<HashMap><database>biostudies-literature</database><scores/><additional><submitter>Litsios A</submitter><funding>Nvidia</funding><funding>Ontario Research Foundation</funding><funding>Deutsche Forschungsgemeinschaft</funding><funding>NHGRI NIH HHS</funding><funding>National Institutes of Health</funding><funding>Canada Foundation for Innovation</funding><funding>Canadian Institutes of Health Research</funding><pagination>1490-1507.e21</pagination><full_dataset_link>https://www.ebi.ac.uk/biostudies/studies/S-EPMC10947830</full_dataset_link><repository>biostudies-literature</repository><omics_type>Unknown</omics_type><volume>187(6)</volume><pubmed_abstract>Cell cycle progression relies on coordinated changes in the composition and subcellular localization of the proteome. By applying two distinct convolutional neural networks on images of millions of live yeast cells, we resolved proteome-level dynamics in both concentration and localization during the cell cycle, with resolution of ∼20 subcellular localization classes. We show that a quarter of the proteome displays cell cycle periodicity, with proteins tending to be controlled either at the level of localization or concentration, but not both. Distinct levels of protein regulation are preferentially utilized for different aspects of the cell cycle, with changes in protein concentration being mostly involved in cell cycle control and changes in protein localization in the biophysical implementation of the cell cycle program. We present a resource for exploring global proteome dynamics during the cell cycle, which will aid in understanding a fundamental biological process at a systems level.</pubmed_abstract><journal>Cell</journal><pubmed_title>Proteome-scale movements and compartment connectivity during the eukaryotic cell cycle.</pubmed_title><pmcid>PMC10947830</pmcid><funding_grant_id>R01HG005853</funding_grant_id><funding_grant_id>R01 HG005853</funding_grant_id><funding_grant_id>Jo 187/9-1</funding_grant_id><funding_grant_id>MFE - 187913</funding_grant_id><funding_grant_id>PJT-180259</funding_grant_id><pubmed_authors>Myers C</pubmed_authors><pubmed_authors>Billmann M</pubmed_authors><pubmed_authors>Churchman LS</pubmed_authors><pubmed_authors>Couvillion MT</pubmed_authors><pubmed_authors>Andrews BJ</pubmed_authors><pubmed_authors>Boone C</pubmed_authors><pubmed_authors>Litsios A</pubmed_authors><pubmed_authors>Johnsson N</pubmed_authors><pubmed_authors>Grys BT</pubmed_authors><pubmed_authors>Timmermann S</pubmed_authors><pubmed_authors>Forster DT</pubmed_authors><pubmed_authors>Friesen H</pubmed_authors><pubmed_authors>Kraus OZ</pubmed_authors><pubmed_authors>Masinas MPD</pubmed_authors><pubmed_authors>Ross C</pubmed_authors></additional><is_claimable>false</is_claimable><name>Proteome-scale movements and compartment connectivity during the eukaryotic cell cycle.</name><description>Cell cycle progression relies on coordinated changes in the composition and subcellular localization of the proteome. By applying two distinct convolutional neural networks on images of millions of live yeast cells, we resolved proteome-level dynamics in both concentration and localization during the cell cycle, with resolution of ∼20 subcellular localization classes. We show that a quarter of the proteome displays cell cycle periodicity, with proteins tending to be controlled either at the level of localization or concentration, but not both. Distinct levels of protein regulation are preferentially utilized for different aspects of the cell cycle, with changes in protein concentration being mostly involved in cell cycle control and changes in protein localization in the biophysical implementation of the cell cycle program. We present a resource for exploring global proteome dynamics during the cell cycle, which will aid in understanding a fundamental biological process at a systems level.</description><dates><release>2024-01-01T00:00:00Z</release><publication>2024 Mar</publication><modification>2026-04-20T03:22:44.544Z</modification><creation>2025-04-04T01:29:42.186Z</creation></dates><accession>S-EPMC10947830</accession><cross_references><pubmed>38452761</pubmed><doi>10.1016/j.cell.2024.02.014</doi></cross_references></HashMap>