The ability of T cells to undergo robust cell division in response to antigenic stimulation is essential for competent T cell function. However, this ability is reduced with aging and contributes to increased susceptibility to infectious diseases, cancers, and other diseases among older adults. To better understand T cell aging, improved measurements of age-related cellular changes in T cells are necessary. The recent development of machine learning (ML)-assisted transcriptome-based quantification of individual CD8+ T cell age represents a significant step forward in this regard. It reveals both prominent and subtle changes in gene expression and points to potential functional alterations of CD8+ T cells with aging. I argue that single-cell transcriptome-based age prediction in the immune system may have promising future applications.
Keywords: DNA methylation; T cell aging; machine learning; somatic mutation; telomere; transcriptome.
Published by Elsevier Ltd.