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bioRxiv · 10.64898/2025.12.25.696528

Ensemble-DeepSets: an interpretable deep learning framework for single-cell resolution profiling of immunological aging

Xu, Y.·Luo, Z.·He, K.·Zhang, F.·Wang, J.·Wen, H.·Li, Y.·Han, D.
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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 31 methods across independent cohorts demonstrates superior predictive performance. The frameworks interpretability uncovers linear and non-linear transcriptomic dynamics that reveal phase-specific physiological transitions, and identifies pro-youthful or pro-aging cellular contributions. Application to systemic lupus erythematosus (SLE) 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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Xu, Y., Luo, Z., He, K., Zhang, F., Wang, J., Wen, H., Li, Y., Han, D.. 2025-12-26. Ensemble-DeepSets: an interpretable deep learning framework for single-cell resolution profiling of immunological aging. https://doi.org/10.64898/2025.12.25.696528

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