Integrative Single-Cell Analysis of Aging-Associated Changes in Human Hematopoietic Stem Cell Heterogeneity - PROJECT SUMMARY/ABSTRACT The global population is aging rapidly, with the number of people over 65 expected to double by 2050. Aging alters hematopoietic stem cells (HSCs), leading to increased inflammation, immune dysfunction, and clonal hematopoiesis. These changes have been linked to hematological disorders, cardiovascular disease, and other age-related conditions. Dissecting the contribution of human HSC heterogeneity to these disorders has been accelerated by single-cell RNA-sequencing and the development of new algorithms to derive of biologically meaningful insights. These algorithms include consensus non-negative matrix factorization (cNMF) and CellAnnoTator (*CAT), which aim to identify consensus gene expression programs that shape cellular heterogeneity across datasets. However, these algorithms have not been applied to human HSC aging, and the circuitry that controls their role in aging and disease remains poorly understood. There is a here is a critical need to identify the molecular programs that drive age-associated changes in human HSCs. The long-term goal is to mitigate age-related immune dysfunction and its associated diseases. The central hypothesis is that inflammatory signaling is a principal driver of human HSC heterogeneity and that AP-1 factors define an HSC subset that expands with age. To test this hypothesis, the investigators will pursue two specific aims: 1) Identify age-associated changes in HSCs across eleven existing datasets, and 2) Determine how consensus gene expression programs shape HSC heterogeneity and relate to aging. For Aim 1, the working hypothesis is that aging HSCs exhibit consistent gene expression changes, including AP-1 activation in a subset that expands with age. Using publicly available datasets, the investigators will identify conserved gene expression and transcription factor activity changes and leverage single-cell data to quantify an aging-associated subset of inflammatory HSCs. For Aim 2, the working hypothesis is that applying cNMF and *CAT within a unified analytical framework will identify biologically meaningful gene expression programs that underlie HSC heterogeneity, including inflammatory pathways linked to aging. The investigators will identify consensus gene expression programs in HSCs, assess their association with age and inflammation, and develop an R-based computational pipeline for broader community use. The expected outcome is the discovery of programs and genes, led by AP-1, that shape heterogeneity of human HSC aging across datasets. The proposed research is innovative because it shifts from evaluating the human HSC compartment as a whole to linking a distinct HSC subset to aging and because it applies an advanced analytical framework to identify consensus gene expression programs shaping HSC heterogeneity. The significance of this research is that it will establish a foundation for predicting how HSC aging impacts immune fitness, systemic inflammation, and cardiovascular risk.
