Abstract 119: Multi-Omics Investigation of Cardiomyocyte-to-Fibroblast Crosstalk in Human iPSC Models

Introduction: Cardiac cells communicate with each other in part through secreted proteins known as cardiokines. The total repertoire of proteins secreted by cardiac cells is unknown. We aim to apply human iPSC models and large-scale multi-omics to identify secreted proteins and the crosstalk signals they mediate. Method: We optimized an experimental protocol to recover and identify cell-specific secreted proteins from human iPSC-cardiac cells. Human iPSCs were differentiated into cardiomyocyte (CM) and endothelial cells (EC) using established protocols, after which secreted proteins were extracted from the conditioned medium for analysis. We combined multiplexed aptamer-based large-scale protein quantification platform and high-resolution mass spectrometry to identify secreted proteins from iPSC-CM, iPSC-ECs, and primary ventricular fibroblasts. An in-silico filter was implemented to prioritize bona fide cardiokines over proteins externalized by passive lysis. Result: We identified 146 candidate cardiokines at 1% false discovery rate, including cell-specific cardiokines as well as a common core secretome of three cardiac cells. We analyzed the data to identify potential signals secreted by cardiomyocytes to mediate fibroblast function, using a ligand-receptor model based on proteomics and single-cell transcriptomics data to prioritize cardiokines secreted by iPSC-CMs and which bind to fibroblast-expressed receptors. To examine their roles in fibrosis regulation, we selected three candidate cardiokines (PLAU, FGF7, and CXCL12) for verification using immunodetection, and exposed their recombinant proteins at multiple concentrations to human ventricular fibroblasts. The results nominated cardiokine-specific effects on recipient cell gene expression including evidence of modulation of fibroblast transcription and translation pathways. Conclusion: We demonstrate a multi-omics approach in iPSC models to explore the large-scale human secretome of multiple cardiac cell types. The approach holds promise for identifying disease markers and understanding intercellular communication in cardiac development and diseases.