Fluo-Cast-Bright: a deep learning pipeline for the non-invasive prediction of chromatin structure and developmental potential in live oocytes
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 transc...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-01-01
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| Series: | Communications Biology |
| Online Access: | https://doi.org/10.1038/s42003-025-07568-0 |
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| Summary: | 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-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. |
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| ISSN: | 2399-3642 |