Send to

Choose Destination
Assay Drug Dev Technol. 2016 Sep;14(7):416-28. doi: 10.1089/adt.2016.739.

Combining High-Content Imaging and Phenotypic Classification Analysis of Senescence-Associated Beta-Galactosidase Staining to Identify Regulators of Oncogene-Induced Senescence.

Author information

1 Division of Cancer Research, Peter MacCallum Cancer Centre , Melbourne, Australia .
2 Institute for Molecular Medicine Finland, University of Helsinki , Helsinki, Finland .
3 John Curtin School of Medical Research, Australian National University , Canberra, Australia .
4 Department of Biochemistry and Molecular Biology, University of Melbourne , Melbourne, Australia .
5 School of Biomedical Sciences, University of Queensland , Brisbane, Queensland, Australia .
6 Sir Peter MacCallum Department of Oncology, University of Melbourne , Melbourne, Australia .
7 Department of Biochemistry and Molecular Biology, Monash University , Clayton, Australia .
8 Victorian Centre for Functional Genomics, Peter MacCallum Cancer Centre , Melbourne, Australia .
9 Synthetic and Systems Biology Unit, Hungarian Academy of Sciences , Szeged, Hungary .


Hyperactivation of the PI3K/AKT/mTORC1 signaling pathway is a hallmark of the majority of sporadic human cancers. Paradoxically, chronic activation of this pathway in nontransformed cells promotes senescence, which acts as a significant barrier to malignant progression. Understanding how this oncogene-induced senescence is maintained in nontransformed cells and conversely how it is subverted in cancer cells will provide insight into cancer development and potentially identify novel therapeutic targets. High-throughput screening provides a powerful platform for target discovery. Here, we describe an approach to use RNAi transfection of a pre-established AKT-induced senescent cell population and subsequent high-content imaging to screen for senescence regulators. We have incorporated multiparametric readouts, including cell number, proliferation, and senescence-associated beta-galactosidase (SA-βGal) staining. Using machine learning and automated image analysis, we also describe methods to classify distinct phenotypes of cells with SA-βGal staining. These methods can be readily adaptable to high-throughput functional screens interrogating the mechanisms that maintain and prevent senescence in various contexts.

[Indexed for MEDLINE]

Supplemental Content

Full text links

Icon for Atypon
Loading ...
Support Center