@article {69136, title = {Permanent deconstruction of intracellular primary cilia in differentiating granule cell neurons.}, journal = {J Cell Biol.}, volume = {223}, year = {2024}, month = {2024 Aug 13}, pages = {e202404038}, abstract = {

Primary cilia on granule cell neuron progenitors in the developing cerebellum detect sonic hedgehog to facilitate proliferation. Following differentiation, cerebellar granule cells become the most abundant neuronal cell type in the brain. While granule cell cilia are essential during early developmental stages, they become infrequent upon maturation. Here, we provide nanoscopic resolution of cilia in situ using large-scale electron microscopy volumes and immunostaining of mouse cerebella. In many granule cells, we found intracellular cilia, concealed from the external environment. Cilia were disassembled in differentiating granule cell neurons-in a process we call cilia deconstruction-distinct from premitotic cilia resorption in proliferating progenitors. In differentiating granule cells, cilia deconstruction involved unique disassembly intermediates, and, as maturation progressed, mother centriolar docking at the plasma membrane. Unlike ciliated neurons in other brain regions, our results show the deconstruction of concealed cilia in differentiating granule cells, which might prevent mitogenic hedgehog responsiveness. Ciliary deconstruction could be paradigmatic of cilia removal during differentiation in other tissues.

}, doi = {10.1016/j.cub.2024.07.036}, url = {https://doi.org/10.1016/j.cub.2024.07.036 }, author = {Ott, Carolyn M and Constable, Sandii and Nguyen, Tri M and White, Kevin and Lee, Wei-Chung Allen and Lippincott-Schwartz, Jennifer and Mukhopadhyay, Saikat} } @article {68564, title = {Local shape descriptors for neuron segmentation.}, journal = {Nature Methods}, volume = {20}, year = {2023}, month = {2023 Feb 01}, pages = {295-303}, abstract = {

We present an auxiliary learning task for the problem of neuron segmentation in electron microscopy volumes. The auxiliary task consists of the prediction of local shape descriptors (LSDs), which we combine with conventional voxel-wise direct neighbor affinities for neuron boundary detection. The shape descriptors capture local statistics about the neuron to be segmented, such as diameter, elongation, and direction. On a study comparing several existing methods across various specimen, imaging techniques, and resolutions, auxiliary learning of LSDs consistently increases segmentation accuracy of affinity-based methods over a range of metrics. Furthermore, the addition of LSDs promotes affinity-based segmentation methods to be on par with the current state of the art for neuron segmentation (flood-filling networks), while being two orders of magnitudes more efficient-a critical requirement for the processing of future petabyte-sized datasets.

}, issn = {1548-7105}, doi = {10.1038/s41592-022-01711-z}, author = {Sheridan, Arlo and Nguyen, Tri M and Deb, Diptodip and Lee, Wei-Chung Allen and Saalfeld, Stephan and Turaga, Srinivas C and Manor, Uri and Funke, Jan} } @article {68517, title = {Structured cerebellar connectivity supports resilient pattern separation.}, journal = {Nature}, volume = {613}, year = {2023}, month = {2023 Jan 01}, pages = {543-549}, abstract = {

The cerebellum is thought to help\ detect and correct errors between intended and executed commands and is critical for social behaviours, cognition and emotion. Computations for motor control must be performed quickly to correct errors in real time and should be sensitive to small differences between patterns for fine error correction while being resilient to noise. Influential theories of cerebellar information processing have largely assumed random network connectivity, which increases the encoding capacity of the network{\textquoteright}s first layer. However, maximizing encoding capacity reduces the resilience to noise. To understand how neuronal circuits address this fundamental trade-off, we mapped the feedforward connectivity in the mouse cerebellar cortex using automated large-scale transmission electron microscopy and convolutional neural network-based image segmentation. We found that both the input and output layers of the circuit exhibit redundant and selective connectivity motifs, which contrast with prevailing models. Numerical simulations suggest that these redundant, non-random connectivity motifs increase the resilience to noise at a negligible cost to the overall encoding capacity. This work reveals how neuronal network structure can support a trade-off between encoding capacity and redundancy, unveiling principles of biological network architecture with implications for the design of artificial neural networks.

}, issn = {1476-4687}, doi = {10.1038/s41586-022-05471-w}, author = {Nguyen, Tri M and Thomas, Logan A and Jeff L Rhoades and Ricchi, Ilaria and Yuan, Xintong Cindy and Sheridan, Arlo and Hildebrand, David G C and Funke, Jan and Regehr, Wade G and Lee, Wei-Chung Allen} } @article {65725, title = {Dense neuronal reconstruction through X-ray holographic nano-tomography.}, journal = {Nature Neuroscience}, volume = {23}, year = {2020}, month = {2020 Dec -1}, pages = {1637-43}, abstract = {

Imaging neuronal networks provides a foundation for understanding the nervous system, but resolving dense nanometer-scale structures over large volumes remains challenging for light microscopy (LM) and electron microscopy (EM). Here we show that X-ray holographic nano-tomography (XNH) can image millimeter-scale volumes with sub-100-nm resolution, enabling reconstruction of dense wiring in Drosophila melanogaster and mouse nervous tissue. We performed correlative XNH and EM to reconstruct hundreds of cortical pyramidal cells and show that more superficial cells receive stronger synaptic inhibition on their apical dendrites. By combining multiple XNH scans, we imaged an adult Drosophila leg with sufficient resolution to comprehensively catalog mechanosensory neurons and trace individual motor axons from muscles to the central nervous system. To accelerate neuronal reconstructions, we trained a convolutional neural network to automatically segment neurons from XNH volumes. Thus, XNH bridges a key gap between LM and EM, providing a new avenue for neural circuit discovery.

}, issn = {1546-1726}, doi = {10.1038/s41593-020-0704-9}, author = {Kuan, Aaron T and Phelps, Jasper S and Thomas, Logan A and Nguyen, Tri M and Han, Julie and Chen, Chiao-Lin and Azevedo, Anthony W and Tuthill, John C and Funke, Jan and Cloetens, Peter and Pacureanu, Alexandra and Lee, Wei-Chung Allen} }