imAgeScore
preclinicalQuantify cellular age in vitro in a scalable and biologically meaningful way for discovery of pharmacological interventions that modulate aging.
Machine-learning model trained on high-content Cell Painting features to predict phenotypic age of primary human dermal fibroblasts from microscope-image morphology.
2026 bioRxiv preprint reported imAgeScore and its use in an automated high-throughput screening pipeline.
imAgeScore correlated with chronological and DNA methylation-based age estimates, detected age acceleration during serial propagation, detected age reduction after partial reprogramming, classified damaging versus rejuvenating cellular states, identified candidate age-modulating compounds, observed inter-individual response variability, found additive effects in selected combinations, and functionally validated leading candidates in a scratch wound assay.