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
Subject 1313 Molecular Medicine
3002 Drug Discovery
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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