Mechanisms Of Age-related Changes in Transcriptional Regulation in lympphocytes

National Institute On Aging

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Abstract

The alteration of CD8+ T cell subpopulations and functions contributes to deteriorating health with aging, but the mechanisms that underlie this age-associated change is not well understood. We used 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 were identified. Age-related changes in gene expression resulted from changes in the percentage of cells expressing a given transcript, quantitative changes in the transcript level, or a combination of these two. We developed a machine learning model capable of predicting the age of individual cells based on their transcriptomic features, which were closely associated with their differentiation and mutation burden. Finally, we validated this model in two separate contexts of CD8+ T cell aging: HIV infection and CAR T cell expansion in vivo. Mutation is a contributing factor for developing cancer and aging. However, the degree of mutation and its potential impact on human CD8+ T cell aging have not been analyzed. DNA-based single-cell mutation detection methods have been developed but are hindered by the limited capability of cell number examined and compounded by errors from PCR amplification and sequencing methods. Here, we developed a UMI-based scRNAseq mutation detection (USCMD) method that allowed us to examine 4500 cells per sample with 14.4% reduced mutation calls and 0.15% recovered false negative mutations compared to the gold standard Genomic Analysis Tool Kit (GATK) method. Our longitudinal analysis revealed highly individualized mutation changes in human CD8+ T cells with age. Novel missense mutations were associated with either gain or loss of their carrying cells over time, as well as an increase or decrease in mRNA levels of the mutated genes compared to the unmutated genes. Together, this USCMD method improves the accuracy of mutational analysis of scRNAseq data and allows parallel transcriptional assessment of mutated and unmutated cells to understand their potential functional consequences.