bioRxivGenomicsPreprint
Single-cell transcriptomic and epigenomic analysis reveals X-linked sex differences in aging mouse hypothalamus
Doudou YuiD, Ilya Osipov, Lexi-Amber HasselliD, Neta A Shwartz, Kaitlyn H. HajdaroviciD, Harold Marin, Ashley E. WebbiD
Abstract
Sex differences contribute to brain aging, neurodegenerative diseases, and more broadly in determining rates of aging across species. The hypothalamus plays a central role in physiological homeostasis and healthy aging, yet how its cellular and molecular landscape diverges between males and females over the lifespan remains poorly understood. Here, we present a single-nucleus multi-omics analysis of the hypothalamus in young, middle aged, and aged male and female mice. We identified major hypothalamic cell types and characterized their sex- and age-dependent transcriptional and chromatin accessibility profiles. Notably, female-specific changes on the X chromosome (chrX) emerged as a prominent feature of aging, including changes to the X inactivation center and an overall increase in chrX gene expression and accessibility in immune cells and neurons. Pseudotime analysis of immune cells revealed an aging trajectory with sex-specific multi-omic programs, featuring increased inflammation in females compared to males. Delving deeper into the epigenetic signatures associated with these sex differences, we found that H3K27me3 - the repressive histone mark enriched on the inactive X in females - increased in abundance and underwent substantial genome-wide redistribution with age in both sexes, particularly on the inactive chrX in females. Collectively, these findings highlight distinct cell-type-specific aging trajectories in the male and female hypothalamus, identify female aging signatures associated with X-linked epigenetic regulatory programs, and provide a comprehensive resource for understanding the molecular basis of sex differences in brain aging.
- organism: mouse
- assay: scRNA-seq
- assay: ATAC-seq
- research focus: cell type annotation
- research focus: differential expression
- research focus: gene regulatory network inference
- approach: statistical model
- Citations
- 0
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