Dataset Information

Optimal, Large-Scale Propagation of Mouse Mammary Tumor Organoids.

ABSTRACT: Tumor organoids mimic the architecture and heterogeneity of in vivo tumors and enable studies of collective interactions between tumor cells as well as with their surrounding microenvironment. Although tumor organoids hold significant promise as cancer models, they are also more costly and labor-intensive to cultivate than traditional 2D cell culture. We sought to identify critical factors regulating organoid growth ex vivo, and to use these observations to develop a more efficient organoid expansion method. Using time-lapse imaging of mouse mammary tumor organoids in 3D culture, we observed that outgrowth potential varies non-linearly with initial organoid size. Maximal outgrowth occurred in organoids with a starting size between ~10 to 1000 cells. Based on these observations, we developed a suspension culture method that maintains organoids in the ideal size range, enabling expansion from 1 million to over 100 million cells in less than 2 weeks and less than 3 hours of hands-on time. Our method facilitates the rapid, cost-effective expansion of organoids for CRISPR based studies and other assays requiring a large amount of organoid starting material.

SUBMITTER: Wrenn ED

PROVIDER: S-EPMC7587543 | biostudies-literature | 2020 Oct

REPOSITORIES: biostudies-literature

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Publications

Optimal, Large-Scale Propagation of Mouse Mammary Tumor Organoids.

Wrenn Emma D ED Moore Breanna M BM Greenwood Erin E McBirney Margaux M Cheung Kevin J KJ

Journal of mammary gland biology and neoplasia 20201026 4

Tumor organoids mimic the architecture and heterogeneity of in vivo tumors and enable studies of collective interactions between tumor cells as well as with their surrounding microenvironment. Although tumor organoids hold significant promise as cancer models, they are also more costly and labor-intensive to cultivate than traditional 2D cell culture. We sought to identify critical factors regulating organoid growth ex vivo, and to use these observations to develop a more efficient organoid expa ...[more]

PMID: 33106923

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Project description:Many automatically analyzable scientific questions are well-posed and a variety of information about expected outcomes is available a priori. Although often neglected, this prior knowledge can be systematically exploited to make automated analysis operations sensitive to a desired phenomenon or to evaluate extracted content with respect to this prior knowledge. For instance, the performance of processing operators can be greatly enhanced by a more focused detection strategy and by direct information about the ambiguity inherent in the extracted data. We present a new concept that increases the result quality awareness of image analysis operators by estimating and distributing the degree of uncertainty involved in their output based on prior knowledge. This allows the use of simple processing operators that are suitable for analyzing large-scale spatiotemporal (3D+t) microscopy images without compromising result quality. On the foundation of fuzzy set theory, we transform available prior knowledge into a mathematical representation and extensively use it to enhance the result quality of various processing operators. These concepts are illustrated on a typical bioimage analysis pipeline comprised of seed point detection, segmentation, multiview fusion and tracking. The functionality of the proposed approach is further validated on a comprehensive simulated 3D+t benchmark data set that mimics embryonic development and on large-scale light-sheet microscopy data of a zebrafish embryo. The general concept introduced in this contribution represents a new approach to efficiently exploit prior knowledge to improve the result quality of image analysis pipelines. The generality of the concept makes it applicable to practically any field with processing strategies that are arranged as linear pipelines. The automated analysis of terabyte-scale microscopy data will especially benefit from sophisticated and efficient algorithms that enable a quantitative and fast readout.

Dataset Information

Optimal, Large-Scale Propagation of Mouse Mammary Tumor Organoids.

Publications

Optimal, Large-Scale Propagation of Mouse Mammary Tumor Organoids.

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