Modeling myotonic dystrophy type 1 with hiPSCs-derived cardiac organoids reveals key disease mechanisms.

BACKGROUND

Myotonic dystrophy type 1 (DM1) is a multisystemic disorder characterized by conduction defects and arrhythmias that contribute significantly to cardiac morbidity and sudden death. Human-induced pluripotent stem cell (hiPSC)-derived cardiomyocytes provide a powerful platform to model disease-specific cardiac dysfunction; however, immature 2-dimensional cultures may not fully capture late-stage pathological mechanisms.

OBJECTIVE

This study aimed to investigate the temporal evolution of cardiac dysfunction in DM1 using developmentally mature 3-dimensional cardioids derived from patient-specific hiPSCs.

METHODS

Control and DM1 hiPSC lines were differentiated into left ventricular and atrial cardioids following chamber-specific protocols and characterized at multiple developmental stages. Electrophysiological properties were assessed using optical mapping and multielectrode arrays, whereas contractile performance was quantified via motion analysis (MUSCLEMOTION). Gene-expression and immunofluorescence analyses were performed to evaluate molecular and structural maturation.

RESULTS

DM1 cardioids displayed a stage-dependent trajectory of dysfunction. Early-stage cardioids showed electrical hyperexcitability with shortened action potential duration and increased spontaneous activity. As differentiation progressed, DM1 cardioids exhibited significant conduction slowing, impaired calcium handling, and reduced contractile amplitude compared with controls. These progressive alterations were accompanied by dysregulation of ion channel and gap junction gene expression.

CONCLUSION

Our findings demonstrate that DM1 cardiac pathology evolves dynamically during maturation, transitioning from early hyperexcitability to late-stage conduction and contractile defects. This study underscores the necessity of using developmentally mature 3-dimensional cardiac models to capture the full spectrum of DM1 pathophysiology and improve translational modeling for therapeutic discovery.