The regulation of energy in the brain has garnered substantial attention in recent years due to its significant implications in various disorders and aging. The brain’s energy metabolism is a dynamic and tightly regulated network that balances energy demand and supply by engaging complementary molecular pathways. The crosstalk among these pathways enables the system to switch its preferred fuel source based on substrate availability, activity levels, and cell state-related factors such as redox balance. Brain energy production relies on multi-cellular cooperation and is continuously supplied by fuel from the blood due to limited internal energy stores. Astrocytes, which interface with neurons and blood vessels, play a crucial role in coordinating the brain’s metabolic activity, and their dysfunction can have detrimental effects on brain health. This review characterizes the major energy substrates (glucose, lactate, glycogen, ketones and lipids) in astrocyte metabolism and their role in brain health, focusing on recent developments in the field.

Bottom-up proteomics holds significant promise for clinical applications due to its high sensitivity and precision but is limited by labor-intensive, low-throughput sample preparation methods. Advanced automation is essential to enhance throughput, reproducibility, and accuracy and to allow standardization to make bottom-up proteomics amenable to large-scale studies. We developed a fully integrated, automated sample preparation platform that covers the entire process from biological sample input to mass-spectrometry-ready peptide output and can be applied to a multitude of biological samples. With this end-to-end solution, we achieved high intra- and interplate reproducibility, as well as longitudinal consistency, resulting in precise and reproducible workflows. We showed that our automated workflow surpasses established manual and semiautomated workflows, while improving time efficiency. Finally, we demonstrated the suitability of our automated sample preparation platform for drug development by performing high-content compound characterization for targeted protein degradation, where high throughput and quantitative accuracy are indispensable. For this, we coupled application-specific workflows to perform proteome profiling and confirm target degradation by precise protein quantification. Overall, our results highlight the selective degradation of specific proteins of interest for ten selected compounds across two cell lines. Thus, the automated sample preparation platform facilitates rapid adaptation to emerging developments in proteomics sample preparation, combining standardization, flexibility, and high-throughput capabilities to drive significant advancements in clinical assays and proteomics research.

Background Normal cognitive aging is accompanied by molecular changes in the brain and periphery, but the specific proteomic and peptide-level alterations remain poorly defined. This study aimed to characterize age-related protein and peptide modifications by analyzing matched cerebrospinal fluid (CSF) and plasma samples from cognitively normal individuals. Methods Mass spectrometry was used to profile the proteome and peptide-level data of CSF and plasma samples from young (n = 52; mean age 29 ± 5.9 years; 4% male) and older (n = 40; mean age 69 ± 6.3 years; 48% male) adults. Differential abundance analysis, gene set enrichment, and weighted correlation network analyses were performed to identify age-associated pathways. Protein cleavage, alternative splicing, and phosphorylation events were examined to capture post-translational modifications linked to aging. Results In CSF, aging is associated with significant upregulation of extracellular matrix (ECM) components, coagulation, and inflammatory pathways. In plasma, the insulin-like growth factor-1 (IGF-1) signaling pathway is notably downregulated. Peptide-level analysis reveals novel alternative cleavage and phosphorylation patterns in key proteins involved in lipid metabolism, ECM structure, axonogenesis, and synaptic activity, including APP, APOE, COL4A2, NRXN1, and NRCAM. These findings highlight distinct and compartment-specific molecular changes that occur with aging. Conclusions This study provides a comprehensive proteomic and peptide-level landscape of normal cognitive aging, identifying both protein-level shifts and novel age-associated cleavage and phosphorylation events.

Disruptions of energy supply to the brain are associated with many neurodegenerative pathologies and are difficult to study due to numerous interlinked metabolic pathways. We explored the effects of diminished energy supply on brain metabolism using a computational model of the neuro-glia-vasculature ensemble, in the form of a neuron, an astrocyte and local blood supply. As a case study, we investigated the glucose transporter type-1 deficiency syndrome (GLUT1-DS), a childhood affliction characterized by impaired glucose utilization and associated with phenotypes including seizures. Compared to neurons, astrocytes exhibited markedly higher metabolite concentration variabilities for all but a few redox species. This effect could signal a role for astrocytes in absorbing the shock of blood nutrient fluctuations. Redox balances were disrupted in GLUT1-DS with lower levels of reducing equivalent carriers NADH and ATP. The best non-glucose nutrient or pharmacotherapies for re-establishing redox normalcy involved lactate, the keto-diet (β-hydroxybutyrate), NAD and Q10 supplementation, suggesting a possible glucose sparing mechanism. GLUT1-DS seizures resulted from after-discharge neuronal firing caused by post-stimulus ATP reductions and impaired Na⁺/K⁺-ATPase, which can be rescued by restoring either normal glucose or by relatively small increases in neuronal ATP.

Bottom-up proteomics holds significant promise for clinical applications due to its high sensitivity and precision, but is limited by labor-intensive, low-throughput sample preparation methods. Advanced automation is essential to enhance throughput, reproducibility, and accuracy and to allow standardization to make bottom-up proteomics amenable for large-scale studies. We developed a fully integrated, automated sample preparation platform that covers the entire process from biological sample input to mass spectrometry-ready peptide output and can be applied on a multitude of biological samples. With this end-to-end solution, we achieved high intra- and inter-plate reproducibility, as well as longitudinal consistency, resulting in precise and reproducible workflows. We showed that our automated workflow surpasses established manual and semi-automated workflows, while improving time efficiency. Finally, we demonstrated the suitability of our automated sample preparation platform for drug development by performing a high-content compound characterization for targeted protein degradation, where high throughput and quantitative accuracy are indispensable. For this, we coupled application-specific workflows to perform proteome profiling and confirm target degradation by precise protein quantification. Overall, our results highlight the selective degradation of specific proteins of interest for ten selected compounds across two cell lines. Thus, the automated sample preparation platform facilitates rapid adaptation to emerging developments in proteomics sample preparation, combining standardization, flexibility, and high-throughput capabilities to drive significant advancements in clinical assays and proteomics research.

The study presented explores the complex relationship between the aging brain, energy metabolism, blood flow and neuronal activity by introducing a comprehensive, data-driven molecular model of the neuro-glial vascular system, including all key enzymes, transporters, metabolites, and blood flow vital for neuronal electrical activity with 16’800 interaction pathways. We find significant alterations in metabolite concentrations and differential effects on ATP supply in neurons and astrocytes and within subcellular compartments within aged brains, and identify reduced Na ⁺ /K ⁺ -ATPase as the leading cause of impaired neuronal action potentials. The model predicts that the metabolic pathways cluster more closely in the aged brain, suggesting a loss of robustness and adaptability. Additionally, the aged metabolic system displays reduced flexibility, undermining its capacity to efficiently respond to stimuli and recover from damage. Through transcription factor analysis, the estrogen-related receptor alpha (ESRRA) emerged as a central target connected to these aging-related changes. An unguided optimization search pinpointed potential interventions capable of restoring the brain’s metabolic flexibility and restoring action potential generation. These strategies include increasing the NADH cytosol-mitochondria shuttle, NAD+ pool, ketone β-hydroxybutyrate, lactate and Na ⁺ /K ⁺ -ATPase and reducing blood glucose levels. The model is open-sourced to help guide further research in brain metabolism.