Deriving waveform parameters from calcium transients in human iPSC-derived cardiomyocytes to predict cardiac activity with machine learning

Yang, Hong-Chang; Stebbeds, Will; Francis, Jo; Pointon, Amy; Obrezanova, Olga; Beattie, Kylie A.; Clements, Peter; Harvey, James S.; Smith, G. F.; Bender, Andreas

doi:10.1016/j.stemcr.2022.01.009

Cited by 10 publications

(6 citation statements)

References 37 publications

(44 reference statements)

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“…Sobie and Wehrens (2009) reviewed computational and experimental models that may be used to explain the mechanisms of Ca 2+ -dependent arrythmias, like CPVT. Montero-Calle et al (2022) have utilized computational modeling to guide the fabrication of biomaterials to support the assembly of cardiac microtissues. Furthermore, other researchers have generated in silico mathematical models of hiPSC-CMs to estimate adult-CM behavior (Koivumäki et al, 2018;Tveito et al, 2018).…”

Section: Discussionmentioning

confidence: 99%

“…Yang et al (2022) introduced an alternative ML method to identify drug treatment of hiPSC-CMs, of 63 unique compounds, using calcium waveforms. The custom CardioWave pipeline extracts 38 parameters from waveforms, including metrics on rise/decay time, number of peaks, peak frequency, amplitude, and shoulder characteristics.…”

Section: In Calcium Handling and Cellcell Couplingmentioning

confidence: 99%

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A review on machine learning approaches in cardiac tissue engineering

Kalkunte,

Cisneros,

Castillo

et al. 2024

Front. Biomater. Sci.

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Cardiac tissue engineering (CTE) holds promise in addressing the clinical challenges posed by cardiovascular disease, the leading global cause of mortality. Human induced pluripotent stem cells (hiPSCs) are pivotal for cardiac regeneration therapy, offering an immunocompatible, high density cell source. However, hiPSC-derived cardiomyocytes (hiPSC-CMs) exhibit vital functional deficiencies that are not yet well understood, hindering their clinical deployment. We argue that machine learning (ML) can overcome these challenges, by improving the phenotyping and functionality of these cells via robust mathematical models and predictions. This review paper explores the transformative role of ML in advancing CTE, presenting a primer on relevant ML algorithms. We focus on how ML has recently addressed six key address six key challenges in CTE: cell differentiation, morphology, calcium handling and cell-cell coupling, contraction, and tissue assembly. The paper surveys common ML models, from tree-based and probabilistic to neural networks and deep learning, illustrating their applications to better understand hiPSC-CM behavior. While acknowledging the challenges associated with integrating ML, such as limited biomedical datasets, computational costs of learning data, and model interpretability and reliability, we examine suggestions for improvement, emphasizing the necessity for more extensive and diverse datasets that incorporate temporal and imaging data, augmented by synthetic generative models. By integrating ML with mathematical models and existing expert knowledge, we foresee a fruitful collaboration that unites innovative data-driven models with biophysics-informed models, effectively closing the gaps within CTE.

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Section: Discussionmentioning

confidence: 99%

Section: In Calcium Handling and Cellcell Couplingmentioning

confidence: 99%

A review on machine learning approaches in cardiac tissue engineering

Kalkunte,

Cisneros,

Castillo

et al. 2024

Front. Biomater. Sci.

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“…An attempt was made by the authors of reference [16] in community-based groups to fnd and describe homogeneous echocardiographic characteristics. Studying phenotypes defned by echocardiography and their connections to vascular function and circulating biomarkers was done on the STANISLAS cohort's frst generation.…”

Section: Related Workmentioning

confidence: 99%

Application of Machine Learning for Cardiovascular Disease Risk Prediction

Dalal

Goel

Onyema

et al. 2023

Computational Intelligence and Neuroscience

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Cardiovascular diseases (CVDs) are a common cause of heart failure globally. The need to explore possible ways to tackle the disease necessitated this study. The study designed a machine learning model for cardiovascular disease risk prediction in accordance with a dataset that contains 11 features which may be used to forecast the disease. The dataset from Kaggle on cardiovascular disease includes approximately 70,000 patient records that were used to determine the outcome. Compared to the UCI dataset, the Kaggle dataset has many more training and validation records. Models created using neural networks, random forests, Bayesian networks, C5.0, and QUEST were compared for this dataset. On training and testing data sets, the results acquired a high accuracy (99.1 percent), which is significantly superior to previous methods. Ahead-of-time detection and diagnosis of cardiac disease, as well as better treatment outcomes, are strong possibilities for the suggested prediction model. Additionally, it may help patients better manage their illness or life forms in order to increase their chances of recovery/survival. The result showed greater accuracy and promising signs that machine-learning algorithms can indeed assist in early identification of the disease and improvement of the treatment outcome.

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“…Phenotypic assays include assessment of key organelles including ER integrity and mitochondrial membrane potential (MMP) (Archer et al 2018 ), previously reported as a predictive approach for SCT and physiological processes. For example, the calcium transient assay, 2 when combined with the software CardioWave (Yang et al 2022 ), can derive up to 40 parameters. These parameters have been used to build machine learning models to study acute cardiotoxicity (Yang et al 2022 ).…”

Section: Introductionmentioning

confidence: 99%

“…For example, the calcium transient assay, 2 when combined with the software CardioWave (Yang et al 2022 ), can derive up to 40 parameters. These parameters have been used to build machine learning models to study acute cardiotoxicity (Yang et al 2022 ).…”

Section: Introductionmentioning

confidence: 99%

Computational approaches identify a transcriptomic fingerprint of drug-induced structural cardiotoxicity

Au Yeung,

Obrezanova,

Zhou

et al. 2024

Cell Biol Toxicol

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Structural cardiotoxicity (SCT) presents a high-impact risk that is poorly tolerated in drug discovery unless significant benefit is anticipated. Therefore, we aimed to improve the mechanistic understanding of SCT. First, we combined machine learning methods with a modified calcium transient assay in human-induced pluripotent stem cell-derived cardiomyocytes to identify nine parameters that could predict SCT. Next, we applied transcriptomic profiling to human cardiac microtissues exposed to structural and non-structural cardiotoxins. Fifty-two genes expressed across the three main cell types in the heart (cardiomyocytes, endothelial cells, and fibroblasts) were prioritised in differential expression and network clustering analyses and could be linked to known mechanisms of SCT. This transcriptomic fingerprint may prove useful for generating strategies to mitigate SCT risk in early drug discovery. Graphical Abstract

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Deriving waveform parameters from calcium transients in human iPSC-derived cardiomyocytes to predict cardiac activity with machine learning

Cited by 10 publications

References 37 publications

A review on machine learning approaches in cardiac tissue engineering

A review on machine learning approaches in cardiac tissue engineering

Application of Machine Learning for Cardiovascular Disease Risk Prediction

Computational approaches identify a transcriptomic fingerprint of drug-induced structural cardiotoxicity

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