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<p>Alzheimer's disease (AD) is a neurodegenerative disorder with synaptic pathology as a core theme in aging-related research. Conventional imaging and limited tools fail to resolve AD-relevant nanoscale structures. Here, we present an automated pipeline integrating volume electron microscopy (vEM) with deep learning to analyze synapses and subcellular components, enabling 3D reconstruction of neuronal architecture in ~130,000 μm<sup>3</sup> of rat prefrontal cortex (PFC). Preliminary results from small animal samples show that, vs. controls, AD rats have fewer mushroom spines but more thin/stubby spines. Synaptic and mitochondrial surface area/volume are reduced, with increased multi-contact synapses. These findings suggest mature spine loss with compensatory morphological changes, potentially impairing dendritic integration. Our method highlights the potential of vEM-deep learning integration for detailed ultrastructural quantification in neurodegenerative models, laying a foundation for future large-sample pathological studies.</p>

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Ultrastructural Evidence of Altered Dendritic Morphology in the Prefrontal Cortex of Alzheimer's Model Rats

  • 期刊论文

  • 发表日期：

    2026年3月25日

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  • Jinyue Guo,
  • Jing Liu,
  • Yanchao Zhang,
  • Jiazheng Liu,
  • Hao Zhai,
  • Linlin Li,
  • Hua Han

摘要

Alzheimer's disease (AD) is a neurodegenerative disorder with synaptic pathology as a core theme in aging-related research. Conventional imaging and limited tools fail to resolve AD-relevant nanoscale structures. Here, we present an automated pipeline integrating volume electron microscopy (vEM) with deep learning to analyze synapses and subcellular components, enabling 3D reconstruction of neuronal architecture in ~130,000 μm3 of rat prefrontal cortex (PFC). Preliminary results from small animal samples show that, vs. controls, AD rats have fewer mushroom spines but more thin/stubby spines. Synaptic and mitochondrial surface area/volume are reduced, with increased multi-contact synapses. These findings suggest mature spine loss with compensatory morphological changes, potentially impairing dendritic integration. Our method highlights the potential of vEM-deep learning integration for detailed ultrastructural quantification in neurodegenerative models, laying a foundation for future large-sample pathological studies.