic profiling are lacking. To gain insights into endothelial pathways affected by aging, we developed a magnetic-activated cell sorting-based mouse BEC enrichment protocol compatible with proteomics and resolved the profiles of protein abundance changes during aging. Unsupervised cluster analysis revealed a segregation of age-related protein dynamics with biological functions, including a downregulation of vesicle-mediated transport. We found a dysregulation of key regulators of endocytosis and receptor recycling (most prominently Arf6), macropinocytosis and lysosomal degradation. In gene deletion and overexpression experiments, Arf6 affected endocytosis pathways in endothelial cells. Our approach uncovered changes not picked up by transcriptomic studies, such as accumulation of vesicle cargo and receptor ligands, including Apoe. Proteomic analysis of BECs from Apoe-deficient mice revealed a signature of accelerated aging. Our findings provide a resource for analysing BEC function during aging.

The Effect of Sleep Deprivation and Subsequent Recovery Period on the Synaptic Proteome of Rat Cerebral Cortex.

Sleep deprivation (SD) is commonplace in the modern way of life and has a substantial social, medical, and human cost. Sleep deprivation induces cognitive impairment such as loss of executive attention, working memory decline, poor emotion regulation, increased reaction times, and higher cognitive functions are particularly vulnerable to sleep loss. Furthermore, SD is associated with obesity, diabetes, cardiovascular diseases, cancer, and a vast majority of psychiatric and neurodegenerative disorders are accompanied by sleep disturbances. Despite the widespread scientific interest in the effect of sleep loss on synaptic function, there is a lack of investigation focusing on synaptic transmission on the proteome level. In the present study, we report the effects of SD and recovery period (RP) on the cortical synaptic proteome in rats. Synaptosomes were isolated after 8 h of SD performed by gentle handling and after 16 h of RP. The purity of synaptosome fraction was validated with western blot and electron microscopy, and the protein abundance alterations were analyzed by mass spectrometry. We observed that SD and RP have a wide impact on neurotransmitter-related proteins at both the presynaptic and postsynaptic membranes. The abundance of synaptic proteins has changed to a greater extent in consequence of SD than during RP: we identified 78 proteins with altered abundance after SD and 39 proteins after the course of RP. Levels of most of the altered proteins were upregulated during SD, while RP showed the opposite tendency, and three proteins (Gabbr1, Anks1b, and Decr1) showed abundance changes with opposite direction after SD and RP. The functional cluster analysis revealed that a majority of the altered proteins is related to signal transduction and regulation, synaptic transmission and synaptic assembly, protein and ion transport, and lipid and fatty acid metabolism, while the interaction network analysis revealed several connections between the significantly altered proteins and the molecular processes of synaptic plasticity or sleep. Our proteomic data implies suppression of SNARE-mediated synaptic vesicle exocytosis and impaired endocytic processes after sleep deprivation. Both SD and RP altered GABA neurotransmission and affected protein synthesis, several regulatory processes and signaling pathways, energy homeostatic processes, and metabolic pathways.

Proteomic comparison of different synaptosome preparation procedures.

Synaptosomes are frequently used research objects in neurobiology studies focusing on synaptic transmission as they mimic several aspects of the physiological synaptic functions. They contain the whole apparatus for neurotransmission, the presynaptic nerve ending with synaptic vesicles, synaptic mitochondria and often a segment of the postsynaptic membrane along with the postsynaptic density is attached to its outer surface. As being artificial functional organelles, synaptosomes are viable for several hours, retain their activity, membrane potential, and capable to store, release, and reuptake neurotransmitters. Synaptosomes are ideal subjects for proteomic analysis. The recently available separation and protein detection techniques can cope with the reduced complexity of the organelle and enable the simultaneous qualitative and quantitative analysis of thousands of proteins shaping the structural and functional characteristics of the synapse. Synaptosomes are formed during the homogenization of nervous tissue in the isoosmotic milieu and can be isolated from the homogenate by various approaches. Each enrichment method has its own benefits and drawbacks and there is not a single method that is optimal for all research purposes. For a proper proteomic experiment, it is desirable to preserve the native synaptic structure during the isolation procedure and keep the degree of contamination from other organelles or cell types as low as possible. In this article, we examined five synaptosome isolation methods from a proteomic point of view by the means of electron microscopy, Western blot, and liquid chromatography-mass spectrometry to compare their efficiency in the isolation of synaptosomes and depletion of contaminating subcellular structures. In our study, the different isolation procedures led to a largely overlapping pool of proteins with a fairly similar distribution of presynaptic, active zone, synaptic vesicle, and postsynaptic proteins; however, discrete differences were noticeable in individual postsynaptic proteins and in the number of identified transmembrane proteins. Much pronounced variance was observed in the degree of contamination with mitochondrial and glial structures. Therefore, we suggest that in selecting the appropriate isolation method for any neuroproteomics experiment carried out on synaptosomes, the degree and sort/source of contamination should be considered as a primary aspect.

Machine learning analysis of whole mouse brain vasculature.

Tissue clearing methods enable the imaging of biological specimens without sectioning. However, reliable and scalable analysis of large imaging datasets in three dimensions remains a challenge. Here we developed a deep learning-based framework to quantify and analyze brain vasculature, named Vessel Segmentation & Analysis Pipeline (VesSAP). Our pipeline uses a convolutional neural network (CNN) with a transfer learning approach for segmentation and achieves human-level accuracy. By using VesSAP, we analyzed the vascular features of whole C57BL/6J, CD1 and BALB/c mouse brains at the micrometer scale after registering them to the Allen mouse brain atlas. We report evidence of secondary intracranial collateral vascularization in CD1 mice and find reduced vascularization of the brainstem in comparison to the cerebrum. Thus, VesSAP enables unbiased and scalable quantifications of the angioarchitecture of cleared mouse brains and yields biological insights into the vascular function of the brain.