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Updated: Jan 15, 2026

Simultaneous Imaging and Flow-Cytometry-based Detection of Multiple Fluorescent Senescence Markers in Therapy-Induced Senescent Cancer Cells
Published on: July 12, 2022
Single-cell and spatial detection of senescent cells using DeepScence
Yilong Qu1, Beijie Ji2, Runze Dong3
1Department of Biostatistics and Bioinformatics, Duke University School of Medicine, Durham, NC, USA.
Abstract:
Accurately identifying senescent cells is essential for studying their spatial and molecular features. We developed DeepScence, a method based on deep neural networks, to identify senescent cells in single-cell and spatial transcriptomics data. DeepScence is based on CoreScence, a senescence-associated gene set we curated that incorporates information from multiple published gene sets. We demonstrate that DeepScence can accurately identify senescent cells in single-cell gene expression data collected both in vitro and in vivo, as well as in spatial transcriptomics data generated by different platforms, substantially outperforming existing methods.

