Dataset Information

Novel 3D Liquid Cell Culture Method for Anchorage-independent Cell Growth, Cell Imaging and Automated Drug Screening.

ABSTRACT: Cells grown in three-dimensional (3D) cultures are more likely to have native cell-cell and cell-matrix interactions than in 2D cultures that impose mechanical constraints to cells. However, most 3D cultures utilise gel matrix which, while serving as a scaffold, limits application due to its solid and opaque nature and inconsistency in cell exposure to exogenous signals. In 3D culture without gel matrix, cells tend to adhere to each other and form clumps with necrotic zone at the centre, making them unsuitable for analyses. Here we report that addition of low-molecular-weight agar named LA717 to culture media allows cells to grow as dispersed clonal spheroids in 3D. LA717 maintains cells dispersed and settled to the bottom of the medium while keeping the medium clear with little additional viscosity, making it suitable for microscopic observation. Importantly, cancer spheroids formed in LA717-containing medium show higher sensitivity to anti-cancer drugs such as Trametinib and MK-2206 that are not as effective in 2D. Because of the small and consistent size of spheroids, cell viability and drug toxicity are readily detectable in automated imaging analysis. These results demonstrate that LA717 offers a novel 3D culture system with great in vivo reflection and practicality.

SUBMITTER: Abe-Fukasawa N

PROVIDER: S-EPMC5827526 | biostudies-literature | 2018 Feb

REPOSITORIES: biostudies-literature

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Novel 3D Liquid Cell Culture Method for Anchorage-independent Cell Growth, Cell Imaging and Automated Drug Screening.

Abe-Fukasawa Natsuki N Otsuka Keiichiro K Aihara Ayako A Itasaki Nobue N Nishino Taito T

Scientific reports 20180226 1

Cells grown in three-dimensional (3D) cultures are more likely to have native cell-cell and cell-matrix interactions than in 2D cultures that impose mechanical constraints to cells. However, most 3D cultures utilise gel matrix which, while serving as a scaffold, limits application due to its solid and opaque nature and inconsistency in cell exposure to exogenous signals. In 3D culture without gel matrix, cells tend to adhere to each other and form clumps with necrotic zone at the centre, making ...[more]

PMID: 29483620

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Project description:There is no segmentation method that performs perfectly with any dataset in comparison to human segmentation. Evaluation procedures for segmentation algorithms become critical for their selection. The problems associated with segmentation performance evaluations and visual verification of segmentation results are exaggerated when dealing with thousands of three-dimensional (3D) image volumes because of the amount of computation and manual inputs needed. We address the problem of evaluating 3D segmentation performance when segmentation is applied to thousands of confocal microscopy images (z-stacks). Our approach is to incorporate experimental imaging and geometrical criteria, and map them into computationally efficient segmentation algorithms that can be applied to a very large number of z-stacks. This is an alternative approach to considering existing segmentation methods and evaluating most state-of-the-art algorithms. We designed a methodology for 3D segmentation performance characterization that consists of design, evaluation and verification steps. The characterization integrates manual inputs from projected surrogate 'ground truth' of statistically representative samples and from visual inspection into the evaluation. The novelty of the methodology lies in (1) designing candidate segmentation algorithms by mapping imaging and geometrical criteria into algorithmic steps, and constructing plausible segmentation algorithms with respect to the order of algorithmic steps and their parameters, (2) evaluating segmentation accuracy using samples drawn from probability distribution estimates of candidate segmentations and (3) minimizing human labour needed to create surrogate 'truth' by approximating z-stack segmentations with 2D contours from three orthogonal z-stack projections and by developing visual verification tools. We demonstrate the methodology by applying it to a dataset of 1253 mesenchymal stem cells. The cells reside on 10 different types of biomaterial scaffolds, and are stained for actin and nucleus yielding 128 460 image frames (on average, 125 cells/scaffold × 10 scaffold types × 2 stains × 51 frames/cell). After constructing and evaluating six candidates of 3D segmentation algorithms, the most accurate 3D segmentation algorithm achieved an average precision of 0.82 and an accuracy of 0.84 as measured by the Dice similarity index where values greater than 0.7 indicate a good spatial overlap. A probability of segmentation success was 0.85 based on visual verification, and a computation time was 42.3 h to process all z-stacks. While the most accurate segmentation technique was 4.2 times slower than the second most accurate algorithm, it consumed on average 9.65 times less memory per z-stack segmentation.

Dataset Information

Novel 3D Liquid Cell Culture Method for Anchorage-independent Cell Growth, Cell Imaging and Automated Drug Screening.

Publications

Novel 3D Liquid Cell Culture Method for Anchorage-independent Cell Growth, Cell Imaging and Automated Drug Screening.

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