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IDEAL-Age: an interpretable deep learning framework for single-cell resolution profiling of immunological aging
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DOI：10.1186/s13059-026-04188-7.png)
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Abstract
En 中文
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.
Keywords:
Interpretable deep learning framework
ScRNA-seq
Immunological aging
Aging clock
Single-cell resolution
PBMC
Systemic lupus erythematosus (SLE)
Accelerated aging
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