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Replicative Cell Senescence

Replicative Cell Senescence

Replicative cell senescence is a property of cells that allows them to divide a finite number of times throughout the organism's lifespan while preventing excessive proliferation. Replicative senescence is associated with the gradual loss of the telomere — short, repetitive DNA sequences found at the end of the chromosomes. Telomeres are bound by a group of proteins to form a protective cap on the ends of chromosomes. Embryonic stem cells express telomerase — an enzyme that adds...

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Simultaneous Imaging and Flow-Cytometry-based Detection of Multiple Fluorescent Senescence Markers in Therapy-Induced Senescent Cancer Cells

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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.

|October 8, 2025

Summary

We created DeepScence, a new deep learning method for accurately identifying senescent cells. This tool analyzes single-cell and spatial transcriptomics data, improving upon existing senescence detection methods.

Keywords:

aging cellular senescence machine learning scRNA-seq senescence gene set senescence identification spatial transcriptomics

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Area of Science:

Cellular senescence
Transcriptomics
Bioinformatics

Background:

Accurate identification of senescent cells is crucial for understanding their roles in health and disease.
Existing methods for senescent cell detection have limitations in accuracy and scope.
Senescence-associated gene expression patterns are key indicators of cellular senescence.

Purpose of the Study:

To develop a novel computational method for precise senescent cell identification.
To leverage deep neural networks for analyzing transcriptomic data.
To create a robust senescence-associated gene set for improved detection.

Main Methods:

Development of DeepScence, a deep neural network-based method.
Creation of CoreScence, a curated senescence-associated gene set.
Application of DeepScence to single-cell and spatial transcriptomics datasets.

Main Results:

DeepScence accurately identifies senescent cells in diverse single-cell gene expression datasets (in vitro and in vivo).
The method demonstrates high performance on spatial transcriptomics data from various platforms.
DeepScence significantly outperforms existing methods for senescent cell identification.

Conclusions:

DeepScence provides a powerful and accurate tool for senescent cell identification in transcriptomic studies.
The CoreScence gene set enhances the specificity and sensitivity of senescence detection.
This method advances the study of cellular senescence in both molecular and spatial contexts.