Multi Scale Graph Neural Network for Alzheimer's Disease

TL;DR

This paper introduces ALZ PINNACLE, a multiscale graph neural network that integrates brain omics data to better understand Alzheimer's disease by capturing cellular context and genetic risk factors.

Contribution

The study develops a novel multiscale GNN model, ALZ PINNACLE, that learns context-aware representations of proteins, cell types, and tissues in Alzheimer's disease.

Findings

APOE embeddings are similar in microglial, neuronal, and CD8 cells.

Fine-tuning reveals cell type-specific roles of APOE in AD.

ALZ PINNACLE uncovers new neurobiological insights into AD.

Abstract

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.