@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 {67228, title = {Local Shape Descriptors for Neuron Segmentation}, journal = {bioRxiv}, year = {2021}, month = {2021/01}, abstract = {

We present a simple, yet effective, 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 are designed to capture local statistics about the neuron to be segmented, such as diameter, elongation, and direction. On a large study comparing several existing methods across various specimen, imaging techniques, and resolutions, we find that auxiliary learning of LSDs consistently increases segmentation accuracy of affinity-based methods over a range of metrics. Furthermore, the addition of LSDs promotes affinitybased segmentation methods to be on par with the current state of the art for neuron segmentation (Flood-Filling Networks, FFN), while being two orders of magnitudes more efficient{\textemdash}a critical requirement for the processing of future petabyte-sized datasets. Implementations of the new auxiliary learning task, network architectures, training, prediction, and evaluation code, as well as the datasets used in this study are publicly available as a benchmark for future method contributions.Competing Interest StatementThe authors have declared no competing interest.

}, doi = {10.1101/2021.01.18.427039}, url = {https://www.biorxiv.org/content/early/2021/01/18/2021.01.18.427039}, author = {Sheridan, Arlo and Nguyen, Tri and Deb, Diptodip and Lee, Wei-Chung Allen and Saalfeld, Stephan and Turaga, Srini and Manor, Uri and Funke, Jan} } @article {65678, title = {Actin chromobody imaging reveals sub-organellar actin dynamics.}, journal = {Nature Methods}, volume = {17}, year = {2020}, month = {2020 Sep 01}, pages = {917-21}, abstract = {

The actin cytoskeleton plays multiple critical roles in cells, from cell migration to organelle dynamics. The small and transient actin structures regulating organelle dynamics are challenging to detect with fluorescence microscopy, making it difficult to determine whether actin filaments are directly associated with specific membranes. To address these limitations, we developed fluorescent-protein-tagged actin nanobodies, termed {\textquoteright}actin chromobodies{\textquoteright} (ACs), targeted to organelle membranes to enable high-resolution imaging of sub-organellar actin dynamics.

}, issn = {1548-7105}, doi = {10.1038/s41592-020-0926-5}, author = {Schiavon, Cara R and Zhang, Tong and Zhao, Bing and Moore, Andrew S and Wales, Pauline and Andrade, Leonardo R and Wu, Melissa and Sung, Tsung-Chang and Dayn, Yelena and Feng, Jasmine W and Quintero, Omar A and Shadel, Gerald S and Grosse, Robert and Manor, Uri} } @article {48116, title = {Sonic Hedgehog Pathway activation increases mitochondrial abundance and activity in hippocampal neurons.}, journal = {Molecular Biology of the Cell}, year = {2016}, month = {2016 Dec 08}, abstract = {

Mitochondria are essential organelles whose biogenesis, structure, and function are regulated by many signaling pathways. In this study we present evidence that, in hippocampal neurons, activation of the Sonic hedgehog (Shh) signaling pathway impacts multiple aspects of mitochondria. Mitochondrial mass was increased significantly in neurons treated with Shh. Using biochemical and fluorescence imaging analyses, we show that Shh signaling activity reduces mitochondrial fission and promotes mitochondrial elongation, at least in part, via suppression of the mitochondrial fission protein dynamin-like GTPase Drp1. Mitochondria from Shh-treated neurons were more electron-dense as revealed by electron microscopy, and had higher membrane potential and respiratory activity. We further show that Shh protects neurons against a variety of stresses, including the mitochondrial poison rotenone, amyloid β-peptide, hydrogen peroxide, and high levels of glutamate. Collectively, our data suggest a link between Shh pathway activity and the physiological properties of mitochondria in hippocampal neurons.

}, issn = {1939-4586}, doi = {10.1091/mbc.E16-07-0553}, author = {Yao, Pamela J and Manor, Uri and Petralia, Ronald S and Brose, Rebecca D and Wu, Ryan T Y and Ott, Carolyn and Wang, Ya-Xian and Charnoff, Ari and Lippincott-Schwartz, Jennifer and Mattson, Mark P} }