Across-polarity quantification method for broad metabolome coverage based on consecutive nanoconfined liquid phase nanoextraction technology: Application in discovering the plasma potential biomarkers of different types of cancer

LC-MS-based metabolomics offers the potential of discovering biomarkers and exploring the mechanisms of underlying diseases. However, given the enormous polarity difference between metabolites, simultaneous across-polarity quantification for broad metabolome coverage has still been challenged by limited sample preparation methods and other hurdles. Herein, we proposed a consecutive extraction strategy based on nanoconfined liquid phase nanoextraction (NLPNE) technique. By modulating the nanoconfined solvents and coupling with LC-MS/MS, this method could simultaneously quantify metabolites with different polarities assigned to three classes, including amines (high polarity), steroids (middle polarity) and lysophosphatidylcholines (LPCs, low polarity) with high selectivity and high efficiency. During the systematical optimization of the extraction workflow, response surface methodology (RSM) was used for key parameters optimization. And consecutive extraction mode and parallel extraction mode were proposed in the choice of integrated extraction strategy. Then the consecutive NLPNE method was compared with two conventional sample preparation methods in metabolomics, protein precipitation (PP) and liquid-liquid extraction (LLE). After systematical validation, the consecutive NLPNE method coupled with LC-MS/MS was successfully applied in the identification of multi-metabolites indexes for lung, colorectal, and gastric cancer plasma samples from healthy controls, and among different types of cancer with student's t-test, partial least squares discriminant analysis (PLS-DA) and logistic regression-receiver operating characteristic (ROC) curve analysis. Taken together, the developed methodology is a versatile candidate in metabolomics for high coverage detection and may be used as a powerful tool for cancer diagnosis.