An Explainable AI Framework for Identifying Universal Aging Signatures in Cell Embeddings

Abstract

Aging is a complex biological process marked by progressive physiological decline and increased disease vulnerability. Single-cell RNA sequencing offers unprecedented resolution for studying aging, yet isolating aging-related signatures remains challenging because gene expression is primarily shaped by other factors such as cell type, tissue, and sex. We present ACE (Aging Cell Embeddings), an explainable deep generative framework that disentangles aging-related gene expression changes from background biological variation. ACE employs two latent representations: one capturing aging-related signatures and another representing non-aging-related variation in the data. Through explainable AI, ACE identifies key genes and pathways associated with aging amid dominant non-aging-related variations. Applied to large-scale mouse, fly, and human datasets, ACE uncovers aging signatures both within specific tissue-cell-type contexts and across all tissues and cell types, enabling accurate prediction of biological age. Moreover, ACE identifies aging genes conserved across species, highlighting its ability to reveal shared biological mechanisms of aging. Experimental RNAi knockdowns in C. elegans validate ACE\'s findings, confirming its ability to prioritize novel aging genes affecting lifespan. ACE reveals key pathways involved in proteostasis, immune regulation, and extracellular matrix remodeling, and identifies Uba52 through the cross-species model as an important aging gene, whose knockdown in C. elegans significantly shortens lifespan. By providing interpretable and generalizable aging embeddings, ACE establishes a foundation for cross-species single-cell aging studies and translational geroscience.