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Bushong, E. A.

Publications and source records attributed to Bushong, E. A..

4 recordsLinked to original sources

Asymmetric ephaptic inhibition between compartmentalized olfactory receptor neurons

In the Drosophila antenna, different subtypes of olfactory receptor neurons (ORNs) housed in the same sensory hair (sensillum) can inhibit each other non-synaptically. However, the mechanisms underlying this unusual form of lateral inhibition remain unclear. Here we use recordings from pairs of sensilla impaled by the same tungsten electrode to prove that direct electrical (\"ephaptic\") interactions mediate lateral inhibition between ORNs. Intriguingly, within individual sensilla, we find that ephaptic lateral inhibition is asymmetric such that one ORN exerts greater influence onto its neighbor. Serial block-face scanning electron microscopy of genetically identified ORNs and circuit modeling indicate that asymmetric lateral inhibition reflects a surprisingly simple mechanism: the physically larger ORN in a pair corresponds to the dominant neuron in ephaptic interactions. Thus, morphometric differences between compartmentalized ORNs account for highly specialized inhibitory interactions that govern information processing at the earliest stages of olfactory coding.

neuroscience

Verifying, Challenging, and Discovering New Synapses among Fully EM-Reconstructed Neurons in the Leech Ganglion

Neural circuits underpin the production of animal behavior, largely based upon the precise pattern of synaptic connectivity among the neurons involved. For large numbers of neurons, determining such \"connectomes\" by direct physiological means is difficult, as physiological accessibility is ultimately required to verify and characterize the function of synapses. We collected a volume of images spanning an entire ganglion of the juvenile leech nervous system via serial blockface electron microscopy (SBEM). We validated this approach by reconstructing a well-characterized circuit of motor neurons involved in the swimming behavior of the leech by locating the synapses among them. We confirm that there are multiple synaptic contacts between connected pairs of neurons in the leech, and that these synapses are widely distributed across the region of neuropil in which the neurons arbors overlap. We verified the anatomical existence of connections that had been described physiologically among longitudinal muscle motor neurons. We also found that some physiological connections were not present anatomically. We then drew upon the SBEM dataset to design additional physiological experiments. We reconstructed an uncharacterized neuron and one of its presynaptic partners identified from the SBEM dataset. We subsequently interrogated this cell pair via intracellular electrophysiology in an adult ganglion and found that the anatomically-discovered synapse was also functional physiologically. Our findings demonstrate the value of combining a connectomics approach with electrophysiology in the leech nervous system.\n\nSignificance StatementThe function of any nervous system depends on the arrangement of its component neurons into circuits. Determining this precise pattern requires an account of which neurons are linked by synapses, and where. Here we use serial electron microscopy to confirm, challenge, and discover synapses in the neuropil of one ganglion from a juvenile leech. Relying on the homology of the ganglion from animal to animal, we demonstrate that we can identify synapses we knew existed from previous physiological work, and that we can confirm a new anatomically-discovered synapse by subsequently recording from the same neurons in a different animal. Here we show how analyses of anatomical detail and physiologically determined interactions complementarily yield insight into how neural circuits produce behavior.

neuroscience

CDeep3M - Plug-and-Play cloud based deep learning for image segmentation of light, electron and X-ray microscopy

As biological imaging datasets increase in size, deep neural networks are considered vital tools for efficient image segmentation. While a number of different network architectures have been developed for segmenting even the most challenging biological images, community access is still limited by the difficulty of setting up complex computational environments and processing pipelines, and the availability of compute resources. Here, we address these bottlenecks, providing a ready-to-use image segmentation solution for any lab, with a pre-configured, publicly available, cloud-based deep convolutional neural network on Amazon Web Services (AWS). We provide simple instructions for training and applying CDeep3M for segmentation of large and complex 2D and 3D microscopy datasets of diverse biomedical imaging modalities.

cell biology

High-quality ultrastructural preservation using cryofixation for 3D electron microscopy of genetically labeled tissues

Electron microscopy (EM) offers unparalleled power to study cell substructures at the nanoscale. Cryofixation by high-pressure freezing offers optimal morphological preservation, as it captures cellular structures instantaneously in their near-native states. However, the applicability of cryofixation is limited by its incompatibilities with diaminobenzidine labeling using genetic EM tags and the high-contrast en bloc staining required for serial block-face scanning electron microscopy (SBEM). In addition, it is challenging to perform correlated light and electron microscopy (CLEM) with cryofixed samples. Consequently, these powerful methods cannot be applied to address questions requiring optimal morphological preservation and high temporal resolution. Here we developed an approach that overcomes these limitations; it enables genetically labeled, cryofixed samples to be characterized with SBEM and 3D CLEM. Our approach is broadly applicable, as demonstrated in cultured cells, Drosophila olfactory organ and mouse brain. This optimization exploits the potential of cryofixation, allowing quality ultrastructural preservation for diverse EM applications.

biophysics