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.
Metabolomics-based mass spectrometry methods to analyze the chemical content of 3D organoid models
Metabolomics, the study of metabolites present in biological samples, can provide a global view of sample state as well as insights into biological changes caused by disease or environmental interactions. Mass spectrometry (MS) is commonly used for metabolomics analysis given its high-throughput capabilities, high sensitivity, and capacity to identify multiple compounds in complex samples simultaneously. MS can be coupled to separation methods that can handle small volumes, making it well suited for analyzing the metabolome of organoids, miniaturized three-dimensional aggregates of stem cells that model in vivo organs. Organoids are being used in research efforts to study human disease and development, and in the design of personalized drug treatments. For organoid models to be useful, they need to recapitulate morphological and chemical aspects, such as the metabolome, of the parent tissue. This review highlights the separation- and imaging-based MS-based metabolomics methods that have been used to analyze the chemical contents of organoids. Future perspectives on how MS techniques can be optimized to determine the accuracy of organoid models and expand the field of organoid research are also discussed.
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- Award ID(s):
- 1735252
- NSF-PAR ID:
- 10334480
- Date Published:
- Journal Name:
- The Analyst
- ISSN:
- 0003-2654
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Abstract Complex three‐dimensional
in vitro organ‐like models, or organoids, offer a unique biological tool with distinct advantages over two‐dimensional cell culture systems, which can be too simplistic, and animal models, which can be too complex and may fail to recapitulate human physiology and pathology. Significant progress has been made in driving stem cells to differentiate into different organoid types, though several challenges remain. For example, many organoid models suffer from high heterogeneity, and it can be difficult to fully incorporate the complexity ofin vivo tissue and organ development to faithfully reproduce human biology. Successfully addressing such limitations would increase the viability of organoids as models for drug development and preclinical testing. On April 3–6, 2022, experts in organoid development and biology convened at the Keystone Symposium “Organoids as Tools for Fundamental Discovery and Translation” to discuss recent advances and insights from this relatively new model system into human development and disease.
- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1039/D2AN00599A
