Combining high-content imaging and phenotypic classification analysis of senescence-associated beta-galactosidase staining to identify regulators of oncogene-induced senescence

Chan, Keefe T., Paavolainen, Lassi, Hannan, Katherine M., George, Amee J., Hannan, Ross D., Simpson, Kaylene J., Horvath, Peter and Pearson, Richard B. (2016) Combining high-content imaging and phenotypic classification analysis of senescence-associated beta-galactosidase staining to identify regulators of oncogene-induced senescence. Assay and Drug Development Technologies, 14 7: 416-428. doi:10.1089/adt.2016.739


Author Chan, Keefe T.
Paavolainen, Lassi
Hannan, Katherine M.
George, Amee J.
Hannan, Ross D.
Simpson, Kaylene J.
Horvath, Peter
Pearson, Richard B.
Title Combining high-content imaging and phenotypic classification analysis of senescence-associated beta-galactosidase staining to identify regulators of oncogene-induced senescence
Journal name Assay and Drug Development Technologies   Check publisher's open access policy
ISSN 1557-8127
1540-658X
Publication date 2016-09-01
Sub-type Article (original research)
DOI 10.1089/adt.2016.739
Open Access Status Not yet assessed
Volume 14
Issue 7
Start page 416
End page 428
Total pages 13
Place of publication New Rochelle, NY, United States
Publisher Mary Ann Liebert
Language eng
Abstract 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.
Q-Index Code C1
Q-Index Status Provisional Code
Institutional Status UQ

Document type: Journal Article
Sub-type: Article (original research)
Collections: HERDC Pre-Audit
School of Biomedical Sciences Publications
 
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