Bryan J. Prazen

A guided tour of the Trans‐Proteomic Pipeline

The Trans‐Proteomic Pipeline (TPP) is a suite of software tools for the analysis of MS/MS data sets. The tools encompass most of the steps in a proteomic data analysis workflow in a single, integrated software system. Specifically, the TPP supports all steps from spectrometer output file conversion to protein‐level statistical validation, including quantification by stable isotope ratios. We describe here the full workflow of the TPP and the tools therein, along with an example on a sample data set, demonstrating that the setup and use of the tools are straightforward and well supported and do not require specialized informatic resources or knowledge.

Comprehensive two-dimensional gas chromatography with time-of-flight mass spectrometry detection: analysis of amino acid and organic acid trimethylsilyl derivatives, with application to the analysis of metabolites in rye grass samples.

First, standard mixtures of trimethylsilyl (TMS) derivatives of amino acid and organic acid are analyzed by comprehensive two-dimensional (2D) gas chromatography (GC) coupled to time-of-flight mass spectrometry (GC x GC/TOFMS) in order to illustrate important issues regarding application of the technique. Specifically of interest is the extent to which the peak capacity of the 2D separation space has been utilized and the procedure by which the derivative standards are identified in the 2D separations using the mass spectral information. The resulting 2D separation is found to make extensive use of the GC x GC separation space provided by the complementary stationary phases employed. Second, in order to demonstrate GC x GC/TOFMS on two real sample types, trimethylsilyl metabolite derivatives were analyzed from extracts of common lawn grass samples (i.e., perennial rye grass), as a means to provide insight into both the pre and post harvest physiology. Various chemical components in the two rye grass extract samples were found to either emerge or disappear in relation to the trauma response. For example, a significant difference in the peak for the TMS derivative of malic acid was found. The successful analysis of various components was readily facilitated by the 2D separation, while a 1D separation would have produced too much peak overlap, thus impeding the analysis. The importance of using a GC x GC separation approach for the analysis of complex samples, such as metabolite extracts, is therefore demonstrated. The real-time analysis capability of GC x GC/TOFMS for multidimensional metabolite analysis makes this technique well suited to the high-throughput analysis of metabolomic samples, especially compared to slower, stopped-flow type separation approaches.

HDL in humans with cardiovascular disease exhibits a proteomic signature.

BACKGROUND Alterations in protein composition and oxidative damage of high density lipoprotein (HDL) have been proposed to impair the cardioprotective properties of HDL. We tested whether relative levels of proteins in HDL(2) could be used as biomarkers for coronary artery disease (CAD). METHODS Twenty control and eighteen CAD subjects matched for HDL-cholesterol, age, and sex were studied. HDL(2) isolated from plasma was digested with trypsin and analyzed by high-resolution matrix-assisted laser desorption ionization mass spectrometry (MALDI-MS) and pattern recognition analysis. RESULTS Partial least squares discriminant analysis (PLS-DA) of mass spectra clearly differentiated CAD from control subjects with area under the receiver operating characteristic curve (ROC(AUC)) of 0.94. Targeted tandem mass spectrometric analysis of the models significant features revealed that HDL(2) of CAD subjects contained oxidized methionine residues of apolipoprotein A-I and elevated levels of apolipoprotein C-III. A proteomic signature composed of MALDI-MS signals from apoA-I, apoC-III, Lp(a) and apoC-I accurately classified CAD and control subjects (ROC(AUC)=0.82). CONCLUSIONS HDL(2) of CAD subjects carries a distinct protein cargo and that protein oxidation helps generate dysfunctional HDL. Moreover, models based on selected identified peptides in MALDI-TOF mass spectra of the HDL may have diagnostic potential.

Enhancing the Limit of Detection for Comprehensive Two‐Dimensional Gas Chromatography (GC×GC) using Bilinear Chemometric Analysis

The chemometric method referred to as the generalized rank annihilation method (GRAM) is used to improve the precision, accuracy, and resolution of comprehensive two-dimensional gas chromatography (GC x GC) data. Because GC × GC signals follow a bilinear structure, GC x GC signals can be readily extracted from noise by chemometric techniques such as GRAM. This resulting improvement in signal-to-noise ratio (S/N) and detectability is referred to as bilinear signal enhancement. Here, GRAM uses bilinear signal enhancement on both resolved and unresolved GC x GC peaks that initially have a low S/N in the original GC × GC data. In this work, the chemometric method of GRAM is compared to two traditional peak integration methods for quantifying GC x GC analyte signals. One integration method uses a threshold to determine the signal of a peak of interest. With this integration method only those data points above the limit of detection and within a selected area are integrated to produce the total analyte signal for calibration and quantification. The other integration method evaluated did not employ a threshold, and simply summed all the data points in a selected region to obtain a total analyte signal. Substantial improvements in quantification precision, accuracy, and limit of detection are obtained by using GRAM, as compared to when either peak integration method is applied. In addition, the GRAM results are found to be more accurate than results obtained by peak integration, because GRAM more effectively corrects for the slight baseline offset remaining after the background subtraction of data. In the case of a 2.7-ppm propylbenzene synthetic sample the quantification result with GRAM is 2.6 times more precise and 4.2 times more accurate than the integration method without a threshold, and 18 times more accurate than the integration method with a threshold. The limit of detection for propylbenzene was 0.6 ppm (parts per million by mass) using GRAM, without implementing any sample preconcentration prior to injection. GRAM is also demonstrated as a means to resolve overlapped signals, while enhancing the S/N. Four alkyl benzene signals of low S/ N which were not resolved by GC x GC are mathematically resolved and quantified.

Comprehensive two-dimensional gas chromatography of volatile and semi-volatile components using a diaphragm valve-based instrument.

A high-temperature configuration for a diaphragm valve-based gas chromatography (GCXGC) instrument is demonstrated. GCxGC is a powerful instrumental tool often used to analyze complex mixtures. Previously, the temperature limitations of valve-based GCxGC instruments were set by the maximum operating temperature of the valve, typically 175 degrees C. Thus, valve-based GCxGC was constrained to the analysis of mainly volatile components; however, many complex mixtures contain semi-volatile components as well. A new configuration is described that extends the working temperature range of diaphragm valve-based GCxGC instruments to significantly higher temperatures, so both volatile and semi-volatile compounds can be readily separated. In the current investigation, separations at temperatures up to 250 degrees C are demonstrated. This new design features both chromatographic columns in the same oven with the valve interfacing the two columns mounted in the side of the oven wall so the valve is both partially inside as well as outside the oven. The diaphragm and the sample ports in the valve are located inside the oven while the temperature-restrictive portion of the valve (containing the O-rings) is outside the oven. Temperature measurements on the surface of the valve indicate that even after a sustained oven temperature of 240 degrees C, the portions of the valve directly involved with the sampling from the first column to the second column track the oven temperature to within 1.2% while the portions of the valve that are temperature-restrictive remain well below the maximum temperature of 175 degrees C. A 26-component mixture of alkanes, ketones, and alcohols whose boiling points range from 65 degrees C (n-hexane) to 270 degrees C (n-pentadecane) is used to test the new design. Peak shapes along the first column axis suggest that sample condensation or carry-over in the valve is not a problem. Chemometric data analysis is performed to demonstrate that the resulting data have a bilinear structure. After over 6 months of use and temperature conditions up to 265 degrees C, no deterioration of the valve or its performance has been observed.

GC × GC temperature programming requirements to produce bilinear data for chemometric analysis

A diaphragm valve-based comprehensive two-dimensional gas chromatography (GC x GC) instrument with the two columns under independent temperature control is demonstrated. A fifteen-component mixture of alkanes, alkyl aromatics, ketones, and alcohols was separated using this system in only 45 s. Independent temperature control of the two columns allows for high-speed analysis of complex samples while retaining the bilinear data structure that is necessary to apply many chemometric peak-resolving methods. An important part of high-speed GC x GC is sharp injections onto the second column. In this work, 10-ms peak widths on the second column are demonstrated. A peak capacity per time of 240 peaks/min was readily achieved. This work is aimed at providing a high-speed GC system for the quantitative and qualitative analysis of complex process streams, such as natural products.

High-Speed Gas Chromatographic Separations with Diaphragm Valve-Based Injection and Chemometric Analysis as a Gas Chromatographic''Sensor''

Abstract A high-speed gas chromatography system, the gas chromatographic sensor (GCS), is developed and evaluated. The GCS combines fast separations and chemometric analysis to produce an instrument capable of high-speed, high-throughput screening and quantitative analysis of complex chemical mixtures on a similar time scale as typical chemical sensors. The GCS was evaluated with 28 test mixtures consisting of 15 compounds from four chemical classes: alkanes, ketones, alkyl benzenes, and alcohols. The chromatograms are on the order of one second in duration, which is considerably faster than the traditional use of gas chromatography. While complete chromatographic separation of each analyte peak is not aimed for, chemical information is readily extracted through chemometric data analysis and quantification of the samples is achieved in considerably less time than conventional gas chromatography. Calibration models to predict percent volume content of either alkanes or ketones were constructed using partial least squares (PLS) regression on calibration sets consisting of the five replicate GCS runs of six different samples. The percent volume content of the alkane and ketone chemical classes were predicted on five replicate runs of the 22 remaining samples ranging from 0 to 50 or 60% depending on the class. Root mean square errors of prediction were 2–3% relative to the mean percent volume values for either alkane or ketone prediction models, depending on the samples chosen for the calibration set of that model. The alkyl benzenes and alcohols present in the calibration sets or samples were treated as variable background interference. It is anticipated that the GCS will eventually be used to rapidly sample and directly analyze industrial processes or for the high throughput analysis of batches of samples.

Characterization and use of a Raman liquid-core waveguide sensor using preconcentration principles

A novel Raman sensor using a liquid-core optical waveguide is reported, implementing a Teflon-AF 2400 tube filled with water. An aqueous analyte mixture of benzene, toluene and p-xylene was introduced using a 1000 microl sample loop to the liquid-core waveguide (LCW) sensor and the analytes were preconcentrated on the inside surface of the waveguide tubing. The analytes were then eluted from the waveguide using an acetonitrile-water solvent mixture injected via a 30 microl eluting solvent loop. The preconcentration factor was experimentally determined to be 14-fold, in reasonable agreement with the theoretical preconcentration factor of 33 based upon the sample volume to elution volume ratio. Raman spectra of benzene, toluene and p-xylene were obtained during elution. It was found that analytically useful Raman signals for benzene, toluene and p-xylene were obtained at 992, 1004 and 1206 cm(-1), respectively. The relative standard deviation of the method was 3% for three replicate measurements. The limit of detection (LOD) was determined to be 730 ppb (parts per billion by volume) for benzene, exceptional for a system that does not resort to surface enhancement or resonance Raman approaches. The Raman spectra of these test analytes were evaluated for qualitative and quantitative analysis utility.

HDL-apolipoprotein A-I exchange is independently associated with cholesterol efflux capacity.

HDL is the primary mediator of cholesterol mobilization from the periphery to the liver via reverse cholesterol transport (RCT). A critical first step in this process is the uptake of cholesterol from lipid-loaded macrophages by HDL, a function of HDL inversely associated with prevalent and incident cardiovascular disease. We hypothesized that the dynamic ability of HDL to undergo remodeling and exchange of apoA-I is an important and potentially rate-limiting aspect of RCT. In this study, we investigated the relationship between HDL-apoA-I exchange (HAE) and serum HDL cholesterol (HDL-C) efflux capacity. We compared HAE to the total and ABCA1-specific cholesterol efflux capacity of 77 subjects. We found that HAE was highly correlated with both total (r = 0.69, P < 0.0001) and ABCA1-specific (r = 0.47, P < 0.0001) efflux, and this relationship remained significant after adjustment for HDL-C or apoA-I. Multivariate models of sterol efflux capacity indicated that HAE accounted for approximately 25% of the model variance for both total and ABCA1-specific efflux. We conclude that the ability of HDL to exchange apoA-I and remodel, as measured by HAE, is a significant contributor to serum HDL efflux capacity, independent of HDL-C and apoA-I, indicating that HDL dynamics are an important factor in cholesterol efflux capacity and likely RCT.

Sequential injection analysis with dynamic surface tension detection. High throughput analysis of the interfacial properties of surface-active samples

A sequential injection analysis (SIA) system is coupled with dynamic surface tension detection (DSTD) for the purpose of studying the interfacial properties of surface-active samples. DSTD is a novel analyzer based upon a growing drop method, utilizing a pressure sensor measurement of drop pressure. The pressure signal depends on the surface tension properties of sample solution drops that grow and detach at the end of a capillary tip. In this work, SIA was used for creating a reagent concentration gradient, and for blending the reagent gradient with a steady-state sample. The sample, consisting of either sodium dodecyl sulfate (SDS) or poly(ethylene glycol) at 1470 g mol(-1) (PEG 1470), elutes with a steady-state concentration at the center of the sample plug. Reagents such as Brij(R)35, tetrabutylammonium (TBA) hydroxide and beta-cyclodextrin were introduced as a concentration gradient that begins after the sample plug has reached the steady-state concentration. By blending the reagent concentration gradient with the sample plug using SIA/DSTD, the kinetic surface pressure signal of samples mixed with various reagent concentrations is observed and evaluated in a high throughput fashion. It was found that the SIA/DSTD method consumes lesser reagent and required significantly less analysis time than traditional FIA/DSTD. Four unique chemical systems were studied with regard to how surface activity is influenced, as observed through the surface tension signal: surface activity addition, surface activity reduction due to competition, surface activity enhancement due to ion-pair formation, and surface activity reduction due to bulk phase binding chemistry.