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A suspension technique for efficient large-scale cancer organoid culturing and perturbation screens.

ABSTRACT: Organoid cell culture methodologies are enabling the generation of cell models from healthy and diseased tissue. Patient-derived cancer organoids that recapitulate the genetic and histopathological diversity of patient tumours are being systematically generated, providing an opportunity to investigate new cancer biology and therapeutic approaches. The use of organoid cultures for many applications, including genetic and chemical perturbation screens, is limited due to the technical demands and cost associated with their handling and propagation. Here we report and benchmark a suspension culture technique for cancer organoids which allows for the expansion of models to tens of millions of cells with increased efficiency in comparison to standard organoid culturing protocols. Using whole-genome DNA and RNA sequencing analyses, as well as medium-throughput drug sensitivity testing and genome-wide CRISPR-Cas9 screening, we demonstrate that cancer organoids grown as a suspension culture are genetically and phenotypically similar to their counterparts grown in standard conditions. This culture technique simplifies organoid cell culture and extends the range of organoid applications, including for routine use in large-scale perturbation screens.

SUBMITTER: Price S

PROVIDER: S-EPMC8976852 | biostudies-literature | 2022 Apr

REPOSITORIES: biostudies-literature

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A suspension technique for efficient large-scale cancer organoid culturing and perturbation screens.

Price Stacey S Bhosle Shriram S Gonçalves Emanuel E Li Xiaodun X McClurg Dylan P DP Barthorpe Syd S Beck Alex A Hall Caitlin C Lightfoot Howard H Farrow Luke L Ansari Rizwan R Jackson David A DA Allen Laura L Roberts Kirsty K Beaver Charlotte C Francies Hayley E HE Garnett Mathew J MJ

Scientific reports 20220402 1

Organoid cell culture methodologies are enabling the generation of cell models from healthy and diseased tissue. Patient-derived cancer organoids that recapitulate the genetic and histopathological diversity of patient tumours are being systematically generated, providing an opportunity to investigate new cancer biology and therapeutic approaches. The use of organoid cultures for many applications, including genetic and chemical perturbation screens, is limited due to the technical demands and c ...[more]

PMID: 35368031

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Project description:BackgroundCancer cells reprogram their metabolism to survive and propagate. Thus, targeting metabolic rewiring in tumors is a promising therapeutic strategy. Genome-wide RNAi and CRISPR screens are powerful tools for identifying genes essential for cancer cell proliferation and survival. Integrating loss-of-function genetic screens with genomics and transcriptomics datasets reveals molecular mechanisms that underlie cancer cell dependence on specific genes; though explaining cell line-specific essentiality of metabolic genes was recently shown to be especially challenging.ResultsWe find that variability in tissue culture medium between cell lines in a genetic screen is a major confounding factor affecting cell line-specific essentiality of metabolic genes-while, quite surprisingly, not being previously accounted for. Additionally, we find that altered expression level of a metabolic gene in a certain cell line is less indicative of its essentiality than for other genes. However, cell line-specific essentiality of metabolic genes is significantly correlated with changes in the expression of neighboring enzymes in the metabolic network. Utilizing a machine learning method that accounts for tissue culture media and functional association between neighboring enzymes, we generated predictive models for cancer cell line-specific dependence on 162 metabolic genes (representing a ~ 2.2-fold increase compared to previous studies). The generated predictive models reveal numerous novel associations between molecular features and cell line-specific dependency on metabolic genes. Specifically, we demonstrate how cancer cell dependence on one-carbon metabolic enzymes is explained based on cancer lineage, oncogenic mutations, and RNA expression of neighboring enzymes.ConclusionsConsidering culture media as well as accounting for molecular features of functionally related metabolic enzymes in a metabolic network significantly improves our understanding of cancer cell line-specific dependence on metabolic genes. We expect our approach and predictive models of metabolic gene essentiality to be a useful tool for investigating metabolic abnormalities in cancer.

Dataset Information

A suspension technique for efficient large-scale cancer organoid culturing and perturbation screens.

Publications

A suspension technique for efficient large-scale cancer organoid culturing and perturbation screens.

Similar Datasets

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