Mark Libardoni

DISCO: Distance and Spectrum Correlation Optimization Alignment for Two Dimensional Gas Chromatography Time-of-Flight Mass Spectrometry-based Metabolomics

A novel peak alignment algorithm using a distance and spectrum correlation optimization (DISCO) method has been developed for two-dimensional gas chromatography time-of-flight mass spectrometry (GCxGC/TOF-MS)-based metabolomics. This algorithm uses the output of the instrument control software, ChromaTOF, as its input data. It detects and merges multiple peak entries of the same metabolite into one peak entry in each input peak list. After a z-score transformation of metabolite retention times, DISCO selects landmark peaks from all samples based on both two-dimensional retention times and mass spectrum similarity of fragment ions measured by Pearsons correlation coefficient. A local linear fitting method is employed in the original two-dimensional retention time space to correct retention time shifts. A progressive retention time map searching method is used to align metabolite peaks in all samples together based on optimization of the Euclidean distance and mass spectrum similarity. The effectiveness of the DISCO algorithm is demonstrated using data sets acquired under different experiment conditions and a spiked-in experiment.

Detection of an extended human volatome with comprehensive two-dimensional gas chromatography time-of-flight mass spectrometry.

Background Comprehensive two-dimensional gas chromatography coupled with time-of-flight mass spectrometry (GCxGC-TOF MS) has been proposed as a powerful new tool for multidimensional analysis of complex chemical mixtures. We investigated GCxGC-TOF MS as a new method for identifying volatile organic compounds (VOCs) in normal human breath. Methods Samples of alveolar breath VOCs and ambient room air VOC were collected with a breath collection apparatus (BCA) onto separate sorbent traps from 34 normal healthy volunteers (mean age = 40 yr, SD = 17 yr, male/female = 19/15). VOCs were separated on two serial capillary columns separated by a cryogenic modulator, and detected with TOF MS. The first and second dimension columns were non-polar and polar respectively. Results BCA collection combined with GC×GC-TOF MS analysis identified approximately 2000 different VOCs in samples of human breath, many of which have not been previously reported. The 50 VOCs with the highest alveolar gradients (abundance in breath minus abundance in ambient room air) mostly comprised benzene derivatives, acetone, methylated derivatives of alkanes, and isoprene. Conclusions Collection and analysis of breath VOCs with the BCA-GC×GC-TOF MS system extended the size of the detectable human volatile metabolome, the volatome, by an order of magnitude compared to previous reports employing one-dimensional GC-MS. The size of the human volatome has been under-estimated in the past due to coelution of VOCs in one-dimensional GC analytical systems.

The Importance of Detector Acquisition Rate in Comprehensive Two-Dimensional Gas Chromatography (GC×GC)

The use of comprehensive two-dimensional gas chromatography (GC×GC) is becoming more popular for complex sample analysis and the optimization of this technique is paramount for accurate qualitative and quantitative reporting. In this work, the effect of detector data acquisition rate (or detector sampling speed) for GC×GC is investigated and summarized. Effects on chromatographic signal such as peak area, height, and width, as well as analytical figures of merit such as resolution and limit of detection, are investigated at four data acquisition rates using a multi-component test mixture. Adjusting the data acquisition rate of the detector has a profound effect on peak areas, heights, and peak widths. In addition, the increase in detected signal and decreased peak widths led to improved chromatographic resolution and limits of detection (LOD) under optimized conditions.

Breath biomarkers of whole-body gamma irradiation in the Göttingen minipig.

AbstractThere is widespread interest in the development of tools to estimate radiation exposures. Exhaled breath provides a novel matrix for assessing biomarkers that could be correlated with exposures. The use of exhaled breath for estimating radiation exposure is warranted, as studies have shown that external exposure to ionizing radiation causes oxidative stress that accelerates lipid peroxidation of polyunsaturated fatty acids, liberating alkanes and alkane metabolites that are excreted in the breath as volatile organic compounds (VOCs). As a proof of principle study, small groups (n = 4) of Göttingen minipigs were whole-body irradiated with gamma rays delivered by a 60Co source at absorbed doses of 0, 0.25, 0.5, 0.75, 1, 1.25, 2, and 4 Gy. Additional groups (n = 4) were treated with lipopolysaccharide (LPS) or granulocyte colony stimulating factor (G-CSF), with and without concurrent 60Co exposure, at an absorbed dose of 1 Gy. Breath and background air VOC samples were collected on days −3, −2, −1, 0 pre-irradiation, then at 0.25, 24, 48, 72, and 168 h post-irradiation. VOCs were analyzed by automated thermal desorption with two-dimensional gas chromatography and time-of-flight mass spectrometry (ATD GCxGC TOF MS). The results show significant changes in 58 breath VOCs post-irradiation, mainly consisting of methylated and other derivatives of alkanes, alkenes, and benzene. Using a multivariate combination of these VOCs, a radiation response function was constructed, which was significantly elevated at 15 min post irradiation and remained elevated throughout the study (to 168 h post irradiation). As a binary test of radiation absorbed doses ≥ 0.25 Gy, the radiation response function distinguished irradiated animals from shams (0 Gy) with 83–84% accuracy. A randomly derived radiation response function was robust: When half of the biomarkers were removed, accuracy was 75%. An optimally derived function with two biomarkers was 82% accurate. As a binary test of radiation absorbed doses ≥ 0.5 Gy, the radiation response function identified irradiated animals with an accuracy of 87% at 15 min post irradiation and 75.5% at 168 h post irradiation. Treatment with LPS and G-CSF did not affect the radiation response function. This proof-of-principle study supports the hypothesis that breath VOCs may be used for estimating radiation exposures. Further studies will be required to validate the sensitivity and specificity of these potential biomarkers.

Nonhuman primate breath volatile organic compounds associate with developmental programming and cardio-metabolic status

Rodent and nonhuman primate studies indicate that developmental programming by reduced perinatal nutrition negatively impacts life course cardio-metabolic health. We have developed a baboon model in which we feed control mothers (CON) ad libitum while nutrient restricted mothers are fed 70% of ad libitum global feed in pregnancy and lactation. Offspring of nutrient restricted mothers are intrauterine growth restricted (IUGR) at term. By 3.5 years IUGR baboons showed signs of insulin resistance, indicating a pre-diabetic phenotype, in contrast to healthy CON offspring. We hypothesized that a novel breath analysis approach would provide markers of the altered cardio-metabolic state in a non-invasive manner. Here we assess whether exhaled breath volatile organic compounds (VOCs) collected from this unique cohort of juvenile baboons with documented cardio-metabolic dysfunction resulting from in utero programming can be detected from their breath signatures. Breath was collected from male and female CON and IUGR baboons at 4.8 ± 0.2 years (human equivalent ~13 years). Breath VOCs were quantified using a two-dimensional gas chromatography mass spectrometer. Two-way ANOVA, on 76 biologically relevant VOCs identified 27 VOCs (p < 0.05) with altered abundances between groups (sex, birthweight, and sex x birthweight). The 27 VOCs included 2-pentanone, 2-octanone, 2,2,7,7-tetramethyloctane and 3-methyl-1-heptene, which have not previously been associated with cardio-metabolic disease. Unsupervised principal component analysis of these VOCs could discriminate the four clusters defining males, females, CON and IUGR. This study, which is the first to assess quantifiable breath signatures associated with cardio-metabolic programing for any model of IUGR, demonstrates the translational value of this unique model to identify metabolites of programmed cardio-metabolic dysfunction in breath signatures. Future studies are required to validate the translatability of these findings to humans.