Pasta, an age-shift transcriptomic clock, maps the chemical and genetic determinants of aging and rejuvenation

With the growing burden of age-related diseases, understanding and modulating the aging process has become a priority. Transcriptomic aging clocks (TACs) can track biological age but remain limited by platform dependence, tissue specificity, or restricted accessibility. To address this, we developed Pasta, a robust and broadly applicable human TAC built using a novel age-shift learning framework. Pasta accurately predicted relative age across diverse tissues, data types, including bulk and single-cell RNA-Seq as well as microarray data, and species. Its predictions aligned with senescent and stem-like cellular states, with model coefficients enriched for p53 and DNA damage response pathways. Pastas age scores correlated with tumor grade and patient survival in several cancer types, indicating potential clinical relevance. Applied to the Connectivity Map L1000 dataset, Pasta identified both established and previously unrecognized age-modulatory compounds and genetic perturbations, highlighting mitochondrial translation and mRNA splicing as key determinants of cellular propensity for aging and rejuvenation, respectively. Experimental validation confirmed pralatrexate as a potent senescence inducer and piperlongumine as a rejuvenating agent. Together, these findings establish Pasta as a versatile and accessible tool for aging research and therapeutic discovery.