Deep learning predicts haematopoietic stem cell ageing from 3D chromatin images.

The functional decline of the haematopoietic system during ageing propagates detrimental effects on the whole organism, ultimately eroding life and healthspan. Quantifying haematopoietic ageing holds great scientific and clinical relevance. Alterations in chromatin architecture are a well-established hallmark of ageing that encode rich and informative signatures of the ageing process, yet they remain largely unexplored as quantitative markers. Here, we present an interpretable deep learning approach based on convolutional neural networks, ChromAgeNet, that learns changes in the spatial features of chromatin architecture during natural aging of Hematopoietic Stem Cells (HSCs). We trained our algorithm on 3D microscope images of DAPI-stained HSC nuclei to discriminate between young and aged murine HSCs, achieving and AUROC of 0.77 ± 0.03. This approach outperforms classical machine learning models trained on handcrafted chromatin features from the same dataset. We then applied explainable artificial intelligence techniques, identifying chromatin entropy, peripheral heterochromatin and chromatin condensates as predictive markers. As a proof of concept, we evaluated the potential of our model as a phenotypic screening tool for aged HSCs treated with epigenetic drugs to detect rejuvenation. Altogether, we demonstrate that changes in chromatin organization can be modeled via machine learning to predict cellular ageing in the hematopoietic compartment. Our developed framework, ChromAgeNet, serves as an interpretable algorithm to unravel the intricate relationship between chromatin changes and cellular ageing, and advance high throughput drug screening for rejuvenation therapies.