An improved LC-ESI-MS-MS method for simultaneous quantitation of rosiglitazone and N-desmethyl rosiglitazone in human plasma.

A fast, sensitive, and selective method for the simultaneous quantitation of rosiglitazone and N-desmethyl rosiglitazone in human plasma, using rosiglitazone-d(4) and N-desmethyl rosiglitazone-d(4) as the respective internal standards, has been developed and validated. The analytes in human plasma (50 microL sample aliquot) were isolated through supported liquid/liquid extraction (SLE) and separated by isocratic HPLC over a 3-min period. The precursor and product ions were detected by ESI-MS-MS with multiple reaction monitoring (MRM) in a triple quadrupole mass spectrometer. For both rosiglitazone and N-desmethyl rosiglitazone, the lower limit of quantitation (LLOQ) was 1.00 ng/mL, and the quantitation range was 1.00-500 ng/mL (with an average correlation coefficient >0.9990). The intra-assay and inter-assay precision had a maximum %CV of 9.37%, and the accuracy had a maximum %difference from theoretical of 12.7%. This method was applied to a clinical study where 16 healthy volunteers were administered a single dose of 4.0mg rosiglitazone. The pharmacokinetic parameters of rosiglitazone and N-desmethyl rosiglitazone were consistent with the results reported in the literature.

Selective metabolite and peptide capture/mass detection using fluorous affinity tags.

A new and general methodology is described for the targeted enrichment and subsequent direct mass spectrometric characterization of sample subsets bearing various chemical functionalities from highly complex mixtures of biological origin. Specifically, sample components containing a chemical moiety of interest are first selectively labeled with perfluoroalkyl groups, and the entire sample is then applied to a perfluoroalkyl-silylated porous silicon (pSi) surface. Due to the unique hydrophobic and lipophobic nature of the perfluorinated tags, unlabeled sample components are readily removed using simple surface washes, and the enriched sample fraction can then directly be analyzed by desorption/ionization on silicon mass spectrometry (DIOS-MS). Importantly, this fluorous-based enrichment methodology provides a single platform that is equally applicable to both peptide as well as small molecule focused applications. The utility of this technique is demonstrated by the enrichment and mass spectrometric analysis of both various peptide subsets from protein digests as well as amino acids from serum.

Mobilization of pro-inflammatory lipids in obese Plscr3-deficient mice.

BACKGROUND: The obesity epidemic has prompted the search for candidate genes capable of influencing adipose function. One such candidate, that encoding phospholipid scramblase 3 (PLSCR3), was recently identified, as genetic deletion of it led to lipid accumulation in abdominal fat pads and changes characteristic of metabolic syndrome. Because adipose tissue is increasingly recognized as an endocrine organ, capable of releasing small molecules that modulate disparate physiological processes, we examined the plasma from wild-type, Plscr1-/-, Plscr3-/- and Plscr1&3-/- mice. Using an untargeted comprehensive metabolite profiling approach coupled with targeted gene expression analyses, the perturbed biochemistry and functional redundancy of PLSCR proteins was assessed. RESULTS: Nineteen metabolites were differentially and similarly regulated in both Plscr3-/- and Plscr1&3-/- animals, of which five were characterized from accurate mass, tandem mass spectrometry data and their correlation to the Metlin database as lysophosphatidylcholine (LPC) species enriched with C16:1, C18:1, C20:3, C20:5 and C22:5 fatty acids. No significant changes in the plasma metabolome were detected upon elimination of PLSCR1, indicating that increases in pro-inflammatory lipids are specifically associated with the obese state of Plscr3-deficient animals. Correspondingly, increases in white adipose lipogenic gene expression confirm a role for PLSCR3 in adipose lipid metabolism. CONCLUSION: The untargeted profiling of circulating metabolites suggests no detectable functional redundancies between PLSCR proteins; however, this approach simultaneously identified previously unrecognized lipid metabolites that suggest a novel molecular link between obesity, inflammation and the downstream consequences associated with PLSCR3-deficiency.

Mass spectrometry reveals specific and global molecular transformations during viral infection.

Mass spectrometry analysis was used to target three different aspects of the viral infection process: the expression kinetics of viral proteins, changes in the expression levels of cellular proteins, and the changes in cellular metabolites in response to viral infection. The combination of these methods represents a new, more comprehensive approach to the study of viral infection revealing the complexity of these events within the infected cell. The proteins associated with measles virus (MV) infection of human HeLa cells were measured using a label-free approach. On the other hand, the regulation of cellular and Flock House Virus (FHV) proteins in response to FHV infection of Drosophila cells was monitored using stable isotope labeling. Three complementary techniques were used to monitor changes in viral protein expression in the cell and host protein expression. A total of 1500 host proteins was identified and quantified, of which over 200 proteins were either up- or down-regulated in response to viral infection, such as the up-regulation of the Drosophila apoptotic croquemort protein, and the down-regulation of proteins that inhibited cell death. These analyses also demonstrated the up-regulation of viral proteins functioning in replication, inhibition of RNA interference, viral assembly, and RNA encapsidation. Over 1000 unique metabolites were also observed with significant changes in over 30, such as the down-regulated cellular phospholipids possibly reflecting the initial events in cell death and viral release. Overall, the cellular transformation that occurs upon viral infection is a process involving hundreds of proteins and metabolites, many of which are structurally and functionally uncharacterized.

Nonlinear data alignment for UPLC-MS and HPLC-MS based metabolomics: quantitative analysis of endogenous and exogenous metabolites in human serum.

A nonlinear alignment strategy was examined for the quantitative analysis of serum metabolites. Two small-molecule mixtures with a difference in relative concentration of 20-100% for 10 of the compounds were added to human serum. The metabolomics protocol using UPLC and XCMS for LC-MS data alignment could readily identify 8 of 10 spiked differences among more than 2700 features detected. Normalization of data against a single factor obtained through averaging the XCMS integrated response areas of spiked standards increased the number of identified differences. The original data structure was well preserved using XCMS, but reintegration of identified differences in the original data reduced the number of false positives. Using UPLC for separation resulted in 20% more detected components compared to HPLC. The length of the chromatographic separation also proved to be a crucial parameter for a number of detected features. Moreover, UPLC displayed better retention time reproducibility and signal-to-noise ratios for spiked compounds over HPLC, making this technology more suitable for nontargeted metabolomics applications.

Solvent-dependent metabolite distribution, clustering, and protein extraction for serum profiling with mass spectrometry.

The aim of metabolite profiling is to monitor all metabolites within a biological sample for applications in basic biochemical research as well as pharmacokinetic studies and biomarker discovery. Here, novel data analysis software, XCMS, was used to monitor all metabolite features detected from an array of serum extraction methods, with application to metabolite profiling using electrospray liquid chromatography/mass spectrometry (ESI-LC/MS). The XCMS software enabled the comparison of methods with regard to reproducibility, the number and type of metabolite features detected, and the similarity of these features between different extraction methods. Extraction efficiency with regard to metabolite feature hydrophobicity was examined through the generation of unique feature density distribution plots, displaying feature distribution along chromatographic time. Hierarchical clustering was performed to highlight similarities in the metabolite features observed between the extraction methods. Protein extraction efficiency was determined using the Bradford assay, and the residual proteins were identified using nano-LC/MS/MS. Additionally, the identification of four of the most intensely ionized serum metabolites using FTMS and tandem mass spectrometry was reported. The extraction methods, ranging from organic solvents and acids to heat denaturation, varied widely in both protein removal efficiency and the number of mass spectral features detected. Methanol protein precipitation followed by centrifugation was found to be the most effective, straightforward, and reproducible approach, resulting in serum extracts containing over 2000 detected metabolite features and less than 2% residual protein. Interestingly, the combination of all approaches produced over 10,000 unique metabolite features, a number that is indicative of the complexity of the human metabolome and the potential of metabolomics in biomarker discovery.

XCMS: processing mass spectrometry data for metabolite profiling using nonlinear peak alignment, matching, and identification.

Metabolite profiling in biomarker discovery, enzyme substrate assignment, drug activity/specificity determination, and basic metabolic research requires new data preprocessing approaches to correlate specific metabolites to their biological origin. Here we introduce an LC/MS-based data analysis approach, XCMS, which incorporates novel nonlinear retention time alignment, matched filtration, peak detection, and peak matching. Without using internal standards, the method dynamically identifies hundreds of endogenous metabolites for use as standards, calculating a nonlinear retention time correction profile for each sample. Following retention time correction, the relative metabolite ion intensities are directly compared to identify changes in specific endogenous metabolites, such as potential biomarkers. The software is demonstrated using data sets from a previously reported enzyme knockout study and a large-scale study of plasma samples. XCMS is freely available under an open-source license at http://metlin.scripps.edu/download/.

METLIN: a metabolite mass spectral database.

Endogenous metabolites have gained increasing interest over the past 5 years largely for their implications in diagnostic and pharmaceutical biomarker discovery. METLIN (http://metlin.scripps.edu), a freely accessible web-based data repository, has been developed to assist in a broad array of metabolite research and to facilitate metabolite identification through mass analysis. METLINincludes an annotated list of known metabolite structural information that is easily cross-correlated with its catalogue of high-resolution Fourier transform mass spectrometry (FTMS) spectra, tandem mass spectrometry (MS/MS) spectra, and LC/MS data.