Dataset Information

Anticancer Drug Affects Metabolomic Profiles in Multicellular Spheroids: Studies Using Mass Spectrometry Imaging Combined with Machine Learning.

ABSTRACT: Multicellular spheroids (hereinafter referred to as spheroids) are 3D biological models. The metabolomic profiles inside spheroids provide crucial information reflecting the molecular phenotypes and microenvironment of cells. To study the influence of an anticancer drug on the spatially resolved metabolites, spheroids were cultured using HCT-116 colorectal cancer cells, treated with the anticancer drug Irinotecan under a series of time- and concentration-dependent conditions. The Single-probe mass spectrometry imaging (MSI) technique was utilized to conduct the experiments. The MSI data were analyzed using advanced data analysis methods to efficiently extract metabolomic information. Multivariate curve resolution alternating least square (MCR-ALS) was used to decompose each MS image into different components with grouped species. To improve the efficiency of data analysis, both supervised (Random Forest) and unsupervised (cluster large applications (CLARA)) machine learning (ML) methods were employed to cluster MS images according to their metabolomic features. Our results indicate that anticancer drug significantly affected the abundances of a variety of metabolites in different regions of spheroids. This integrated experiment and data analysis approach can facilitate the studies of metabolites in different types of 3D tumor models and tissues and potentially benefit the drug discovery, therapeutic resistance, and other biological research fields.

SUBMITTER: Tian X

PROVIDER: S-EPMC6573030 | biostudies-literature | 2019 May

REPOSITORIES: biostudies-literature

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Anticancer Drug Affects Metabolomic Profiles in Multicellular Spheroids: Studies Using Mass Spectrometry Imaging Combined with Machine Learning.

Tian Xiang X Zhang Genwei G Zou Zhu Z Yang Zhibo Z

Analytical chemistry 20190415 9

Multicellular spheroids (hereinafter referred to as spheroids) are 3D biological models. The metabolomic profiles inside spheroids provide crucial information reflecting the molecular phenotypes and microenvironment of cells. To study the influence of an anticancer drug on the spatially resolved metabolites, spheroids were cultured using HCT-116 colorectal cancer cells, treated with the anticancer drug Irinotecan under a series of time- and concentration-dependent conditions. The Single-probe ma ...[more]

PMID: 30951294

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Project description:Multicellular tumor spheroids (MCTS) are valuable in vitro tumor models frequently used to evaluate the penetration and efficacy of therapeutics. In this study, we evaluated potential differences in epigenetic markers, i.e., histone post-translational modifications (PTMs), in the layers of the HCT116 colon carcinoma MCTS. Cells were grown in agarose-coated 96 well plates, forming reproducible 1-mm-diameter MCTS. The MCTS were fractionated into three radially concentric portions, generating samples containing cells from the core, the mid and the external layers. Using mass spectrometry (MS)-based proteomics and EpiProfile, we quantified hundreds of histone peptides in different modified forms; by combining the results of all experiments, we quantified the abundance of 258 differently modified peptides, finding significant differences in their relative abundance across layers. Among these differences, we detected higher amounts of the repressive mark H3K27me3 in the external layers, compared to the core. We then evaluated the epigenetic response of MCTS following UNC1999 treatment, a drug targeting the enzymes that catalyze H3K27me3, namely, the polycomb repressive complex 2 (PRC2) subunits enhancer of zeste 1 (EZH1) and enhancer of zeste 2 (EZH2). UNC1999 treatment resulted in significant differences in MCTS diameter under drug treatment of varying duration. Using matrix-assisted laser desorption/ionization (MALDI) imaging, we determined that the drug penetrates the entire MCTS. Proteomic analysis revealed a decrease in abundance of H3K27me3, compared to the untreated sample, as expected. Interestingly, we observed a comparable growth curve for MCTS under constant drug treatment over 13 days with those treated for only 4 days at the beginning of their growth. We thus demonstrate that MS-based proteomics can define significant differences in histone PTM patterns in submillimetric layers of three-dimensional (3D) cultures. Moreover, we show that our model is suitable for monitoring drug localization and regulation of histone PTMs after drug treatment.

Dataset Information

Anticancer Drug Affects Metabolomic Profiles in Multicellular Spheroids: Studies Using Mass Spectrometry Imaging Combined with Machine Learning.

Publications

Anticancer Drug Affects Metabolomic Profiles in Multicellular Spheroids: Studies Using Mass Spectrometry Imaging Combined with Machine Learning.

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