BMI Distinguished Seminar // Anne Brunet: Modeling and targeting aging

Old age is associated with a decline in cognitive function and an increase in neurodegenerative disease risk. However, the mechanisms of We generated spatiotemporal data at single-cell resolution for the mouse brain across lifespan and we developed machine learning models based on spatial transcriptomics (‘spatial aging clocks’) to reveal cell proximity effects during brain aging and rejuvenation. We identified spatial and cell type-specific transcriptomic fingerprints of aging, rejuvenation, and disease, including for rare cell types. Interestingly, we identify that specific cells have. These results suggest that rare cells can have a drastic influence on their neighbors and could be targeted to counter tissue aging. We anticipate that these spatial aging clocks will not only allow scalable assessment of the efficacy of interventions for aging and disease but also represent a new tool for studying cell-cell interactions in many spatial contexts.