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A Mouse Model to Assess Innate Immune Response to Staphylococcus aureus Infection

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Reading the immune clock: a machine learning model predicts mouse immune age from cellular patterns.

Hyun Bo Sim¹, Ji-Hun Jang², Seul-Ki Mun^1,3

¹Department of Biomedical Science, Sunchon National University, Suncheon, Republic of Korea.

Nature Communications

|December 10, 2025

Summary

Researchers developed a machine learning model to predict immune system aging using protein data from mouse immune cells. This tool accurately assesses immunological age and has potential for disease-related immune senescence identification.

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Area of Science:

Immunology
Computational Biology
Gerontology

Background:

Aging significantly alters the immune system, but predicting immunological age accurately is difficult.
Current methods like transcriptomics offer insights, but protein-level analysis and machine learning tools for immune aging are underdeveloped.

Purpose of the Study:

To develop and validate a machine learning model for predicting immunological age using protein expression data.
To assess the model's generalizability and translational potential in identifying immune senescence.

Main Methods:

Mass cytometry was used to profile 30 protein markers on murine splenic immune cells across different age groups.
Machine learning, specifically support vector regression (SVR), was employed to predict immunological age based on 103 molecular features from six major immune subsets.
The model's performance was validated on independent test samples and in an obese mouse model.

Main Results:

A robust machine learning model was trained to predict immunological age using multidimensional protein expression data.
The model demonstrated strong generalizability, accurately predicting age in unseen samples.
The model's robustness was confirmed in an obese mouse model exhibiting immune senescence.

Conclusions:

A reliable framework for predicting immune aging based on protein expression and machine learning has been established.
This quantitative tool aids in assessing immune aging and has translational potential for identifying disease-associated immune senescence, including in obesity.