Exploring Promising Biomarkers for Alzheimer’s Disease through the Computational Analysis of Peripheral Blood Single-Cell RNA Sequencing Data

Abstract

Alzheimer’s disease (AD) represents one of the most important healthcare challenges of the current century, characterized as an expanding, “silent pandemic”. Recent studies suggest that the peripheral immune system may participate in AD development; however, the molecular components of these cells in AD remain poorly understood. Although single-cell RNA sequencing (scRNA-seq) offers a sufficient exploration of various biological processes at the cellular level, the number of existing works is limited, and no comprehensive machine learning (ML) analysis has yet been conducted to identify effective biomarkers in AD. Herein, we introduced a computational workflow using both deep learning and ML processes examining scRNA-seq data obtained from the peripheral blood of both Alzheimer’s disease patients with an amyloid-positive status and healthy controls with an amyloid-negative status, totaling 36,849 cells. The output of our pipeline contained transcripts ranked by their level of significance, which could serve as reliable genetic signatures of AD pathophysiology. The comprehensive functional analysis of the most dominant genes in terms of biological relevance to AD demonstrates that the proposed methodology has great potential for discovering blood-based fingerprints of the disease. Furthermore, the present approach paves the way for the application of ML techniques to scRNA-seq data from complex disorders, providing new challenges to identify key biological processes from a molecular perspective.

Exploring Intervention Techniques for Alzheimer's Disease: Conventional Methods and the Role of AI in Advancing Care

“…The study underscores the value of integrating diverse neuroimaging modalities through data fusion, showcasing the efficacy of DL and DFC features in predicting AD progression. The paper by Krokidis et al (2023) addresses the lack of comprehensive AD single-cell Ribonucleic acid (RNA) sequencing (scRNA-seq) analysis. The proposed computational workflow identifies potential genetic signatures from peripheral blood cells, offering a promising avenue for uncovering blood-based biomarkers and understanding AD pathophysiology at the molecular level.…”

Machine Learning Analysis of Alzheimer’s Disease Single-Cell RNA-Sequencing Data across Cortex and Hippocampus Regions

“…The expression values of AD-related genes obtained from blood samples of ADNI, ANM1, and ANM2 were classified using five feature selection methods and five classifiers; AD-related genes were enriched with several pathways, showing their immune, inflammation, energy metabolism, and Wnt signaling involvement [10]. In a recent study by our group, we also examined the impact of an ensemble pipeline including the SCALEX integration tool in the context of single-cell AD studies from the peripheral blood of both AD patients with an amyloid-positive status and healthy amyloid-negative controls [11].…”