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Updated: Jun 19, 2026

Induction and Validation of Cellular Senescence in Primary Human Cells

Induction and Validation of Cellular Senescence in Primary Human Cells

Published on: June 20, 2018

Detection of senescence using machine learning algorithms based on nuclear features.

Imanol Duran^1,2, Joaquim Pombo^1,2, Bin Sun^1,2

¹MRC Laboratory of Medical Sciences (LMS), Du Cane Road, London, W12 0NN, UK.

Nature Communications

|February 3, 2024

Summary

This summary is machine-generated.

Machine learning accurately identifies cellular senescence using nuclear features. This breakthrough aids in developing senotherapies for cancer, aging, and liver disease.

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Published on: July 12, 2022

Area of Science:

Cellular and Molecular Biology
Biotechnology
Gerontology

Background:

Cellular senescence is a biological stress response with significant roles in aging and disease.
Senotherapies offer potential for treating cancer and age-related conditions, but their efficacy is hindered by challenges in identifying senescent cells.
Reliable methods for detecting senescence are crucial for advancing senotherapeutic development and application.

Purpose of the Study:

To develop accurate machine-learning classifiers for identifying cellular senescence based on nuclear morphology.
To utilize these classifiers for characterizing senolytic drugs and screening for novel senotherapeutic agents.
To establish a tissue senescence score for assessing senolytic efficacy and detecting senescence in various disease models and human patients.

Main Methods:

Utilized nuclear morphology features of senescent cells to train machine-learning classifiers.
Applied the developed classifiers to diverse cell types and tissues under various stress conditions.
Employed classifiers to characterize senolytics, screen for senescence-inducing drugs, and calculate a tissue senescence score.

Main Results:

Achieved accurate prediction of senescence induced by diverse stressors across different cell types and tissues.
Successfully characterized senolytics and identified drugs selectively inducing senescence in cancer cells.
Demonstrated the utility of the tissue senescence score in evaluating senolytic efficacy and detecting senescence in models of liver cancer, aging, fibrosis, and human fatty liver disease.

Conclusions:

Machine-learning classifiers based on nuclear morphology provide a reliable method for identifying cellular senescence.
These classifiers are valuable tools for senolytic characterization, drug screening, and assessing therapeutic efficacy.
The developed approach facilitates the detection of pathophysiological senescence and the discovery and validation of novel senotherapies.