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Organoid Intelligence ushers in a new era by seamlessly integrating cutting-edge organoid technology with the power of artificial intelligence. Organoids, three-dimensional miniature organ-like structures cultivated from stem cells, offer an unparalleled opportunity to simulate complex human organ systems in vitro. Through the convergence of organoid technology and AI, researchers gain the means to accelerate discoveries and insights across various disciplines. Artificial intelligence algorithms enable the comprehensive analysis of intricate organoid behaviors, intricate cellular interactions, and dynamic responses to stimuli. This synergy empowers the development of predictive models, precise disease simulations, and personalized medicine approaches, revolutionizing our understanding of human development, disease mechanisms, and therapeutic interventions. Organoid Intelligence holds the promise of reshaping how we perceive in vitro modeling, propelling us toward a future where these advanced systems play a pivotal role in biomedical research and drug development. 

Costameres, as striated muscle-specific cell adhesions, anchor both M-lines and Z-lines of the sarcomeres to the extracellular matrix. Previous studies have demonstrated that costameres intimately participate in the initial assembly of myofibrils. However, how costamere maturation cooperates with myofibril growth is still underexplored. In this work, we analyzed zyxin (costameres), α-actinin (Z-lines) and myomesin (M-lines) to track the behaviors of costameres and myofibrils within the cardiomyocytes derived from human induced pluripotent stem cells (hiPSC-CMs). We quantified the assembly and maturation of costameres associated with the process of myofibril growth within the hiPSC-CMs in a time-dependent manner. We found that asynchrony existed not only between the maturation of myofibrils and costameres, but also between the formation of Z-costameres and M-costameres that associated with different structural components of the sarcomeres. This study helps us gain more understanding of how costameres assemble and incorporate into the cardiomyocyte sarcomeres, which sheds a light on cardiomyocyte mechanobiology. 

Since the term “smart materials” was put forward in the 1980s, stimuli-responsive biomaterials have been used as powerful tools in tissue engineering, mechanobiology, and clinical applications. For the purpose of myocardial repair and regeneration, stimuli-responsive biomaterials are employed to fabricate hydrogels and nanoparticles for targeted delivery of therapeutic drugs and cells, which have been proved to alleviate disease progression and enhance tissue regeneration. By reproducing the sophisticated and dynamic microenvironment of the native heart, stimuli-responsive biomaterials have also been used to engineer dynamic culture systems to understand how cardiac cells and tissues respond to progressive changes in extracellular microenvironments, enabling the investigation of dynamic cell mechanobiology. Here, we provide an overview of stimuli-responsive biomaterials used in cardiovascular research applications, with a specific focus on cardiac tissue engineering and dynamic cell mechanobiology. We also discuss how these smart materials can be utilized to mimic the dynamic microenvironment during heart development, which might provide an opportunity to reveal the fundamental mechanisms of cardiomyogenesis and cardiac maturation. 

Abstract Over the past decades, mesenchymal stromal cells (MSCs) have been extensively investigated as a potential therapeutic cell source for the treatment of various disorders. Differentiation of MSCs from human induced pluripotent stem cells (iMSCs) has provided a scalable approach for the biomanufacturing of MSCs and related biological products. Although iMSCs shared typical MSC markers and functions as primary MSCs (pMSCs), there is a lack of lineage specificity in many iMSC differentiation protocols. Here, a stepwise hiPSC‐to‐iMSC differentiation method is employed via intermediate cell stages of neural crest and cytotrophoblast to generate lineage‐specific MSCs with varying differentiation efficiencies and gene expression. Through a comprehensive comparison between early developmental cell types (hiPSCs, neural crest, and cytotrophoblast), two lineage‐specific iMSCs, and six source‐specific pMSCs, are able to not only distinguish the transcriptomic differences between MSCs and early developmental cells, but also determine the transcriptomic similarities of iMSC subtypes to postnatal or perinatal pMSCs. Additionally, it is demonstrated that different iMSC subtypes and priming conditions affected EV production, exosomal protein expression, and cytokine cargo.