Deep learning-based high-resolution time inference for deciphering dynamic gene regulation from fixed embryos
Abstract Embryo development is driven by the spatiotemporal dynamics of complex gene regulatory networks. Uncovering these dynamics requires simultaneous tracking of multiple fluctuating molecular species over time, which exceeds the capabilities of traditional live-imaging approaches. Fixed-embryo...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-07-01
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| Series: | Nature Communications |
| Online Access: | https://doi.org/10.1038/s41467-025-61907-7 |
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| author | Huihan Bao Shihe Zhang Zhiyang Yu Heng Xu |
| author_facet | Huihan Bao Shihe Zhang Zhiyang Yu Heng Xu |
| author_sort | Huihan Bao |
| collection | DOAJ |
| description | Abstract Embryo development is driven by the spatiotemporal dynamics of complex gene regulatory networks. Uncovering these dynamics requires simultaneous tracking of multiple fluctuating molecular species over time, which exceeds the capabilities of traditional live-imaging approaches. Fixed-embryo imaging offers the necessary sensitivity and capacity but lacks temporal resolution. Here, we present a multi-scale ensemble deep learning approach to precisely infer absolute developmental time with 1-minute resolution from nuclear morphology in fixed Drosophila embryo images. Applying this approach to quantitative imaging of fixed wild-type embryos, we resolve the spatiotemporal regulation of the endogenous segmentation gene Krüppel (Kr) by multiple transcription factors (TFs) during early development without genetic modification. Integrating a time-resolved theoretical model of single-molecule mRNA statistics, we further uncover the unsteady-state bursty kinetics of the endogenous segmentation gene, hunchback (hb), driven by dynamic TF binding. Our method provides a versatile framework for deciphering complex gene network dynamics in genetically unmodified organisms. |
| format | Article |
| id | doaj-art-8706ee7c8b6a48d5a424ece030828097 |
| institution | Kabale University |
| issn | 2041-1723 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Nature Communications |
| spelling | doaj-art-8706ee7c8b6a48d5a424ece0308280972025-08-20T04:02:56ZengNature PortfolioNature Communications2041-17232025-07-0116111610.1038/s41467-025-61907-7Deep learning-based high-resolution time inference for deciphering dynamic gene regulation from fixed embryosHuihan Bao0Shihe Zhang1Zhiyang Yu2Heng Xu3School of Physics and Astronomy, Shanghai Jiao Tong UniversitySchool of Physics and Astronomy, Shanghai Jiao Tong UniversitySchool of Physics and Astronomy, Shanghai Jiao Tong UniversitySchool of Physics and Astronomy, Shanghai Jiao Tong UniversityAbstract Embryo development is driven by the spatiotemporal dynamics of complex gene regulatory networks. Uncovering these dynamics requires simultaneous tracking of multiple fluctuating molecular species over time, which exceeds the capabilities of traditional live-imaging approaches. Fixed-embryo imaging offers the necessary sensitivity and capacity but lacks temporal resolution. Here, we present a multi-scale ensemble deep learning approach to precisely infer absolute developmental time with 1-minute resolution from nuclear morphology in fixed Drosophila embryo images. Applying this approach to quantitative imaging of fixed wild-type embryos, we resolve the spatiotemporal regulation of the endogenous segmentation gene Krüppel (Kr) by multiple transcription factors (TFs) during early development without genetic modification. Integrating a time-resolved theoretical model of single-molecule mRNA statistics, we further uncover the unsteady-state bursty kinetics of the endogenous segmentation gene, hunchback (hb), driven by dynamic TF binding. Our method provides a versatile framework for deciphering complex gene network dynamics in genetically unmodified organisms.https://doi.org/10.1038/s41467-025-61907-7 |
| spellingShingle | Huihan Bao Shihe Zhang Zhiyang Yu Heng Xu Deep learning-based high-resolution time inference for deciphering dynamic gene regulation from fixed embryos Nature Communications |
| title | Deep learning-based high-resolution time inference for deciphering dynamic gene regulation from fixed embryos |
| title_full | Deep learning-based high-resolution time inference for deciphering dynamic gene regulation from fixed embryos |
| title_fullStr | Deep learning-based high-resolution time inference for deciphering dynamic gene regulation from fixed embryos |
| title_full_unstemmed | Deep learning-based high-resolution time inference for deciphering dynamic gene regulation from fixed embryos |
| title_short | Deep learning-based high-resolution time inference for deciphering dynamic gene regulation from fixed embryos |
| title_sort | deep learning based high resolution time inference for deciphering dynamic gene regulation from fixed embryos |
| url | https://doi.org/10.1038/s41467-025-61907-7 |
| work_keys_str_mv | AT huihanbao deeplearningbasedhighresolutiontimeinferencefordecipheringdynamicgeneregulationfromfixedembryos AT shihezhang deeplearningbasedhighresolutiontimeinferencefordecipheringdynamicgeneregulationfromfixedembryos AT zhiyangyu deeplearningbasedhighresolutiontimeinferencefordecipheringdynamicgeneregulationfromfixedembryos AT hengxu deeplearningbasedhighresolutiontimeinferencefordecipheringdynamicgeneregulationfromfixedembryos |