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This paper develops a deep learning model to predict haematopoietic stem cell ageing from 3D chromatin images, achieving an AUROC of 0.77 and identifying key chromatin features as predictive markers.

The paper constructs a single-cell multiomics atlas of the human pancreas across development, health, and T2D, identifying plastic cell states and lineage trajectories.

This study shows that reducing RhoA activity in aged HSCs lowers nuclear tension, restores chromatin structure, and improves regenerative capacity and lineage balance.

This paper reviews deep learning applications in single-cell omics, finding that while promising, DL models still lack biological interpretability and have not yet solved major challenges in the field.