The global increase in life expectancy over the past century has led to a rise in aging populations. However, lifespan extension does not necessarily equate to prolonged healthspan, and elderly people have a higher prevalence of chronic diseases, placing an increasing burden on healthcare systems worldwide and highlighting the urgent need for research on aging and age associated-diseases. Despite advances in this field, the underlying complexities of this process are far from fully understood.
Epiproteomic changes during aging
Histone post-translational modifications (hPTMs) act as dynamic epigenetic switches, modulating chromatin architecture, regulating gene expression, and maintaining genome integrity. During aging, patterns of hPTMs progressively shift, disrupting transcriptional fidelity, accelerating epigenetic clock signatures, and contributing to functional decline. Experimental approaches aimed at re establishing youthful histone modification profiles hold significant promise for slowing or potentially reversing aspects of aging.
AI-based discovery of anti-aging interventions
Aging clocks have proven useful to find epigenomic or transcriptomic determinants of aging, with biomarker and/or causal potential. Additionally, their readouts have been successfully used to test the effect of anti-aging interventions. However, these machine learning models typically rely on bulk data from whole tissues, obscuring cell-specific contributions. Single-cell technologies promise a deeper understanding by capturing data at cell resolution, enabling precise evaluation of individual cell type and gene contributions to aging. Nonetheless, existing single-cell aging clocks often collapse data into pseudobulk samples, sacrificing the granularity provided by single-cell resolution and restricting applicability due to cell-type specificity.