Innovative AI Method Measures and Monitors Aging Cells

Recent advancements in high-resolution imaging combined with artificial intelligence (AI) have led to a novel approach for tracking cellular aging and damage. Researchers at NYU Langone Health have developed a machine learning-based tool capable of analyzing changes in cell nuclei, which are indicative of cellular senescence—a state where cells stop dividing and contribute to aging-related diseases such as cancer and heart disease. This AI-assisted technique allows scientists to quantify specific nuclear features, such as size, shape irregularities, and genetic material density, providing a comprehensive senescence score.

The study involved training a computer system using animal cells subjected to chemical stress to simulate aging. The AI identified measurable changes in nuclear morphology that closely correlated with the degree of cell aging. These features include nuclear expansion, denser centers or foci, and irregular shapes, along with lighter-staining genetic material. Validation experiments confirmed that cells with these nuclear characteristics exhibited typical signs of senescence, including halted reproduction and DNA damage.

To facilitate widespread research, the team developed the nuclear morphometric pipeline (NMP), which condenses nuclear features into a single senescence score. This score successfully distinguished between healthy and senescent cells across different age groups and tissue types, from young to aged mice. The NMP also effectively tracked tissue repair processes, identifying senescent cells in injured muscle tissue and distinguishing aged cartilage cells from younger ones, highlighting its potential for studying aging and related diseases.

The researchers plan to expand their work to include human tissues and explore the integration of the NMP with other biomarker tools. Their ultimate goal is to leverage this technology to develop interventions that can prevent or reverse cellular aging, accelerating research on anti-aging therapies. The approach, which relies on commonly used nuclear staining, offers a reliable and accessible alternative to previous, more complex methods for detecting senescent cells. This breakthrough promises to enhance understanding of tissue regeneration, aging processes, and disease progression, paving the way for innovative treatments.

Source: https://medicalxpress.com/news/2025-07-ai-technique-track-aging-cells.html