Biology subjects

Yu, S. B.

Publications and source records attributed to Yu, S. B..

4 recordsLinked to original sources

bioRxiv2023

FluxNorm: Toolbox for metabolic flux assay normalization by in situ cell counting

Plate-based quantitative metabolic flux analysis has emerged as the central technology to examine cellular metabolism and mitochondrial bioenergetics. However, accurate interpretation of metabolic activity between different experimental conditions in multi-well microplates requires data normalization based on in situ cell counts. Here, we describe FluxNorm, a platform-independent semi-automated computational workflow, validated for three different cell types, to normalize cell density for accurate assessment of cellular bioenergetics.

cell biology↗

bioRxiv2023

Glycolytic Metabolon Assembly on Mitochondria via Hexokinase O-GlcNAcylation Promotes Metabolic Efficiency

Glucose, the primary cellular energy source, is metabolized through glycolysis initiated by the rate-limiting enzyme Hexokinase (HK). In energy-demanding tissues like the brain, HK1 is the dominant isoform, primarily localized on mitochondria, crucial for efficient glycolysis-oxidative phosphorylation coupling and optimal energy generation. This study unveils a unique mechanism regulating HK1 activity, glycolysis, and the dynamics of mitochondrial coupling, mediated by the metabolic sensor enzyme O-GlcNAc transferase (OGT). OGT catalyzes reversible O-GlcNAcylation, a post-translational modification, influenced by glucose flux. Elevated OGT activity induces dynamic O-GlcNAcylation of HK1s regulatory domain, subsequently promoting the assembly of the glycolytic metabolon on the outer mitochondrial membrane. This modification enhances HK1s mitochondrial association, orchestrating glycolytic and mitochondrial ATP production. Mutations in HK1s O-GlcNAcylation site reduce ATP generation, affecting synaptic functions in neurons. The study uncovers a novel pathway that bridges neuronal metabolism and mitochondrial function via OGT and the formation of the glycolytic metabolon, offering new prospects for tackling metabolic and neurological disorders.

cell biology↗

bioRxiv2023

Neuronal activity-driven O-GlcNAcylation promotes mitochondrial plasticity

Neuronal activity is an energy-intensive process that is largely sustained by instantaneous fuel utilization and ATP synthesis. However, how neurons couple ATP synthesis rate to fuel availability is largely unknown. Here, we demonstrate that the metabolic sensor enzyme O-GlcNAc transferase regulates neuronal activity-driven mitochondrial bioenergetics. We show that neuronal activity upregulates O-GlcNAcylation mainly in mitochondria. Mitochondrial O-GlcNAcylation is promoted by activity-driven fuel consumption, which allows neurons to compensate for high energy expenditure based on fuel availability. To determine the proteins that are responsible for these adjustments, we mapped the mitochondrial O-GlcNAcome of neurons. Finally, we determine that neurons fail to meet activity-driven metabolic demand when O-GlcNAcylation dynamics are prevented. Our findings suggest that O-GlcNAcylation provides a fuel-dependent feedforward control mechanism in neurons to optimize mitochondrial performance based on neuronal activity. This mechanism thereby couples neuronal metabolism to mitochondrial bioenergetics and plays a key role in sustaining energy homeostasis.

neuroscience↗

bioRxiv2019

Deep Learning-Based Point-Scanning Super-Resolution Imaging

Point scanning imaging systems (e.g. scanning electron or laser scanning confocal microscopes) are perhaps the most widely used tools for high resolution cellular and tissue imaging. Like all other imaging modalities, the resolution, speed, sample preservation, and signal-to-noise ratio (SNR) of point scanning systems are difficult to optimize simultaneously. In particular, point scanning systems are uniquely constrained by an inverse relationship between imaging speed and pixel resolution. Here we show these limitations can be mitigated via the use of deep learning-based super-sampling of undersampled images acquired on a point-scanning system, which we termed point-scanning super-resolution (PSSR) imaging. Oversampled, high SNR ground truth images acquired on scanning electron or Airyscan laser scanning confocal microscopes were crappified to generate semi-synthetic training data for PSSR models that were then used to restore real-world undersampled images. Remarkably, our EM PSSR model could restore undersampled images acquired with different optics, detectors, samples, or sample preparation methods in other labs. PSSR enabled previously unattainable 2 nm resolution images with our serial block face scanning electron microscope system. For fluorescence, we show that undersampled confocal images combined with a multiframe PSSR model trained on Airyscan timelapses facilitates Airyscan-equivalent spatial resolution and SNR with [~]100x lower laser dose and 16x higher frame rates than corresponding high-resolution acquisitions. In conclusion, PSSR facilitates point-scanning image acquisition with otherwise unattainable resolution, speed, and sensitivity.\n\n\n\nO_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=116 SRC=\"FIGDIR/small/740548v8_ufig1.gif\" ALT=\"Figure 1\">\nView larger version (90K):\norg.highwire.dtl.DTLVardef@fbf19aorg.highwire.dtl.DTLVardef@b2cc88org.highwire.dtl.DTLVardef@921e1corg.highwire.dtl.DTLVardef@f6c5af_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioinformatics↗