Based on literature research and patient records we selected over 100 genes with known pathogenic cardiomyopathy mutations and performed an arrayed, phenotypic, knock-out (KO) screen based on structural and functional readouts that included key positive and negative controls using our platform and CRISPR/Cas9.
The aim was to determine the feasibility and dynamic range of possible Cardioid-based phenotypes on a structural/morphological as well as functional level. Therefore, we first selected and categorized 108 genes based on their reported function. Only genes with confirmed pathogenic mutations in patients were selected (Figure 1).
We also included a number of key controls such as scrambled gRNAs, key heart development/signaling genes as well as genes not expected to have a stark effect.
Then we used the CRISPR/Cas9 system with validated gRNAs against the relevant genes and performed a transfection followed by an immediate Cardioid generation. Each KO population was aggregated in one column of a 96-well plate and Cardioids were generated. Their development and calcium transients were then monitored and analyzed.
Crucially, all technical controls worked as expected with our control Cardioids forming normally (Figure 2, CTRL) and KO of key signaling hubs such as gene 24 leading to an almost complete impairment of Cardioid development. Similarly, several gene KO did not show a meaningful morphological/structural defect.
Overall, we could observe a large dynamic range of structural phenotypes that were either coupled to, or independent from a functional phenotype (Figure 3). In fact, while the ion channel KO of SCN5A produced primarily functional defects compared to the control, a KO of MYL3, the ventricular isoform of the myosin essential light chain, caused both functional and structural defects.
These results from our case study point to Cardioids’ ability to model complex structural and functional phenotypes, a prospect we are actively exploring for the screening and pre-clinical development of new therapeutics using a variety of specific HCM/DCM and ACM disease modeling approaches.