Linking COVID-19 to Neurodegeneration: A Single-Cell Deep Learning Study of PBMCs in Multiple Sclerosis and Alzheimer’s Disease

Abstract COVID-19 has increasingly been linked to neurological complications that may overlap with those observed in neurodegenerative and autoimmune diseases. In this study, we analyzed single-cell RNA-sequencing data from peripheral blood mononuclear cells (PBMCs) of patients with COVID-19, multiple sclerosis (MS), and Alzheimer’s disease (AD). Using a deep neural network combining autoencoders and adversarial learning, we uncovered distinct and shared transcriptional signatures across these conditions. Top-ranked genes—including HLA-DRB5, XIST, and DDX3X—were not necessarily differentially expressed but demonstrated strong functional relevance through pathway enrichment and protein interaction analysis, highlighting latent biomarkers often missed by traditional DEG-based methods. Importantly, these candidate genes may aid in the detection of MS and AD among individuals with severe COVID-19 and a family history of these disorders, offering a non-invasive strategy for risk stratification and early intervention. Our findings underscore the value of PBMC-based scRNA-seq and deep neural network frameworks for discovering non-invasive biomarkers and highlight systemic and neuroinflammatory pathways that may connect COVID-19 to long-term neurological outcomes. This integrative approach may pave the way for novel diagnostic and therapeutic strategies, emphasizing the shared immunological underpinnings of these complex diseases.