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.

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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
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Single-cell and spatial detection of senescent cells using DeepScence

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1 citations
Cell Genomics
Single-cell and spatial transcriptomics
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Single-cell and spatial detection of senescent cells using DeepScence

Liangcai Gu, Runze Dong, Andrew B. Nixon, Jichun Xie, Carolyn Glass, Bing Ji, Zhicheng Ji, Yilong Qu, Xiao‐Fan Wang, Cliburn Chan
article en
1 citations

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.

Cell GenomicsVol. 5(12)
Duke University (US), University of Washington (US)
Openalex Percentile: Top 23%
Single-cell and spatial transcriptomics
0.81
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