Integrated machine learning and multimodal data fusion for patho-phenotypic feature recognition in iPSC models of dilated cardiomyopathy.

Integration of multiple data sources presents a challenge for accurate prediction of molecular patho-phenotypic features in automated analysis of data from human model systems. Here, we applied a machine learning-based data integration to distinguish patho-phenotypic features at the subcellular level for dilated cardiomyopathy (DCM). We employed a human induced pluripotent stem cell-derived cardiomyocyte (iPSC-CM) model of a DCM mutation in the sarcomere protein troponin T (TnT), TnT-R141W, compared to isogenic healthy (WT) control iPSC-CMs.

An Alternative Mechanism of Subcellular Iron Uptake Deficiency in Cardiomyocytes.

Background: Systemic defects in intestinal iron absorption, circulation, and retention cause iron deficiency in 50% of patients with heart failure. Defective subcellular iron uptake mechanisms that are independent of systemic absorption are incompletely understood. The main intracellular route for iron uptake in cardiomyocytes is clathrin-mediated endocytosis.

Non-negative blind deconvolution for signal processing in a CRISPR-edited iPSC-cardiomyocyte model of dilated cardiomyopathy.

We developed an integrated platform for analysis of parameterized data from human disease models. We report a non-negative blind deconvolution (NNBD) approach to quantify calcium (Ca ) handling, beating force and contractility in human-induced pluripotent stem cell-derived cardiomyocytes (iPSC-CMs) at the single-cell level. We employed CRISPR/Cas gene editing to introduce a dilated cardiomyopathy (DCM)-causing mutation in troponin T (TnT), TnT-R141W, into wild-type control iPSCs (MUT).