Direct 3D‐Bioprinting of hiPSC‐Derived Cardiomyocytes to Generate Functional Cardiac Tissues

Esser, Tilman U.; Anspach, Annalise; Muenzebrock, Katrin A.; Kah, Delf; Schrüfer, Stefan; Schenk, Joachim; Heinze, Katrin G.; Schubert, Dirk W.; Fabry, Ben; Engel, Felix B.

doi:10.1002/adma.202305911

Cited by 13 publications

(2 citation statements)

References 51 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…While the self-assembly of hiPSC-CMs into engineered heart tissues has produced largely functional 3D myocardium, its scalability and lack of vasculature pose limitations (Ronaldson-Bouchard et al, 2018). Various approaches, including scaffold or scaffold free systems (Feinberg et al, 2013;Nunes et al, 2013;Kobayashi et al, 2019;Qasim et al, 2019) such as bioprinting have been explored for 3D cardiac tissue assembly (Ong et al, 2018;Esser et al, 2023;Finkel et al, 2023). ML offers a potential avenue to expedite the optimization of scaffold and bioink parameters for supporting multicellular culture and facilitating functional tissue assembly.…”

Section: In Tissue Assemblymentioning

confidence: 99%

A review on machine learning approaches in cardiac tissue engineering

Kalkunte,

Cisneros,

Castillo

et al. 2024

Front. Biomater. Sci.

View full text Add to dashboard Cite

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.

show abstract

Section: In Tissue Assemblymentioning

confidence: 99%

A review on machine learning approaches in cardiac tissue engineering

Kalkunte,

Cisneros,

Castillo

et al. 2024

Front. Biomater. Sci.

View full text Add to dashboard Cite

show abstract

“…New technologies, like 3D-bioprinting, can be leveraged to improve the embedding of cells and matrices still further in physiologically relevant ex vivo tissues, as has been done using hiPSC-derived cardiomyocytes in ventricle-shaped cardiac tissues. 118 These approaches show promise for tailored production of patient-specific tissues to address cardiac defects or predisposing conditions. However, significant hurdles must be overcome in terms of testing before these novel approaches can be integrated clinically.…”

Section: Use Of Evts For Disease Modelingmentioning

confidence: 99%

Use of iPSC-Derived Smooth Muscle Cells to Model Physiology and Pathology

Kwartler,

Esparza Pinelo

2024

ATVB

View full text Add to dashboard Cite

The implementation of human-induced pluripotent stem cell models has introduced an additional tool for identifying molecular mechanisms of disease that complement animal models. Patient-derived or CRISPR/Cas9-edited induced pluripotent stem cells differentiated into smooth muscle cells (SMCs) have been leveraged to discover novel mechanisms, screen potential therapeutic strategies, and model in vivo development. The field has evolved over almost 15 years of research using human-induced pluripotent stem cell-SMCs and has made significant strides toward overcoming initial challenges such as the lineage specificity of SMC phenotypes. However, challenges both specific (eg, the lack of specific markers to thoroughly validate human-induced pluripotent stem cell-SMCs) and general (eg, a lack of transparency and consensus around methodology in the field) remain. In this review, we highlight the recent successes and remaining challenges of the human-induced pluripotent stem cell-SMC model.

show abstract

Bioprinting‐Assisted Tissue Assembly for Structural and Functional Modulation of Engineered Heart Tissue Mimicking Left Ventricular Myocardial Fiber Orientation

Hwang,

Choi,

Yong

et al. 2024

Advanced Materials

View full text Add to dashboard Cite

Left ventricular twist is influenced by the unique oriented structure of myocardial fibers. Replicating this intricate structural‐functional relationship in an in vitro heart model remains challenging, mainly due to the difficulties in achieving a complex structure with synchrony between layers. This study introduces a novel approach through the utilization of bioprinting‐assisted tissue assembly (BATA)—a synergistic integration of bioprinting and tissue assembly strategies. By flexibly manufacturing tissue modules and assembly platforms, BATA can create structures that traditional methods find difficult to achieve. This approach integrates engineered heart tissue (EHT) modules, each with intrinsic functional and structural characteristics, into a layered, multi‐oriented tissue in a controlled manner. EHTs assembled in different orientations exhibit various contractile forces and electrical signal patterns. The BATA is capable of constructing complex myocardial fiber orientations within a chamber‐like structure (MoCha). MoCha replicates the native cardiac architecture by exhibiting three layers and three alignment directions, and it reproduces the left ventricular twist by exhibiting synchronized contraction between layers and mimicking the native cardiac architecture. The potential of BATA extends to engineering tissues capable of constructing and functioning as complete organs on a large scale. This advancement holds the promise of realizing future organ‐on‐demand technology.This article is protected by copyright. All rights reserved

show abstract

Direct 3D‐Bioprinting of hiPSC‐Derived Cardiomyocytes to Generate Functional Cardiac Tissues

Cited by 13 publications

References 51 publications

A review on machine learning approaches in cardiac tissue engineering

A review on machine learning approaches in cardiac tissue engineering

Use of iPSC-Derived Smooth Muscle Cells to Model Physiology and Pathology

Bioprinting‐Assisted Tissue Assembly for Structural and Functional Modulation of Engineered Heart Tissue Mimicking Left Ventricular Myocardial Fiber Orientation

Contact Info

Product

Resources

About