Exploratory Single-Cell RNA Sequencing of Peripheral Blood Identifies Candidate Signatures Associated with Alzheimer’s Disease

Background. Alzheimer's disease (AD) is a progressive neurodegenerative disorder with a complex etiology, often diagnosed late in its course. Early detection of AD biomarkers could aid in timely intervention and management. Single-cell RNA sequencing (scRNA-seq) offers a powerful tool to unravel cellular heterogeneity and identify molecular signatures associated with disease states. Here, we employ scRNA-seq on peripheral blood samples to investigate potential predictive biomarkers for AD. Methods. We analyzed the publicly available scRNA-seq dataset GSE181279, comprising peripheral blood cells from three individuals with Alzheimer’s disease and two healthy controls. Single-cell RNA sequencing was performed on these samples to profile the transcriptomic landscape of individual cells. Bioinformatics analyses were employed to identify differentially expressed genes and cellular subtypes associated with AD pathology. Machine learning algorithms were utilized to develop predictive models based on gene expression patterns, aiming to discriminate between AD patients and healthy controls. Results. Our scRNA-seq (GSE181279) analysis revealed distinct gene expression profiles and cellular subtypes in peripheral blood samples from AD patients compared to healthy controls. We identified several dysregulated genes and cell populations associated with AD pathology, including immune cell activation and neuroinflammatory processes. Differential-expression and enrichment analyses identified candidate genes and pathways associated with immune activation, stress-response signaling, and altered cellular homeostasis in AD. In an exploratory leave-one-out analysis, a two-gene model incorporating BTG1 and DUSP1 separated AD from healthy controls within this very small dataset; these findings require validation in larger independent cohorts. Conclusions. This exploratory analysis suggests that peripheral-blood scRNA-seq may help identify candidate biomarkers associated with AD. The identified gene expression signatures and cellular subtypes associated with AD pathology provide valuable insights into the underlying molecular mechanisms of the disease. Furthermore, the development of accurate predictive models based on scRNA-seq data suggests a promising avenue for early diagnosis and intervention in AD. Further validation and prospective studies are warranted to assess the clinical utility and generalizability of these findings in larger cohorts.