Cellular senescence is a molecular process that is activated in response to a large variety of distinct stress signals. Mechanistically, cellular senescence is characterized by an arrest in cell cycle accompanied by phenotypic adaptations and physiological alterations including changes in the secretory profile of senescent cells termed the senescence-associated secretory phenotype (SASP). Here we describe a detailed, automation- compatible method for the detection of senescence-associated beta galactosidase (SA-β-gal) activity as a hallmark of cellular senescence using a conventional fluorescent microscope equipped with a transmitted light module. Moreover, we outline a protocol for the automated analysis of cellular senescence using convolutional neural networks (CNNs) and mathematical morphology. In sum, we provide a toolset for the high throughput assessment of cellular senescence based on light microscopy and automated image analysis.
Keywords: Aging; Cancer; Deep neural networks; Immunity; SA-βgal; Senolytics.
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