In 24 out of 30 RNA-seq datasets, Pasta effectively distinguished between senescent, proliferative, and quiescent cells, demonstrating high accuracy in tracking cell aging.

The tool showed that age scores from cancer patient samples correlated more strongly with tumor histological grades than chronological age, indicating its clinical relevance.

Pasta was trained on over 17,000 healthy profiles from various RNA sequencing studies, outperforming previous models in predicting age-related gene expressions.

Aging-prone cell lines exhibited traits related to oxidative stress, while rejuvenation-prone lines showed over-expression of chromatin-remodeling proteins, providing insights for personalized therapies.

Two selected compounds, pralatrexate and piperlongumine, were experimentally confirmed to induce cellular aging and rejuvenation effects, respectively.

Researchers have developed an open-source transcriptomic aging clock called Pasta, aimed at enhancing regenerative medicine and longevity therapies.

Aging and rejuvenation propensity scores were computed to understand cellular responses to aging cues, revealing critical mutations in TP53 as influential in aging outcomes.

Pasta is anchored on p53-mediated DNA-damage responses, linking it to cellular senescence, stemness, and cancer biology.

The model utilizes a ridge-regularized classifier to analyze gene responses across different platforms without the need for re-alignment or batch correction.

The researchers screened approximately 3 million profiles to identify 259 aging and 59 rejuvenating compounds, with many aging compounds being FDA-approved chemotherapeutics.