Dataset Information

High-Resolution Imaging of Tumor Spheroids and Organoids Enabled by Expansion Microscopy.

ABSTRACT: Three-dimensional cell cultures are able to better mimic the physiology and cellular environments found in tissues in vivo compared to cells grown in two dimensions. In order to study the structure and function of cells in 3-D cultures, light microscopy is frequently used. The preparation of 3-D cell cultures for light microscopy is often destructive, including physical sectioning of the samples, which can result in the loss of 3-D information. In order to probe the structure of 3-D cell cultures at high resolution, we have explored the use of expansion microscopy and compared it to a simple immersion clearing protocol. We provide a practical method for the study of spheroids, organoids and tumor-infiltrating immune cells at high resolution without the loss of spatial organization. Expanded samples are highly transparent, enabling high-resolution imaging over extended volumes by significantly reducing light scatter and absorption. In addition, the hydrogel-like nature of expanded samples enables homogenous antibody labeling of dense epitopes throughout the sample volume. The improved labeling and image quality achieved in expanded samples revealed details in the center of the organoid which were previously only observable following serial sectioning. In comparison to chemically cleared spheroids, the improved signal-to-background ratio of expanded samples greatly improved subsequent methods for image segmentation and analysis.

SUBMITTER: Edwards SJ

PROVIDER: S-EPMC7543521 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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Publications

High-Resolution Imaging of Tumor Spheroids and Organoids Enabled by Expansion Microscopy.

Edwards Steven J SJ Carannante Valentina V Kuhnigk Kyra K Ring Henrik H Tararuk Tatsiana T Hallböök Finn F Blom Hans H Önfelt Björn B Brismar Hjalmar H

Frontiers in molecular biosciences 20200924

Three-dimensional cell cultures are able to better mimic the physiology and cellular environments found in tissues <i>in vivo</i> compared to cells grown in two dimensions. In order to study the structure and function of cells in 3-D cultures, light microscopy is frequently used. The preparation of 3-D cell cultures for light microscopy is often destructive, including physical sectioning of the samples, which can result in the loss of 3-D information. In order to probe the structure of 3-D cell ...[more]

PMID: 33195398

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Project description:Artificial intelligence algorithms that aid mini-microscope imaging are attractive for numerous applications. In this paper, we optimize artificial intelligence techniques to provide clear, and natural biomedical imaging. We demonstrate that a deep learning-enabled super-resolution method can significantly enhance the spatial resolution of mini-microscopy and regular-microscopy. This data-driven approach trains a generative adversarial network to transform low-resolution images into super-resolved ones. Mini-microscopic images and regular-microscopic images acquired with different optical microscopes under various magnifications are collected as our experimental benchmark datasets. The only input to this generative-adversarial-network-based method are images from the datasets down-sampled by the Bicubic interpolation. We use independent test set to evaluate this deep learning approach with other deep learning-based algorithms through qualitative and quantitative comparisons. To clearly present the improvements achieved by this generative-adversarial-network-based method, we zoom into the local features to explore and highlight the qualitative differences. We also employ the peak signal-to-noise ratio and the structural similarity, to quantitatively compare alternative super-resolution methods. The quantitative results illustrate that super-resolution images obtained from our approach with interpolation parameter α=0.25 more closely match those of the original high-resolution images than to those obtained by any of the alternative state-of-the-art method. These results are significant for fields that use microscopy tools, such as biomedical imaging of engineered living systems. We also utilize this generative adversarial network-based algorithm to optimize the resolution of biomedical specimen images and then generate three-dimensional reconstruction, so as to enhance the ability of three-dimensional imaging throughout the entire volumes for spatial-temporal analyses of specimen structures.

Dataset Information

High-Resolution Imaging of Tumor Spheroids and Organoids Enabled by Expansion Microscopy.

Publications

High-Resolution Imaging of Tumor Spheroids and Organoids Enabled by Expansion Microscopy.

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