Direct Assessment of Phytochemicals Inherent in Plant Tissues Using Extractive Electrospray Ionization Mass Spectrometry

An ambient pressure ionization mass spectrometric strategy called internal extractive electrospray ionization mass spectrometry (iEESI-MS) has been developed and applied for direct profiling of labile phytochemicals inherent in various native plant tissues, including leaves, roots, and fruits. By passing the electrospray solvent through the plant tissue, a variety of phytochemicals, such as amino acids, sugars (e.g., glucose, sucrose, polysaccharides, etc.), and alkaloids, were continuously extracted from the sample interior, driven toward the natural/cut electro-spraying tip, and vaporized into gaseous ions for mass spectrometric interrogation. Phytochemical patterns obtained by iEESI–MS permit a rapid differentiation between various species of ginkgo plant and strawberry maturity stages, as well as characterization of physiological/pathologic conditions of chlorophytum comosum. Our experimental results further demonstrate that the established iEESI–MS approach is potentially useful for direct phytochemomics studies with minimal biodegradation, allowing elucidation of plant metabolism with high speed, specificity, and simplicity of analysis

Direct Assessment of Phytochemicals Inherent in Plant Tissues Using Extractive Electrospray Ionization Mass Spectrometry

An ambient pressure ionization mass spectrometric strategy called internal extractive electrospray ionization mass spectrometry (iEESI-MS) has been developed and applied for direct profiling of labile phytochemicals inherent in various native plant tissues, including leaves, roots, and fruits. By passing the electrospray solvent through the plant tissue, a variety of phytochemicals, such as amino acids, sugars (e.g., glucose, sucrose, polysaccharides, etc.), and alkaloids, were continuously extracted from the sample interior, driven toward the natural/cut electro-spraying tip, and vaporized into gaseous ions for mass spectrometric interrogation. Phytochemical patterns obtained by iEESI–MS permit a rapid differentiation between various species of ginkgo plant and strawberry maturity stages, as well as characterization of physiological/pathologic conditions of chlorophytum comosum. Our experimental results further demonstrate that the established iEESI–MS approach is potentially useful for direct phytochemomics studies with minimal biodegradation, allowing elucidation of plant metabolism with high speed, specificity, and simplicity of analysis

Differentiation Using Microwave Plasma Torch Desorption Mass Spectrometry of Navel Oranges Cultivated in Neighboring Habitats

The molecular fingerprinting of intact fruit samples combined with statistical data analysis can allow the assessment of fruit quality and location of origin. Herein, microwave plasma torch desorption ionization mass spectrometry (MPT-MS) was applied to produce molecular fingerprints for the juice sac and exocarp of navel oranges cultivated in three closely located habitats, and the mass spectrometric fingerprints were differentiated by principal component analysis (PCA). Because of the relatively high temperature and high ionization efficiency of MPT, the volatile aroma compounds and semivolatile chemicals in the navel oranges were sensitively detected and confidently identified by collision induced dissociation (CID). The limit of detection (LOD) of MPT-MS for vanillin was 0.119 μg/L, with the relative standard deviation (RSD, <i>n</i> = 10) of 1.7%. The results showed that MPT-MS could be a powerful analytical platform for the sensitive molecular analysis of fruits at molecular level with high chemical specificity, allowing differentiation between the same sorts grown in neighboring habitats

Additional file 1: Tables S1-S4. of A network-based predictive gene-expression signature for adjuvant chemotherapy benefit in stage II colorectal cancer

indicate additional results of Cox regression analysis and genes involved in the 11-PPI-mod. Figures S1-S4. show additional information of data processing, feature selection and Kaplan-Meier analysis. (DOC 1262 kb