Rachel E. Mohler

Cyclic changes in metabolic state during the life of a yeast cell

Budding yeast undergo robust oscillations in oxygen consumption during continuous growth in a nutrient-limited environment. Using liquid chromatography-mass spectrometry and comprehensive 2D gas chromatography-mass spectrometry-based metabolite profiling methods, we have determined that the intracellular concentrations of many metabolites change periodically as a function of these metabolic cycles. These results reveal the logic of cellular metabolism during different phases of the life of a yeast cell. They may further indicate that oscillation in the abundance of key metabolites might help control the temporal regulation of cellular processes and the establishment of a cycle. Such oscillations in metabolic state might occur during the course of other biological cycles.

Comprehensive analysis of yeast metabolite GC×GC–TOFMS data: combining discovery-mode and deconvolution chemometric software

The first extensive study of yeast metabolite GC x GC-TOFMS data from cells grown under fermenting, R, and respiring, DR, conditions is reported. In this study, recently developed chemometric software for use with three-dimensional instrumentation data was implemented, using a statistically-based Fisher ratio method. The Fisher ratio method is fully automated and will rapidly reduce the data to pinpoint two-dimensional chromatographic peaks differentiating sample types while utilizing all the mass channels. The effect of lowering the Fisher ratio threshold on peak identification was studied. At the lowest threshold (just above the noise level), 73 metabolite peaks were identified, nearly three-fold greater than the number of previously reported metabolite peaks identified (26). In addition to the 73 identified metabolites, 81 unknown metabolites were also located. A Parallel Factor Analysis graphical user interface (PARAFAC GUI) was applied to selected mass channels to obtain a concentration ratio, for each metabolite under the two growth conditions. Of the 73 known metabolites identified by the Fisher ratio method, 54 were statistically changing to the 95% confidence limit between the DR and R conditions according to the rigorous Students t-test. PARAFAC determined the concentration ratio and provided a fully-deconvoluted (i.e. mathematically resolved) mass spectrum for each of the metabolites. The combination of the Fisher ratio method with the PARAFAC GUI provides high-throughput software for discovery-based metabolomics research, and is novel for GC x GC-TOFMS data due to the use of the entire data set in the analysis (640 MB x 70 runs, double precision floating point).

Identification and Quantification of Aqueous Aromatic Hydrocarbons Using SH-Surface Acoustic Wave Sensors

A need exists for compact sensor systems capable of in situ monitoring of groundwater for accidental releases of fuel and oil. The work reported here addresses this need, using shear horizontal surface acoustic wave (SH-SAW) sensors, which function effectively in liquid environments. To achieve enhanced sensitivity and partial selectivity for hydrocarbons, the devices are coated with thin chemically sensitive polymer films. Various polymer materials are investigated with the goal of identifying a set of coatings suitable for a sensor array. The system is tested with compounds indicative of fuel and oil releases, in particular, the BTEX compounds (benzene, toluene, ethylbenzene, and xylenes), in the low milligrams/liters to high micrograms/liters concentration range. Particular emphasis is placed on detection of benzene, a known carcinogen. It was observed that within the above concentration range, responses to multiple analytes in a mixture are additive, and there is a characteristic response time for each coating/analyte pair, which is largely independent of concentration. With the use of both the steady-state and transient-response information of SH-SAW sensor devices coated with three different polymer materials, poly(ethyl acrylate), poly(epichlorohydrin), and poly(isobutylene), a response pattern was obtained for benzene that is easily distinguishable from those of the other BTEX compounds. The time courses of the responses to binary analyte mixtures were modeled accurately using dual-exponential fits, yielding a characteristic concentration-independent time constant for each analyte/coating pair. Benzene concentration was quantified in the aqueous phase in the presence of the other BTEX compounds.

A Review of Chemometrics Applied to Comprehensive Two-dimensional Separations from 2008–2010

This review article covers developments in multidimensional separations combined with chemometrics that were published in 2008 through 2010, specifically for multidimensional gas chromatography, liquid chromatography, and electrophoresis. Although different instrumentation is used to generate multidimensional separations data, many similar data processing options and chemometrics can be applied in order to objectively distill the data into useful knowledge while reducing manual analysis and preserving data integrity. This review article describes the chemometrics employed in the referenced studies in terms of unsupervised, supervised, preprocessing, resolution, and image analysis algorithms. Other factors that affect converting data into useful knowledge are the structure of the data and the format of the data submitted to the analysis methods, so the studies are also described in terms of data dimensionality and data format (i.e., whether peak tables or raw data points were analyzed).

Analysis of binary mixtures of aqueous aromatic hydrocarbons with low-phase-noise shear-horizontal surface acoustic wave sensors using multielectrode transducer designs.

The present work investigates a compact sensor system that provides rapid, real-time, in situ measurements of the identities and concentrations of aromatic hydrocarbons at parts-per-billion concentrations in water through the combined use of kinetic and thermodynamic response parameters. The system uses shear-horizontal surface acoustic wave (SH-SAW) sensors operating directly in the liquid phase. The 103 MHz SAW sensors are coated with thin sorbent polymer films to provide the appropriate limits of detection as well as partial selectivity for the analytes of interest, the BTEX compounds (benzene, toluene, ethylbenzene, and xylenes), which are common indicators of fuel and oil accidental releases in groundwater. Particular emphasis is placed on benzene, a known carcinogen and the most challenging BTEX analyte with regard to both regulated levels and its solubility properties. To demonstrate the identification and quantification of individual compounds in multicomponent aqueous samples, responses to binary mixtures of benzene with toluene as well as ethylbenzene were characterized at concentrations below 1 ppm (1 mg/L). The use of both thermodynamic and kinetic (i.e., steady-state and transient) responses from a single polymer-coated SH-SAW sensor enabled identification and quantification of the two BTEX compounds in binary mixtures in aqueous solution. The signal-to-noise ratio was improved, resulting in lower limits of detection and improved identification at low concentrations, by designing and implementing a type of multielectrode transducer pattern, not previously reported for chemical sensor applications. The design significantly reduces signal distortion and root-mean-square (RMS) phase noise by minimizing acoustic wave reflections from electrode edges, thus enabling limits of detection for BTEX analytes of 9-83 ppb (calculated from RMS noise); concentrations of benzene in water as low as ~100 ppb were measured directly. Reliable quantification of BTEX analytes in binary mixtures is demonstrated in the sub-parts-per-million concentration range.

5.4.2 Quantification of Benzene in Groundwater Using SH-Surface Acoustic Wave Sensors

A need exists for compact sensor systems capable of in-situ monitoring of groundwater for fuel and oil contamination. The work reported here addresses this need using shear horizontal surface acoustic wave (SH-SAW) sensors, which function effectively in the liquid phase. To achieve enhanced sensitivity and partial selectivity for hydrocarbons, the devices are coated with thin chemically sensitive polymer films. Various polymer materials are investigated with the goal of identifying a set of coatings suitable for a sensor array. The system is tested with compounds indicative of fuel and oil contamination, in particular, BTEX (benzene, toluene, ethylbenzene and xylenes), at relatively low concentrations. Of particular importance is benzene, a known carcinogen. Using responses of the SHSAW sensor devices coated with three different polymer materials, benzene was quantified in the aqueous phase in the presence of other aromatic interferents. It is shown that various concentrations of BTEX in water can be identified and quantified by evaluation of both steady-state and transient response information.

Design of SH-surface acoustic wave sensors for detection of ppb concentrations of BTEX in water

To address the need to protect public health from contamination of drinking water and water for recreational use, a compact sensor system for in-situ detection of fuel and oil components in water is being investigated. The system makes use of shear-horizontal surface acoustic wave (SH-SAW) sensors coated with thin chemically sensitive polymer films. For this work, the BTEX compounds (benzene, toluene, ethylbenzene, and xylenes) were selected as target analytes because they are good indicators of fuel and oil releases, but also because they include known carcinogens (benzene, ethylbenzene). Chemical selectivity is achieved by combining sensors with various polymer coatings into a sensor array, and by evaluating both steady-state and transient response information. This work focuses on the influence of interdigital transducer (IDT) design on signal distortion and rms (root-mean-square) noise level. It is demonstrated that for suitable IDT design and experimental approach, it is possible to detect all BTEX compounds at concentration levels of 100 ppb or below, and to quantify benzene concentration in binary analyte mixtures at concentrations well below 1 ppm.

Identification of ester metabolites from petroleum hydrocarbon biodegradation in groundwater using GC×GC‐TOFMS

In an effort to understand the nature and toxicity of petroleum hydrocarbon degradation metabolites, 2-dimensional gas chromatography linked to a time-of-flight mass spectrometer (GC×GC-TOFMS) was used to conduct nontargeted analysis of the extracts of 61 groundwater samples collected from 10 fuel release sites. An unexpected result was the tentative identification of 197 unique esters. Although esters are known to be part of specific hydrocarbon degradative pathways, they are not commonly considered or evaluated in field studies of petroleum biodegradation. In addition to describing the compounds identified, the present study discusses the role for nontargeted analysis in environmental studies. Overall, the low toxicological profile of the identified esters, along with the limited potential for exposure, renders them unlikely to pose any significant health risk.

Online Chemical Sensor Signal Processing Using Estimation Theory: Quantification of Binary Mixtures of Organic Compounds in the Presence of Linear Baseline Drift and Outliers

Compact sensor systems for on-site monitoring of groundwater for trace organic compounds in the liquid phase are currently under development in our laboratories. Potential challenges include sensor baseline drift and the presence of outliers in the data, along with difficulties extracting the contribution of individual BTEX compound (benzene, toluene, ethylbenzene, and xylenes) from the sensor response to mixtures containing multiple chemically similar compounds. As a first step, the approach presented here permits online estimation of analyte concentrations in binary mixtures of BTEX compounds in the presence of linear baseline drift and outliers. This paper investigates a sensor signal-processing approach based on estimation theory, specifically, Kalman filter (KF), extended KF, and discrete low-pass filter. The approach permits online linear baseline drift correction, filtering of outlier points, and estimation of analyte concentration(s) in binary mixtures and single analyte samples, before the sensor response reaches steady state. Sensor signals from mixtures of BTEX compounds were analyzed because these compounds are good indicators of accidental releases of fuel and oil into groundwater. Models were first developed for the sensor response so that estimation theory can be used to obtain the sensor parameters. The baseline-drift correction technique uses KF to perform online linear extrapolation or interpolation. The presented combination of sensor signal-processing techniques was simultaneously tested using actual measured data. Unknown sensor parameters and identification of analytes in samples were obtained within a relatively short period of time (8 min or less for the present sensor system), well before the sensor response reaches equilibrium.

Near-real-time analysis of binary mixtures of organic compounds in water using SH-SAW sensors and estimation theory

Sensor systems for on-site monitoring of contaminated water for trace organic compounds are currently under development. To permit near-real-time analysis of samples containing multiple analytes, we investigate a sensor signal processing approach based on estimation theory, specifically, the Kalman Filter. The approach permits estimation of analyte concentration(s) in binary mixtures on-line, before the sensor response reaches equilibrium. Sensor signals from binary mixtures of BTEX compounds (benzene, toluene, ethylbenzene, and xylenes) were analyzed because these compounds are good indicators of accidental releases of fuel and oil into groundwater. Based on previous and recent experimental results, models for the sensor response to binary mixtures were developed. The sensor response model was transformed into a state-space representation so that estimation theory could be used to estimate the sensor parameters. The state-space form was tested using the available measured data; the results indicate that relatively accurate estimates of analyte concentration(s) can be obtained within a short period of time (four - six minutes or less for the tested sensor system) well before the sensor response reaches equilibrium (10 - 16 minutes).