aceDeep Learning Predicts Hematopoietic Stem Cell Aging From 3D Chromatin Images
The functional decline of the hematopoietic system during aging affects organismal function and contributes to reduced healthspan. Quantifying hematopoietic aging holds great scientific and clinical relevance. Alterations in chromatin architecture are a well-established hallmark of aging that encode rich and informative signatures of the aging process, yet they remain largely unexplored as quantitative markers. Here, we present an interpretable deep learning approach based on convolutional neural networks, Chrom…
A convolutional neural network trained on 3D images of stained mouse stem cell nuclei separated young from aged cells better than classical models and flagged interpretable chromatin markers of aging.
The model's discrimination is moderate (AUROC 0.77), and the drug-screening application is described only as a proof of concept in murine stem cells.
Evidence
- Peer-reviewedAging Cell2026-10-01
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Truvace Impact Record TRV-2026-1512, v1: “Deep Learning Predicts Hematopoietic Stem Cell Aging From 3D Chromatin Images.” Truvace, 2026-10-16. /record/TRV-2026-1512 (accessed at citation time). sha256 eef06d37e56388dd…
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