Spatial mapping of mitochondrial networks and bioenergetics in lung cancer.

Mitochondria are critical to the governance of metabolism and bioenergetics in cancer cells. The mitochondria form highly organized networks, in which their outer and inner membrane structures define their bioenergetic capacity. However, in vivo studies delineating the relationship between the structural organization of mitochondrial networks and their bioenergetic activity have been limited.

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

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 olfactory receptor neurons (ORNs) across the majority of genetically identified sensory hairs.

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.

Microglial G-dependent dynamics regulate brain network hyperexcitability.

Microglial surveillance is a key feature of brain physiology and disease. Here, we found that G-dependent microglial dynamics prevent neuronal network hyperexcitability. By generating Mg mice to genetically inhibit G 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.

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.