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.
@article{arxiv.2511.02860,
title = {AI-driven Large-scale Electron Microscopy enables Whole-tissue Subcellular Digitization},
author = {Li Xiao and Liqing Liu and Hongjun Wu and Jiayi Zhong and Xixia Li and Yan Zhang and Junjie Hu and Sun Fei and Ge Yang and Tao Xu},
journal= {arXiv preprint arXiv:2511.02860},
year = {2026}
}