Although graphene oxide (GO) nanosheets are widely used in different fields, the mechanism of their toxicity remains relatively unknown. NMR‐based metabolomics was used to study in vivo time and dose‐dependent toxicity of GO nanosheets in mice. Sixty serum samples from mice in four different time intervals including 24 and 72 h and 7 and 21 days after injection of 0‐, 1‐, and 10‐mg/kg b.w. were analyzed based on 1HNMR spectra of each sample and multivariate methods. In comparison with the control group, 12 changed metabolites were identified in GO nanosheet‐treated mice groups. These metabolites are involved in steroid hormone biosynthesis and steroid biosynthesis pathways. It was seen that the time factor is more important than the dose factor and the groups were separated in a time direction, completely. We found that GO nanosheets has toxicity and can affect steroidal hormones. However, this study shows that after 21 days, the treated groups regardless of their GO nanosheet dose are very close to the control group. This means that in one step exposure to GO nanosheets, their toxicity diminished after 21 days.

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Extracting accurate biological information from complex datasets is a main challenge in 1H-NMR-based metabolomics research. One of the crucial steps to achieve this goal is to apply an appropriate pretreatment method before multivariate data analysis in 1H-NMR based metabolomics studies. One of the most important pretreatment methods in metabolomics studies is scaling techniques. In this study, the effect of different pretreatment approaches such as auto-, pareto-, level-, range- and vast-scaling in addition to mean-centering are investigated on both experimental and simulated datasets. The goal is linear classification modeling of the toxicity induced by different doses of graphene oxide (GO) in metabolomics context, employing partial least squares- discriminant analysis) PLS-DA). The experimental dataset includes 1H-NMR spectra of mice serum samples exposed to different doses of GO nano-sheets. Here, it is shown that type of applied pre-treatment method has a considerable effect on data analysis results. In this study, auto-, pareto- and vast-scaling lead to a better separation of classes using PLS-DA modeling and PCA. From the results of this study, it was concluded that there is no general rule for the selection of the best scaling method in the analysis of 1H-NMR metabolomics datasets, and different ways of scaling should be tested.

Extracting accurate biological information from complex datasets is a main challenge in 1H-NMR-based metabolomics research. One of the crucial steps to achieve this goal is to apply an appropriate pretreatment method before multivariate data analysis in 1H-NMR based metabolomics studies. One of the most important pretreatment methods in metabolomics studies is scaling techniques. In this study, the effect of different pretreatment approaches such as auto-, pareto-, level-, range- and vast-scaling in addition to mean-centering are investigated on both experimental and simulated datasets. The goal is linear classification modeling of the toxicity induced by different doses of graphene oxide (GO) in metabolomics context, employing partial least squares- discriminant analysis) PLS-DA). The experimental dataset includes 1H-NMR spectra of mice serum samples exposed to different doses of GO nano-sheets. Here, it is shown that type of applied pre-treatment method has a considerable effect on data analysis results. In this study, auto-, pareto- and vast-scaling lead to a better separation of classes using PLS-DA modeling and PCA. From the results of this study, it was concluded that there is no general rule for the selection of the best scaling method in the analysis of 1H-NMR metabolomics datasets, and different ways of scaling should be tested.