Deep learning the dynamic regulatory sequence code of cardiac organoid differentiation

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), establishing its essential role in ventricular specification. Integration of chromatin, transcriptional, and genetic data further highlighted regulatory regions and disease-associated variants that perturb differentiation state transitions, supporting evidence that suggests congenital heart disease emerges early in development. This work bridges developmental gene regulation with disease genetics, providing a foundation for mechanistic and therapeutic insights into congenital diseases.