The HZI team has developed an AI-powered computer model that, for the first time, reveals the aging process at the cellular level.
As we age, our immune system ages as well. We become more susceptible to infections, vaccinations become less effective, and the risk of developing immune-related disorders such as autoimmune diseases increases.
“In order to better understand how and where exactly the immune system changes with age and which factors trigger or accelerate aging processes, we need to focus on the players of our immune system – the immune cells,” says Prof. Yang Li, head of the department “Computation Biology for Individualised Medicine” and Director of the CiiM.
Yang Li’s team set out to answer a key research question: How does aging affect different types of immune cells? To explore this, the researchers analyzed thousands of transcriptome datasets—records of all active genes in a cell at a given time—for five distinct immune cell types. These datasets were compiled from publicly available sources and scientific literature.
In total, the team examined data from more than two million individual immune cells, collected from blood samples of approximately 1,000 healthy individuals ranging in age from 18 to 97. Using this extensive dataset, they developed a machine learning model to track cellular aging. The result was a computational tool they named the Single-Cell Immune Aging Clock, designed to map how immune cells change over time.
Discovering Aging Markers
“We were able to identify specific genes for each type of immune cell that are involved in important immunological processes and whose activity changes during the aging process. These serve as marker genes for the respective immune cell type and as a reference in the subsequent application of the model,” explains Yang Li. “Incidentally, the genes we identified play a decisive role in the development of inflammatory processes. It is well known that aging processes are particularly associated with inflammatory processes. We were able to confirm this once again with our study.”
Case Study: COVID-19 and Immune Aging
The research team then applied the aging clock in two case studies using patient data. They wanted to find out how a COVID-19 infection or a tuberculosis vaccination affects the aging processes within the different immune cell types. In COVID-19 patients, aging processes were only evident in one type of immune cell, the so-called monocytes. However, in people with a mild course of the disease, aging was significantly less pronounced. “Our results suggest that severe infections can cause our immune cells to age more quickly,” says Yang Li. “But – and this is good news – these changes seem to be reversible: After about three weeks, as COVID-19 patients slowly recover, the monocytes start to return to their original age profile.”
Case Study: Tuberculosis Vaccination and Immune Rejuvenation
In the second case study, the researchers used the aging clock to look at the age of different immune cell types in people who had been vaccinated against tuberculosis. Here, the team discovered an interesting correlation: The vaccination had very different effects within one immune cell type, the so-called CD8 T cells, depending on how much inflammation was going on in the body. However, in people with high levels of inflammation, the vaccination had a rejuvenating effect on the immune cells.
“The Single-Cell Immune Aging Clock opens up incredibly exciting insights into cellular aging processes within different immune cell types for the first time,” says Yang Li. “It is a powerful tool that could be used in the future to uncover further dynamics of immune aging, to better understand the effects of infections and vaccinations and to develop new approaches for therapies and preventive measures that promote healthy aging.”
Reference: “Single-cell immune aging clocks reveal inter-individual heterogeneity during infection and vaccination” by Wenchao Li, Zhenhua Zhang, Saumya Kumar, Javier Botey-Bataller, Martijn Zoodsma, Ali Ehsani, Qiuyao Zhan, Ahmed Alaswad, Liang Zhou, Inge Grondman, Valerie Koeken, Jian Yang, Gang Wang, Sonja Volland, Tania O. Crişan, Leo A. B. Joosten, Thomas Illig, Cheng-Jian Xu, Mihai G. Netea and Yang Li, 5 March 2025, Nature Aging.
DOI: 10.1038/s43587-025-00819-z
The research team is making the “Single-Cell Immune Aging Clock” freely available so that it can be used for further research projects (https://github.com/CiiM-Bioinformatics-group/scImmuAging).
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