Cellular senescence is a hallmark of aging and a promising target for therapeutic approaches. The identification of senescent cells requires multiple biomarkers and complex experimental procedures, resulting in increased variability and reduced sensitivity. Here, we propose a simple and broadly applicable imaging flow cytometry (IFC) method. This method is based on measuring autofluorescence and morphological parameters and on applying recent artificial intelligence (AI) and machine learning (ML) tools. We show that the results of this method are superior to those obtained measuring the classical senescence marker, senescence-associated beta-galactosidase (SA-β-Gal). We provide evidence that this method has the potential for diagnostic or prognostic applications as it was able to detect senescence in cardiac pericytes isolated from the hearts of patients affected by end-stage heart failure. We additionally demonstrate that it can be used to quantify senescence "in vivo" and can be used to evaluate the effects of senolytic compounds. We conclude that this method can be used as a simple and fast senescence assay independently of the origin of the cells and the procedure to induce senescence.
Keywords: artificial intelligence and machine learning; cellular senescence; imaging flow cytometry; replicative senescence; senolytics.