Zhang Xiangyu, Baumann Claudia, De La Fuente Rabindranath
Department of Physiology and Pharmacology, College of Veterinary Medicine, University of Georgia, Athens, GA, 30602, USA.
Regenerative Bioscience Center (RBC), University of Georgia, Athens, GA, 30602, USA.
Commun Biol. 2025 Jan 29;8(1):141. doi: 10.1038/s42003-025-07568-0.
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-invasive selection revealed that meiotically competent oocytes exhibit a higher expression of transcripts associated with RNA and protein nuclear export, maternal mRNA deadenylation, histone modifications, chromatin remodeling and signaling pathways regulating microtubule dynamics during the metaphase-I to metaphase-II transition. Fluo-Cast-Bright provides fast and non-invasive selection of meiotically competent oocytes for downstream research and clinical applications.
在哺乳动物卵母细胞中，大规模染色质组织调控转录、核结构以及为减数分裂起始做准备时染色体稳定性的维持。具有不同染色质构型的排卵前卵母细胞在代谢和转录谱方面表现出显著差异，这些差异最终决定减数分裂能力和发育潜能。在此，我们开发了一种深度学习流程，用于对活的小鼠卵母细胞中的染色质结构和发育潜能进行无创预测。我们的明场荧光预测与分类（Fluo-Cast-Bright）流程在固定卵母细胞染色质状态分类中准确率达到91.3%，在活卵母细胞中准确率达到85.7%。重要的是，无创筛选后的转录组分析表明，减数分裂能力强的卵母细胞在第一次减数分裂中期到第二次减数分裂中期转变过程中，与RNA和蛋白质核输出、母体mRNA去腺苷酸化、组蛋白修饰、染色质重塑以及调节微管动力学的信号通路相关的转录本表达较高。Fluo-Cast-Bright为下游研究和临床应用提供了快速且无创的减数分裂能力强的卵母细胞筛选方法。
