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<p>Immunosenescence increases susceptibility to infection and reduces vaccine responsiveness, yet bulk transcriptomic clocks obscure the cellular heterogeneity underlying this process. Here, we present IDEAL-Age, an interpretable deep learning framework that operates directly on single-cell PBMC transcriptomes. Benchmarking against 35 methods across independent cohorts demonstrates superior predictive performance. The framework’s interpretability uncovers linear and non-linear gene contribution trajectories that reveal phase-specific physiological transitions, and identifies youth-associated or aging-associated cellular roles. Application to systemic lupus erythematosus reveals accelerated immunological aging driven by interferon-associated monocyte shifts. IDEAL-Age establishes a high-resolution computational framework for deciphering systemic immune aging.</p>

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IDEAL-Age: an interpretable deep learning framework for single-cell resolution profiling of immunological aging

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年7月27日

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  • Yin Xu,
  • Zhengchao Luo,
  • Kai He,
  • Feifan Zhang,
  • Yawei Zhang,
  • Jinzhuo Wang,
  • Han Wen,
  • Yongge Li,
  • Dali Han

摘要

Immunosenescence increases susceptibility to infection and reduces vaccine responsiveness, yet bulk transcriptomic clocks obscure the cellular heterogeneity underlying this process. Here, we present IDEAL-Age, an interpretable deep learning framework that operates directly on single-cell PBMC transcriptomes. Benchmarking against 35 methods across independent cohorts demonstrates superior predictive performance. The framework’s interpretability uncovers linear and non-linear gene contribution trajectories that reveal phase-specific physiological transitions, and identifies youth-associated or aging-associated cellular roles. Application to systemic lupus erythematosus reveals accelerated immunological aging driven by interferon-associated monocyte shifts. IDEAL-Age establishes a high-resolution computational framework for deciphering systemic immune aging.