Project description:Colorectal cancer organoids (CCOs) recapitulate the gene expression signatures of their respective primary tumors. These CCOs provide valuable model for studying the primary cancers.
Project description:Colorectal cancer organoids (CCOs) recapitulate the gene expression signatures of their respective primary tumors. These CCOs provide valuable model for studying the primary cancers.
Project description:As metabolic rewiring is crucial for cancer cell proliferation, metabolic phenotyping of patient-derived organoids is desirable to identify drug-induced changes and trace metabolic vulnerabilities of tumor subtypes. We established a novel protocol for metabolomic and lipidomic profiling of colorectal cancer organoids by LC-QTOF-MS facing the challenge of capturing metabolic information from minimal sample amount (< 500 cells/injection) in the presence of extracellular matrix (ECM). The best procedure of the tested protocols included ultrasonic metabolite extraction with acetonitrile/methanol/water (2:2:1, v/v/v) without ECM removal. To eliminate ECM-derived background signals, we implemented a data filtering procedure based on p-value and fold change cut-offs which retained features with signal intensities >120% compared to matrix-derived signals present in blank samples. As a proof-of-concept, the method was applied to examine the early metabolic response of colorectal cancer organoids to 5-fluorouracil treatment. Statistical analysis revealed dose-dependent changes in the metabolic profiles of treated organoids including elevated levels of 2'-deoxyuridine, 2'-O-methylcytidin, inosine and 1-methyladenosine and depletion of 2'-deoxyadenosine and specific phospholipids. In accordance with the mechanism of action of 5-fluorouracil, changed metabolites are mainly involved in purine and pyrimidine metabolism. The novel protocol provides a first basis for the assessment of metabolic drug response phenotypes in 3D organoid models.
Project description:Here, we generated bulk RNA-seq data on colon organoids derived from both healthy and familial adenomatous polyposis patients. We related findings observed within this dataset to differential expression findings from a publicly available colorectal cancer cohort.