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Abstract BackgroundCardiac pathological outcome of metabolic remodeling is difficult to model using cardiomyocytes derived from human-induced pluripotent stem cells (hiPSC-CMs) due to low metabolic maturation. MethodshiPSC-CM spheres were treated with AMP-activated protein kinase (AMPK) activators and examined for hiPSC-CM maturation features, molecular changes and the response to pathological stimuli. ResultsTreatment of hiPSC-CMs with AMPK activators increased ATP content, mitochondrial membrane potential and content, mitochondrial DNA, mitochondrial function and fatty acid uptake, indicating increased metabolic maturation. Conversely, the knockdown of AMPK inhibited mitochondrial maturation of hiPSC-CMs. In addition, AMPK activator-treated hiPSC-CMs had improved structural development and functional features—including enhanced Ca²⁺transient kinetics and increased contraction. Transcriptomic, proteomic and metabolomic profiling identified differential levels of expression of genes, proteins and metabolites associated with a molecular signature of mature cardiomyocytes in AMPK activator-treated hiPSC-CMs. In response to pathological stimuli, AMPK activator-treated hiPSC-CMs had increased glycolysis, and other pathological outcomes compared to untreated cells. ConclusionAMPK activator-treated cardiac spheres could serve as a valuable model to gain novel insights into cardiac diseases. 

Abstract Human-induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs) provide an excellent platform for potential clinical and research applications. Identifying abnormal Ca²⁺transients is crucial for evaluating cardiomyocyte function that requires labor-intensive manual effort. Therefore, we develop an analytical pipeline for automatic assessment of Ca²⁺transient abnormality, by employing advanced machine learning methods together with an Analytical Algorithm. First, we adapt an existing Analytical Algorithm to identify Ca²⁺transient peaks and determine peak abnormality based on quantified peak characteristics. Second, we train a peak-level Support Vector Machine (SVM) classifier by using human-expert assessment of peak abnormality as outcome and profiled peak variables as predictive features. Third, we train another cell-level SVM classifier by using human-expert assessment of cell abnormality as outcome and quantified cell-level variables as predictive features. This cell-level SVM classifier can be used to assess additional Ca²⁺transient signals. By applying this pipeline to our Ca²⁺transient data, we trained a cell-level SVM classifier using 200 cells as training data, then tested its accuracy in an independent dataset of 54 cells. As a result, we obtained 88% training accuracy and 87% test accuracy. Further, we provide a free R package to implement our pipeline for high-throughput CM Ca²⁺analysis.