measuring-single-cell-aging-with-an-imaging-based-biomarker-chromatin

Measuring Single-Cell Aging with an Imaging-based Biomarker of Chromatin and Epigenetic Aging

To advance personalized healthcare and assess potential rejuvenation strategies, biomarkers reflecting biological age are crucial. We have introduced ...

The University of Western Australia

Author Correction: ImAge quantitates aging and rejuvenation.

For efficient, cost-effective and personalized healthcare, biomarkers that capture aspects of functional, biological aging, thus predicting disease risk and lifespan more accurately and reliably than chronological age, are essential. We developed an imaging-based chromatin and epigenetic age (ImAge) that captures intrinsic age-related trajectories of the spatial organization of chromatin and epigenetic marks in single nuclei, in mice. We show that such trajectories readily emerge as principal changes in each individual dataset without regression on chronological age, and that ImAge can be computed using several epigenetic marks and DNA labeling. We find that interventions known to affect biological aging induce corresponding effects on ImAge, including increased ImAge upon chemotherapy treatment and decreased ImAge upon caloric restriction and partial reprogramming by transient OSKM expression in liver and skeletal muscle. Further, ImAge readouts from chronologically identical mice inversely correlated with their locomotor activity, suggesting that ImAge may capture elements of biological and functional age. In sum, we developed ImAge, an imaging-based biomarker of aging with single-cell resolution rooted in the analysis of spatial organization of epigenetic marks.

ImAge quantitates aging and rejuvenation.

ImAge: quantifying epigenetic ageing with single-cell images.

Prediction of severe disease (SVD) in patients with coronavirus disease (COVID-19) pneumonia at an early stage could allow for more appropriate triage and improve patient prognosis. Moreover, the visualization of the topological properties of COVID-19 pneumonia could help clinical physicians describe the reasons for their decisions. We aimed to construct predictive models of SVD in patients with COVID-19 pneumonia at an early stage on computed tomography (CT) images using SVD-specific features that can be visualized on accumulated Betti number (BN) maps. BN maps (b0 and b1 maps) were generated by calculating the BNs within a shifting kernel in a manner similar to a convolution. Accumulated BN maps were constructed by summing BN maps (b0 and b1 maps) derived from a range of multiple-threshold values. Topological features were computed as intrinsic topological properties of COVID-19 pneumonia from the accumulated BN maps. Predictive models of SVD were constructed with two feature selection methods and three machine learning models using nested fivefold cross-validation. The proposed model achieved an area under the receiver-operating characteristic curve of 0.854 and a sensitivity of 0.908 in a test fold. These results suggested that topological image features could characterize COVID-19 pneumonia at an early stage as SVD.

Article	Year
Author Correction: ImAge quantitates aging and rejuvenation. Nature aging\| PubMed ID: 40764433	2025
ImAge quantitates aging and rejuvenation. Nature aging\| PubMed ID: 39210148	2024
ImAge: quantifying epigenetic ageing with single-cell images. Nature reviews. Cancer\| PubMed ID: 40082708	2025
Predictive models of severe disease in patients with COVID-19 pneumonia at an early stage on CT images using topological properties. Radiological physics and technology\| PubMed ID: 40293683	2025

Kenta Ninomiya

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