A combination of high -resolution and mechanical learning imaging, also known as artificial intelligence (AI), can monitor cells that have been damaged by injury, aging or illness and that they are no longer developed and reproduced normally, shows a new study.
These aging cells are known to play a key role in repairing wounds and aging -related diseases, such as cancer and heart disease, so as to monitor their progress, the researchers say, could lead to a better understanding of how they are in their Diseases. The tool could also provide information on reversal treatments.
Led by researchers in the Department of Orthopedic Surgery of Nyu Langone Health, the study included the preparation of a computer system to analyze animal cells that have been damaged by increasing the concentrations of chemicals over time to reproduce human beings. Cells that constantly treat environmental or biological stress are known to be known that SENESCE, which means that they stop reproducing and start releasing underline molecules showing that they have suffered injury.
Published in the magazine Nature communications On July 7, the researchers’ AI analysis revealed several measurable features associated with the cell control center (its core) which, when taken together, is closely monitored with the degree of aging in the tissue or group of cells. This included signs that the core had expanded, had denser centers or fireplaces and had become less circular and more irregular. Its genetic material is also lighter than normal with standard chemical dyes.
Further tests confirmed that the cells with these characteristics were indeed aging, showing signs that they had stopped reproducing, had damaged DNA and had densely packaged lysosomes-storing enzymes. The cells also shown a response to existing senolytic drugs.
From their analysis, the researchers created what they call a nuclear morphometric pipeline (NMP) that uses the changing natural characteristics of the core to produce a unique senator score to describe a series of cells. For example, groups of fully aging cells could be compared to a cluster of healthy cells on a minus of 20 to plus 20.
To validate the NMP rating, researchers showed that it could accurately distinguish between healthy and weak mice from younger mice, aged 3 months of up to 2 years. The earlier cell clusters had significantly lower NMP scores than the newest cell clusters.
The researchers also examined the NMP tool in five types of cells in mice of different ages with injured muscle tissue as it underwent repair. The NMP was found to monitor closely with the changing levels of mesenchymal stem cells, muscle stem cells, endothelial cells and immune cells in young, adult and geriatric mice. For example, the use of NMP was able to confirm that the muscle stem cells were absent in control mice that were not injured, but present in large numbers in injured mice immediately after muscle injury (when helping to start repair).
Final tests have shown that NMP could successfully distinguish healthy and aging cartilage cells, which were 10 times more dominant in geriatric mice with osteoarthritis than in younger, healthy mice. Osteoarthritis is known to progressively worsen with age.
Our study shows that this particular nuclear morphometry can serve as a reliable tool for identifying and monitoring aging cells, which we believe is the key to future research and understanding of regeneration, aging and progressive disease. “
Michael N. Wosczyna, PhD, Study Senior Researcher
Dr. Wosczyna is Assistant Professor in the Department of Orthopedic Surgery of the NYU Grossman Medical School.
Dr. Wosczyna reports that his team’s study confirms the widespread application of NMP to study aging cells at all ages and different types of tissues and in different diseases.
He says the team is planning further experiments to examine the use of NMP in human tissues, as well as combine NMP with other biomarkers tools to examine aging and its various roles in repair, aging and diseases.
Researchers say their ultimate goal for NMP, for which NYU has applied for patents, is to use it to develop treatments that prevent or reverse the negative effects of aging on human health.
“Our test platform offers a rigorous method for easier than before studying aging cells and testing the efficacy of therapeutics, such as Senolytics, targeting these cells in different tissues and pathologies,” said Dr. Wosczyna, who plans to make NMP freely available.
“Existing methods for detecting aging cells are difficult to use, making them less reliable by the nuclear morphometric conductor or NMP, which is based on a more commonly used stain for the core,” said study researcher Sahil Mapkar. Mapkar is a PhD candidate at the Nyu Tandon School of Engineering.
The funding for the study is provided by the National Institutes of Health Grant R01AG053438 and the Department of Orthopedic Surgery in Nyu Langone.
Except for Dr. Wosczyna and Mapkar, Nyu Langone researchers participating in this study are Sarah Bliss researchers and Edgar Perez Carbajal and co-founders Sean Murray, Zhiru Li, Anna Wilson, Vikrant Piprode, Youjin Lee, Thorsten Kirsch, Kerina Petrof and Fengyuan Liu.
Source:
Magazine report:
Mapkar, Sa, et al. (2025). Nuclear morphometry combined with mechanical learning determines the dynamic states of aging across age. Nature communications. Doi.org/10.1038/S41467-025-60975-Z.
