A diagnostic plasma omics-biomarker for Alzheimer's disease informed by microglial single-cell transcriptomics: A...

Area of Science:

• Neuroscience
• Genomics
• Biomarker Discovery

Background:

• Current Alzheimer's disease (AD) diagnosis relies on late-stage neuropathological markers, limiting early detection.
• Neurodegeneration onset precedes current diagnostic capabilities, necessitating earlier biomarkers.

Purpose of the Study:

• To develop a novel framework for omic-informed, blood-based diagnostic biomarkers for early-stage Alzheimer's disease.
• To identify gene panels from transcriptomic data predictive of AD.

Main Methods:

• Analyzed microglial gene expression from single nucleus RNA sequencing (snRNA-seq) data using six statistical methods.
• Evaluated 78 gene panels (30-2000 genes) for AD patient vs. control classification.
• Mapped top gene panels (300, 50, 30 genes) to blood (monocyte) transcriptomic data using graph-based mapping and optimal transport.

Main Results:

• The 300-gene panel achieved 75% AD accuracy (AUC 0.7) but had lower normal control prediction (53%).
• Reduced panels (50 and 30 genes) showed similar AUCs but improved balanced accuracy between AD and normal prediction.
• The 50-gene panel yielded an AUC of 0.7, with 65% AD accuracy and 71% normal accuracy.

Conclusions:

• Integrating multiomics datasets enhances precision in Alzheimer's disease biomarker discovery.
• This approach offers a powerful modality for advancing AD research and clinical applications.
• Omic-informed blood biomarkers show potential for early and comprehensive AD diagnosis.