Dataset Information

Untargeted LC-MS/MS analysis reveals metabolomics feature of osteosarcoma stem cell response to methotrexate.

ABSTRACT:

Background

Cancer stem cell (CSC) is identified in osteosarcoma (OS) and considered resistant to chemotherapeutic agents. However, the mechanism of osteosarcoma stem cell (OSC) resistant to chemotherapy remains debatable and vague, and the metabolomics feature of OSC is not clarified.

Materials and methods

OSC was isolated by using sphere forming assay and identified. Untargeted LC-MS/MS analysis was performed to reveal the metabolomics feature of OSC and underlying mechanisms of OSC resistant to methotrexate (MTX).

Results

OSC was efficiently isolated and identified from human OS 143B and MG63 cell lines with enhanced chemo-resistance to MTX. The untargeted LC-MS analysis revealed that OSC showed differential metabolites and perturbed signaling pathways, mainly involved in metabolisms of fatty acid, amino acid, carbohydrate metabolism and nucleic acid. After treated with MTX, metabolomics feature of OSC was mainly involved metabolisms of amino acid, fatty acid, energy and nucleic acid. Moreover, compared with their parental OS cells response to MTX, the differential metabolites and perturbed signaling pathways were mainly involved in metabolism of amino acid, fatty acid and nucleic acid. What's more, Rap1 signaling pathway and Ras signaling pathway were involved in OS cells and their SCs response to MTX.

Conclusion

Sphere-forming assay was able to efficiently isolate OSC from human OS cell lines and the untargeted LC-MS/MS analysis was suggested a sufficient methodology to investigate metabolomics features of OS cells and OSCs. Moreover, the metabolomics features of OSCs response to MTX might reveal a further understanding of chemotherapeutic resistance in OS.

SUBMITTER: Wang F

PROVIDER: S-EPMC7313215 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Untargeted LC-MS/MS analysis reveals metabolomics feature of osteosarcoma stem cell response to methotrexate.

Wang Feng F Zhang Zhiyu Z Li Qin Q Yu Tao T Ma Chengbin C

Cancer cell international 20200624

<h4>Background</h4>Cancer stem cell (CSC) is identified in osteosarcoma (OS) and considered resistant to chemotherapeutic agents. However, the mechanism of osteosarcoma stem cell (OSC) resistant to chemotherapy remains debatable and vague, and the metabolomics feature of OSC is not clarified.<h4>Materials and methods</h4>OSC was isolated by using sphere forming assay and identified. Untargeted LC-MS/MS analysis was performed to reveal the metabolomics feature of OSC and underlying mechanisms of ...[more]

PMID: 32587477

Similar Datasets

Project description:BackgroundLiquid chromatography-mass spectrometry is widely used in untargeted metabolomics for composition profiling. In multi-run analysis scenarios, features of each run are aligned into consensus features by feature alignment algorithms to observe the intensity variations across runs. However, most of the existing feature alignment methods focus more on accurate retention time correction, while underestimating the importance of feature matching. None of the existing methods can comprehensively consider feature correspondences among all runs and achieve optimal matching.ResultsTo comprehensively analyze feature correspondences among runs, we propose G-Aligner, a graph-based feature alignment method for untargeted LC-MS data. In the feature matching stage, G-Aligner treats features and potential correspondences as nodes and edges in a multipartite graph, considers the multi-run feature matching problem an unbalanced multidimensional assignment problem, and provides three combinatorial optimization algorithms to find optimal matching solutions. In comparison with the feature alignment methods in OpenMS, MZmine2 and XCMS on three public metabolomics benchmark datasets, G-Aligner achieved the best feature alignment performance on all the three datasets with up to 9.8% and 26.6% increase in accurately aligned features and analytes, and helped all comparison software obtain more accurate results on their self-extracted features by integrating G-Aligner to their analysis workflow. G-Aligner is open-source and freely available at https://github.com/CSi-Studio/G-Aligner under a permissive license. Benchmark datasets, manual annotation results, evaluation methods and results are available at https://doi.org/10.5281/zenodo.8313034 CONCLUSIONS: In this study, we proposed G-Aligner to improve feature matching accuracy for untargeted metabolomics LC-MS data. G-Aligner comprehensively considered potential feature correspondences between all runs, converting the feature matching problem as a multidimensional assignment problem (MAP). In evaluations on three public metabolomics benchmark datasets, G-Aligner achieved the highest alignment accuracy on manual annotated and popular software extracted features, proving the effectiveness and robustness of the algorithm.

Project description:Diabetes in the elderly increases cognitive impairment, but the underlying mechanisms are still far from fully understood. A non-targeted metabolomics approach based on liquid chromatography-mass spectrometry (LC-MS) was performed to screen out the serum biomarkers of diabetic mild cognitive impairment (DMMCI) in rats. Total 48 SD rats were divided into three groups, Normal control (NC) group, high-fat diet (HFD) fed group and type 2 diabetes mellitus (T2DM) group. The T2DM rat model was induced by intraperitoneal administration of streptozotocin (STZ, 35 mg/kg) after 6 weeks of high-fat diet (HFD) feeding. Then each group was further divided into 4-week and 8-week subgroups, which were calculated from the time point of T2DM rat model establishment. The novel object recognition test (NORT) and the Morris water maze (MWM) method were used to evaluate the cognitive deficits in all groups. Compared to the NC-8w and HFD-8w groups, both NOR and MWM tests indicated significant cognitive dysfunction in the T2DM-8w group, which could be used as an animal model of DMMCI. Serum was ultimately collected from the inferior vena cava after laparotomy. Metabolic profiling analysis was conducted using ultra high performance liquid chromatography coupled with quadrupole time-of-flight mass spectrometry (UPLC-Q-TOF-MS) technology. Principal component analysis (PCA) and orthogonal partial least squares discriminant analysis (OPLS-DA) were used to verify the stability of the model. According to variable importance in the project (VIP > 1) and the p-value of t-test (P < 0.05) obtained by the OPLS-DA model, the metabolites with significant differences were screened out as potential biomarkers. In total, we identified 94 differentially expressed (44 up-regulated and 50 down-regulated) endogenous metabolites. The 10 top up-regulated and 10 top down-regulated potential biomarkers were screened according to the FDR significance. These biomarkers by pathway topology analysis were primarily involved in the metabolism of sphingolipid (SP) metabolism, tryptophan (Trp) metabolism, Glycerophospholipid (GP) metabolism, etc. Besides, SP metabolism, Trp metabolism and GP metabolism mainly belonging to the lipid metabolism showed marked perturbations over DMMCI and may contribute to the development of disease. Taken collectively, our results revealed that T2DM could cause cognitive impairment by affecting a variety of metabolic pathways especially lipid metabolism. Besides, serum PE, PC, L-Trp, and S1P may be used as the most critical biomarkers for the early diagnosis of DMMCI.

Dataset Information

Untargeted LC-MS/MS analysis reveals metabolomics feature of osteosarcoma stem cell response to methotrexate.

Background

Materials and methods

Results

Conclusion

Publications

Untargeted LC-MS/MS analysis reveals metabolomics feature of osteosarcoma stem cell response to methotrexate.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets