Cellular senescence is a major driver of aging and age-related diseases. Quantification of senescent cells remains challenging due to the lack of senescence-specific markers and generalist, unbiased methodology. Here, we describe the Fully-Automated Senescence Test (FAST), an image-based method for the high-throughput, single-cell assessment of senescence in cultured cells. FAST quantifies three of the most widely adopted senescence-associated markers for each cell imaged: senescence-associated β-galactosidase activity (SA-β-Gal) using X-Gal, proliferation arrest via lack of 5-ethynyl-2’-deoxyuridine (EdU) incorporation, and enlarged morphology via increased nuclear area. The presented workflow entails microplate image acquisition, image processing, data analysis, and graphing. Standardization was achieved by (i) quantifying colorimetric SA-β-Gal via optical density; (ii) implementing staining background controls; and (iii) automating image acquisition, image processing, and data analysis. In addition to the automated threshold-based scoring, a multivariate machine learning approach is provided. We show that FAST accurately quantifies senescence burden and is agnostic to cell type and microscope setup. Moreover, it effectively mitigates false-positive senescence marker staining, a common issue arising from culturing conditions. Using FAST, we compared X-Gal with fluorescent C₁₂FDG live-cell SA-β-Gal staining on the single-cell level. We observed only a modest correlation between the two, indicating that those stains are not trivially interchangeable. Finally, we provide proof of concept that our method is suitable for screening compounds that modify senescence burden. This method will be broadly useful to the aging field by enabling rapid, unbiased, and user-friendly quantification of senescence burden in culture, as well as facilitating large-scale experiments that were previously impractical.

细胞衰老是衰老和与年龄相关的疾病的主要驱动因素。由于缺乏衰老特异性标记物和通用、公正的方法，衰老细胞的定量仍然具有挑战性。在这里，我们描述了全自动衰老测试（FAST），这是一种基于图像的方法，用于对培养细胞中的衰老进行高通量、单细胞评估。 FAST 对每个成像细胞量化了三种最广泛采用的衰老相关标记：使用 X-Gal 的衰老相关 β-半乳糖苷酶活性 (SA-β-Gal)、通过缺乏 5-乙炔基-2'-脱氧尿苷而导致的增殖停滞（ EdU）合并，并通过增加核面积扩大形态。所提出的工作流程需要微孔板图像采集、图像处理、数据分析和绘图。标准化是通过 (i) 通过光密度量化比色 SA-β-Gal 来实现的； (ii) 实施染色背景控制； (iii) 自动化图像采集、图像处理和数据分析。除了基于阈值的自动评分之外，还提供了多元机器学习方法。我们证明 FAST 可以准确量化衰老负担，并且与细胞类型和显微镜设置无关。此外，它还能有效减轻假阳性衰老标记染色，这是培养条件引起的常见问题。使用 FAST，我们在单细胞水平上比较了 X-Gal 与荧光 C ₁₂ FDG 活细胞 SA-β-Gal 染色。我们观察到两者之间只有适度的相关性，这表明这些污渍不能轻易互换。最后，我们提供了概念证明，证明我们的方法适合筛选改变衰老负担的化合物。 该方法将广泛用于衰老领域，能够快速、公正且用户友好地量化培养物中的衰老负担，并促进以前不切实际的大规模实验。