Design optimization of geometrically confined cardiac organoids enabled by machine learning techniques.
Cell Rep Methods
; 4(6): 100798, 2024 Jun 17.
Article
en En
| MEDLINE
| ID: mdl-38889687
ABSTRACT
Stem cell organoids are powerful models for studying organ development, disease modeling, drug screening, and regenerative medicine applications. The convergence of organoid technology, tissue engineering, and artificial intelligence (AI) could potentially enhance our understanding of the design principles for organoid engineering. In this study, we utilized micropatterning techniques to create a designer library of 230 cardiac organoids with 7 geometric designs. We employed manifold learning techniques to analyze single organoid heterogeneity based on 10 physiological parameters. We clustered and refined the cardiac organoids based on their functional similarity using unsupervised machine learning approaches, thus elucidating unique functionalities associated with geometric designs. We also highlighted the critical role of calcium transient rising time in distinguishing organoids based on geometric patterns and clustering results. This integration of organoid engineering and machine learning enhances our understanding of structure-function relationships in cardiac organoids, paving the way for more controlled and optimized organoid design.
Palabras clave
Texto completo:
1
Banco de datos:
MEDLINE
Asunto principal:
Organoides
/
Ingeniería de Tejidos
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Aprendizaje Automático
Límite:
Animals
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Humans
Idioma:
En
Año:
2024
Tipo del documento:
Article