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33 reviewsOld age is associated with a decline in cognitive function and an increase in Open accessneurodegenerative disease risk1. Brain ageing is complex and is accompanied by many Check for updatescellular changes2. Furthermore, the infuence that aged cells have on neighbouring cells and how this contributes to tissue decline is unknown. More generally, the tools to systematically address this question in ageing tissues have not yet been developed. Here we generate a spatially resolved single-cell transcriptomics brain atlas of 4.2 million cells from 20 distinct ages across the adult lifespan and across two rejuvenating interventions—exercise and partial reprogramming. We build spatial ageing clocks, machine learning models trained on this spatial transcriptomics atlas, to identify spatial and cell-type-specifc transcriptomic fngerprints of ageing, rejuvenation and disease, including for rare cell types. Using spatial ageing clocks and deep learning, we fnd that T cells, which increasingly infltrate the brain with age, have a marked pro-ageing proximity efect on neighbouring cells. Surprisingly, neural stem cells have a strong pro-rejuvenating proximity efect on neighbouring cells. We also identify potential mediators of the pro-ageing efect of T cells and the pro-rejuvenating efect of neural stem cells on their neighbours. These results suggest that rare cell types can have a potent infuence on their neighbours and could be targeted to counter tissue ageing. Spatial ageing clocks represent a useful tool for studying cell–cell interactions in spatial contexts and should allow scalable assessment of the efcacy of interventions for ageing and disease.