TY  - JOUR
AU  - Duran, Imanol
AU  - Pombo, Joaquim
AU  - Sun, Bin
AU  - Gallage, Suchira Upeksha
AU  - Kudo, Hiromi
AU  - McHugh, Domhnall
AU  - Bousset, Laura
AU  - Barragan Avila, Jose Efren
AU  - Forlano, Roberta
AU  - Manousou, Pinelopi
AU  - Heikenwalder, Mathias
AU  - Withers, Dominic J
AU  - Vernia, Santiago
AU  - Goldin, Robert D
AU  - Gil, Jesús
TI  - Detection of senescence using machine learning algorithms based on nuclear features.
JO  - Nature Communications
VL  - 15
IS  - 1
SN  - 2041-1723
CY  - [London]
PB  - Nature Publishing Group UK
M1  - DKFZ-2024-00274
SP  - 1041
PY  - 2024
AB  - 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.
LB  - PUB:(DE-HGF)16
C6  - pmid:38310113
DO  - DOI:10.1038/s41467-024-45421-w
UR  - https://inrepo02.dkfz.de/record/287620
ER  -