{"ID":2846953,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2511.02860","arxiv_id":"2511.02860","title":"AI-driven Large-scale Electron Microscopy enables Whole-tissue Subcellular Digitization","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.","short_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 to...","url_abs":"https://arxiv.org/abs/2511.02860","url_pdf":"https://arxiv.org/pdf/2511.02860v2","authors":"[\"Li Xiao\",\"Liqing Liu\",\"Hongjun Wu\",\"Jiayi Zhong\",\"Xixia Li\",\"Yan Zhang\",\"Junjie Hu\",\"Sun Fei\",\"Ge Yang\",\"Tao Xu\"]","published":"2025-11-02T05:19:59Z","proceeding":"physics.bio-ph","tasks":"[\"physics.bio-ph\",\"cs.AI\"]","methods":"[\"Generative Adversarial Network\"]","has_code":false}