Single-cell and spatial detection of senescent cells using DeepScence
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.
Authors
- Liangcai Gu (ORCID: https://orcid.org/0000-0002-8232-6025)
- Runze Dong (ORCID: https://orcid.org/0000-0002-7294-3070)
- Andrew B. Nixon (ORCID: https://orcid.org/0000-0003-3971-2964)
- Jichun Xie (ORCID: https://orcid.org/0000-0001-5905-6728)
- Carolyn Glass (ORCID: https://orcid.org/0000-0002-8850-9906)
- Bing Ji (ORCID: https://orcid.org/0000-0003-1326-4120)
- Zhicheng Ji (ORCID: https://orcid.org/0000-0002-4837-7240)
- Yilong Qu
- Xiao‐Fan Wang (ORCID: https://orcid.org/0000-0002-2534-0410)
- Cliburn Chan
Institutions
- Duke University (US)
- University of Washington (US)
Publication Details
- Journal
- Cell Genomics
- Published
- 2025-10-07
- DOI
- https://doi.org/10.1016/j.xgen.2025.101035
- Citations
- 1
- Primary Topic
- Single-cell and spatial transcriptomics
- Type
- article
- Field-Weighted Citation Impact
- 0.81