AI detects signs of aging in blood stem cells from nuclear images
· Medical Xpressby IDIBELL-Bellvitge Biomedical Research Institute
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Aging progressively affects how our bodies function. Among other effects, it reduces the ability of the hematopoietic system—the organs and tissues responsible for producing blood cells—to maintain adequate blood cell production. Understanding and measuring this process is especially important for studying how blood stem cells age and identifying ways to preserve or restore their function.
A study led by Dr. Maria Carolina Florian, a researcher in the Regenerative Medicine program at the Bellvitge Biomedical Research Institute (IDIBELL) and an ICREA research professor, and Dr. Paula Petrone, a researcher at the Barcelona Supercomputing Center—Centro Nacional de Supercomputación (BSC-CNS) and the Barcelona Institute for Global Health (ISGlobal), a center supported by the "la Caixa" Foundation, presents ChromAgeNet. The artificial intelligence-based tool identifies aging-associated patterns in microscopy images of hematopoietic stem cells by analyzing the three-dimensional organization of chromatin.
Chromatin is the material in the cell nucleus, made up mainly of DNA and proteins, that packages DNA and regulates which genes are active, helping determine cell identity and function. The work has been a central part of the doctoral thesis of Pablo Iañez, a researcher at ISGlobal, and brings together expertise in stem cell biology, aging, image analysis and artificial intelligence. The results are published in Aging Cell.
To develop the model, the researchers analyzed three-dimensional images of mouse hematopoietic stem cell nuclei stained with DAPI, a simple, widely used technique for visualizing DNA. Using a convolutional neural network—a type of artificial intelligence model designed to analyze images—ChromAgeNet learned to distinguish young cells from aged cells.
Based on the appearance of the nucleus, the model had a 77% probability of correctly classifying cells as young or aged, outperforming a machine learning model based on chromatin features the researchers had previously defined.
Detecting what the eye cannot see
Differences associated with aging are not necessarily visible to the naked eye in microscopic images. Stem cell aging is a heterogeneous process, and changes in the architecture of the nucleus can be subtle. ChromAgeNet, however, can identify combinations of spatial chromatin features that contain information about a cell's aging state.
The researchers also analyzed the model's performance to determine which image features were most helpful in distinguishing young cells from aged cells. They identified chromatin entropy, heterochromatin at the periphery of the nucleus and certain chromatin condensates as features predictive of an age-associated state.
Knowing which elements of DNA organization the model uses to make its predictions goes beyond classifying cells as young or aged. It provides information about the nuclear architecture associated with aging. This approach can complement other age biomarkers, such as the so-called epigenetic clocks, which estimate biological age based on certain chemical changes in DNA, including those related to methylation.
A new way to look for rejuvenation strategies
The team also explored the potential of ChromAgeNet as a tool for screening treatments that could modify age-associated characteristics. As a proof of concept, the researchers applied the model to aged hematopoietic stem cells treated with different epigenetic drugs to assess whether the treatments produced changes in chromatin organization consistent with a younger state.
The results do not demonstrate that the treatments functionally rejuvenated the cells. They do, however, show the potential of ChromAgeNet to detect age-associated changes in response to interventions and help researchers explore possible rejuvenation strategies.
DAPI is a low-cost stain that is easy to incorporate into microscopy protocols. Its use, along with the model's small number of parameters, could make ChromAgeNet suitable for high-throughput microscopy workflows, which allow researchers to analyze large numbers of samples. In the future, this could help researchers study many compounds and accelerate the identification of candidates for cell rejuvenation strategies.
The researchers have also made a dataset of three-dimensional images of hematopoietic stem cells, along with ChromAgeNet, available to the scientific community. This resource could support the development and validation of new computational tools to study the aging of these cells, an area with few publicly available imaging datasets.
Overall, the study demonstrates that the three-dimensional organization of DNA contains quantifiable information about the aging of blood stem cells and that artificial intelligence can help extract it from microscopic images. ChromAgeNet offers a new tool for studying cellular aging and exploring ways to preserve or restore hematopoietic stem cell function more quickly and at a larger scale.
Publication details
Deep learning predicts hematopoietic stem cell aging from 3D chromatin images, Aging Cell (2026).
Journal information: Aging Cell
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Laboratory medicineHealthy aging Provided by IDIBELL-Bellvitge Biomedical Research Institute Who's behind this story?
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