Language

English

Publication Date

1-1-2026

Journal

Computational and Structural Biotechnology Journal

DOI

10.34133/csbj.0108

PMID

42146901

PMCID

PMC13172580

PubMedCentral® Posted Date

5-14-2026

PubMedCentral® Full Text Version

Post-print

Abstract

Alzheimer's disease is characterized by complex molecular and cellular heterogeneity, which complicates efforts to identify consistent biomarkers and therapeutic targets. To better characterize the heterogeneity, we applied latent factor modeling to RNA sequencing data from approximately 2,500 human Alzheimer's disease brain samples, uncovering underlying patterns in gene expression. These transcriptional groups demonstrated unique gene expression profiles related to synaptic and neuronal pathways, vasculature development, and protein folding and antigen processing. Notably, this latent factor reflects variation in spatial sampling. Adjusting for the latent factor improved the identification of differentially expressed genes in disease samples. This finding suggests that spatial heterogeneity is a pervasive driver of transcriptomic variation and has important implications for future studies of Alzheimer's disease and related neurological disorders.

Published Open-Access

yes

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