five

Form follows function: Nuclear morphology as a quantifiable predictor of cellular senescence

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NIAID Data Ecosystem2026-05-02 收录
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https://www.ncbi.nlm.nih.gov/sra/SRP576053
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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. Overall design: The goal of this RNA sequencing experiment was to test in an unbiased manner whether changes in nuclear shape, size, and abundance—induced by exposing cells to subsaturating concentrations of the microtubule-targeting agent Docetaxel (DTX)—would trigger an adaptive transcriptional program consistent with a senescence-like phenotype. To this end, RNA was isolated and sequenced from HeLa Kyoto cells that were either untreated (Control, Day 0) or treated with 10 nM DTX for 72 hours (Day 3). The experiment was performed in triplicate, with replicate numbers indicated next to each sample name. The main conclusion from this experiment is that the cellular transcriptome undergoes global changes in response to DTX-induced perturbations in nuclear morphogenesis (and potentially chromatin organization). Many of the observed transcriptomic alterations are consistent with the activation of a stress-induced, senescence-like program. The resulting transcriptomic dataset will be of broad interest to researchers studying drug resistance mechanisms, cellular stress responses, senescence and senescence-like states, as well as nuclear architecture and cytoskeletal biology.
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2025-05-23
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