Predicting Alzheimer's disease subtypes and understanding their molecular characteristics in living patients with transcriptomic trajectory profiling

Abstract

Introduction: Deciphering the diverse molecular mechanisms in living Alzheimer's disease (AD) patients is a big challenge but is pivotal for disease prognosis and precision medicine development. Methods: Utilizing an optimal transport approach, we conducted graph-based mapping of transcriptomic profiles to transfer AD subtype labels from ROSMAP monocyte samples to ADNI and ANMerge peripheral blood mononuclear cells. Subsequently, differential expression followed by comparative pathway and diffusion pseudotime analysis were applied to each cohort to infer the progression trajectories. Survival analysis with real follow-up time was used to obtain potential biomarkers for AD prognosis. Results: AD subtype labels were accurately transferred onto the blood samples of ADNI and ANMerge living patients. Pathways and associated genes in neutrophil degranulation-like immune process, immune acute phase response, and IL-6 signaling were significantly associated with AD progression. Discussion: The work enhanced our understanding of AD progression in different subtypes, offering insights into potential biomarkers and personalized interventions for improved patient care. Highlights: We applied an innovative optimal transport-based approach to map transcriptomic data from different Alzheimer's disease (AD) cohort studies and transfer known AD subtype labels from ROSMAP monocyte samples to peripheral blood mononuclear cell (PBMC) samples within ADNI and ANMerge cohorts. Through comprehensive trajectory and comparative analysis, we investigated the molecular mechanisms underlying different disease progression trajectories in AD. We validated the accuracy of our AD subtype label transfer and identified prognostic genetic markers associated with disease progression, facilitating personalized treatment strategies. By identifying and predicting distinctive AD subtypes and their associated pathways, our study contributes to a deeper understanding of AD heterogeneity.