AI Article Synopsis
- - The study investigates how CD8 T cell functions decline with aging, utilizing single-cell RNA sequencing to explore changes in T cell heterogeneity and gene expression over nine decades of life.
- - Researchers identified eleven subpopulations of CD8 T cells that change dynamically with age, revealing that gene expression alterations stem from varying percentages of cells and differing transcript levels.
- - A machine learning model was created to predict the age of individual CD8 T cells based on their transcriptomic features, validated in the contexts of HIV infection and CAR T cell expansion.
Article Abstract
The decline of CD8 T cell functions contributes to deteriorating health with aging, but the mechanisms that underlie this phenomenon are not well understood. We use single-cell RNA sequencing with both cross-sectional and longitudinal samples to assess how human CD8 T cell heterogeneity and transcriptomes change over nine decades of life. Eleven subpopulations of CD8 T cells and their dynamic changes with age are identified. Age-related changes in gene expression result from changes in the percentage of cells expressing a given transcript, quantitative changes in the transcript level, or a combination of these two. We develop a machine learning model capable of predicting the age of individual cells based on their transcriptomic features, which are closely associated with their differentiation and mutation burden. Finally, we validate this model in two separate contexts of CD8 T cell aging: HIV infection and CAR T cell expansion in vivo.
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Source |
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| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC9436929 | PMC |
| http://dx.doi.org/10.1038/s41467-022-32869-x | DOI Listing |
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