Qingping Tao

Informatics for cross-sample analysis with comprehensive two-dimensional gas chromatography and high-resolution mass spectrometry (GCxGC–HRMS)

This paper describes informatics for cross-sample analysis with comprehensive two-dimensional gas chromatography (GCxGC) and high-resolution mass spectrometry (HRMS). GCxGC-HRMS analysis produces large data sets that are rich with information, but highly complex. The size of the data and volume of information requires automated processing for comprehensive cross-sample analysis, but the complexity poses a challenge for developing robust methods. The approach developed here analyzes GCxGC-HRMS data from multiple samples to extract a feature template that comprehensively captures the pattern of peaks detected in the retention-times plane. Then, for each sample chromatogram, the template is geometrically transformed to align with the detected peak pattern and generate a set of feature measurements for cross-sample analyses such as sample classification and biomarker discovery. The approach avoids the intractable problem of comprehensive peak matching by using a few reliable peaks for alignment and peak-based retention-plane windows to define comprehensive features that can be reliably matched for cross-sample analysis. The informatics are demonstrated with a set of 18 samples from breast-cancer tumors, each from different individuals, six each for Grades 1-3. The features allow classification that matches grading by a cancer pathologist with 78% success in leave-one-out cross-validation experiments. The HRMS signatures of the features of interest can be examined for determining elemental compositions and identifying compounds.

Smart Templates for Peak Pattern Matching with Comprehensive Two-Dimensional Liquid Chromatography

Comprehensive two-dimensional liquid chromatography (LCxLC) generates information-rich but complex peak patterns that require automated processing for rapid chemical identification and classification. This paper describes a powerful approach and specific methods for peak pattern matching to identify and classify constituent peaks in data from LCxLC and other multidimensional chemical separations. The approach records a prototypical pattern of peaks with retention times and associated metadata, such as chemical identities and classes, in a template. Then, the template pattern is matched to the detected peaks in subsequent data and the metadata are copied from the template to identify and classify the matched peaks. Smart Templates employ rule-based constraints (e.g., multispectral matching) to increase matching accuracy. Experimental results demonstrate Smart Templates, with the combination of retention-time pattern matching and multispectral constraints, are accurate and robust with respect to changes in peak patterns associated with variable chromatographic conditions.

Features for non-targeted cross-sample analysis with comprehensive two-dimensional chromatography

This review surveys different approaches for generating features from comprehensive two-dimensional chromatography for non-targeted cross-sample analysis. The goal of non-targeted cross-sample analysis is to discover relevant chemical characteristics (such as compositional similarities or differences) from multiple samples. In non-targeted analysis, the relevant characteristics are unknown, so individual features for all chemical constituents should be analyzed, not just those for targeted or selected analytes. Cross-sample analysis requires matching the corresponding features that characterize each constituent across multiple samples so that relevant characteristics or patterns can be recognized. Non-targeted, cross-sample analysis requires generating and matching all features across all samples. Applications of non-targeted cross-sample analysis include sample classification, chemical fingerprinting, monitoring, sample clustering, and chemical marker discovery. Comprehensive two-dimensional chromatography is a powerful technology for separating complex samples and so is well suited for non-targeted cross-sample analysis. However, two-dimensional chromatographic data is typically large and complex, so the computational tasks of extracting and matching features for pattern recognition are challenging. This review examines five general approaches that researchers have applied to these difficult problems: visual image comparisons, datapoint feature analysis, peak feature analysis, region feature analysis, and peak-region feature analysis.

Global and selective detection of organohalogens in environmental samples by comprehensive two-dimensional gas chromatography–tandem mass spectrometry and high-resolution time-of-flight mass spectrometry

We successfully detected halogenated compounds from several kinds of environmental samples by using a comprehensive two-dimensional gas chromatograph coupled with a tandem mass spectrometer (GC×GC-MS/MS). For the global detection of organohalogens, fly ash sample extracts were directly measured without any cleanup process. The global and selective detection of halogenated compounds was achieved by neutral loss scans of chlorine, bromine and/or fluorine using an MS/MS. It was also possible to search for and identify compounds using two-dimensional mass chromatograms and mass profiles obtained from measurements of the same sample with a GC×GC-high resolution time-of-flight mass spectrometer (HRTofMS) under the same conditions as those used for the GC×GC-MS/MS. In this study, novel software tools were also developed to help find target (halogenated) compounds in the data provided by a GC×GC-HRTofMS. As a result, many dioxin and polychlorinated biphenyl congeners and many other halogenated compounds were found in fly ash extract and sediment samples. By extracting the desired information, which concerned organohalogens in this study, from huge quantities of data with the GC×GC-HRTofMS, we reveal the possibility of realizing the total global detection of compounds with one GC measurement of a sample without any pre-treatment.

Non-targeted analysis of electronics waste by comprehensive two-dimensional gas chromatography combined with high-resolution mass spectrometry: Using accurate mass information and mass defect analysis to explore the data.

Comprehensive two-dimensional gas chromatography (GC×GC) and high-resolution mass spectrometry (HRMS) offer the best possible separation of their respective techniques. Recent commercialization of combined GC×GC-HRMS systems offers new possibilities for the analysis of complex mixtures. However, such experiments yield enormous data sets that require new informatics tools to facilitate the interpretation of the rich information content. This study reports on the analysis of dust obtained from an electronics recycling facility by using GC×GC in combination with a new high-resolution time-of-flight (TOF) mass spectrometer. New software tools for (non-traditional) Kendrick mass defect analysis were developed in this research and greatly aided in the identification of compounds containing chlorine and bromine, elements that feature in most persistent organic pollutants (POPs). In essence, the mass defect plot serves as a visual aid from which halogenated compounds are recognizable on the basis of their mass defect and isotope patterns. Mass chromatograms were generated based on specific ions identified in the plots as well as region of the plot predominantly occupied by halogenated contaminants. Tentative identification was aided by database searches, complementary electron-capture negative ionization experiments and elemental composition determinations from the exact mass data. These included known and emerging flame retardants, such as polybrominated diphenyl ethers (PBDEs), hexabromobenzene, tetrabromo bisphenol A and tris (1-chloro-2-propyl) phosphate (TCPP), as well as other legacy contaminants such as polychlorinated biphenyls (PCBs) and polychlorinated terphenyls (PCTs).

Comparative analysis of peak-detection techniques for comprehensive two-dimensional chromatography

Comprehensive two-dimensional gas chromatography (GC×GC) is a powerful technology for separating complex samples. The typical goal of GC×GC peak detection is to aggregate data points of analyte peaks based on their retention times and intensities. Two techniques commonly used for two-dimensional peak detection are the two-step algorithm and the watershed algorithm. A recent study [4] compared the performance of the two-step and watershed algorithms for GC×GC data with retention-time shifts in the second-column separations. In that analysis, the peak retention-time shifts were corrected while applying the two-step algorithm but the watershed algorithm was applied without shift correction. The results indicated that the watershed algorithm has a higher probability of erroneously splitting a single two-dimensional peak than the two-step approach. This paper reconsiders the analysis by comparing peak-detection performance for resolved peaks after correcting retention-time shifts for both the two-step and watershed algorithms. Simulations with wide-ranging conditions indicate that when shift correction is employed with both algorithms, the watershed algorithm detects resolved peaks with greater accuracy than the two-step method.

Alignment for Comprehensive Two-Dimensional Gas Chromatography with Dual Secondary Columns and Detectors

In each sample run, comprehensive two-dimensional gas chromatography with dual secondary columns and detectors (GC × 2GC) provides complementary information in two chromatograms generated by its two detectors. For example, a flame ionization detector (FID) produces data that is especially effective for quantification and a mass spectrometer (MS) produces data that is especially useful for chemical-structure elucidation and compound identification. The greater information capacity of two detectors is most useful for difficult analyses, such as metabolomics, but using the joint information offered by the two complex two-dimensional chromatograms requires data fusion. In the case that the second columns are equivalent but flow conditions vary (e.g., related to the operative pressure of their different detectors), data fusion can be accomplished by aligning the chromatographic data and/or chromatographic features such as peaks and retention-time windows. Chromatographic alignment requires a mapping from the retention times of one chromatogram to the retention times of the other chromatogram. This paper considers general issues and experimental performance for global two-dimensional mapping functions to align pairs of GC × 2GC chromatograms. Experimental results for GC × 2GC with FID and MS for metabolomic analyses of human urine samples suggest that low-degree polynomial mapping functions out-perform affine transformation (as measured by root-mean-square residuals for matched peaks) and achieve performance near a lower-bound benchmark of inherent variability. Third-degree polynomials slightly out-performed second-degree polynomials in these results, but second-degree polynomials performed nearly as well and may be preferred for parametric and computational simplicity as well as robustness.

Reliable Peak Selection for Multisample Analysis with Comprehensive Two-Dimensional Chromatography

Comprehensive two-dimensional chromatography is a powerful technology for analyzing the patterns of constituent compounds in complex samples, but matching chromatographic features for comparative analysis across large sample sets is difficult. Various methods have been described for pairwise peak matching between two chromatograms, but the peaks indicated by these pairwise matches commonly are incomplete or inconsistent across many chromatograms. This paper describes a new, automated method for postprocessing the results of pairwise peak matching to address incomplete and inconsistent peak matches and thereby select chromatographic peaks that reliably correspond across many chromatograms. Reliably corresponding peaks can be used both for directly comparing relative compositions across large numbers of samples and for aligning chromatographic data for comprehensive comparative analyses. To select reliable features for a set of chromatograms, the Consistent Cliques Method (CCM) represents all peaks from all chromatograms and all pairwise peak matches in a graph, finds the maximal cliques, and then combines cliques with shared peaks to extract reliable features. The parameters of CCM are the minimum number of chromatograms with complete pairwise peak matches and the desired number of reliable peaks. A particular threshold for the minimum number of chromatograms with complete pairwise matches ensures that there are no conflicts among the pairwise matches for reliable peaks. Experimental results with samples of complex bio-oils analyzed by comprehensive two-dimensional gas chromatography (GCxGC) coupled with mass spectrometry (GCxGC-MS) indicate that CCM provides a good foundation for comparative analysis of complex chemical mixtures.

Comprehensive feature analysis for sample classification with comprehensive two-dimensional LC

Comprehensive two-dimensional LC (LC x LC) is a powerful tool for analysis of complex biological samples. With its multidimensional separation power and increased peak capacity, LC x LC generates information-rich, but complex, chromatograms, which require advanced data analysis to produce useful information. An important analytical challenge is to classify samples on the basis of chromatographic features, e.g., to extract and utilize biomarkers indicative of health conditions, such as disease or response to therapy. This study presents a new approach to extract comprehensive non-target chromatographic features from a set of LC x LC chromatograms for sample classification. Experimental results with urine samples indicate that the chromatographic features generated by this approach can be used to effectively classify samples. Based on the extracted features, a support vector machine successfully classified urine samples by individual, before/after procedure, and concentration with leave-one-out and replicate K-fold cross-validation. The new method for comprehensive chromatographic feature analysis of LC x LC separations provides a potentially powerful tool for classifying complex biological samples.

Gradient-based value mapping for pseudocolor images

We develop a method for automatic colorization of im- ages (or two-dimensional fields) in order to visualize pixel values and their local differences. In many applications, local differences in pixel values are as important as their values. For example, in topog- raphy, both elevation and slope often must be considered. Gradient- based value mapping (GBVM) is a technique for colorizing pixels based on value (e.g., intensity or elevation) and gradient (e.g., local differences or slope). The method maps pixel values to a color scale (either gray-scale or pseudocolor) in a manner that emphasizes gra- dients in the image while maintaining ordinal relationships of values. GBVM is especially useful for high-precision data, in which the num- ber of possible values is large. Colorization with GBVM is demon- strated with data from comprehensive two-dimensional gas chroma- tography (GCxGC), using both gray-scale and pseudocolor to visualize both small and large peaks, and with data from the Global Land One-Kilometer Base Elevation (GLOBE) Project, using gray- scale to visualize features that are not visible in images produced with popular value-mapping algorithms.