Longevity & AgingDeepScence AI Tool Maps Senescent Cells with Unprecedented Accuracy
Researchers at Duke University developed DeepScence, an unsupervised deep-learning tool built on an autoencoder architecture, to detect senescent cells in single-cell RNA sequencing and spatial transcriptomics data. The team first created CoreScence, a curated gene set of 39 genes consistently reported across at least five published senescence gene databases, addressing the massive disagreement among existing gene sets. DeepScence uses CoreScence as input and learns complex, nonlinear gene expression patterns to score cells on a senescence continuum. Tested across in vitro and in vivo datasets from multiple platforms, DeepScence substantially outperformed existing scoring methods and the supervised SVM-based SenCID tool, achieving AUROCs exceeding 0.9 across all datasets and generalizing across species, tissues, and senescence induction contexts.
