Supporting data for "A collection of yeast cellular electron cryotomography data"

Cells are powered by a large set of macromolecular complexes, which work together in a crowded environment. The <em>in situ</em> mechanisms of these complexes are unclear because their 3-D distribution, organization, and interactions are largely unknown. Electron cryotomography (cryo-ET) is a key tool to address these knowledge gaps because it produces cryotomograms 3-D images that reveal biological structure at approximately 4-nm resolution. Cryo-ET does not involve any fixation, dehydration, staining, or plastic embedment, meaning that cellular features are visualized in a life-like, frozen-hydrated state. To study chromatin and mitotic machinery <em>in situ</em>, we have subjected yeast cells to a variety of genetic and/or chemical perturbations, cryosectioned them, and then imaged the cells by cryo-ET. Every study from our group has generated more cryo-ET data than needed. Only the small subset of data that contributed to figures in these studies have been publicly shared. Here we share more than 1,000 cryo-ET raw datasets of cryosectioned budding yeast <em>S. cerevisiae</em>. This data will be valuable to cell biologists who are interested in the nanoscale organization of yeasts and of eukaryotic cells in general. To facilitate access, all the unpublished tilt series and a subset of corresponding cryotomograms have been deposited in the EMPIAR resource for the cell-biology community to use freely. To improve tilt series discoverability, we have uploaded metadata and preliminary notes to publicly accessible Google Sheets, EMPIAR and GigaDB. Cellular cryo-ET data can be mined to obtain new cell-biological, structural, and 3-D statistical insights <em>in situ</em>. Because these data capture cells in a life-like state, they contain some structures that are either absent or not visible in traditional EM data. Template matching and subtomogram averaging of known macromolecular complexes can reveal their 3-D 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 who are learning cellular cryo-ET.