@article {68807,
	title = {Architecture and dynamics of a desmosome-endoplasmic reticulum complex.},
	journal = {Nature Cell Biology},
	volume = {25},
	year = {2023},
	month = {2023 Jun 16},
	pages = {823-835},
	abstract = {<p>The endoplasmic reticulum (ER) forms a dynamic network that contacts other cellular membranes to regulate stress responses, calcium signalling and lipid transfer. Here, using high-resolution volume electron microscopy, we find that the ER forms a previously unknown association with keratin intermediate filaments and desmosomal cell-cell junctions. Peripheral ER assembles into mirror image-like arrangements at desmosomes and exhibits nanometre proximity to keratin filaments and the desmosome cytoplasmic plaque. ER tubules exhibit stable associations with desmosomes, and perturbation of desmosomes or keratin filaments alters ER organization, mobility and expression of ER stress transcripts. These findings indicate that desmosomes and the keratin cytoskeleton regulate the distribution, function and dynamics of the ER network. Overall, this study reveals a previously unknown subcellular architecture defined by the structural integration of ER tubules with an epithelial intercellular junction.</p>
},
	keywords = {Cytoskeleton, Desmosomes, Endoplasmic Reticulum, Intermediate Filaments, Keratins},
	issn = {1476-4679},
	doi = {10.1038/s41556-023-01154-4},
	author = {Bharathan, Navaneetha Krishnan and Giang, William and Hoffman, Coryn L and Aaron, Jesse S and Khuon, Satya and Chew, Teng-Leong and Preibisch, Stephan and Trautman, Eric T and Heinrich, Larissa and Bogovic, John and Bennett, Davis and Ackerman, David and Park, Woohyun and Petruncio, Alyson and Weigel, Aubrey V and Saalfeld, Stephan and Wayne Vogl, A and Stahley, Sara N and Kowalczyk, Andrew P}
}
@article {65832,
	title = {Automatic Detection of Synaptic Partners in a Whole-Brain Drosophila EM Dataset},
	journal = {Nature Methods},
	volume = {18},
	year = {2021},
	month = {2021 Jul 1},
	pages = {771-4},
	abstract = {<p>The study of neural circuits requires the reconstruction of neurons and the identification of synaptic connections between them. To scale the reconstruction to the size of whole-brain datasets, semi-automatic methods are needed to solve those tasks. Here, we present an automatic method for synaptic partner identification in insect brains, which uses convolutional neural networks to identify post-synaptic sites and their pre-synaptic partners. The networks can be trained from human generated point annotations alone and requires only simple post-processing to obtain final predictions. We used our method to extract 244 million putative synaptic partners in the fifty-teravoxel full adult fly brain (FAFB) electron microscopy (EM) dataset and evaluated its accuracy on 146,643 synapses from 702 neurons with a total cable length of 312 mm in four different brain regions. The predicted synaptic connections can be used together with a neuron segmentation to infer a connectivity graph with high accuracy: 96\% of edges between connected neurons are correctly classified as weakly connected (less than five synapses) and strongly connected (at least five synapses). Our synaptic partner predictions for the FAFB dataset are publicly available, together with a query library allowing automatic retrieval of up- and downstream neurons.</p>
},
	doi = {10.1038/s41592-021-01183-7},
	author = {Buhmann, Julia and Sheridan, Arlo and Gerhard, Stephan and Krause, Renate and Nguyen, Tri and Heinrich, Larissa and Schlegel, Philipp and Lee, Wei-Chung Allen and Wilson, Rachel and Saalfeld, Stephan and Jefferis, Gregory and Bock, Davi and Turaga, Srinivas and Cook, Matthew and Funke, Jan}
}
