A combination of high-resolution imaging and machine learning, also known as artificial intelligence (AI), can track damaged cells from damage, aging, or disease.
As these senescent cells are known to play an important role in wound repair and aging-related diseases such as cancer and heart disease, tracking progress can help you better understand the ability of tissues to regenerate over time and how they lose the fuel for the disease, researchers say. This tool can also provide insight into treatments to reverse the damage.
The study, led by researchers in the Orthopedic Surgery Division at NYU Langone Health, involved training computer systems to analyze damaged animal cells by increasing concentrations over time to replicate human aging. Cells that are continuously facing environmental or biological stress are known to senesce. This means that it will stop breeding and begin releasing the telltale molecule, indicating that it has been injured.
Published in the journal Natural Communication Online on July 7th, researchers' AI analysis revealed several measurable features connected to the cell's control center (its nucleus). This is when closely tracked by the degree of aging in tissue or group of cells. This included signs that the nucleus had expanded, had a more dense center or foci, had less circulation and became more irregular. The genetic material was dyed lighter than usual with standard chemical dyes.
Further testing confirms that cells with these properties are indeed aging, showing signs that they have stopped breeding, damaged DNA and packed closely with enzyme-storage lysosomes. Cells also demonstrated response to existing senescent drugs.
From their analysis, the researchers created what they call the Nuclear Morphology Pipeline (NMP), which uses the altered physical properties of the nucleus to generate a single senescence score to describe the range of cells. For example, a group of fully senescent cells can be compared to clusters of healthy cells on a scale of minus 20 to plus 20.
To validate the NMP score, the researchers showed that healthy mouse cells can be accurately distinguished from young to older mice over three months to two years. Older cell clusters had significantly lower NMP scores than younger cell clusters.
The researchers also tested NMP tools on five types of cells from mice of different ages of muscle tissue that were damaged when undergoing repair. NMPs were found to closely track changes in senescence and levels of non-trophic mesenchymal stem cells, muscle stem cells, endothelial cells, and immune cells in young, adult, and geriatric mice. For example, the use of NMP could be confirmed to be absent from senile muscle stem cells in uninjured control mice, but gradually loses as tissue regenerates and is present in large quantities in injured mice (when starting repair).
Final examinations showed that NMPs could better distinguish between 10 times more general health and aged chondrocytes in elderly osteoarthritis than in young healthy mice. Osteoarthritis is known to gradually worsen with age.
Our research demonstrates that specific nuclear morphometry serves as a reliable tool for identifying and tracking senescent cells. We believe this is key to future research and understanding of tissue regeneration, aging and progressive disease. ”
Michael N. Wosczyna, Senior Research Investigator
Dr. Wosczyna is an assistant professor in the Department of Orthopaedics at the NYU Grossman School of Medicine.
Dr. Wosczyna says that in his team's research, the wide range of applications of NMP for the study of senescent cells of all ages and different tissue types, and various diseases.
He says the team plans to plan further experiments to investigate the use of NMP in human tissues, combining NMP with other biomarker tools with examining its various roles in aging, wound repair, aging and disease.
Researchers say the ultimate goal of the NMP, which NYU filed its patent application, is to use it to develop treatments that prevent or reverse the adverse effects of aging.
“Our testing platform provides a more simple and easy way to provide senescent cells than before we studied them, and provides a rigorous method to test the efficacy of therapeutics targeting these cells in a variety of tissues and pathologies,” says Dr. Wosczyna, who plans to make NMPs freely available to other researchers.
“Existing methods of identifying senescent cells are difficult to use and are less reliable than nuclear morphometric pipelines or NMPs. This depends on staining commonly used by the nucleus.” Mapkar is a doctoral candidate for the NYU Tandon School of Engineering.
Funding for this study was provided by National Institutes of Health Grant R01AG053438 and the Orthopaedic Surgery Office, NYU Langone.
In addition to Dr. Wosczyna and Dr. Mapkar, Nyu Langone researchers involved in this study are co-lead investigators Sarah Bliss and Edgar Perez Carbajal and researchers collaborators Sean Murray, Zhiru Li, Anna Wilson, Vikrant Piprode, Youjin Lee, Thorsten Kirsch, Katerina Petroff, and Fengyuani.
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Journal Reference:
Mapkar, SA, et al. (2025). Nuclear morphometry, coupled with machine learning, identifies the dynamic state of aging across age. Natural Communication. doi.org/10.1038/S41467-025-60975-Z.
