AI-driven Large-scale Electron Microscopy enables Whole-tissue Subcellular Digitization
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866910028575801344 |
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| author | Xiao, Li Liu, Liqing Wu, Hongjun Zhong, Jiayi Li, Xixia Zhang, Yan Hu, Junjie Fei, Sun Yang, Ge Xu, Tao |
| author_facet | Xiao, Li Liu, Liqing Wu, Hongjun Zhong, Jiayi Li, Xixia Zhang, Yan Hu, Junjie Fei, Sun Yang, Ge Xu, Tao |
| contents | The distribution and interactions of cellular organelles play a critical role in mediating cellular physiology and pathology. Large-scale electron microscopy enables visualization of organelle distribution and interactions at the tissue level with nanometer resolution, but robust and efficient computational analysis tools are lacking. Here, we present a deep learning tool for universal large-scale 2D/3D electron microscopy analysis, DeepOrganelle. This new tool enables high-throughput, cell-resolved spatiotemporal mapping and digitization of organelle distribution and interactions. When applied to spermatogenesis across 12 stages and 22 differentiation status of the germ cells, DeepOrganelle uncovered previously unrecognized, stage-dependent dynamics of mitochondria-endoplasmic reticulum contact sites within one subphase of prophase I during meiosis. It also revealed coordinated organelle redistribution in Sertoli cells towards the blood-testis barrier, digitizing the remodeling dynamics of the tissue. This study demonstrates that DeepOrganelle provides a powerful framework that captures subcellular dynamics at the whole-tissue level. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_02860 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | AI-driven Large-scale Electron Microscopy enables Whole-tissue Subcellular Digitization Xiao, Li Liu, Liqing Wu, Hongjun Zhong, Jiayi Li, Xixia Zhang, Yan Hu, Junjie Fei, Sun Yang, Ge Xu, Tao Biological Physics Artificial Intelligence The distribution and interactions of cellular organelles play a critical role in mediating cellular physiology and pathology. Large-scale electron microscopy enables visualization of organelle distribution and interactions at the tissue level with nanometer resolution, but robust and efficient computational analysis tools are lacking. Here, we present a deep learning tool for universal large-scale 2D/3D electron microscopy analysis, DeepOrganelle. This new tool enables high-throughput, cell-resolved spatiotemporal mapping and digitization of organelle distribution and interactions. When applied to spermatogenesis across 12 stages and 22 differentiation status of the germ cells, DeepOrganelle uncovered previously unrecognized, stage-dependent dynamics of mitochondria-endoplasmic reticulum contact sites within one subphase of prophase I during meiosis. It also revealed coordinated organelle redistribution in Sertoli cells towards the blood-testis barrier, digitizing the remodeling dynamics of the tissue. This study demonstrates that DeepOrganelle provides a powerful framework that captures subcellular dynamics at the whole-tissue level. |
| title | AI-driven Large-scale Electron Microscopy enables Whole-tissue Subcellular Digitization |
| topic | Biological Physics Artificial Intelligence |
| url | https://arxiv.org/abs/2511.02860 |