@article {68193,
	title = {Architecture and dynamics of a novel desmosome-endoplasmic reticulum organelle},
	journal = {bioRxiv},
	year = {2022},
	month = {2022 Jul 08},
	abstract = {<p>The endoplasmic reticulum (ER) forms a dynamic network that contacts other cellular membranes to regulate stress responses, calcium signaling, and lipid transfer. 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 nanometer 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 and mobility. These findings indicate that desmosomes and the keratin cytoskeleton pattern the distribution 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>
},
	doi = {10.1101/2022.07.07.499185},
	url = {https://doi.org/10.1101/2022.07.07.499185},
	author = {Navaneetha Krishnan Bharathan and William Giang and Jesse S. Aaron and Satya Khuon and Teng-Leong Chew and Stephan Preibisch and Eric T. Trautman and Larissa Heinrich and John Bogovic and Davis Bennett and David Ackerman and Woohyun Park and Alyson Petruncio and Aubrey V. Weigel and Stephan Saalfeld and COSEM Project Team and A. Wayne Vogl and Sara N. Stahley and Andrew P. Kowalczyk}
}
@article {67994,
	title = {Petascale pipeline for precise alignment of images from serial section electron microscopy.},
	journal = {bioRxiv},
	year = {2022},
	month = {2022 Mar 27},
	abstract = {<p>The reconstruction of neural circuits from serial section electron microscopy (ssEM) images is being accelerated by automatic image segmentation methods. Segmentation accuracy is often limited by the preceding step of aligning 2D section images to create a 3D image stack. Precise and robust alignment in the presence of image artifacts is challenging, especially as datasets are attaining the petascale. We present a computational pipeline for aligning ssEM images with several key elements. Self-supervised convolutional nets are trained via metric learning to encode and align image pairs, and they are used to initialize iterative fine-tuning of alignment. A procedure called vector voting increases robustness to image artifacts or missing image data. For speedup the series is divided into blocks that are distributed to computational workers for alignment. The blocks are aligned to each other by composing transformations with decay, which achieves a global alignment without resorting to a time-consuming global optimization. We apply our pipeline to a whole fly brain dataset, and show improved accuracy relative to prior state of the art. We also demonstrate that our pipeline scales to a cubic millimeter of mouse visual cortex. Our pipeline is publicly available through two open source Python packages.</p>
},
	doi = {10.1101/2022.03.25.485816},
	url = {https://doi.org/10.1101/2022.03.25.485816},
	author = {Sergiy Popovych and Thomas Macrina and Nico Kemnitz and Manuel Castro and Barak Nehoran and Zhen Jia and J. Alexander Bae and Eric Mitchell and Shang Mu and Eric T. Trautman and Stephan Saalfeld and Kai Li and Sebastian Seung}
}
