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

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.

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Publication Details

Journal
Genome biology
Published
2026-07-26
DOI
https://doi.org/10.1186/s13059-026-04188-7
Primary Topic
Single-cell and spatial transcriptomics
Type
article
Field-Weighted Citation Impact
0.00

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article

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

Yin Xu, Dali Han, Yongge Li, Yi-Xiang Wang et al.
Genome biology
Single-cell and spatial transcriptomics
article

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

Yin Xu, Dali Han, Yongge Li, Yi-Xiang Wang, Yawei Zhang, Feifan Zhang, Zhengchao Luo, Kai He, Han Wen
article en

Abstract

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.

Genome biologyVol. 27(1)
Peking University (CN), Beijing Institute of Genomics (CN), Shanghai FRP Research Institute (China) (CN), Sino-Danish Centre for Education and Research (CN), China National Biotechnology (China) (CN), Taipei Municipal Jen-Ai Hospital (TW), SP Technology (South Korea) (KR), Beijing Proteome Research Center (CN), Shanghai Center For Bioinformation Technology (CN), Chongqing Three Gorges University (CN)
National Natural Science Foundation of China, Science and Technology Commission of Shanghai Municipality, National Key Research and Development Program of China
Openalex Percentile: Top 15%
Single-cell and spatial transcriptomics
DOIOpen Access PDFOpenAlex
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