Dataset Information

Human induced pluripotent stem cell-based platform for modeling cardiac ischemia.

ABSTRACT: Ischemic heart disease is a major cause of death worldwide, and the only available therapy to salvage the tissue is reperfusion, which can initially cause further damage. Many therapeutics that have been promising in animal models have failed in human trials. Thus, functional human based cardiac ischemia models are required. In this study, a human induced pluripotent stem cell derived-cardiomyocyte (hiPSC-CM)-based platform for modeling ischemia-reperfusion was developed utilizing a system enabling precise control over oxygen concentration and real-time monitoring of the oxygen dynamics as well as iPS-CM functionality. In addition, morphology and expression of hypoxia-related genes and proteins were evaluated as hiPSC-CM response to 8 or 24 h hypoxia and 24 h reoxygenation. During hypoxia, initial decrease in hiPSC-CM beating frequency was observed, after which the CMs adapted to the conditions and the beating frequency gradually increased already before reoxygenation. During reoxygenation, the beating frequency typically first surpassed the baseline before settling down to the values close the baseline. Furthermore, slowing on the field potential propagation throughout the hiPSC-CM sheet as well as increase in depolarization time and decrease in overall field potential duration were observed during hypoxia. These changes were reversed during reoxygenation. Disorganization of sarcomere structures was observed after hypoxia and reoxygenation, supported by decrease in the expression of sarcomeric proteins. Furthermore, increase in the expression of gene encoding glucose transporter 1 was observed. These findings indicate, that despite their immature phenotype, hiPSC-CMs can be utilized in modeling ischemia-reperfusion injury.

SUBMITTER: Hakli M

PROVIDER: S-EPMC7893031 | biostudies-literature | 2021 Feb

REPOSITORIES: biostudies-literature

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Human induced pluripotent stem cell-based platform for modeling cardiac ischemia.

Häkli Martta M Kreutzer Joose J Mäki Antti-Juhana AJ Välimäki Hannu H Lappi Henna H Huhtala Heini H Kallio Pasi P Aalto-Setälä Katriina K Pekkanen-Mattila Mari M

Scientific reports 20210218 1

Ischemic heart disease is a major cause of death worldwide, and the only available therapy to salvage the tissue is reperfusion, which can initially cause further damage. Many therapeutics that have been promising in animal models have failed in human trials. Thus, functional human based cardiac ischemia models are required. In this study, a human induced pluripotent stem cell derived-cardiomyocyte (hiPSC-CM)-based platform for modeling ischemia-reperfusion was developed utilizing a system enabl ...[more]

PMID: 33603154

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Project description:Causative mutations and variants associated with cardiac diseases have been found in genes encoding cardiac ion channels, accessory proteins, cytoskeletal components, junctional proteins, and signaling molecules. In most cases the functional evaluation of the genetic alteration has been carried out by expressing the mutated proteins in in-vitro heterologous systems. While these studies have provided a wealth of functional details that have greatly enhanced the understanding of the pathological mechanisms, it has always been clear that heterologous expression of the mutant protein bears the intrinsic limitation of the lack of a proper intracellular environment and the lack of pathological remodeling. The results obtained from the application of the next generation sequencing technique to patients suffering from cardiac diseases have identified several loci, mostly in non-coding DNA regions, which still await functional analysis. The isolation and culture of human embryonic stem cells has initially provided a constant source of cells from which cardiomyocytes (CMs) can be obtained by differentiation. Furthermore, the possibility to reprogram cellular fate to a pluripotent state, has opened this process to the study of genetic diseases. Thus induced pluripotent stem cells (iPSCs) represent a completely new cellular model that overcomes the limitations of heterologous studies. Importantly, due to the possibility to keep spontaneously beating CMs in culture for several months, during which they show a certain degree of maturation/aging, this approach will also provide a system in which to address the effect of long-term expression of the mutated proteins or any other DNA mutation, in terms of electrophysiological remodeling. Moreover, since iPSC preserve the entire patients' genetic context, the system will help the physicians in identifying the most appropriate pharmacological intervention to correct the functional alteration. This article summarizes the current knowledge of cardiac genetic diseases modelled with iPSC.

Project description:Human cardiomyocytes (CMs) have potential for use in therapeutic cell therapy and high-throughput drug screening. Because of the inability to expand adult CMs, their large-scale production from human pluripotent stem cells (hPSC) has been suggested. Significant improvements have been made in understanding directed differentiation processes of CMs from hPSCs and their suspension culture-based production at chemically defined conditions. However, optimization experiments are costly, time-consuming, and highly variable, leading to challenges in developing reliable and consistent protocols for the generation of large CM numbers at high purity. This study examined the ability of data-driven modeling with machine learning for identifying key experimental conditions and predicting final CM content using data collected during hPSC-cardiac differentiation in advanced stirred tank bioreactors (STBRs). Through feature selection, we identified process conditions, features, and patterns that are the most influential on and predictive of the CM content at the process endpoint, on differentiation day 10 (dd10). Process-related features were extracted from experimental data collected from 58 differentiation experiments by feature engineering. These features included data continuously collected online by the bioreactor system, such as dissolved oxygen concentration and pH patterns, as well as offline determined data, including the cell density, cell aggregate size, and nutrient concentrations. The selected features were used as inputs to construct models to classify the resulting CM content as being "sufficient" or "insufficient" regarding pre-defined thresholds. The models built using random forests and Gaussian process modeling predicted insufficient CM content for a differentiation process with 90% accuracy and precision on dd7 of the protocol and with 85% accuracy and 82% precision at a substantially earlier stage: dd5. These models provide insight into potential key factors affecting hPSC cardiac differentiation to aid in selecting future experimental conditions and can predict the final CM content at earlier process timepoints, providing cost and time savings. This study suggests that data-driven models and machine learning techniques can be employed using existing data for understanding and improving production of a specific cell type, which is potentially applicable to other lineages and critical for realization of their therapeutic applications.

Dataset Information

Human induced pluripotent stem cell-based platform for modeling cardiac ischemia.

Publications

Human induced pluripotent stem cell-based platform for modeling cardiac ischemia.

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