Deep learning the dynamic regulatory sequence code of cardiac organoid differentiation.

ABSTRACT: Defining the temporal gene regulatory programs that drive human organogenesis is essential for understanding the origins of congenital disease. We combined a time-resolved, single-cell multi-omic atlas of human iPSC-derived cardiac organoids with deep learning models that predict chromatin accessibility from DNA sequence, enabling the discovery of the regulatory syntax underlying early heart development. This framework uncovered cell-state-specific rules of cardiogenesis, including context-dependent activities of TEAD, HAND, and TBX transcription factor families, and linked these motifs to their target genes. We identified distinct programs guiding lineage divergence, such as ventricular versus pacemaker cardiomyocytes, and validated predictions by perturbing Myocardin (MYOCD), esta

SUBMITTER: Metzl-Raz E 

PROVIDER: S-EPMC12632746 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature