c-Abl kinase-mediated phosphorylation of γ-tubulin promotes γ-tubulin ring complexes assembly and microtubule nucleation.

Cytoskeletal microtubules (MTs) are nucleated from γ-tubulin ring complexes (γTuRCs) located at MT organizing centers (MTOCs), such as the centrosome. However, the exact regulatory mechanism of γTuRC assembly is not fully understood. Here, we showed that the nonreceptor tyrosine kinase c-Abl was associated with and phosphorylated γ-tubulin, the essential component of the γTuRC, mainly on the Y443 residue by in vivo (immunofluorescence and immunoprecipitation) or in vitro (surface plasmon resonance) detection. We further demonstrated that phosphorylation deficiency significantly impaired γTuRC assembly, centrosome construction, and MT nucleation. c-Abl/Arg deletion and γ-tubulin Y443F mutation resulted in an abnormal morphology and compromised spindle function during mitosis, eventually causing uneven chromosome segregation. Our findings reveal that γTuRC assembly and nucleation function are regulated by Abl kinase-mediated γ-tubulin phosphorylation, revealing a fundamental mechanism that contributes to the maintenance of MT function.

Extending the breadth of saliva metabolome fingerprinting by smart template strategies and effective pattern realignment on comprehensive two-dimensional gas chromatographic data.

Comprehensive two-dimensional gas chromatography with time-of-flight mass spectrometry (GC × GC-TOFMS) is one the most powerful analytical platforms for chemical investigations of complex biological samples. It produces large datasets that are rich in information, but highly complex, and its consistency may be affected by random systemic fluctuations and/or changes in the experimental parameters. This study details the optimization of a data processing strategy that compensates for severe 2D pattern misalignments and detector response fluctuations for saliva samples analyzed across 2 years. The strategy was trained on two batches: one with samples from healthy subjects who had undergone dietary intervention with high/low-Maillard reaction products (dataset A), and the second from healthy/unhealthy obese individuals (dataset B). The combined untargeted and targeted pattern recognition algorithm (i.e., UT fingerprinting) was tuned for key process parameters, the signal-to-noise ratio (S/N), and MS spectrum similarity thresholds, and then tested for the best transform function (global or local, affine or low-degree polynomial) for pattern realignment in the temporal domain. Reliable peak detection achieved its best performance, computed as % of false negative/positive matches, with a S/N threshold of 50 and spectral similarity direct match factor (DMF) of 700. Cross-alignment of bi-dimensional (2D) peaks in the temporal domain was fully effective with a supervised operation including multiple centroids (reference peaks) and a match-and-transform strategy using affine functions. Regarding the performance-derived response fluctuations, the most promising strategy for cross-comparative analysis and data fusion included the mass spectral total useful signal (MSTUS) approach followed by Z-score normalization on the resulting matrix.

Magnetic Susceptibility Difference-Induced Nucleus Positioning in Gradient Ultrahigh Magnetic Field.

Despite the importance of magnetic properties of biological samples for biomagnetism and related fields, the exact magnetic susceptibilities of most biological samples in their physiological conditions are still unknown. Here we used superconducting quantum interferometer device to detect the magnetic properties of nonfixed, nondehydrated live cell and cellular fractions at a physiological temperature of 37°C (310 K). It is obvious that there are paramagnetic components within human nasopharyngeal carcinoma CNE-2Z cells. More importantly, the magnetic properties of the cytoplasm and nucleus are different. Although within a single cell, the magnetic susceptibility difference between cellular fractions (nucleus and cytoplasm) could only cause ∼41-130 pN forces to the nucleus by gradient ultrahigh magnetic fields of 13.1-23.5 T (92-160 T/m), these forces are enough to cause a relative position shift of the nucleus within the cell. This not only demonstrates the importance of magnetic susceptibility in the biological effects of magnetic field but also illustrates the potential application of high magnetic fields in biomedicine.

Moderate intensity low frequency rotating magnetic field inhibits breast cancer growth in mice.

Moderate intensity low frequency rotating magnetic field (LF-RMF) has been shown to inhibit melanoma, liver and lung cancer growth in mice. However, its effects on other types of cancers have not been investigated in vivo. Here, we show that 0-0.15T moderate intensity 4.2 Hz LF-RMF can inhibit tumor growth in mice bearing MDA-MB231 and MCF7 human breast cancer cells by over 30%. In contrast, the human gastrointestinal stromal tumor GIST-T1 growth was not inhibited by LF-RMF. In all RMF treatments, there were no apparent adverse effects on mice organs, body weight or water/food consumptions. However, the alanine aminotransferase (ALT) level was decreased in LF-RMF-treated mice bearing MCF7 and GIST-T1 cells, which indicated alleviated liver damage. Therefore, our study shows that moderate intensity LF-RMF might be a safe physical method that has clinical potentials to be used to inhibit breast cancer growth in the future.

Effectiveness of Global, Low-Degree Polynomial Transformations for GCxGC Data Alignment.

As columns age and differ between systems, retention times for comprehensive two-dimensional gas chromatography (GCxGC) may vary between runs. To properly analyze GCxGC chromatograms, it often is desirable to align the retention times of chromatographic features, such as analyte peaks, between chromatograms. Previous work by the authors has shown that global, low-degree polynomial transformation functions, namely affine, second-degree polynomial, and third-degree polynomial, are effective for aligning pairs of two-dimensional chromatograms acquired with dual second columns and detectors (GC×2GC). This work assesses the experimental performance of these global methods on more general GCxGC chromatogram pairs and compares their performance to that of a recent, robust, local alignment algorithm for GCxGC data [ Gros Anal. Chem. 2012 , 84 , 9033 ]. Measuring performance with the root-mean-square (RMS) residual differences in retention times for matched peaks suggests that global, low-degree polynomial transformations outperform the local algorithm given a sufficiently large set of alignment points, and are able to improve misalignment by over 95% based on a lower-bound benchmark of inherent variability. However, with small sets of alignment points, the local method demonstrated lower error rates (although with greater computational overhead). For GCxGC chromatogram pairs with only slight initial misalignment, none of the global or local methods performed well. In some cases with initial misalignment near the inherent variability of the system, these methods worsened alignment, suggesting that it may be better not to perform alignment in such cases.

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.

Augmented visualization by computer vision and chromatographic fingerprinting on comprehensive two-dimensional gas chromatographic patterns: Unraveling diagnostic signatures in food volatilome.

Computer Vision is an approach of Artificial Intelligence (AI) that conceptually enables "computers and systems to derive useful information from digital images" giving access to higher-level information and "take actions or make recommendations based on that information". Comprehensive two-dimensional chromatography gives access to highly detailed, accurate, yet unstructured information on the sample's chemical composition, and makes it possible to exploit the AI concepts at the data processing level (e.g., by Computer Vision) to rationalize raw data explorations. The goal is the understanding of the biological phenomena interrelated to a specific/diagnostic chemical signature. This study introduces a novel workflow for Computer Vision based on pattern recognition algorithms (i.e., combined untargeted and targeted UT fingerprinting) which includes the generation of composite Class Images for representative samples' classes, their effective re-alignment and registration against a comprehensive feature template followed by Augmented Visualization by comparative visual analysis. As an illustrative application, a sample set originated from a Research Project on artisanal butter (from raw sweet cream to ripened butter) is explored, capturing the evolution of volatile components along the production chain and the impact of different microbial cultures on the finished product volatilome. The workflow has significant advantages compared to the classical one-step pairwise comparison process given the ability to realign and pairwise compare both targeted and untargeted chromatographic features belonging to Class Images resembling chemical patterns from many different samples with intrinsic biological variability.

Exploring the Extra-Virgin Olive Oil Volatilome by Adding Extra Dimensions to Comprehensive Two-Dimensional Gas Chromatography and Time-of-Flight Mass Spectrometry Featuring Tandem Ionization: Validation of Ripening Markers in Headspace Linearity Conditions.

BACKGROUND: Comprehensive two-dimensional gas chromatography (GC×GC) combined with time-of-flight (TOF) MS is the most informative analytical approach for chemical characterization of the complex food volatilome. Key analytical features include separation power and resolution enhancement, improved sensitivity, and structured separation patterns from chemically correlated analytes. OBJECTIVE: In this study, we explore the complex extra-virgin olive oil volatilome by combining headspace (HS) solid-phase microextraction (SPME), applied under HS linearity conditions to GC×GC-TOF MS and featuring hard and soft ionization in tandem. METHOD: Multiple analytical dimensions are combined in a single run and evaluated in terms of chemical dimensionality, method absolute and relative sensitivity, identification reliability provided by spectral signatures acquired at 70 and 12 eV, and dynamic and linear range of response provided by soft ionization. RESULTS: Method effectiveness is validated on a sample set of oils from Picual olives at different ripening stages. Ripening markers [3,4-diethyl-1,5-hexadiene (RS/SR), 3,4-diethyl-1,5-hexadiene (meso), (5Z)-3-ethyl-1,5-octadiene, (5E)-3-ethyl-1,5-octadiene, (E, Z)-3,7-decadiene and (E, E)-3,7-decadiene, (Z)-2-hexenal, (Z)-3-hexenal and (Z)-3-hexenal, (E)-2-pentenal, (Z)-2-pentenal, 1-pentanol, 1-penten-3-ol, 3-pentanone, and 1-penten-3-one] and quality indexes [(Z)-3-hexenal/nonanal, (Z)-3-hexenal/octane, (E)-2-pentenal/nonanal, and (E)-2-pentenal/octane] are confirmed for their validity in HS linearity conditions. CONCLUSIONS: For the complex olive oil volatilome, the proposed approach offers concrete advantages for the validation of the informative role of existing analytes while suggesting new potential markers to be studied in larger sample sets. HIGHLIGHTS: The accurate fingerprinting of volatiles by HS-SPME operating in HS linearity conditions followed by GC×GC-TOF MS featuring tandem ionization gives the opportunity to improve the quality of analytical data and reliability of results.

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.

Chromatographic Fingerprinting by Template Matching for Data Collected by Comprehensive Two-Dimensional Gas Chromatography.

Data processing and evaluation are critical steps of comprehensive two-dimensional gas chromatography (GCxGC), particularly when coupled to mass spectrometry. The rich information encrypted in the data may be highly valuable but difficult to access efficiently. Data density and complexity can lead to long elaboration times and require laborious, analyst-dependent procedures. Effective yet accessible data processing tools, therefore, are key to enabling the spread and acceptance of this advanced multidimensional technique in laboratories for daily use. The data analysis protocol presented in this work uses chromatographic fingerprinting and template matching to achieve the goal of highly automated deconstruction of complex two-dimensional chromatograms into individual chemical features for advanced recognition of informative patterns within individual chromatograms and across sets of chromatograms. The protocol delivers high consistency and reliability with little intervention. At the same time, analyst supervision is possible in a variety of settings and constraint functions that can be customized to provide flexibility and capacity to adapt to different needs and goals. Template matching is shown here to be a powerful approach to explore extra-virgin olive oil volatilome. Cross-alignment of peaks is performed not only for known targets, but also for untargeted compounds, which significantly increases the characterization power for a wide range of applications. Examples are presented to evidence the performance for the classification and comparison of chromatographic patterns from sample sets analyzed under similar conditions.

Untargeted and Targeted Fingerprinting of Extra Virgin Olive Oil Volatiles by Comprehensive Two-Dimensional Gas Chromatography with Mass Spectrometry: Challenges in Long-Term Studies.

Comprehensive two-dimensional gas chromatography coupled with mass spectrometric detection (GC × GC-MS) offers an information-rich basis for effective chemical fingerprinting of food. However, GC × GC-MS yields 2D-peak patterns (i.e., sample 2D fingerprints) whose consistency may be affected by variables related to either the analytical platform or to the experimental parameters adopted for the analysis. This study focuses on the complex volatile fraction of extra-virgin olive oil and addresses 2D-peak patterns variations, including MS signal fluctuations, as they may occur in long-term studies where pedo-climatic, harvest year, or shelf life changes are studied. The 2D-pattern misalignments are forced by changing chromatographic settings and MS acquisition. All procedural steps, preceding pattern recognition by template matching, are analyzed and a rational workflow defined to accurately realign patterns and analytes metadata. Signal-to-noise ratio (SNR) detection threshold, reference spectra extraction, and similarity match factor threshold are critical to avoid false-negative matches. Distance thresholds and polynomial transform parameters are key for effective template matching. In targeted analysis (supervised workflow) with optimized parameters, method accuracy reaches 92.5% (i.e., % of true-positive matches) while for combined untargeted and targeted ( UT) fingerprinting (unsupervised workflow), accuracy reaches 97.9%. Response normalization also is examined, evidencing good performance of multiple internal standard normalization that effectively compensates for discriminations occurring during injection of highly volatile compounds. The resulting workflow is simple, effective, and time efficient.

Benchmarking machine learning methods for comprehensive chemical fingerprinting and pattern recognition.

Machine learning (ML) has been used previously to recognize particular patterns of constituent compounds. Here, ML is used with comprehensive chemical fingerprints that capture the distribution of all constituent compounds to flexibly perform various pattern recognition tasks. Such pattern recognition requires a sequence of chemical analysis, data analysis, and pattern analysis. Chemical analysis with comprehensive multidimensional chromatography is a maturing approach for highly effective separations of complex samples and so provides a solid foundation for undertaking comprehensive chemical fingerprinting. Data analysis with smart templates employs marker peaks and chemical logic for chromatographic alignment and peak-regions to delineate chromatographic windows in which analytes are quantified and matched consistently across chromatograms to create chemical profiles that serve as complete fingerprints. Pattern analysis uses ML techniques with the resulting fingerprints to recognize sample characteristics, e.g., for classification. Our experiments evaluated the effectiveness of seventeen different ML techniques for various classification problems with chemical fingerprints from a rich data set from 126 wine samples of different varieties, geographic regions, vintages, and wineries. Results of these experiments showed an accuracy range from 58% to 88% for different ML methods on the most difficult classification problems and 96% to 100% for different ML methods on the least difficult classification problems. Averaged over 14 classification problems, accuracy for the different methods ranged from 80% to 90%, with some relatively simple ML techniques among the top-performing methods.

Comprehensive two-dimensional gas chromatography coupled with time of flight mass spectrometry featuring tandem ionization: Challenges and opportunities for accurate fingerprinting studies.

The capture of volatile patterns from food is a fingerprinting that opens access to a high level of information related to functional variables (origin, processing, shelf-life etc.) and their impact on sample composition and quality. When the focus is on food volatilome, comprehensive two-dimensional gas chromatography combined with time-of-flight mass spectrometry (GC×GC-TOF MS) is undoubtedly the most effective technique to obtain a highly representative fingerprinting. A recently patented ion source, featuring variable-energy EI, when operated at low energies (10 eV, 12 eV, 14 eV), claims enhanced intensity of structure-indicating ions while minimizing the inherent loss of sensitivity due to low EI energies. The spectral acquisition is done by multiplexing between two ionization energies and generates tandem data streams in a single run. This study explores the potentials of combined untargeted/targeted (UT) fingerprinting with tandem signals to study the complex volatile metabolome of high quality cocoa. The quality of the spectra at 70 eV is confirmed by similarity match factors above a fixed threshold (950) while spectral differences between hard (70 eV) and soft (12 eV, 14 eV) ionization are computed in terms of spectral similarity and signal-to-noise ratio (SNR). Tandem signals are then processed independently and after fusion in a single stream (summed signal) by the UT fingerprinting work-flow; signal characteristics (SNR, detectable 2D peaks, spectral peak intensities) are then computed and adopted to define the best strategy to discriminate and classify samples. Classification performance, on processed cocoa from four different origins, is validated by cross-comparing results between single ionization channels and fused data streams and considering both targeted and untargeted features. Classification results indicate the potential for superior performances of UT fingerprinting with fused data streams (summed signals), while signal characteristics at low ionization energies not only offer additional elements to better discriminate and/or identify isomeric analytes but also to achieve wider dynamic range of exploration.

Kernels for generalized multiple-instance learning.

The multiple-instance learning (MIL) model has been successful in numerous application areas. Recently, a generalization of this model and an algorithm for it were introduced, showing significant advantages over the conventional MIL model on certain application areas. Unfortunately, that algorithm is not scalable to high dimensions. We adapt that algorithm to one using a support vector machine with our new kernel k\wedge. This reduces the time complexity from exponential in the dimension to polynomial. Computing our new kernel is equivalent to counting the number of boxes in a discrete, bounded space that contain at least one point from each of two multisets. We show that this problem is #P-complete, but then give a fully polynomial randomized approximation scheme (FPRAS) for it. We then extend k\wedge by enriching its representation into a new kernel kmin, and also consider a normalized version of k\wedge that we call k\wedge/\vee (which may or may not not be a kernel, but whose approximation yielded positive semidefinite Gram matrices in practice). We then empirically evaluate all three measures on data from content-based image retrieval, biological sequence analysis, and the musk data sets. We found that our kernels performed well on all data sets relative to algorithms in the conventional MIL model.

Advanced fingerprinting of high-quality cocoa: Challenges in transferring methods from thermal to differential-flow modulated comprehensive two dimensional gas chromatography.

The possibility to transfer methods from thermal to differential-flow modulated comprehensive two-dimensional gas chromatographic (GC×GC) platforms opens interesting perspectives for routine analysis of complex samples. Flow modulated platforms avoid the use of cryogenics, thereby simplifying laboratory operations and analyst supervision during intensive analytical sessions. This study evaluates the feasibility of transferring a fingerprinting method capable of classifying and discriminating cocoa samples based on the volatiles fraction composition according to their origin and processing steps. Previously developed principles of GC×GC method translation are applied to an original fingerprinting method, developed for a loop-type thermal modulated GC×GC-MS system, to engineer a method for a reverse-injection differential flow modulated platform (GC×2GC-MS/FID) with a dual-parallel secondary column and dual detection. Effective method translation preserves analytes elution order, 1D resolution, and 2D pattern coherence. The experimental results confirm the feasibility of translating fingerprinting method conditions while preserving the informative power of 2D peak patterns for sample classification and discrimination. Correct translation enables effective transfer of metadata (e.g., compound names and MS fragmentation patterns) by automatic template transformation and matching from the original/reference method to its translated counterpart. Although the adoption of a narrow bore (i.e. 0.1mm dc) column in the first-dimension enabled operation under close-to-optimal conditions with the differential-flow modulation platform, due to the dual-parallel columns in the second-dimension, it resulted in lower overall method sensitivity. Nevertheless, fingerprinting accuracy was preserved and most of the key-aroma compounds and technological markers were effectively mapped, thus limiting the loss of fingerprinting information.

Pixel-by-pixel correction of retention time shifts in chromatograms from comprehensive two-dimensional gas chromatography coupled to high resolution time-of-flight mass spectrometry.

A pixel-by-pixel method for correcting retention time (RT) shifts in whole chromatograms from comprehensive two-dimensional gas chromatography coupled to high-resolution time-of-flight mass spectrometry (GC×GC-HRTOFMS) is introduced. A previously developed robust algorithm for correcting RT shifts was extended to high-resolution mass-spectral data. The performance of the new method in terms of decreasing RT shifts and peak volume changes was tested on GC×GC-HRTOFMS data. The RT shift correction algorithm, using linear interpolation for the 1st dimension and Sibson natural neighbor interpolation for the 2nd dimension, performed well for systematically shifted data acquired using two different temperature programs in terms of decreasing RT differences and alterations to the peak volumes and mass spectra. A modified RT shift correction algorithm, using Sibson natural neighbor for both dimensions, performed better for RT shifts caused by column damage, for which the original interpolation method did not appropriately correct RT shifts. Although further investigation would be required for more types of severe shifts, this study shows that the developed method is useful for correcting RT shifts with GC×GC-HRTOFMS.

Comparative visualization for comprehensive two-dimensional gas chromatography.

This paper investigates methods for comparing datasets produced by comprehensive two-dimensional gas chromatography (GC x GC). Chemical comparisons are useful for process monitoring, sample classification or identification, correlative determinations, and other important tasks. GC x GC is a powerful new technology for chemical analysis, but methods for comparative visualization must address challenges posed by GC x GC data: inconsistency and complexity. The approach extends conventional techniques for image comparison by utilizing specific characteristics of GC x GC data and developing new methods for comparative visualization and analysis. The paper describes techniques that register (or align) GC x GC datasets to remove retention-time variations; normalize intensities to remove sample amount variations; compute differences in local regions to remove slight misregistrations and differences in peak shapes; employ color (hue), intensity, and saturation to simultaneously visualize differences and values; and use tools for masking, three-dimensional visualization, and tabular presentation with controls for graphical highlights to significantly improve comparative analysis of GC x GC datasets. Experimental results indicate that the comparative methods preserve chemical information and support qualitative and quantitative analyses.

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).