Saeid Ansaryan, Yung-Cheng Chiang, Yen-Cheng Liu, Jiayi Tan, Luis Francisco Lorenzo-Martín, Matthias P Lutolf, Genrich Tolstonog, Hatice Altug
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引用次数: 0
Abstract
Advances in organoid models, as ex vivo mini-organs, and the development of screening imaging technologies have continuously driven each other forward. A complete understanding of organoids requires detailed insights into the intertwined intraorganoid and extraorganoid activities and how they change across time and space. This study introduces a multiplexed imaging platform that integrates label-free nanoplasmonic biosensing with fluorescence microscopy to offer simultaneous monitoring of dynamics occurring within and around arrays of single spheroids with spatiotemporal resolution. The label-free module employs nanoplasmonic biosensors with extraordinary optical transmission to track biomolecular secretions into the surroundings, while concurrent fluorescence imaging enables structural analysis and viability assessment. To perform multiparametric interrogation of the data from different channels over extended periods, a deep-learning-augmented image analysis is incorporated. The platform is applied to tumor spheroids to investigate vascular endothelial growth factor A secretion alongside morphometric changes and viability, showcasing its ability to capture variations in secretion and growth dynamics between untreated and drug-treated groups. This integrated approach advances comprehensive insights into organoid models and can complement existing technologies to accelerate discoveries in disease modeling and drug development.
Small MethodsMaterials Science-General Materials Science
CiteScore
17.40
自引率
1.60%
发文量
347
期刊介绍:
Small Methods is a multidisciplinary journal that publishes groundbreaking research on methods relevant to nano- and microscale research. It welcomes contributions from the fields of materials science, biomedical science, chemistry, and physics, showcasing the latest advancements in experimental techniques.
With a notable 2022 Impact Factor of 12.4 (Journal Citation Reports, Clarivate Analytics, 2023), Small Methods is recognized for its significant impact on the scientific community.
The online ISSN for Small Methods is 2366-9608.