Nuclear morphology (size and shape) is a quantifiable marker of senescence. Deep learning algorithms, detecting and measuring nuclear morphology, consistently classify senescent and proliferating cells across various cell types and species, holding clinical potential. Experimental manipulation of nuclear size can trigger a senescence (-like) state, confirming the predictive power of nuclear morphology. A causal link between nuclear morphology and senescence warrants further research.
Enlarged or irregularly shaped nuclei are frequently observed in tissue cells undergoing senescence. However, it remained unclear whether this peculiar morphology is a cause or a consequence of senescence and how informative it is in distinguishing between proliferative and senescent cells. Recent research reveals that nuclear morphology can act as a predictive biomarker of senescence, suggesting an active role for the nucleus in driving senescence phenotypes. By employing deep learning algorithms to analyze nuclear morphology, accurate classification of cells as proliferative or senescent is achievable across various cell types and species both in vitro and in vivo. This quantitative imaging-based approach can be employed to establish links between senescence burden and clinical data, aiding in the understanding of age-related diseases, as well as assisting in disease prognosis and treatment response.
<p>The post Form follows function: Nuclear morphology as a quantifiable predictor of cellular senescence first appeared on Health ShoutOut.</p>
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