Ageing is a degenerative process that leads to tissue dysfunction and death. A proposed cause of ageing is the accumulation of epigenetic noise that disrupts gene expression patterns, leading to decreases in tissue function and regenerative capacity 1 – 3 . Changes to DNA methylation patterns over time form the basis of ageing clocks 4 , but whether older individuals retain the information needed to restore these patterns—and, if so, whether this could improve tissue function—is not known. Over time, the central nervous system (CNS) loses function and regenerative capacity 5 – 7 . Using the eye as a model CNS tissue, here we show that ectopic expression of Oct4 (also known as Pou5f1), Sox2 and Klf4 genes (OSK) in mouse retinal ganglion cells restores youthful DNA methylation patterns and transcriptomes, promotes axon regeneration after injury, and reverses vision loss in a mouse model of glaucoma and in aged mice. The beneficial effects of OSK-induced reprogramming in axon regeneration and vision require the DNA demethylases TET1 and TET2. These data indicate that mammalian tissues retain a record of youthful epigenetic information—encoded in part by DNA methylation—that can be accessed to improve tissue function and promote regeneration in vivo.

Alterations in metabolism, sleep patterns, body composition and hormone status are all key features of aging. While the hypothalamus is a well-conserved brain region that controls these homeostatic and survival-related behaviors, little is known about the intrinsic features of hypothalamic aging. Here, we perform single-nuclei RNA sequencing of 40,064 hypothalamic nuclei from young and aged female mice. We identify cell type-specific signatures of aging in neuronal subtypes as well as astrocytes and microglia. We uncover changes in cell types critical for metabolic regulation and body composition and in an area of the hypothalamus linked to cognition. Our analysis also reveals an unexpected female-specific feature of hypothalamic aging: the master regulator of X inactivation, Xist, is elevated with age, particularly in hypothalamic neurons. Moreover, using machine learning, we show that levels of X chromosome genes and Xist itself, can accurately predict cellular age. This study identifies critical cell-specific changes of the aging hypothalamus in mammals and uncovers a potential marker of neuronal aging in females.

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