PROJECT SUMMARY Alzheimer's Disease (AD) is the most prevalent neurodegenerative disease in United States. Current medications are only effective at improving the symptoms for a short period of time and blood-based biomarkers for the disease are only recently beginning to emerge in research and clinical practice. In this proposal we aim to apply pseudotime analysis on publicly available RNA profiling data to detect both novel molecular processes in brain tissue and blood-based RNA biomarkers associated with AD progression. Pseudotime algorithms are machine learning approaches capable of extracting latent temporal information to order samples along a pseudotemporal progression. These approaches utilize cross-sectional data without the need of disease stage information or longitudinal specimen sampling making them uniquely well suited to the large collection of cross-sectional gene expression data currently available for AD. In Aim 1 we will focus on post-mortem brain gene expression analysis, using RNA sequencing data from bulk sampled brain tissue as well as single cell sequencing studies (e.g., Mount Sinai, ROSMAP) that include clinical and neuropathological variables related to AD staging. After extracting the pseudotime trajectories with the phenoPath method, we will prioritize genes according to their statistical correlation with pseudotime. Molecular processes associated with disease onset and progression will be inferred by Weighted Gene Coexpression Network Analysis (WGCNA). In Aim 2 we will focus on RNA expression profiling data from whole blood. Pseudotime trajectories will be determined from existing AD patient blood-based gene expression data as in aim 1, and genes will be prioritized according to their correlation with pseudotime. Then, we will retain genes highly correlated with pseudotime that simultaneously exhibit significant differential expression when compared to control samples, with the goal of finding genes that demonstrate a gradient of expression change from a non-pathological to a pathological stage that are also correlated with disease progression. Finally, we will validate the findings obtained from whole blood in post-mortem brain data from Aim 1, to assess the correlation with the gold- standard neuropathological-based staging. The findings from this proposal will allow us to identify targets for new AD treatments and identify potential candidate blood-based biomarkers of AD progression.