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Is your immune system over the hill?

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Science Immunology  03 May 2019:
Vol. 4, Issue 35, eaax8198
DOI: 10.1126/sciimmunol.aax8198

Abstract

Longitudinal study of immune function yields cytometric and transcriptomic measures of immune age.

Is our chronological age less important than the “age” of our immune system? There are many age-associated changes in immune function (e.g., increased senescence and decreased naïve T cells). While we have learned a great deal from mouse studies and cross-sectional human studies, Aylet et al. completed a longitudinal investigation of adult immune function. They recruited 135 adult participants, with annual blood draws over 8 years. A small subset of patients had longitudinal samples deeply immunoprofiled as a single batch using CyTOF. After cellular subsets were manually assessed, the longitudinal slopes of each subset were measured both at a group level and individually. While most subsets traveled in the expected age-correlated paths, a few diverged. Overall, immune status changes over the longitudinal study were more affected by personal baseline senescence measures than chronological age. However, this was too small a sample. After accounting for technical variation, they used the annual surveillance immunotyping and gene expression analysis from the entire cohort and found dozens of cell subsets correlated with age. These cellular subsets were divided into three groups by frequency: stable, linear changes over time, and those that asymptotically approached a stable state. They noted that the dynamics of immune aging yield an expected order by which individuals sequentially reach asymptotic levels for core immune subsets (e.g., reaching projected frequency of naïve CD4+ prior to naïve CD8+ T cells). Each cellular immune subset, in each participant, contributed to the overall high-dimensional immune trajectory, calculated using pseudotime analysis. Each person, at any given time, can have an immune age (IMM-AGE) assessed as a position along this trajectory. To move away from high-dimensional cellular immunophenotyping, the authors identified a gene set correlated with IMM-AGE. To connect IMM-AGE to health, a subset gene set was assessed in the Framingham Heart Study; heart disease was correlated with immune aging out of proportion to chronological age. This may improve disease prognosis and therapeutic selection in the future. First, it will be important to test in broader age groups in healthy controls and additional disease cohorts, these analyses may also help clarify testable mechanistic hypotheses underlying immune aging.

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