PROJECT SUMMARY The immune system is a complex system which continuously changes as it responds to the internal and external environments, making it very difficult to understand the variation between individuals and its functional implications. Our SELA derived trajectory captures the variation in immune-cell composition between individuals, thus describes the conserved cascade of cell compositional changes occurring during healthy aging. An individual’s high-dimensional immune cell composition can be translated into a quantitative measure describing its position along this immune-aging cascade reflecting the immune-age. Individuals’ immune-age alters as they advance at different rates along this patterned trajectory, resulting in high inter-individual variation at baseline. We hypothesize that the immune-aging trajectory is conserved between individuals due to epigenetic cell-specific alterations that are induced through an interaction with common physiological and environmental changes that occur with age. Furthermore, we hypothesize that this the baseline variation in immune-age between individuals has functional implications during immune response, which ultimately may lead to variability in clinical outcome, which is observed for the majority of insults and therapies. Project 1 is designed to enrich and refine our immune age metric by expanding our cohort (adding 40-60 years-old twins) and by measuring in addition immune cell types, factors (cytokines, epigenetic modifications, and metabolomics) found to be in correlation with immune aging. Furthermore, we will identify factors that drive progression along the immune age trajectory. In addition, we will gain comprehensive insight into the way age and immune history affect adaptive (B and T) responses elicited by annual influenza vaccination in the old. Lastly, leveraging the 12+ years of bio-banked SELA samples pre and post seasonal influenza vaccination we will test the hypothesis that molecular immune responses differ as a function of baseline immune-age and decouple immune dynamics that are caused by different vaccine compositions from those that are caused by immune-age. We will combine the latter molecular features correlated with immune age with data on flu specific adaptive responses to obtain, for the first-time comprehensive understanding of vaccine responses in older adults, a risk group for infection. We hope that combining immune-age with B and T flu history, will allow to predict influenza vaccine response from baseline, an unsolved problem of high concern for world health.