The availability of genetic testing for inherited cardiac conditions, including dilated cardiomyopathy has revolutionised the care and treatment of patients with these conditions. One of the greatest challenges, however, remains the classification with regards to pathogenicity of many variants identified. This highlights the importance of being able to further classify such variants using functional tests that can either confirm or exclude their cause of the condition.
Taking a personalised medicine approach, we have established a pipeline to mimic the exact patient variants in a dish to understand how normal and patient-derived micro-hearts contract. This model enable us to understand what the potential mechanisms of dysfunction are and uncover whether clinically approved or experimental drugs may be beneficial for patients.
This project will use a machine learning-based pipeline to detect and quantify 4-dimensional (3D volume in time) changes to contractility, cardiomyocyte nuclear shape, and calcium handling in normal micro-heart tissues as a ground truth, as well as micro-hearts that have been genetically engineered to mimic patient variants of unknown significance. After performing such analyses, clinical collaborators and geneticists will be informed as to whether there are functional consequences of such variants, which may inform the best course of treatment.