Brain cross-omics integration in Alzheimer's disease

Abstract

Unbiased data-driven omic approaches are revealing the molecular heterogeneity of Alzheimer disease. Here, we used machine learning approaches to integrate high-throughput bulk and single-nucleus transcriptomic, proteomic, metabolomic, and lipidomic profiles with clinical and neuropathological data from multiple AD cohorts. We discovered four unique multimodal molecular profiles, one showing signs of poor cognitive function, a faster pace of disease progression, shorter survival with the disease, severe neurodegeneration and astrogliosis, and reduced levels of metabolomic profiles. This profile shows similar cellular and molecular profiles in multiple affected cortical regions associated with higher Braak tau scores and significant dysregulation of synapse-related genes and endocytosis, phagosome, mTOR signaling pathways altered in AD early and late stages. The multimodal clusters uncovered cerebrospinal fluid biomarkers to monitor AD progression. AD cross-omics data integration with transcriptomic data from an SNCA mouse model revealed an overlapping signature. Our cross-omics analyses provide novel critical molecular insights into AD.