Back

Self-Assembled Chambered Cardiac Organoids for Modeling Cardiac Chamber Formation and Cardiotoxicity Assessment

Zou, X.; Wang, F.; Zheng, H.; Kong, T.; Pei, D.

2024-07-25 cell biology
10.1101/2024.07.25.605044 bioRxiv
Show abstract

The human heart, crucial for health and longevity, is a primary focus of medical research. Despite advancements in cardiac organoid technology, replicating early cardiac chamber formation stages remains challenging. Here, we develop chambered cardiac organoids (CCOs) by orchestrating cardiac signaling pathways, including the synergistic modulation of FGF and Wnt signaling via the HAND1 transcription factor. These CCOs exhibit stable chambers with self-organized outer myocardial and inner endocardial layers, and express developmental markers such as NKX2.5, TNNT2, and NAPPA, demonstrating physiological functionality including spontaneous contractions and calcium transients. Immunofluorescence and single-cell RNA sequencing confirmed the presence and stability of cardiomyocytes within CCOs. Further validation showed CCOs involvement in critical processes like endothelial-mesenchymal transition (EndoMT) and valvulogenesis. Ultrastructural and electrophysiological analysis revealed organized myofibrils and atrial-like action potentials. Importantly, CCOs proved effective in assessing cardiotoxicity through observable morphological changes, demonstrating specific cellular responses to established cardiotoxic compounds, thereby highlighting their potential for drug testing and safety evaluations. Our findings offer insights into cardiac chamber formation mechanisms and establish a robust platform for drug testing, disease modeling, and personalized medicine, representing a significant advancement in functional cardiac organoid development for both basic research and translational applications.

Matching journals

The top 4 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.