The Journal of Cell BiologyCell BiologyPeer-reviewed journal article
Close-Up of vesicular ER Exit Sites by Volume Electron Imaging using FIB-SEM
Athul NairiD, Akhil NairiD, Patrick StockiD, Arkash JainiD, Elliott SomervilleiD, Anwesha SanyaliD, Jose Inacio Costa-FilhoiD, Tomas L. KirchhauseniD
Abstract
Volume electron microscopy by high-pressure freeze substitution combined with block-face focused ion beam scanning electron microscopy (FIB-SEM) provides comprehensive 3D views of subcellular architecture, essential for understanding cellular activity in context. Aided by Automated Segmentation in Electron Microscopy (ASEM)--a 3D-Unet convolutional neural network trained with sparse annotations--we characterized the spatial organization of endoplasmic reticulum exit sites (ERES), the initial locations for membrane remodeling in protein secretion, in cells not overexpressing secretory cargo. The ASEM model, trained on 50-70 nm in diameter COPI vesicles, successfully identified vesicles adjacent to the Golgi apparatus of HeLa, SVG-A, and iPSC-derived neurons. It also revealed abundant vesicles of similar appearance, often within a group of [~]5-40 vesicles clustered within a 250 nm3 region adjacent to flattened endoplasmic reticulum (ER) domains, forming what we propose are COPII-mediated vesicular ER-exit sites. Elsewhere, similar assemblies appeared alongside tubular ER networks emerging from similarly enlarged ER domains previously reported as the only ERES in HeLa cells. These findings underscore the power of large-scale volume electron microscopy to resolve contradictions regarding membrane organization in vesicular trafficking. We encourage the scientific community to use our publicly accessible repository, containing open-source code, trained models, annotations, predictions, and FIB- SEM datasets, to facilitate continued advances in automated segmentation methods. SummaryNair et. al. show abundance of vesicular ERES in mammalian cells imaged by volume focused ion beam electron microscopy.
- organism: human
- organism: mouse
- assay: electronic health records
- assay: imaging
- research focus: cell type annotation
- research focus: clustering
- research focus: spatial domain detection
- approach: deep learning
- approach: contrastive learning
- Citations
- 8
- FWCI
- 3.97
- Field percentile
- top 5%
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