bioRxiv Science⌕ Search
Home/Archive/bioRxiv

bioRxiv · 10.1101/2025.11.07.687286

An Explainable AI Framework for Identifying Universal Aging Signatures in Cell Embeddings

Qiu, W.·Arian, C.·Weinberger, E.·Yun, S. R.·Mendenhall, A. R.·Young, J. E.·Brbic, M.·Lee, S.-I.
View original source ↗Export metadata ↓

Abstract

Aging is a complex biological process marked by progressive physiological decline and increased disease vulnerability. Single-cell RNA sequencing offers unprecedented resolution for studying aging, yet isolating aging-related signatures remains challenging because gene expression is primarily shaped by other factors such as cell type, tissue, and sex. We present ACE (Aging Cell Embeddings), an explainable deep generative framework that disentangles aging-related gene expression changes from background biological variation. ACE employs two latent representations: one capturing aging-related signatures and another representing non-aging-related variation in the data. Through explainable AI, ACE identifies key genes and pathways associated with aging amid dominant non-aging-related variations. Applied to large-scale mouse, fly, and human datasets, ACE uncovers aging signatures both within specific tissue-cell-type contexts and across all tissues and cell types, enabling accurate prediction of biological age. Moreover, ACE identifies aging genes conserved across species, highlighting its ability to reveal shared biological mechanisms of aging. Experimental RNAi knockdowns in C. elegans validate ACEs findings, confirming its ability to prioritize novel aging genes affecting lifespan. ACE reveals key pathways involved in proteostasis, immune regulation, and extracellular matrix remodeling, and identifies Uba52 through the cross-species model as an important aging gene, whose knockdown in C. elegans significantly shortens lifespan. By providing interpretable and generalizable aging embeddings, ACE establishes a foundation for cross-species single-cell aging studies and translational geroscience.

Explore related subjects

bioinformaticsPreprint

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Qiu, W., Arian, C., Weinberger, E., Yun, S. R., Mendenhall, A. R., Young, J. E., Brbic, M., Lee, S.-I.. 2025-11-10. An Explainable AI Framework for Identifying Universal Aging Signatures in Cell Embeddings. https://doi.org/10.1101/2025.11.07.687286

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related preprints

bioRxiv2026

Senescence-associated KRAS upregulation in peripheral T cells links to premature coronary artery disease

Aims: Premature coronary artery disease (PCAD) lacks specific molecular drivers, and the role of immunosenescence is unclear. We investigated whether aging-related gene dysregulation in T cells contributes to PCAD. Methods: We combined bulk transcriptomics of PBMCs from 12 PCAD patients and 21 controls, single-cell RNA sequencing of PBMCs and human atherosclerotic plaques, weighted gene co-expression network analysis, gene perturbation network analysis, and molecular docking. Results: KRAS was identified as a hub gene intersecting PCAD-associated genes and aging-related genes. Single-cell analysis showed KRAS upregulation predominantly in effector CD8+ T cells, which exhibited the highest senescence scores that were further elevated in disease. Network perturbation of KRAS strongly impacted the cell killing pathway. KRAS-high effector CD8+ T cells were detected in coronary and carotid plaques, displaying enhanced cytotoxicity, exhaustion, and senescence features. Additionally, a candidate small molecule was computationally predicted to bind inactive KRAS. Conclusions: Elevated KRAS expression in senescent, cytotoxic CD8+ T cells is associated with PCAD, bridging immunosenescence and premature atherosclerosis. This finding provides a novel biomarker candidate and potential therapeutic entry point, awaiting further functional validation.

bioinformatics↗
bioRxiv2026

Targeted finetuning enables co-folding models to learn ligand-induced protein conformational states

Advances in protein structure prediction have enabled all-atom protein-ligand co-folding models that predict bound conformations directly from sequence and small-molecule structure. However, these models often fail to generalize to novel binding sites or alternative protein conformational states, limiting their utility for chemical biology and drug discovery. Here we show this limitation reflects training data bias rather than architectural constraints and can be overcome through targeted finetuning. Using ten previously unseen X-ray structures of Werner (WRN) helicase from a drug discovery program, we finetune Boltz-1 to learn both an allosteric binding site and a large conformational change locking the enzyme in an inactive state, while preserving accuracy on the ATP-bound state. The finetuned model generalizes to different chemical series and transfers the conformational logic across RecQ-family helicases in a binding-site sequence-dependent manner. This approach provides a blueprint for adapting foundation models as new structural and mechanistic data emerge, enabling co-folding networks to capture ligand-induced conformational switches and binding poses absent from their training data but central to biological regulation and therapeutic intervention.

bioinformatics↗
bioRxiv2026

Benchmarking single-cell foundation models for aging biology

Single cell foundation models (scFMs) provide representations of cellular states, but their utility across biological questions in aging research remains unclear. We established a benchmark of cellular representations for aging research, evaluating ten general-purpose scFMs, three aging-specific models and conventional methods across five biological questions using more than 2.5 million single cell transcriptomes. Using frozen pretrained representations, Geneformer performed best among scFMs for chronological age prediction and age pseudotime concordance, although 2,000 highly variable genes achieved higher mean performance. Several scFMs captured positive molecular age shifts across three disease contexts, consistent with reported aging-associated changes. SCimilarity performed well for rare cellular state identification across out-of-distribution datasets, exceeding aging specific models and conventional baselines. At the gene level, scGPT showed the highest recovery of reference TF target interactions, including aging-related regulatory hubs. Overall, scFMs supported diverse aging analyses, but performance depended on the biological question, highlighting their utility for rare cellular state identification and regulatory analysis.

bioinformatics↗