A Fully-Automated Senescence Test (FAST) for the high-throughput quantification of senescence-associated markers
Preprint
Abstract
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; 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 C 12 FDG 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.Keywords
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- Published version: GeroScience, 46 (5), 4185.
This publication has 42 references indexed in Scilit:
- Data-driven color augmentation for H&E stained images in computational pathologyJournal of Pathology Informatics, 2023
- Environmental pollutants exposure: A potential contributor for aging and age-related diseasesEnvironmental Toxicology and Pharmacology, 2021
- Cellular Senescence: Defining a Path ForwardCell, 2019
- Suppressors of Superoxide-H 2 O 2 Production at Site I Q of Mitochondrial Complex I Protect against Stem Cell Hyperplasia and Ischemia-Reperfusion InjuryCell Metabolism, 2016
- Mitochondrial Dysfunction Induces Senescence with a Distinct Secretory PhenotypeCell Metabolism, 2016
- Z-Factor2015
- An Essential Role for Senescent Cells in Optimal Wound Healing through Secretion of PDGF-AADevelopmental Cell, 2014
- Senescence Is a Developmental Mechanism that Contributes to Embryonic Growth and PatterningCell, 2013
- Programmed Cell Senescence during Mammalian Embryonic DevelopmentCell, 2013
- Doxorubicin induces senescence and impairs function of human cardiac progenitor cellsBasic Research in Cardiology, 2013