Computers in biology and medicine · 2025
GeneDX-PBMC: An adversarial autoencoder framework for unlocking Alzheimer's disease biomarkers using blood single-cell RNA sequencing data.
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Talebi Hediyeh, Ghiam Shokoofeh, Koli Asiyeh Mirzaei, Yeganeh Pourya Naderi, Eslahchi Changiz
Аннотация
OBJECTIVE: To identify blood-based biomarkers and therapeutic targets for Alzheimer's disease (AD) by leveraging single-cell RNA sequencing (scRNA-seq) data from peripheral blood mononuclear cells (PBMCs) and advanced deep learning techniques.
METHODS: Using scRNA-seq data from PBMCs of AD patients and cognitively normal controls, we developed a deep learning framework that integrates autoencoders, classifiers, and discriminators. This approach analyzed gene expression across various immune cell types-including T cells, B cells, NK cells, and monocytes-by combining both differentially expressed genes (DEGs) and subtle genetic variations typically overlooked by conventional methods. Enrichment analyses were then conducted using Gene Ontology (GO), KEGG pathways, and protein-protein interaction (PPI) networks to assess the biological relevance of the identified genes.
RESULTS: Key genes, such as ZFP36L2, PNRC1, DUSP1, BTG1, YBX1, and CYBA, were identified as significant regulators of inflammation, apoptosis, and cell proliferation. Their overexpression in peripheral immune cells was linked to neuroinflammation, a critical factor in AD progression. Additionally, an observed overlap between aging-associated and AD-related genes reinforced the interconnected nature of these processes. The deep learning model achieved high precision, recall, and F1-scores across T cells, B cells, and NK cells, while Random Forest classifiers effectively managed constraints in monocyte data.
CONCLUSION: Combining scRNA-seq with deep learning provides a powerful non-invasive strategy for the early detection of AD by identifying novel blood-based biomarkers. This integrative approach not only enhances our understanding of immune regulation and neuroinflammatory pathways in AD but also paves the way for innovative diagnostic and therapeutic strategies.
METHODS: Using scRNA-seq data from PBMCs of AD patients and cognitively normal controls, we developed a deep learning framework that integrates autoencoders, classifiers, and discriminators. This approach analyzed gene expression across various immune cell types-including T cells, B cells, NK cells, and monocytes-by combining both differentially expressed genes (DEGs) and subtle genetic variations typically overlooked by conventional methods. Enrichment analyses were then conducted using Gene Ontology (GO), KEGG pathways, and protein-protein interaction (PPI) networks to assess the biological relevance of the identified genes.
RESULTS: Key genes, such as ZFP36L2, PNRC1, DUSP1, BTG1, YBX1, and CYBA, were identified as significant regulators of inflammation, apoptosis, and cell proliferation. Their overexpression in peripheral immune cells was linked to neuroinflammation, a critical factor in AD progression. Additionally, an observed overlap between aging-associated and AD-related genes reinforced the interconnected nature of these processes. The deep learning model achieved high precision, recall, and F1-scores across T cells, B cells, and NK cells, while Random Forest classifiers effectively managed constraints in monocyte data.
CONCLUSION: Combining scRNA-seq with deep learning provides a powerful non-invasive strategy for the early detection of AD by identifying novel blood-based biomarkers. This integrative approach not only enhances our understanding of immune regulation and neuroinflammatory pathways in AD but also paves the way for innovative diagnostic and therapeutic strategies.