用AI实现全组织亚细胞结构高通量数字化，揭示精子发生中线粒体-内质网动态变化。
AI-driven Large-scale Electron Microscopy enables Whole-tissue Subcellular Digitization
- 基于深度学习构建通用2D/3D电镜分析工具DeepOrganelle，支持高通量细胞级分析。
- 在12个阶段、22种分化状态的精子发生中发现减数分裂I前期特定亚阶段的线粒体-内质网接触位点动态变化。
- 首次数字化描绘支持细胞向血睾屏障迁移过程中的协同细胞器重分布，适用于组织水平亚细胞研究。
细胞器的分布与相互作用在调控细胞生理与病理过程中起关键作用。大规模电子显微镜可在组织水平以纳米分辨率可视化细胞器分布与互作，但缺乏鲁棒高效的计算分析工具。本文提出一种通用的大规模2D/3D电镜分析深度学习工具DeepOrganelle，实现了高通量、细胞分辨的时空映射与细胞器分布及互作的数字化。该方法应用于跨12个阶段、22种分化状态的精子发生过程，揭示了减数分裂I前期某一亚阶段中线粒体-内质网接触位点的先前未知的阶段依赖性动态变化；同时发现支持细胞向血睾屏障迁移过程中细胞器的协调重分布，数字化描绘了组织重塑动态。本研究证明DeepOrganelle为全组织水平捕捉亚细胞动态提供了强大框架。
原文摘要 · Abstract (English)
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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