<HashMap><database>biostudies-literature</database><scores/><additional><omics_type>Unknown</omics_type><volume>8(1)</volume><submitter>Zhang X</submitter><pubmed_abstract>In mammalian oocytes, large-scale chromatin organization regulates transcription, nuclear architecture, and maintenance of chromosome stability in preparation for meiosis onset. Pre-ovulatory oocytes with distinct chromatin configurations exhibit profound differences in metabolic and transcriptional profiles that ultimately determine meiotic competence and developmental potential. Here, we developed a deep learning pipeline for the non-invasive prediction of chromatin structure and developmental potential in live mouse oocytes. Our Fluorescence prediction and Classification on Bright-field (Fluo-Cast-Bright) pipeline achieved 91.3% accuracy in the classification of chromatin state in fixed oocytes and 85.7% accuracy in live oocytes. Importantly, transcriptome analysis following non-invasiv</pubmed_abstract><journal>Communications biology</journal><pagination>141</pagination><full_dataset_link>https://www.ebi.ac.uk/biostudies/studies/S-EPMC11779945</full_dataset_link><repository>biostudies-literature</repository><pubmed_title>Fluo-Cast-Bright: a deep learning pipeline for the non-invasive prediction of chromatin structure and developmental potential in live oocytes.</pubmed_title><pmcid>PMC11779945</pmcid><pubmed_authors>Zhang X</pubmed_authors><pubmed_authors>Baumann C</pubmed_authors><pubmed_authors>De La Fuente R</pubmed_authors></additional><is_claimable>false</is_claimable><name>Fluo-Cast-Bright: a deep learning pipeline for the non-invasive prediction of chromatin structure and developmental potential in live oocytes.</name><description>In mammalian oocytes, large-scale chromatin organization regulates transcription, nuclear architecture, and maintenance of chromosome stability in preparation for meiosis onset. Pre-ovulatory oocytes with distinct chromatin configurations exhibit profound differences in metabolic and transcriptional profiles that ultimately determine meiotic competence and developmental potential. Here, we developed a deep learning pipeline for the non-invasive prediction of chromatin structure and developmental potential in live mouse oocytes. Our Fluorescence prediction and Classification on Bright-field (Fluo-Cast-Bright) pipeline achieved 91.3% accuracy in the classification of chromatin state in fixed oocytes and 85.7% accuracy in live oocytes. Importantly, transcriptome analysis following non-invasiv</description><dates><release>2025-01-01T00:00:00Z</release><publication>2025 Jan</publication><modification>2025-04-03T23:26:47.318Z</modification><creation>2025-04-03T23:26:47.318Z</creation></dates><accession>S-EPMC11779945</accession><cross_references><pubmed>39880880</pubmed><doi>10.1038/s42003-025-07568-0</doi></cross_references></HashMap>