# Integrative Proteomics of Extracellular Vesicles from hiPSC-Derived Cardiac Organoids Reveals Heart Tissue-like Molecular Representativity

## TL;DR

This study shows that EVs from cardiac organoids resemble those from real heart tissue, supporting their use in studying heart development and disease.

## Contribution

The study demonstrates molecular similarity between EVs from cardiac organoids and human heart tissue using proteomics.

## Key findings

- cardEVs share 48.9% of their proteins with EVs from human heart explants.
- Shared biological processes include 'Metabolism' and 'Cardiac Function'.
- Cardiac organoids are validated as a relevant model for studying heart development and disease.

## Abstract

Cardiovascular diseases remain a growing concern worldwide. Hence, it is critical to understand cardiac development and disease in a relevant human-based in vitro model. Human cardiac organoids are an alternative approach to studying cardiogenesis, in the context of cell–cell communication, and disease etiology, using human induced pluripotent stem cells (hiPSCs). Extracellular vesicles (EVs) are nanosized particles harboring proteins, nucleic acids, and metabolites and are implicated in intercellular communication. Since cardiac development requires a complex interplay between several cell types, we hypothesize that EVs may mediate this communication. Here, we isolated EVs from hiPSC-derived cardiac organoids (cardEVs). LC-MS/MS was performed to analyze their protein cargo and compare it with those from a cardiomyocyte cell line (AC10 CM EVs) and from human heart explants of cadaveric donors (heEVs) using a bioinformatic approach. cardEVs share 48.9% of their proteins with heEVs, with important biological processes such as “Metabolism” and “Cardiac Function” highlighted in both proteomes. This overlap between the proteomes of cardEVs and heEVs suggests a molecular similarity between the two models. Therefore, we reiterate the importance of cardiac organoids as an excellent model for studying cardiac development and disease modeling, as well as to explore the complexity of intercellular communication.