Detection of senescence using machine learning algorithms based on nuclear features.

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  • Additional Information
    • Source:
      Publisher: Nature Pub. Group Country of Publication: England NLM ID: 101528555 Publication Model: Electronic Cited Medium: Internet ISSN: 2041-1723 (Electronic) Linking ISSN: 20411723 NLM ISO Abbreviation: Nat Commun Subsets: MEDLINE
    • Publication Information:
      Original Publication: [London] : Nature Pub. Group
    • Subject Terms:
      Aging*/physiology ; Cellular Senescence*/physiology; Animals ; Mice ; Humans ; Fibrosis
    • Abstract:
      Cellular senescence is a stress response with broad pathophysiological implications. Senotherapies can induce senescence to treat cancer or eliminate senescent cells to ameliorate ageing and age-related pathologies. However, the success of senotherapies is limited by the lack of reliable ways to identify senescence. Here, we use nuclear morphology features of senescent cells to devise machine-learning classifiers that accurately predict senescence induced by diverse stressors in different cell types and tissues. As a proof-of-principle, we use these senescence classifiers to characterise senolytics and to screen for drugs that selectively induce senescence in cancer cells but not normal cells. Moreover, a tissue senescence score served to assess the efficacy of senolytic drugs and identified senescence in mouse models of liver cancer initiation, ageing, and fibrosis, and in patients with fatty liver disease. Thus, senescence classifiers can help to detect pathophysiological senescence and to discover and validate potential senotherapies.
      (© 2024. The Author(s).)
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    • Grant Information:
      United Kingdom WT_ Wellcome Trust; 28647 United Kingdom CRUK_ Cancer Research UK; MC_U120085810 United Kingdom MRC_ Medical Research Council; MC_U120097114 United Kingdom MRC_ Medical Research Council
    • Publication Date:
      Date Created: 20240203 Date Completed: 20240205 Latest Revision: 20240224
    • Publication Date:
      20240224
    • Accession Number:
      PMC10838307
    • Accession Number:
      10.1038/s41467-024-45421-w
    • Accession Number:
      38310113