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

ABSTRACT: 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

SUBMITTER: Picazo PI 

PROVIDER: S-EPMC12746025 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature