Search for: All records

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.

 

In this review, we explore the growing role of artificial intelligence (AI) in advancing the biomedical applications of human pluripotent stem cell (hPSC)‐derived organoids. Stem cell‐derived organoids, these miniature organ replicas, have become essential tools for disease modeling, drug discovery, and regenerative medicine. However, analyzing the vast and intricate datasets generated from these organoids can be inefficient and error‐prone. AI techniques offer a promising solution to efficiently extract insights and make predictions from diverse data types generated from microscopy images, transcriptomics, metabolomics, and proteomics. This review offers a brief overview of organoid characterization and fundamental concepts in AI while focusing on a comprehensive exploration of AI applications in organoid‐based disease modeling and drug evaluation. It provides insights into the future possibilities of AI in enhancing the quality control of organoid fabrication, label‐free organoid recognition, and three‐dimensional image reconstruction of complex organoid structures. This review presents the challenges and potential solutions in AI‐organoid integration, focusing on the establishment of reliable AI model decision‐making processes and the standardization of organoid research.

 

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. 

Traditionally, tissue-specific organoids are generated as 3D aggregates of stem cells embedded in Matrigel or hydrogels, and the aggregates eventually end up a spherical shape and suspended in the matrix. Lack of geometrical control of organoid formation makes these spherical organoids limited for modeling the tissues with complex shapes. To address this challenge, we developed a new method to generate 3D spatial-organized cardiac organoids from 2D micropatterned human induced pluripotent stem cell (hiPSC) colonies, instead of directly from 3D stem cell aggregates. This new approach opens the possibility to create cardiac organoids that are templated by 2D non-spherical geometries, which potentially provides us a deeper understanding of biophysical controls on developmental organogenesis. Here, we designed 2D geometrical templates with quadrilateral shapes and pentagram shapes that had same total area but different geometrical shapes. Using this templated substrate, we grew cardiac organoids from hiPSCs and collected a series of parameters to characterize morphological and functional properties of the cardiac organoids. In quadrilateral templates, we found that increasing the aspect ratio impaired cardiac tissue 3D self-assembly, but the elongated geometry improved the cardiac contractile functions. However, in pentagram templates, cardiac organoid structure and function were optimized with a specific geometry of an ideal star shape. This study will shed a light on “organogenesis-by-design” by increasing the intricacy of starting templates from external geometrical cues to improve the organoid morphogenesis and functionality.

 

Mechanical forces impact cardiac cells and tissues over their entire lifespan, from development to growth and eventually to pathophysiology. However, the mechanobiological pathways that drive cell and tissue responses to mechanical forces are only now beginning to be understood, due in part to the challenges in replicating the evolving dynamic microenvironments of cardiac cells and tissues in a laboratory setting. Although many in vitro cardiac models have been established to provide specific stiffness, topography, or viscoelasticity to cardiac cells and tissues via biomaterial scaffolds or external stimuli, technologies for presenting time-evolving mechanical microenvironments have only recently been developed. In this review, we summarize the range of in vitro platforms that have been used for cardiac mechanobiological studies. We provide a comprehensive review on phenotypic and molecular changes of cardiomyocytes in response to these environments, with a focus on how dynamic mechanical cues are transduced and deciphered. We conclude with our vision of how these findings will help to define the baseline of heart pathology and of how these in vitro systems will potentially serve to improve the development of therapies for heart diseases. 

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.