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

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 oncogeneinduced 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-bGal) staining. Using machine learning and automated image analysis, we also describe methods to classify distinct phenotypes of cells with SA-bGal staining. These methods can be readily adaptable to high-throughput functional screens interrogating the mechanisms that maintain and prevent senescence in various contexts.