San Yuan Wang

Batch Normalizer: a fast total abundance regression calibration method to simultaneously adjust batch and injection order effects in liquid chromatography/time-of-flight mass spectrometry-based metabolomics data and comparison with current calibration methods.

Metabolomics is a powerful tool for understanding phenotypes and discovering biomarkers. Combinations of multiple batches or data sets in large cross-sectional epidemiology studies are frequently utilized in metabolomics, but various systematic biases can introduce both batch and injection order effects and often require proper calibrations prior to chemometric analyses. We present a novel algorithm, Batch Normalizer, to calibrate large scale metabolomic data. Batch Normalizer utilizes a regression model with consideration of the total abundance of each sample to improve its calibration performance, and it is able to remove both batch effect and injection order effects. This calibration method was tested using liquid chromatography/time-of-flight mass spectrometry (LC/TOF-MS) chromatograms of 228 plasma samples and 23 pooled quality control (QC) samples. We evaluated the performance of Batch Normalizer by examining the distribution of relative standard deviation (RSD) for all peaks detected in the pooled QC samples, the average Pearson correlation coefficients for all peaks between any two of QC samples, and the distribution of QC samples in the scores plot of a principal component analysis (PCA). After calibration by Batch Normalizer, the number of peaks in QC samples with RSD less than 15% increased from 11 to 914, all of the QC samples were closely clustered in PCA scores plot, and the average Pearson correlation coefficients for all peaks of QC samples increased from 0.938 to 0.976. This method was compared to 7 commonly used calibration methods. We discovered that using Batch Normalizer to calibrate LC/TOF-MS data produces the best calibration results.

The metabolome profiling and pathway analysis in metabolic healthy and abnormal obesity

Objectives:Mechanisms of the development of abnormal metabolic phenotypes among obese population are not yet clear. In this study, we aimed to screen metabolomes of both healthy and subjects with abnormal obesity to identify potential metabolic pathways that may regulate the different metabolic characteristics of obesity.Methods:We recruited subjects with body mass index (BMI) over 25 from the weight-loss clinic of a central hospital in Taiwan. Metabolic healthy obesity (MHO) is defined as without having any form of hyperglycemia, hypertension and dyslipidemia, while metabolic abnormal obesity (MAO) is defined as having one or more abnormal metabolic indexes. Serum-based metabolomic profiling using both liquid chromatography–mass spectrometry and gas chromatography–mass spectrometry of 34 MHO and MAO individuals with matching age, sex and BMI was performed. Conditional logistic regression and partial least squares discriminant analysis were applied to identify significant metabolites between the two groups. Pathway enrichment and topology analyses were conducted to evaluate the regulated pathways.Results:A differential metabolite panel was identified to be significantly differed in MHO and MAO groups, including L-kynurenine, glycerophosphocholine (GPC), glycerol 1-phosphate, glycolic acid, tagatose, methyl palmitate and uric acid. Moreover, several metabolic pathways were relevant in distinguishing MHO from MAO groups, including fatty acid biosynthesis, phenylalanine metabolism, propanoate metabolism, and valine, leucine and isoleucine degradation.Conclusion:Different metabolomic profiles and metabolic pathways are important for distinguishing between MHO and MAO groups. We have identified and discussed the key metabolites and pathways that may prove important in the regulation of metabolic traits among the obese, which could provide useful clues to study the underlying mechanisms of the development of abnormal metabolic phenotypes.

Thermogravimetric Analysis of Corn Cob Impregnated With Zinc Chloride for Preparation of Activated Carbon

The thermochemical decomposition of agricultural by-product corn cob impregnated with ZnCl2, as a precursor material for producing the activated carbons, was investigated by thermogravimetric (TG) analysis at the heating rate of 5 and 10°C min–1 under a controlled atmosphere of nitrogen (60 ml min–1). The appearance of a peak in the differential thermogravimetric plot (DTG) in the temperature range of 400–600°C is significantly related to the extent of impregnation. The DTG curve of the sample impregnated with the optimal impregnation ratio of 175% (i.e., the ratio of ZnCl2 mass of 87.5 g in the 200 cm3 of water to corn cobmass of 50 g), which yields an optimal BET surface area of the activated carbon and displays a DTG peak at about 500°C. This may be partially due to the intense chemical activation and results in the formation of a porous structure in the activated solid residue. This observation is also in close agreement with previous results at optimal pyrolysis temperatures of 500°C and with similar experimental conditions. In order to support the results in the TG-DTG analysis, the development of pore structure of the resulting activated carbons thus obtained by previous studies was also examined and explained using the scanning electron microscopy (SEM).

Chromaligner: a web server for chromatogram alignment

UNLABELLEDnChromaligner is a tool for chromatogram alignment to align retention time for chromatographic methods coupled to spectrophotometers such as high performance liquid chromatography and capillary electrophoresis for metabolomics works. Chromaligner resolves peak shifts by a constrained chromatogram alignment. For a collection of chromatograms and a set of defined peaks, Chromaligner aligns the chromatograms on defined peaks using correlation warping (COW). Chromaligner is faster than the original COW algorithm by k(2) times, where k is the number of defined peaks in a chromatogram. It also provides alignments based on known component peaks to reach the best results for further chemometric analysis.nnnAVAILABILITYnChromaligner is freely accessible at http://cmdd.csie.ntu.edu.tw/~chromaligner.

A Portable Micro Gas Chromatography System for Lung Cancer Associated Volatile Organic Compound Detection

With the help of micro-electromechanical systems (MEMS) and complementary metal-oxide-semiconductor (CMOS) technology, a portable micro gas chromatography (μGC) system for lung cancer associated volatile organic compounds (VOCs) detection is realized for the first time. The system is composed of an MEMS preconcentrator, an MEMS separation column, and a CMOS system-on-chip (SoC). The preconcentrator provides a concentration ratio of 2170. The separation column can separate more than seven types of lung cancer associated VOCs. The SoC is fabricated by a TSMC 0.35 μm 2P4M process including the CMOS VOCs detector, sensor calibration circuit, low-noise chopper instrumentation amplifier (IA), 10 bit analog to digital converter, and the microcontrol unit (MCU). Experimental results show that the system is able to detect seven types of lung cancer associated VOCs (acetone, 2-butanone, benzene, heptane, toluene, m-xylene, 1,3,5-trimethylbenzene). The concentration linearity is R2 = 0.985 and the detection sensitivity is up to 15 ppb with 1,3,5-trimethylbenzene.

Metabolomic characterization of rhubarb species by capillary electrophoresis and ultra‐high‐pressure liquid chromatography

This study developed CE and ultra‐high‐pressure LC (UHPLC) methods coupled with UV detectors to characterize the metabolomic profiles of different rhubarb species. The optimal CE conditions used a BGE with 15 mM sodium tetraborate, 15 mM sodium dihydrogen phosphate monohydrate, 30 mM sodium deoxycholate, and 30% ACN v/v at pH 8.3. The optimal UHPLC conditions used a mobile phase composed of 0.05% phosphate buffer and ACN with gradient elution. The gradient profile increased linearly from 10 to 21% ACN within the first 25 min, then increased to 33% ACN for the next 10 min. It took another 5 min to reach the 65% ACN, then for the next 5 min, it stayed unchanged. Sixteen samples of Rheum officinale and Rheum tanguticum collected from various locations were analyzed by CE and UHPLC methods. The metabolite profiles of CE were aligned and baseline corrected before chemometric analysis. Metabolomic signatures of rhubarb species from CE and UHPLC were clustered using principle component analysis and distance‐based redundancy analysis; the clusters were not only able to discriminate different species but also different cultivation regions. Similarity measurements were performed by calculating the correlation coefficient of each sample with the authentic samples. Hybrid rhizome was clearly identified through similarity measurement of UHPLC metabolite profile and later confirmed by gene sequencing. The present study demonstrated that CE and UHPLC are efficient and effective tools to identify and authenticate herbs even coupled with simple detectors.

Plasma metabolomic profiles predict near-term death among individuals with lower extremity peripheral arterial disease.

BACKGROUNDnIndividuals with peripheral arterial disease (PAD) have a nearly two-fold increased risk of all-cause and cardiovascular disease mortality compared to those without PAD. This pilot study determined whether metabolomic profiling can accurately identify patients with PAD who are at increased risk of near-term mortality.nnnMETHODSnWe completed a case-control study using (1)H NMR metabolomic profiling of plasma from 20 decedents with PAD, without critical limb ischemia, who had blood drawn within 8 months prior to death (index blood draw) and within 10 to 28 months prior to death (preindex blood draw). Twenty-one PAD participants who survived more than 30 months after their index blood draw served as a control population.nnnRESULTSnResults showed distinct metabolomic patterns between preindex decedent, index decedent, and survivor samples. The major chemical signals contributing to the differential pattern (between survivors and decedents) arose from the fatty acyl chain protons of lipoproteins and the choline head group protons of phospholipids. Using the top 40 chemical signals for which the intensity was most distinct between survivor and preindex decedent samples, classification models predicted near-term all-cause death with overall accuracy of 78% (32/41), a sensitivity of 85% (17/20), and a specificity of 71% (15/21). When comparing survivor with index decedent samples, the overall classification accuracy was optimal at 83% (34/41) with a sensitivity of 80% (16/20) and a specificity of 86% (18/21), using as few as the top 10 to 20 chemical signals.nnnCONCLUSIONSnOur results suggest that metabolomic profiling of plasma may be useful for identifying PAD patients at increased risk for near-term death. Larger studies using more sensitive metabolomic techniques are needed to identify specific metabolic pathways associated with increased risk of near-term all-cause mortality among PAD patients.

Using precursor ion scan of 184 with liquid chromatography-electrospray ionization-tandem mass spectrometry for concentration normalization in cellular lipidomic studies

Cellular lipidomic studies have been favored approaches in many biomedical research areas. To provide fair comparisons of the studied cells, it is essential to perform normalization of the determined concentration before lipidomic analysis. This study proposed a cellular lipidomic normalization method by measuring the phosphatidylcholine (PC) and sphingomyelin (SM) contents in cell extracts. To provide efficient analysis of PC and SM in cell extracts, flow injection analysis-electrospray ionization-tandem mass spectrometry (FIA-ESI-MS/MS) with a precursor ion scan (PIS) of m/z 184 was used, and the parameters affecting the performance of the method were optimized. Good linearity could be observed between the cell extract dilution factor and the reciprocal of the total ion chromatogram (TIC) area in the PIS of m/z 184 within the dilution range of 1- to 16-fold (R2xa0=xa00.998). The calibration curve could be used for concentration adjustment of the unknown concentration of a cell extract. The intraday and intermediate precisions were below 10%. The accuracy ranged from 93.0% to 105.6%. The performance of the new normalization method was evaluated using different numbers of HCT-116xa0cells. Sphingosine, ceramide (d18:1/18:0), SM (d18:1/18:0) and PC (16:1/18:0) were selected as the representative test lipid species, and the results showed that the peak areas of each lipid species obtained from different cell numbers were within a 20% variation after normalization. Finally, the PIS of 184 normalization method was applied to study ischemia-induced neuron injury using oxygen and glucose deprivation (OGD) on primary neuronal cultured cells. Our results showed that the PIS of 184 normalization method is an efficient and effective approach for concentration normalization in cellular lipidomic studies.

An efficient computer-aided structural elucidation strategy for mixtures using an iterative dynamic programming algorithm

AbstractnThe identification of chemical structures in natural product mixtures is an important task in drug discovery but is still a challenging problem, as structural elucidation is a time-consuming process and is limited by the available mass spectra of known natural products. Computer-aided structure elucidation (CASE) strategies seek to automatically propose a list of possible chemical structures in mixtures by utilizing chromatographic and spectroscopic methods. However, current CASE tools still cannot automatically solve structures for experienced natural product chemists. Here, we formulated the structural elucidation of natural products in a mixture as a computational problem by extending a list of scaffolds using a weighted side chain list after analyzing a collection of 243,130 natural products and designed an efficient algorithm to precisely identify the chemical structures. The complexity of such a problem is NP-complete. A dynamic programming (DP) algorithm can solve this NP-complete problem in pseudo-polynomial time after converting floating point molecular weights into integers. However, the running time of the DP algorithm degrades exponentially as the precision of the mass spectrometry experiment grows. To ideally solve in polynomial time, we proposed a novel iterative DP algorithm that can quickly recognize the chemical structures of natural products. By utilizing this algorithm to elucidate the structures of four natural products that were experimentally and structurally determined, the algorithm can search the exact solutions, and the time performance was shown to be in polynomial time for average cases. The proposed method improved the speed of the structural elucidation of natural products and helped broaden the spectrum of available compounds that could be applied as new drug candidates. A web service built for structural elucidation studies is freely accessible via the following link (http://csccp.cmdm.tw/).

21.5 A portable micro gas chromatography system for volatile compounds detection with 15ppb of sensitivity

Existing non-invasive lung cancer diagnostic equipment has difficulty in detecting early stage lung cancer as abnormal tissue is smaller than 0.5cm in size. According to studies showing that volatile organic compounds (VOCs) from human breath gas can provide biomarkers for human disease, especially for lung cancer, a non-invasive method was developed to measure the exhaled air from cancer patients using chromatography-mass spectrometry (GC-MS). However, traditional GC-MS equipment is very expensive and requires specialists to operate. In this research we demonstrate a portable micro gas chromatography (μGC) system to overcome the limitations of conventional methods and realize small chips for big data.