Detection of senescence using machine learning algorithms based on nuclear features

Imanol Duran, Joaquim Pombo, Bin Sun, Suchira Gallage, Hiromi Kudo, Domhnall McHugh, Laura Bousset, Jose Efren Barragan Avila, Roberta Forlano, Pinelopi Manousou, Mathias Heikenwalder, Dominic J. Withers, Santiago Vernia, Robert D. Goldin and Jesús Gil ()
Additional contact information
Imanol Duran: Du Cane Road
Joaquim Pombo: Du Cane Road
Bin Sun: Du Cane Road
Suchira Gallage: Du Cane Road
Hiromi Kudo: Imperial College London
Domhnall McHugh: Du Cane Road
Laura Bousset: Du Cane Road
Jose Efren Barragan Avila: German Cancer Research Center (DKFZ), Heidelberg
Roberta Forlano: Imperial College London
Pinelopi Manousou: Imperial College London
Mathias Heikenwalder: German Cancer Research Center (DKFZ), Heidelberg
Dominic J. Withers: Du Cane Road
Santiago Vernia: Du Cane Road
Robert D. Goldin: Imperial College London
Jesús Gil: Du Cane Road

Nature Communications, 2024, vol. 15, issue 1, 1-20

Abstract: 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.

Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:nat:natcom:v:15:y:2024:i:1:d:10.1038_s41467-024-45421-w

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DOI: 10.1038/s41467-024-45421-w

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