A collection of yeast cellular electron cryotomography data

Author:

Gan Lu1ORCID,Ng Cai Tong1ORCID,Chen Chen1,Cai Shujun1ORCID

Affiliation:

1. Department of Biological Sciences and Centre for BioImaging Sciences, National University of Singapore, 14 Science Drive 4, Singapore 117543

Abstract

Abstract Background 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 All the unpublished tilt series and a subset of corresponding cryotomograms have been deposited in the EMPIAR resource for the community to use freely.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.

Funder

Singapore Ministry of Education

National University of Singapore

Publisher

Oxford University Press (OUP)

Subject

Computer Science Applications,Health Informatics

Cited by 8 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3