Deep learning enables cross-species annotation and attribution of ageing states in haematopoietic stem and immune cells
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Abstract Mouse single-cell ageing studies provide experimentally controlled age contrasts, but using mouse-labelled data to annotate human ageing states is limited by species, donor and assay effects in sparse transcriptomic and chromatin profiles. We developed a cross-species annotation workflow that treats mouse-to-human prediction as a target-validated domain-adaptation problem. The workflow uses orthologue-aligned features, a residual encoder, an age classifier and a species discriminator trained with two-phase adversarial optimisation, and couples prediction with stability-based gene attribution. In haematopoietic stem cells (HSCs), the model achieved held-out human AUROCs of 0.933 in scRNA-seq and 0.953 in scATAC-seq. In an independent CD8+ T-cell scRNA-seq setting, the held-out human AUROC was 0.941. Ablation analyses indicated that residual connections, ELU activation and two-phase training improved predictive performance and attribution stability. Consensus attributions from DeepLIFT, Integrated Gradients and saliency recovered conserved ageing-associated genes with greater cross-species overlap than differential expression alone. In a COVID-19 convalescent cohort, severe disease in younger adults was associated with a higher fraction of CD8+ cells classified as old-like by the pretrained model. These results support a reproducible framework for testing, interpreting and releasing cross-species single-cell ageing models, while highlighting the need for target-domain validation when mouse labels are transferred to human data.
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DOI retrouvé dans Crossref DOI retrouvé ; titre concordant.
- Titre Crossref
- Deep learning enables cross-species annotation and attribution of ageing states in haematopoietic stem and immune cells
- Date Crossref
- 26/07/2026
- Éditeur
- openRxiv
- Type
- posted-content
Ce recoupement confirme des métadonnées liées au DOI. Il ne confirme ni la méthode ni les conclusions de l’étude, et il ne compte pas comme une seconde source scientifique indépendante.
Où se fait cette recherche
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John Radcliffe Hospital pays non établi dans la noticeÉtablissement de santé
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University of Oxford MRC Weatherall Institute of Molecular Medicine pays non établi dans la noticeUniversité ou école supérieure
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MRC Weatherall Institute of Molecular Medicine pays non établi dans la noticeStructure de recherche
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Imperial College London Department of Civil and Environmental Engineering pays non établi dans la noticeUniversité ou école supérieure
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Open Data Institute pays non établi dans la noticeOrganisation à but non lucratif
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University of Cambridge Department of Computer Science and Technology pays non établi dans la noticeUniversité ou école supérieure
John Radcliffe Hospital, MRC Weatherall Institute of Molecular Medicine — University of Oxford et MRC Weatherall Institute of Molecular Medicine, avec 3 autres affiliations.
Une affiliation ne permet pas de déduire la nationalité d’un auteur.