Aging is a multifaceted and challenging process characterized by the deterioration of cellular function, which significantly contributes to various chronic illnesses like cancer, cardiovascular diseases, and neurodegenerative disorders. Consequently, accurately quantifying aging represents a crucial endeavor in our efforts to mitigate illness and prolong a healthy lifespan.

This project aims to address the complexities of aging, a process closely linked to cellular decline and various chronic diseases, with the goal of quantifying aging to enhance overall health and extend lifespan. Traditional molecular aging assessments often fall short due to tissue and cell heterogeneity. To overcome this, the project is constructing transcriptome-based, cell type-specific aging clocks for the human hippocampus. Its three main objectives are to discern differential aging rates among cell types, identify accurate age predictors, and explore how factors like gender, region, and species impact aging. Using post-mortem human hippocampal samples and single nuclei RNA sequencing, the project leverages machine learning to focus on the molecular signatures of immature neurons and glia subtypes, offering valuable insights into the aging process.