Fast, high peak capacity separations in gas chromatography-time-of-flight mass spectrometry

Peak capacity production (i.e., peak capacity per separation run time) is substantially improved for gas chromatography-time-of-flight mass spectrometry (GC-TOFMS) and applied to the fast separation of complex samples. The increase in peak capacity production is achieved by selecting appropriate experimental conditions based on theoretical modeling of on-column band broadening, and by reducing the injection pulse width. Modeling to estimate the on-column band broadening from experimental parameters provided insight for the potential of achieving GC separations in the absence of off-column band broadening, i.e., the additional band broadening not due to the on-column separation process. To optimize GC-TOFMS separations collected with a commercial instrumental platform, off-column band broadening from injection and detection needed to be significantly reduced. Specifically for injection, a commercially available thermal modulator is adapted and applied (referred to herein as thermal injection) to provide a narrow injection pulse, while the TOFMS provided a data collection rate of 500 Hz, initially averaged to 100 Hz for data storage. The use of long, relatively narrow open tubular capillary columns and a 30 °C/min programming rate were explored for GC-TOFMS, specifically a 20 m, 100 μm inner diameter (i.d.) capillary column with a 0.4 μm film thickness to benefit column capacity, operated slightly below the optimal average linear gas velocity (at ~2 mL/min, due to the flow rate constraint of the TOFMS). Standard autoinjection with a 1:100 split resulted in an average peak width of ~1.2 s, hence a peak capacity production of 50 peaks/min. Metabolites in the headspace of urine were sampled by solid-phase microextraction (SPME), followed by thermal injection and a ~7 min GC separation (with a ~6 min separation time window), producing ~660 ms peak widths on average, resulting in a total peak capacity of ~550 peaks (at unit resolution) and a peak capacity production of ~90 peaks/min (~2-fold improvement relative to standard autoinjection with the 1:100 split). This total peak capacity production achieved is equivalent to, or greater than, that currently utilized in metabolomics studies using GC/MS, but with much slower separations, on the order of 40 to 60 min, corresponding to a 5-fold or greater GC/MS analysis throughput rate.     

Comprehensive two-dimensional gas chromatography time-of-flight mass spectrometry analysis of metabolites in fermenting and respiring yeast cells

Comprehensive two-dimensional gas chromatography with time-of-flight mass spectrometry coupled with rapid chemometric analysis were used to identify chemical differences in metabolite extracts isolated from yeast cells either metabolizing glucose (repressed (R) cells) via fermentation or metabolizing ethanol by respiration (derepressed (DR) cells). Principal component analysis (PCA) followed by parallel factor analysis (PARAFAC) in concert with the LECO ChromaTOF software located and identified the differences in composition between the two types of cell extracts and provided a reliable ratio of the metabolite concentrations. In this report, we demonstrate the analytical method developed to provide relatively rapid analysis of three selective mass channels (m/z 73, 205, 387), although in principle all collected mass channels could be analyzed. Twenty-six metabolites that differentiate repressed cells from derepressed cells were identified. The DR/R ratio of metabolite concentrations ranged from 0.02 for glucose to 67 for trehalose. The average biological variation of the sample extracts was 31%. This analysis demonstrates the utility and benefit of using PCA combined with PARAFAC and ChromaTOF software on extremely complex samples to derive useful information from complex three-dimensional chromatographic data objectively and relatively rapidly.     

Time-dependent profiling of metabolites from Snf1 mutant and wild type yeast cells

The effect of sampling time in the context of growth conditions on a dynamic metabolic system was investigated in order to assess to what extent a single sampling time may be sufficient for general application, as well as to determine if useful kinetic information could be obtained. A wild type yeast strain (W) was compared to a snf1Delta mutant yeast strain (S) grown in high-glucose medium (R) and in low-glucose medium containing ethanol (DR). Under these growth conditions, different metabolic pathways for utilizing the different carbon sources are expected to be active. Thus, changes in metabolite levels relating to the carbon source in the growth medium were anticipated. Furthermore, the Snf1 protein kinase complex is required to adapt cellular metabolism from fermentative R conditions to oxidative DR conditions. So, differences in intracellular metabolite levels between the W and S yeast strains were also anticipated. Cell extracts were collected at four time points (0.5, 2, 4, 6 h) after shifting half of the cells from R to DR conditions, resulting in 16 sample classes (WR, WDR, SR, SDR) x (0.5, 2, 4, 6 h). The experimental design provided time course data, so temporal dependencies could be monitored in addition to carbon source and strain dependencies. Comprehensive two-dimensional (2D) gas chromatography coupled to time-of-flight mass spectrometry (GC x GC-TOFMS) was used with discovery-based data mining algorithms ( Anal. Chem. 2006, 78, 5068-5075 (ref 1); J. Chromatogr., A 2008, 1186, 401-411 (ref 2)) to locate regions within the 2D chromatograms (i.e., metabolites) that provided chemical selectivity between the 16 sample classes. These regions were mathematically resolved using parallel factor analysis to positively identify the metabolites and to acquire quantitative results. With these tools, 51 unique metabolites were identified and quantified. Various time course patterns emerged from these data, and principal component analysis (PCA) was utilized as a comparison tool to determine the sources of variance between these 51 metabolites. The effect of sampling time was investigated with separate PCA analyses using various subsets of the data. PCA utilizing all of the time course data, averaged time course data, and each individual time point data set independently were performed to discern the differences. For the yeast strains examined in the current study, data collection at either 4 or 6 h provided information comparable to averaged time course data, albeit with a few metabolites missing using a single sampling time point.     

Constrained mirror placement on the Internet

Web content providers and content distribution network (CDN) operators often set up mirrors of popular content to improve performance. Due to the scale and decentralized administration of the Internet, companies have a limited number of sites (relative to the size of the Internet) where they can place mirrors. We formalize the mirror placement problem as a case of constrained mirror placement, where mirrors can only be placed on a preselected set of candidates. We study performance improvement in terms of client round-trip time (RTT) and server load when clients are clustered by the autonomous systems (AS) in which they reside. Our results show that, regardless of the mirror placement algorithm used, for only a surprisingly small range of values there is an increase in the number of mirror sites (under the constraint) effective in reducing the client to server RTT and server load. In this range, we show that greedy placement performs the best.