An international research team from the Helmholtz Centre for Infection Research (HZI), the Hannover Medical School, the University of Bonn, the Radboud University Medical Center in Nijmegen, the Rotterdam University of Applied Sciences and the Anding Hospital in Beijing, led by Prof. Yang Li from the Centre for Individualized Infection Medicine (CiiM), has created an AI-based computer model that can visualize ageing at the cellular level.

For their study, the scientists used thousands of transcriptome datasets for five different immune cell types from freely accessible data and literature sources. From this, they used machine learning to create a computer model. They tested their model two stress situations that affect ageing processes: a Covid19 infection and a tuberculosis vaccination. In the former, they only observed reversible ageing processes in the monocytes, with severe courses also causing the monocytes to age more quickly. In the second case, a specific cell type, CD8 T cells, was affected differently depending on how many inflammatory processes were currently taking place in the body.

Original paper:
Li W, Zhang Z, Kumar S, Botey-Bataller J, Zoodsma M, Ehsani A, Zhan Q, Alaswad A, Zhou L, Grondman I, Koeken V, Yang J, Wang G, Volland S, Crişan TO, Joosten LAB, Illig T, Xu CJ, Netea MG, Li Y. (2025). Single-cell immune aging clocks reveal inter-individual heterogeneity during infection and vaccination. Nat Aging. 2025 Mar 5. doi: 10.1038/s43587-025-00819-z. Epub ahead of print. PMID: 40044970.

Source and more information:
https://www.laborpraxis.vogel.de/