NYU Langone researchers developed a machine learning method analyzing nuclear morphology to identify and monitor senescent cells at single-cell resolution across diverse tissues and aging stages. Published in Nature Communications, the study demonstrates how nuclear morphometrics can yield quantitative senescence scores, enabling refined differentiation of cell states involved in aging and disease processes. This approach offers a powerful tool for characterizing dynamic senescence profiles and understanding tissue regeneration and chronic inflammation mechanisms.