Interpretation of comprehensive two-dimensional gas chromatography data using advanced chemometrics

The power of comprehensive two-dimensional gas chromatography (GC × GC) for the study of complex mixtures has been indisputably proved in the past several decades. This review encompasses the whole of GC × GC-related data processing and summarizes relevant applications. We include theoretical introduction to some specific methods and studies to aid readers' understanding of chemometrics strategies for advanced data interpretation

Smith-Waterman peak alignment for comprehensive two-dimensional gas chromatography-mass spectrometry

<p>Abstract</p> <p>Background</p> <p>Comprehensive two-dimensional gas chromatography coupled with mass spectrometry (GC × GC-MS) is a powerful technique which has gained increasing attention over the last two decades. The GC × GC-MS provides much increased separation capacity, chemical selectivity and sensitivity for complex sample analysis and brings more accurate information about compound retention times and mass spectra. Despite these advantages, the retention times of the resolved peaks on the two-dimensional gas chromatographic columns are always shifted due to experimental variations, introducing difficulty in the data processing for metabolomics analysis. Therefore, the retention time variation must be adjusted in order to compare multiple metabolic profiles obtained from different conditions.</p> <p>Results</p> <p>We developed novel peak alignment algorithms for both homogeneous (acquired under the identical experimental conditions) and heterogeneous (acquired under the different experimental conditions) GC × GC-MS data using modified Smith-Waterman local alignment algorithms along with mass spectral similarity. Compared with literature reported algorithms, the proposed algorithms eliminated the detection of landmark peaks and the usage of retention time transformation. Furthermore, an automated peak alignment software package was established by implementing a likelihood function for optimal peak alignment.</p> <p>Conclusions</p> <p>The proposed Smith-Waterman local alignment-based algorithms are capable of aligning both the homogeneous and heterogeneous data of multiple GC × GC-MS experiments without the transformation of retention times and the selection of landmark peaks. An optimal version of the SW-based algorithms was also established based on the associated likelihood function for the automatic peak alignment. The proposed alignment algorithms outperform the literature reported alignment method by analyzing the experiment data of a mixture of compound standards and a metabolite extract of mouse plasma with spiked-in compound standards.</p

Automated mass spectrometry-based metabolomics data processing by blind source separation methods

Una de les principals limitacions de la metabolòmica és la transformació de dades crues en informació biològica. A més, la metabolòmica basada en espectrometria de masses genera grans quantitats de dades complexes caracteritzades per la co-elució de compostos i artefactes experimentals. L'objectiu d'aquesta tesi és desenvolupar estratègies automatitzades basades en deconvolució cega del senyal per millorar les capacitats dels mètodes existents que tracten les limitacions de les diferents passes del processament de dades en metabolòmica. L'objectiu d'aquesta tesi és també desenvolupar eines capaces d'executar el flux de treball del processament de dades en metabolòmica, que inclou el preprocessament de dades, deconvolució espectral, alineament i identificació. Com a resultat, tres nous mètodes automàtics per deconvolució espectral basats en deconvolució cega del senyal van ser desenvolupats. Aquests mètodes van ser inclosos en dues eines computacionals que permeten convertir automàticament dades crues en informació biològica interpretable i per tant, permeten resoldre hipòtesis biològiques i adquirir nous coneixements biològics.Una de les principals limitacions de la metabolòmica és la transformació de dades crues en informació biològica. A més, la metabolòmica basada en espectrometria de masses genera grans quantitats de dades complexes caracteritzades per la co-elució de compostos i artefactes experimentals. L'objectiu d'aquesta tesi és desenvolupar estratègies automatitzades basades en deconvolució cega del senyal per millorar les capacitats dels mètodes existents que tracten les limitacions de les diferents passes del processament de dades en metabolòmica. L'objectiu d'aquesta tesi és també desenvolupar eines capaces d'executar el flux de treball del processament de dades en metabolòmica, que inclou el preprocessament de dades, deconvolució espectral, alineament i identificació. Com a resultat, tres nous mètodes automàtics per deconvolució espectral basats en deconvolució cega del senyal van ser desenvolupats. Aquests mètodes van ser inclosos en dues eines computacionals que permeten convertir automàticament dades crues en informació biològica interpretable i per tant, permeten resoldre hipòtesis biològiques i adquirir nous coneixements biològics.Una de las principales limitaciones de la metabolómica es la transformación de datos crudos en información biológica. Además, la metabolómica basada en espectrometría de masas genera grandes cantidades de datos complejos caracterizados por la co-elución de compuestos y artefactos experimentales. El objetivo de esta tesis es desarrollar estrategias automatizadas basadas en deconvolución ciega de la señal para mejorar las capacidades de los métodos existentes que tratan las limitaciones de los diferentes pasos del procesamiento de datos en metabolómica. El objetivo de esta tesis es también desarrollar herramientas capaces de ejecutar el flujo de trabajo del procesamiento de datos en metabolómica, que incluye el preprocessamiento de datos, deconvolución espectral, alineamiento e identificación. Como resultado, tres nuevos métodos automáticos para deconvolución espectral basados en deconvolución ciega de la señal fueron desarrollados. Estos métodos fueron incluidos en dos herramientas computacionales que permiten convertir automáticamente datos crudos en información biológica interpretable y por lo tanto, permiten resolver hipótesis biológicas y adquirir nuevos conocimientos biológicos.One of the major bottlenecks in metabolomics is to convert raw data samples into biological interpretable information. Moreover, mass spectrometry-based metabolomics generates large and complex datasets characterized by co-eluting compounds and with experimental artifacts. This thesis main objective is to develop automated strategies based on blind source separation to improve the capabilities of the current methods that tackle the different metabolomics data processing workflow steps limitations. Also, the objective of this thesis is to develop tools capable of performing the entire metabolomics workflow for GC--MS, including pre-processing, spectral deconvolution, alignment and identification. As a result, three new automated methods for spectral deconvolution based on blind source separation were developed. These methods were embedded into two computation tools able to automatedly convert raw data into biological interpretable information and thus, allow resolving biological answers and discovering new biological insights

Biological studies with comprehensive 2D-GC-HRMS screening: Exploring the human sweat volatilome

A key issue in GCxGC-HRMS data analysis is how to approach large-sample studies in an efficient and comprehensive way. We have developed a semi-automated data-driven workflow from identification to suspect screening, which allows highly selective monitoring of each identified chemical in a large-sample dataset. The example dataset used to illustrate the potential of the approach consisted of human sweat samples from 40 participants, including field blanks (80 samples). These samples have been collected in a Horizon 2020 project to investigate the capacity of body odour to communicate emotion and influence social behaviour. We used dy-namic headspace extraction, which allows comprehensive extraction with high preconcentration capability, and has to date only been used for a few biological applications. We were able to detect a set of 326 compounds from a diverse range of chemical classes (278 identified compounds, 39 class unknowns, and 9 true unknowns). Unlike partitioning-based extraction methods, the developed method detects semi-polar (log P &lt; 2) nitrogen and oxygen-containing compounds. However, it is unable to detect certain acids due to the pH conditions of un-modified sweat samples. We believe that our framework will open up the possibility of efficiently using GCxGC-HRMS for large-sample studies in a wide range of applications such as biological and environmental studies

Innovative solutions for enhanced illicit drugs profiling using comprehensive two-dimensional gas chromatography and mass spectrometry technologies

This project aimed to determine whether comprehensive two-dimensional gas chromatography is suitable for routine use in a forensic laboratory for profiling illicit substances. Abstract Analytical gas chromatographic methods usually rely upon a single dimension (ie single column) high-resolution capillary GC column to provide separation of target analyses. When a matrix is especially complex, the ability of the column to provide adequate resolution is severely compromised. Often, mass spectrometry may provide the ability to uniquely measure the target compounds, but if the matrix generates similar ions to the target compounds, this can lead to confounded analysis. Mass spectrometry offers many potential solutions to the lack of resolution of GC; however, this usually involves selected ion monitoring or similar approaches. This removes the important opportunity to use a full-scan spectrum to match with a database library. In the present project, high-resolution GC analysis using the multidimensional separation method of comprehensive two-dimensional gas chromatography (GCxGC) was used to provide sufficient resolution to allow full-scan acquisition with library confirmation of illicit drug identity. It was shown that the WADA criteria for a selection of test steroid compounds could be suitably met under this new high-resolution environment. In addition, analysis of samples of ecstasy were profiled and all synthetic residues involved in the synthesis of ecstasy could be fully resolved and located in the 2D separation space with excellent library matches, even though the underlying matrix was very complicated and would have strongly interfered in a 1D separation analysis. This will allow facile profiling of the reaction procedure for ecstasy synthesis

DATA ANALYSIS WORKFLOW FOR GAS CHROMATOGRAPHY MASS SPECTROMETRY-BASED METABOLOMICS STUDIES

Metabolomics has emerged as an integral part of systems biology research that attempts to comprehensively study low molecular weight organic and inorganic metabolites under certain conditions within a biological system. Technological advances in the past decade have made it possible to carry out metabolomics studies in a high- throughput fashion using gas chromatography coupled with mass spectrometry. As a result, large volumes of data are produced from these studies and there is a pressing need for algorithms that can efficiently process and analyze the data in a high-throughput fashion as well. To address this need, we have developed computational algorithms and the associated software tool named an Automated Data Analysis Pipeline (ADAP). ADAP allows data to flow seamlessly through the data processing steps that include de- nosing, peak detection, deconvolution, alignment, compound identification and quantitation. The development of ADAP started in 2009 and the past four years have witnessed continuous improvements in its performance from ADAP-GC 1.0, to ADAP- GC 2.0, and to the current ADAP-GC 3.0. As part of the performance assessment of ADAP-GC, we have compared it with three other software tools. In this dissertation, I will present the computational details about these three versions of ADAP-GC, the capabilities of the software tool, and the results from software comparison