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Title: A collection of yeast cellular electron cryotomography data
Authors: Gan, L. 
Ng, C.T. 
Chen, C.
Cai, S. 
Keywords: chromatin
template matching
Issue Date: 2019
Publisher: Oxford University Press
Citation: Gan, L., Ng, C.T., Chen, C., Cai, S. (2019). A collection of yeast cellular electron cryotomography data. GigaScience 8 (6) : giz077. ScholarBank@NUS Repository.
Rights: Attribution 4.0 International
Abstract: Cells are powered by a large set of macromolecular complexes, which work together in a crowded environment. The in situ mechanisms of these complexes are unclear because their 3D distribution, organization, and interactions are largely unknown. Electron cryotomography (cryo-ET) can address these knowledge gaps because it produces cryotomograms - 3D images that reveal biological structure at ?4-nm resolution. Cryo-ET uses no fixation, dehydration, staining, or plastic embedment, so cellular features are visualized in a life-like, frozen-hydrated state. To study chromatin and mitotic machinery in situ, we subjected yeast cells to genetic and chemical perturbations, cryosectioned them, and then imaged the cells by cryo-ET. Findings: Here we share >1,000 cryo-ET raw datasets of cryosectioned budding yeast Saccharomyces cerevisiaecollected as part of previously published studies. These data will be valuable to cell biologists who are interested in the nanoscale organization of yeasts and of eukaryotic cells in general. All the unpublished tilt series and a subset of corresponding cryotomograms have been deposited in the EMPIAR resource for the community to use freely. To improve tilt series discoverability, we have uploaded metadata and preliminary notes to publicly accessible Google Sheets, EMPIAR, and GigaDB. Conclusions: Cellular cryo-ET data can be mined to obtain new cell-biological, structural, and 3D statistical insights in situ. These data contain structures not visible in traditional electron-microscopy data. Template matching and subtomogram averaging of known macromolecular complexes can reveal their 3D distributions and low-resolution structures. Furthermore, these data can serve as testbeds for high-throughput image-analysis pipelines, as training sets for feature-recognition software, for feasibility analysis when planning new structural-cell-biology projects, and as practice data for students. © 2019 The Author(s) 2019. Published by Oxford University Press.
Source Title: GigaScience
ISSN: 2047217X
DOI: 10.1093/gigascience/giz077
Rights: Attribution 4.0 International
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