Flavonol 3,7-di-O-glycosides were investigated by negative ion electrospray ionization tandem mass spectrometry using a quadrupole linear ion trap (LIT) mass spectrometer. The results indicate that the fragmentation behavior of flavonol 3,7-di-O-glycosides is substantially different from that of their isomeric mono-O-diglycosides. In order to characterize a flavonoid as a flavonol 3,7-di-O-glycoside, both [Y3(0) - H]-* and [Y(0) - 2H]- ions should be present in [M - H]- product ion spectrum. The MS(3) product ion spectra of Y3(0)-, [Y3(0) - H]-* and Y7(0)- ions generated from the [M - H]- ion provide sufficient structural information for the determination of glycosylation position. Furthermore, the glycosylation positions are determined by comparing the relative abundances of Y3(0)- and Y7(0)- ions and their specific fragmentation patterns with those of flavonol mono-O-glycosides. In addition, a [Y3(0) - H]-* ion formed by the homolytic cleavage of 3-O glycosidic bond with high abundance points to 3-O glycosylation, while a [Y(0) - 2H]- ion formed by the elimination of the two sugar residues is consistent with glycosylation at both the 3-O and 7-O positions. Investigation of negative ion ESI-MS(2) and MS(3) spectra of flavonol O-glycosides allows their rapid characterization as flavonol 3,7-di-O-glycoside and their differentiation from isomeric mono-O-diglycosides, and also enables their direct analysis in crude plant extracts.

A metabonomics strategy based on rapid resolution liquid chromatography/tandem mass spectrometry (RRLC-MS/MS), multivariate statistics and metabolic correlation networks has been implemented to find biologically significant metabolite biomarkers in breast cancer. RRLC-MS/MS analysis by electrospray ionization (ESI) in both positive and negative ion modes was employed to investigate human urine samples. The resulting data matrices were analyzed using multivariate analysis. Application of orthogonal projections to latent structures discriminate analysis (OPLS-DA) allowed us to extract several discriminated metabolites reflecting metabolic characteristics between healthy volunteers and breast cancer patients. Correlation network analysis between these metabolites has been further applied to select more reliable biomarkers. Finally, high resolution MS and MS/MS analyses were performed for the identification of the metabolites of interest. We identified 12 metabolites as potential biomarkers including amino acids, organic acids, and nucleosides. They revealed elevated tryptophan and nucleoside metabolism as well as protein degradation in breast cancer patients. These studies demonstrate the advantages of integrating metabolic correlation networks with metabonomics for finding significant potential biomarkers: this strategy not only helps identify potential biomarkers, it also further confirms these biomarkers and can even provide biochemical insights into changes in breast cancer.

Diagnostic and therapeutic biomarkers useful for esophageal squamous cell carcinoma (ESCC) have the ability to increase the long term survival of cancer patients. A metabolomics study, using plasma from four groups including ESCC patients before, during, and after chemoradiotherapy (CRT) and healthy controls, was originally carried out by LC-MS to determine global alterations in the metabolic profiles and find biomarkers potentially applicable to diagnosis and monitoring treatment effects. It is worth pointing out that a clear clustering and separation of metabolic data from the four groups was observed, which indicated that disease status and treatment intervention resulted in specific metabolic perturbations in the patients. A series of metabolites were found to be significantly altered in ESCC patients versus healthy controls and in pre-versus post-treatment patients based on multivariate statistical data analysis (MVDA). To further validate the reliability of these potential biomarkers, an independent validation was performed by using the selected reaction monitoring (SRM) based targeted approach. Finally, 18 most significantly altered plasma metabolites in ESCC patients, relative to healthy controls, were tentatively identified as lysophosphatidylcholines (lysoPCs), fatty acids, L-carnitine, acylcarnitines, organic acids, and a sterol metabolite. The classification performance of these metabolites were analyzed by receiver operating characteristic (ROC) 1 analysis and a biomarker panel was generated. Together, biological significance of these metabolites was discussed. Comparison between pre-and post-treatment patients generated 11 metabolites as potential therapeutic biomarkers that were tentatively identified as amino acids, acylcarnitines, and lysoPCs. Levels of three of these (octanoylcarnitine, lysoPC(16:1), and decanoylcarnitine) were closely correlated with treatment effect. Moreover, variation of these three potential biomarkers was investigated over the treatment course. The results suggest that these biomarkers may be useful in diagnosis, as well as in monitoring therapeutic responses and predicting outcomes of the ESCC. Molecular & Cellular Proteomics

An integrated ionization approach of electrospray ionization (ESI), atmospheric pressure chemical ionization (APCI), and atmospheric pressure photoionization (APPI) combining with rapid resolution liquid chromatography mass spectrometry (RRLC-MS) has been developed for performing global metabonomic analysis on complex biological samples. This approach was designed to overcome the low ionization efficiencies of endogenous metabolites due to diverse physicochemical properties as well as ion suppression, and obtain comprehensive metabolite profiles in LC-MS analysis. Ionization capability and applicability were manifested by improved ionization efficiency and enlarged metabolite coverage in analysis on typical urinary metabolite standards and urine samples from healthy volunteers. The method was validated by the limit of detection and precision. When applied to the global metabonomic studies of lung cancer, more comprehensive biomarker candidates were obtained to reflect metabolic traits between healthy volunteers and lung cancer patients, including 74 potential biomarkers in positive ion mode and 59 in negative ion mode. Taking identical potential biomarkers of any two or three ionization methods into account, analysis using ESI-MS in positive (+) and negative (-) ion mode contributed to 70 and 64% of the total potential biomarkers, respectively. The biomarker discovery capability of (+/-) APCI-MS accounted for 45 and 42% of the overall; meanwhile (+/-) APPI-MS amounted for 39 and 54%. These results indicated that potential biomarkers with vital biological information could be missed if only a single ionization method was used. Furthermore, 11 potential biomarkers were identified including amino acids, nucleosides, and a metabolite of indole. They revealed elevated amino acid and nucleoside metabolism as well as protein degradation in lung cancer patients. This proposed approach provided a more comprehensive picture of the metabolic changes and further verified identical biomarkers that were obtained simultaneously using different ionization methods.

Ambient ionization methods are an important research area in mass spectrometry (MS) analysis. Under ambient conditions, the gas flow and atmospheric pressure significantly affect the transfer and focusing of ions. The design and implementation of air flow assisted ionization (AFAI) as a novel and effective, remote sampling method for ambient mass spectrometry are described herein. AFAI benefits from a high extracting air flow rate. A systematic investigation of the extracting air flow in the AFAI system has been carried out, and it has been demonstrated not only that it plays a role in the effective capture and remote transport of charged droplets, but also that it promotes desolvation and ion formation, and even prevents ion fragmentation during the ionization process. Moreover, the sensitivity of remote sampling ambient MS analysis was improved significantly by the AFAI method. Highly polar and nonpolar molecules, including dyes, pharmaceutical samples, explosives, drugs of abuse, protein and volatile compounds, have been successfully analyzed using AFAI-MS. The successful application of the technique to residue detection on fingers, large object analysis and remote monitoring in real time indicates its potential for the analysis of a variety of samples, especially large objects. The ability to couple this technique with most commercially available MS instruments with an API interface further enhances its broad applicability.