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arXiv · 2511.02860

AI-driven Large-scale Electron Microscopy enables Whole-tissue Subcellular Digitization

Li Xiao·Liqing Liu·Hongjun Wu·Jiayi Zhong·Xixia Li·Yan Zhang·Junjie Hu·Sun Fei·Ge Yang·Tao Xu
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Abstract

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.

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Li Xiao, Liqing Liu, Hongjun Wu, Jiayi Zhong, Xixia Li, Yan Zhang, Junjie Hu, Sun Fei, Ge Yang, Tao Xu. 2025-11-02. AI-driven Large-scale Electron Microscopy enables Whole-tissue Subcellular Digitization. https://arxiv.org/abs/2511.02860

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