Spatial mapping of mitochondrial networks and bioenergetics in lung cancer.

Mitochondria are critical to the governance of metabolism and bioenergetics in cancer cells1. The mitochondria form highly organized networks, in which their outer and inner membrane structures define their bioenergetic capacity2,3. However, in vivo studies delineating the relationship between the structural organization of mitochondrial networks and their bioenergetic activity have been limited. Here we present an in vivo structural and functional analysis of mitochondrial networks and bioenergetic phenotypes in non-small cell lung cancer (NSCLC) using an integrated platform consisting of positron emission tomography imaging, respirometry and three-dimensional scanning block-face electron microscopy. The diverse bioenergetic phenotypes and metabolic dependencies we identified in NSCLC tumours align with distinct structural organization of mitochondrial networks present. Further, we discovered that mitochondrial networks are organized into distinct compartments within tumour cells. In tumours with high rates of oxidative phosphorylation (OXPHOSHI) and fatty acid oxidation, we identified peri-droplet mitochondrial networks wherein mitochondria contact and surround lipid droplets. By contrast, we discovered that in tumours with low rates of OXPHOS (OXPHOSLO), high glucose flux regulated perinuclear localization of mitochondria, structural remodelling of cristae and mitochondrial respiratory capacity. Our findings suggest that in NSCLC, mitochondrial networks are compartmentalized into distinct subpopulations that govern the bioenergetic capacity of tumours.

CDeep3M-Plug-and-Play cloud-based deep learning for image segmentation.

As biomedical imaging datasets expand, deep neural networks are considered vital for image processing, yet community access is still limited by setting up complex computational environments and availability of high-performance computing resources. We address these bottlenecks with CDeep3M, a ready-to-use image segmentation solution employing a cloud-based deep convolutional neural network. We benchmark CDeep3M on large and complex two-dimensional and three-dimensional imaging datasets from light, X-ray, and electron microscopy.

Systematic morphological and morphometric analysis of identified olfactory receptor neurons in Drosophila melanogaster.

The biophysical properties of sensory neurons are influenced by their morphometric and morphological features, whose precise measurements require high-quality volume electron microscopy (EM). However, systematic surveys of nanoscale characteristics for identified neurons are scarce. Here, we characterize the morphology of Drosophila olfactory receptor neurons (ORNs) across the majority of genetically identified sensory hairs. By analyzing serial block-face electron microscopy images of cryofixed antennal tissues, we compile an extensive morphometric data set based on 122 reconstructed 3D models for 33 of the 40 identified antennal ORN types. Additionally, we observe multiple novel features-including extracellular vacuoles within sensillum lumen, intricate dendritic branching, mitochondria enrichment in select ORNs, novel sensillum types, and empty sensilla containing no neurons-which raise new questions pertinent to cell biology and sensory neurobiology. Our systematic survey is critical for future investigations into how the size and shape of sensory neurons influence their responses, sensitivity, and circuit function.

Genetic Probe for Visualizing Glutamatergic Synapses and Vesicles by 3D Electron Microscopy.

Communication between neurons relies on the release of diverse neurotransmitters, which represent a key-defining feature of a neuron's chemical and functional identity. Neurotransmitters are packaged into vesicles by specific vesicular transporters. However, tools for labeling and imaging synapses and synaptic vesicles based on their neurochemical identity remain limited. We developed a genetically encoded probe to identify glutamatergic synaptic vesicles at the levels of both light and electron microscopy (EM) by fusing the mini singlet oxygen generator (miniSOG) probe to an intralumenal loop of the vesicular glutamate transporter-2. We then used a 3D imaging method, serial block-face scanning EM, combined with a deep learning approach for automatic segmentation of labeled synaptic vesicles to assess the subcellular distribution of transporter-defined vesicles at nanometer scale. These tools represent a new resource for accessing the subcellular structure and molecular machinery of neurotransmission and for transmitter-defined tracing of neuronal connectivity.

Microglial Gi-dependent dynamics regulate brain network hyperexcitability.

Microglial surveillance is a key feature of brain physiology and disease. Here, we found that Gi-dependent microglial dynamics prevent neuronal network hyperexcitability. By generating MgPTX mice to genetically inhibit Gi in microglia, we show that sustained reduction of microglia brain surveillance and directed process motility induced spontaneous seizures and increased hypersynchrony after physiologically evoked neuronal activity in awake adult mice. Thus, Gi-dependent microglia dynamics may prevent hyperexcitability in neurological diseases.