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The integration of CRISPR-based and stem-cell-based functional genomics has transformed our ability to perform precise, large-scale dissection of gene networks and to validate causality in human-relevant models. A key barrier, however, has been the silencing of the CRISPR machinery and transgene systems during differentiation, limiting their use in lineage-resolved studies using human iPSCs. To enable these investigations, we have engineered genomic safe harbour sites in human embryonic stem cells, allowing for the efficient, robust integration and long-term expression of transgenes. This approach addresses limitations of lentiviral and piggyBac gene delivery systems, which are prone to transgene silencing in terminally differentiated cell types. First, we will discuss a genome-wide loss-of-function CRISPR screening approach using the MYH6-cardiomyocyte-specific reporter system. This unbiased screening approach revealed transcriptional regulatory roadblocks and a crucial NF2-AMOT-YAP ternary complex that governs cardiomyocyte lineage specification, offering mechanistic insights to the transcriptional and gene regulatory mechanisms of cardiac cell fate. Second, we will discuss how integrating CRISPR perturbation systems with safe-harbour-mediated transgene overexpression enable for precise gene validation and interrogation of cell-resolved cis-regulatory elements.

To expand the versatility of these safe-harbour sites, we have also incorporated genetically encoded sensors and reporters that enable real-time, direct measurement of cellular phenotypes, such as calcium handling and ATP bioenergetics, in hESC-derived cardiac cell types. Given the heart’s multicellular complexity – including cardiomyocytes, fibroblasts, vascular cells, and macrophages - we are extending these tools to advanced 3D culture systems. We will discuss the use of a 4-cell-type cardiac microtissue model as a physiologically relevant model to study cardiometabolic stress, enabling multimodal, cell-specific readouts that bridge regulatory genomics to disease modelling.