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Self-assembling cardiac organoids to model myovascular interactions in human cardiac tissue regeneration

$43,061F30FY2025HLNIH

Duke University, Durham NC

Investigators

Abstract

Abstract Heart failure is a leading cause of death worldwide and there is a critical, unmet need for non-surgical therapies. A key barrier to developing curative therapy is that the human heart, unlike that of zebrafish, axolotls, and some neonatal mammals, lacks the ability to recover after acute or chronic injury. Our lab has discovered that in neonatal mice, which can regenerate the heart after injury, cardiomyocytes and endothelial cells proliferate in a spatiotemporally coordinated manner. When key cell-cell signaling factors are lost, such as VEGFA or its receptor VEGFR2, we observe significantly decreased rates of cell cycling in both cardiomyocytes and endothelial cells. This suggests that local signals between these cells are responsible for inducing and maintaining a proliferative state in the neonatal period. For this information to be therapeutically translational, however, we must understand whether these mechanisms are conserved in human cells. We have devised a cardiac organoid (CO) platform enriched for endothelial cells that can be used to model human myovascular interactions. With this model, we will use cardiomyocyte and endothelial cell reporters and live imaging to investigate how modifying endothelial cell content within a human CO impacts organoid function and regeneration after ischemia-reperfusion injury. In addition, we will use a CRISPRa lentiviral construct to overexpress key factors secreted by cycling endothelial cells, with cognate receptors on cycling cardiomyocytes, for effects on cardiomyocyte proliferation. This proposal details the use of a high-throughput CO platform to disentangle the complexities of myovascular interactions in human cells, enabling discovery and validation of novel therapeutic targets.

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Self-assembling cardiac organoids to model myovascular interactions in human cardiac tissue regeneration · GrantIndex