Mechanisms Of Age-related Changes in Transcriptional Regulation in lympphocytes

National Institute On Aging

Investigators

Linked publications & trials

Abstract

The decline of CD8+ T cell functions contributes to deteriorating health with aging but the mechanisms underlying this age-related change are not fully understood. We used scRNAseq to analyze the effect of age on human CD8+ T cell transcriptome cross-sectionally and longitudinally spanning nine decades of life. We identified eleven subpopulations of CD8+ T cells and their changes with age. Age-related changes in expression of a given gene results from variable contributions of change in the percentage of cells expressing a given transcript, quantitative change in the transcript level, or a combination of these two. We developed a machine learning (ML) model capable of predicting the age of individual cells with a high correlation to chronological age. The predicted cell age was closely correlated with the differentiation and mutation burden, and indicative of CD8+ T cell aging in the context of HIV infection and CAR T cell expansion in vivo. Somatic mutation is a contributing factor for developing cancer and aging. DNA-based single cell mutation detection has been developed, but is hindered by the limited number of cells examined and the difficulty to identify errors from PCR amplification and sequencing. Here, we developed a UMI-based mutation detection (USCMD) method using scRNAseq data that allowed us to examine 5000 cells per sample with 40% reduced mutation calls and 0.4% recovered false negative mutation compared to the gold standard Genomic Analysis Tool Kit (GATK) method. Analysis of longitudinal changes in mutation burden of human CD8+ T cells revealed high individual variation of mutation changes per cell and per study subject with age and identified novel missense mutations that were associated with gain or loss of these mutation carrying cells over time. Together, this USCMD method offers mutational analysis of single cells with significantly reduced errors and parallel transcriptional assessment of mutated cells.