A collection of yeast cellular electron cryotomography data

  1. Lu Gan
  2. Cai Tong Ng
  3. Chen Chen
  4. Shujun Cai

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 All the unpublished tilt series and a subset of corresponding cryotomograms have been deposited in the EMPIAR resource for the community to use freely.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

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  1. GigaScience

    Now published in GigaScience doi: 10.1093/gigascience/giz077

    Lu Gan Department of Biological Sciences and Centre for BioImaging Sciences, National University of Singapore, Singapore 117543Find this author on Google ScholarFind this author on PubMedSearch for this author on this siteORCID record for Lu GanFor correspondence: lu@anaphase.orgCai Tong Ng Department of Biological Sciences and Centre for BioImaging Sciences, National University of Singapore, Singapore 117543Find this author on Google ScholarFind this author on PubMedSearch for this author on this siteORCID record for Cai Tong NgChen Chen Department of Biological Sciences and Centre for BioImaging Sciences, National University of Singapore, Singapore 117543Find this author on Google ScholarFind this author on PubMedSearch for this author on this siteShujun Cai Department of Biological Sciences and Centre for BioImaging Sciences, National University of Singapore, Singapore 117543Find this author on Google ScholarFind this author on PubMedSearch for this author on this siteORCID record for Shujun Cai

    A version of this preprint has been published in the Open Access journal GigaScience (see paper https://doi.org/10.1093/gigascience/giz077 ), where the paper and peer reviews are published openly under a CC-BY 4.0 license.

    These peer reviews were as follows:

    Reviewer 1: http://dx.doi.org/10.5524/REVIEW.101801 Reviewer 2: http://dx.doi.org/10.5524/REVIEW.101802

  2. Version published to 10.1093/gigascience/giz077
  3. Version published to 10.1101/594432v1 on bioRxiv