用多尺度图网络分析阿尔茨海默病，揭示基因在不同脑细胞中的作用。
Multi Scale Graph Neural Network for Alzheimer's Disease
- 构建多尺度图神经网络，融合蛋白、细胞类型与组织信息
- 发现APOE基因在微胶质细胞等三类细胞中表达相似，提示共同作用
- 适合研究神经退行性疾病机制的生物学家和计算神经科学家
阿尔茨海默病（AD）是一种复杂的进行性神经退行性疾病，特征包括细胞外Aβ斑块、神经纤维缠结、胶质细胞激活和神经元退化，涉及多种细胞类型与通路。现有模型常忽略这些通路的细胞背景。为此，我们开发了基于脑组学数据的多尺度图神经网络模型ALZ PINNACLE，涵盖从衰老到AD全谱系的捐赠者数据。该模型基于PINNACLE框架，在统一潜在空间中学习蛋白质、细胞类型和组织的上下文感知表示。训练使用14,951种蛋白、206,850个蛋白互作、7种细胞类型及48个细胞亚型或状态。预训练后，我们分析了最大遗传风险因子APOE在不同细胞类型中的嵌入表示，发现其在小胶质细胞、神经元和CD8细胞中高度相似，暗示其在这些细胞中具有相似功能。微调模型于AD风险基因后，识别出与APOE在AD中作用相关的细胞类型背景。结果表明，ALZ PINNACLE可为揭示AD神经生物学新见解提供有效框架。
原文摘要 · Abstract (English)
Alzheimer's disease (AD) is a complex, progressive neurodegenerative disorder characterized by extracellular A\b{eta} plaques, neurofibrillary tau tangles, glial activation, and neuronal degeneration, involving multiple cell types and pathways. Current models often overlook the cellular context of these pathways. To address this, we developed a multiscale graph neural network (GNN) model, ALZ PINNACLE, using brain omics data from donors spanning the entire aging to AD spectrum. ALZ PINNACLE is based on the PINNACLE GNN framework, which learns context-aware protein, cell type, and tissue representations within a unified latent space. ALZ PINNACLE was trained on 14,951 proteins, 206,850 protein interactions, 7 cell types, and 48 cell subtypes or states. After pretraining, we investigated the learned embedding of APOE, the largest genetic risk factor for AD, across different cell types. Notably, APOE embeddings showed high similarity in microglial, neuronal, and CD8 cells, suggesting a similar role of APOE in these cell types. Fine tuning the model on AD risk genes revealed cell type contexts predictive of the role of APOE in AD. Our results suggest that ALZ PINNACLE may provide a valuable framework for uncovering novel insights into AD neurobiology.
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