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

Automated resolution of chromatographic signals by independent component analysis-orthogonal signal deconvolution in comprehensive gas chromatography/mass spectrometry-based metabolomics

Comprehensive gas chromatography-mass spectrometry (GC x GC-MS) provides a different perspective in metabolomics profiling of samples. However, algorithms for GCx GC-MS data processing are needed in order to automatically process the data and extract the purest information about the compounds appearing in complex biological samples. This study shows the capability of independent component analysis-orthogonal signal deconvolution (ICA-OSD), an algorithm based on blind source separation and distributed in an R package called osd, to extract the spectra of the compounds appearing in GCx GC-MS chromatograms in an automated manner. We studied the performance of ICA-OSD by the quantification of 38 metabolites through a set of 20 Jurkat cell samples analyzed by GCx GC-MS. The quantification by ICA-OSD was compared with a supervised quantification by selective ions, and most of the R2 coefficients of determination were in good agreement (R-2>0.90) while up to 24 cases exhibited an excellent linear relation (R-2>0.95). We concluded that ICA-OSD can be used to resolve co-eluted compounds in GC x GC-MS. (C) 2016 Elsevier Ireland Ltd. All rights reserved.Postprint (author's final draft

Updates in metabolomics tools and resources: 2014-2015

Data processing and interpretation represent the most challenging and time-consuming steps in high-throughput metabolomic experiments, regardless of the analytical platforms (MS or NMR spectroscopy based) used for data acquisition. Improved machinery in metabolomics generates increasingly complex datasets that create the need for more and better processing and analysis software and in silico approaches to understand the resulting data. However, a comprehensive source of information describing the utility of the most recently developed and released metabolomics resources—in the form of tools, software, and databases—is currently lacking. Thus, here we provide an overview of freely-available, and open-source, tools, algorithms, and frameworks to make both upcoming and established metabolomics researchers aware of the recent developments in an attempt to advance and facilitate data processing workflows in their metabolomics research. The major topics include tools and researches for data processing, data annotation, and data visualization in MS and NMR-based metabolomics. Most in this review described tools are dedicated to untargeted metabolomics workflows; however, some more specialist tools are described as well. All tools and resources described including their analytical and computational platform dependencies are summarized in an overview Table

Gas chromatography-mass spectrometry based untargeted volatolomics for smoked seafood classification

With the increase of the demand of low flavouring smoked seafood products, there is a need of methodologies able to distinguish between different seafood treatments, as not all of them are allowed in all markers. Following this objective, in the present work an untargeted volatolomics approach was applied to identify volatile markers that demonstrate that Cold smoked products can be distinguished from Tasteless smoke neither Carbon monoxide treated seafood, which are prohibited in the European Union. The use of dynamic headspace for the volatile extraction followed by thermal desorption in combination with Gas Chromatography (GC) coupled to single quadrupole Mass Spectrometry (MS) has been employed for the determination of volatile composition of smoked fish. Data processing consisted on the use of PARADISe software, applied for GC/MS data treatment, followed by the multivariate analysis with PLS_Toolbox (MATLAB), and finally the creation and validation of statistical classification model. All 107 variables obtained allowed the construction of a model reaching the correct classification of 97% of the blind samples, while a simplified model with only 11 variables correctly classified up to 93% of the blind samples. These 11 compounds were elucidated to develop subsequent target volatolomics approaches, if needed. Ordered according to the importance in the classification model, the elucidated compounds were: 3-methylcyclopentanone, ethylbenzene, 2-methyl-2-cyclopenten-1-one, 2-methyl-benzofuran, furfuryl alcohol, 2-acetylfuran, acetophenone, guaiacol, 1-hydroxy-2-butanone, 4-vinylguaicol and acetoin. The results demonstrated the great potential of untargeted volatolomics for smoked seafood treatments classification

Quality classification of Spanish olive oils by untargeted gas chromatography coupled to hybrid quadrupole–time of flight mass spectrometry with atmospheric pressure chemical ionization and metabolomics-based statistical approach

The novel atmospheric pressure chemical ionization (APCI) source has been used in combination with gas chromatography (GC) coupled to hybrid quadrupole time-of-flight (QTOF) mass spectrometry (MS) for determination of volatile components of olive oil, enhancing its potential for classification of olive oil samples according to their quality using a metabolomics-based approach. The full-spectrum acquisition has allowed the detection of volatile organic compounds (VOCs) in olive oil samples, including extra virgin, virgin and lampante qualities. A dynamic headspace extraction with cartridge solvent elution was applied. The metabolomics strategy consisted of three different steps: a full mass spectral alignment of GC-MS data using MzMine 2.0, a multivariate analysis using Ez-Info and the creation of the statistical model with combinations of responses for molecular fragments. The model was finally validated using blind samples, obtaining an accuracy in oil classification of 70 % taking the official established method, “PANEL TEST” as reference.The authors acknowledge the financial support of Generalitat Valenciana, as research group of excellence (PROMETEO II/2014/023) and Collaborative Research on Environment and Food-Safety (ISIC/2012/016). This work and C. Sales grant has been supported by Universitat Jaume I research promotion plans (P1-1B2013-70

Untargeted/Targeted 2D Gas Chromatography/Mass Spectrometry Detection of the Total Volatile Tea Metabolome

Identifying all analytes in a natural product is a daunting challenge, even if fractionated by volatility. In this study, comprehensive two-dimensional gas chromatography/mass spectrometry (GC×GC-MS) was used to investigate relative distribution of volatiles in green, pu-erh tea from leaves collected at two different elevations (1162 m and 1651 m). A total of 317 high and 280 low elevation compounds were detected, many of them known to have sensory and health beneficial properties. The samples were evaluated by two different software. The first, GC Image, used feature-based detection algorithms to identify spectral patterns and peak-regions, leading to tentative identification of 107 compounds. The software produced a composite map illustrating differences in the samples. The second, Ion Analytics, employed spectral deconvolution algorithms to detect target compounds, then subtracted their spectra from the total ion current chromatogram to reveal untargeted compounds. Compound identities were more easily assigned, since chromatogram complexities were reduced. Of the 317 compounds, for example, 34% were positively identified and 42% were tentatively identified, leaving 24% as unknowns. This study demonstrated the targeted/untargeted approach taken simplifies the analysis time for large data sets, leading to a better understanding of the chemistry behind biological phenomena

The metaRbolomics Toolbox in Bioconductor and beyond

Metabolomics aims to measure and characterise the complex composition of metabolites in a biological system. Metabolomics studies involve sophisticated analytical techniques such as mass spectrometry and nuclear magnetic resonance spectroscopy, and generate large amounts of high-dimensional and complex experimental data. Open source processing and analysis tools are of major interest in light of innovative, open and reproducible science. The scientific community has developed a wide range of open source software, providing freely available advanced processing and analysis approaches. The programming and statistics environment R has emerged as one of the most popular environments to process and analyse Metabolomics datasets. A major benefit of such an environment is the possibility of connecting different tools into more complex workflows. Combining reusable data processing R scripts with the experimental data thus allows for open, reproducible research. This review provides an extensive overview of existing packages in R for different steps in a typical computational metabolomics workflow, including data processing, biostatistics, metabolite annotation and identification, and biochemical network and pathway analysis. Multifunctional workflows, possible user interfaces and integration into workflow management systems are also reviewed. In total, this review summarises more than two hundred metabolomics specific packages primarily available on CRAN, Bioconductor and GitHub

The metaRbolomics Toolbox in Bioconductor and beyond

Metabolomics aims to measure and characterise the complex composition of metabolites in a biological system. Metabolomics studies involve sophisticated analytical techniques such as mass spectrometry and nuclear magnetic resonance spectroscopy, and generate large amounts of high-dimensional and complex experimental data. Open source processing and analysis tools are of major interest in light of innovative, open and reproducible science. The scientific community has developed a wide range of open source software, providing freely available advanced processing and analysis approaches. The programming and statistics environment R has emerged as one of the most popular environments to process and analyse Metabolomics datasets. A major benefit of such an environment is the possibility of connecting different tools into more complex workflows. Combining reusable data processing R scripts with the experimental data thus allows for open, reproducible research. This review provides an extensive overview of existing packages in R for different steps in a typical computational metabolomics workflow, including data processing, biostatistics, metabolite annotation and identification, and biochemical network and pathway analysis. Multifunctional workflows, possible user interfaces and integration into workflow management systems are also reviewed. In total, this review summarises more than two hundred metabolomics specific packages primarily available on CRAN, Bioconductor and GitHub

From spectrometric data to metabolic networks: an integrated view of cell metabolism

La biologia molecular ha avançat considerablement gràcies a importants progressos com la seqüenciació del ADN o la seva modificació per CRISPR. Tot i això, per entendre el metabolisme requerim estudiar els perfils metabòlics i les seves reaccions metabòliques. L™objectiu d™aquesta tesi és contribuir en aquest estudi del metabolism, el qual unifica dels camps de la proteòmica i la metabolòmica. Tradicionalment, l™anàlisi de dades òmiques es basa en el tractament independent de les diferents variables encara que està profundament establert que els mecanismes moleculars són controlats per la interacció de diferents molècules, i per tant seria més correcte tractar les dades de la mateixa manera. Avui dia, s™han descrit una gran quantitat de vies metabòliques, incluint els enzims responsables de les transformacions dels metabòlits que les formen, aquesta informació s™ha recopilat en bases de dades, que a la vegada poden ser utilitzades per a construir xarxes metabòliques. En aquesta tesi, s™han utilitzat xarxes metabòliques per a desenvolupar un algoritme que prediu metabòlits desregulats basant-se en el perfil d™expressió d™enzims gràcies a proteòmica quantitativa. Per a validar tals prediccions, és possible mesurar l™abundància d™aquests metabòlits, o el seu flux, o sigui la velocitat a la que s™han transformat, utilitzant experiments de marcatge amb isòtops estables, mesures completades mitjançant metabolòmica. Aqui, mostrem els productes del desenvolupament de dos mètodes per a l™anàlisi de dades de metabolòmica per a experiments amb isòtops estables: el primer per a la quantificació dirigida del flux en metabòlits del metabolisme central; i un segon, per la detecció no-dirigida de metabòlits marcats amb isòtops en altres vies metabòliques. Aquests mètodes han sigut provats en diferents estudis on han aportat resultats remarcables, revelant nous mecanismes moleculars en una complicació de la diabetes o en relació al metabolisme del càncer.La biología molecular ha avanzado considerablemente gracias a progresos como la secuenciación de ADN o su modificación por CRISPR. Sin embargo, para entender el metabolismo es indispensable estudiar los perfiles metabólicos y sus reacciones metabólicas. El objetivo de esta tesis es contribuir en el estudio del metabolismo, el cual implica los campos de la proteómica y la metabolómica. Tradicionalmente, el análisis de datos ómicas se basa en el tratamiento independiente de las diferentes variables aunque está profundamente aceptado que los mecanismos moleculares son controlados por la interacción de diferentes moléculas, y por lo tanto sería más correcto tratar los datos de esa manera. Hoy día, se han descrito una gran cantidad de vías metabólicas, incluyendo las enzimas responsables de las transformaciones de los metabolitos que las forman, esta información se ha recopilado en bases de datos, que a su vez pueden ser utilizadas para construir redes metabólicas . En esta tesis, se han utilizado redes metabólicas para desarrollar un algoritmo que predice metabolitos desregulados basándose en el perfil de expresión de enzimas por proteómica cuantitativa. Para validar tales predicciones, es posible medir la abundancia de estos metabolitos, o su flujo, o sea la velocidad a la que se han transformado, utilizando experimentos de marcado con isótopos estables, estas medidas se obtienen por metabolómica. Aquí, mostramos los productos del desarrollo de dos métodos para el análisis de datos de metabolómica para experimentos con isótopos estables: el primero para la cuantificación dirigida del flujo en metabolitos del metabolismo central; y un segundo, para la detección no-dirigida de metabolitos marcados con isótopos en otras vías metabólicas. Estos métodos han sido probados en diferentes estudios donde han aportado resultados interesantes, revelando nuevos mecanismos moleculares en una complicación de la diabetes o en relación al metabolismo del cáncer.Understanding the molecular basis of life has been in the spotlight of biochemistry research for more than a century already. Molecular biology has taken medicine forward thanks to technological breakthroughs like DNA sequencing and CRISPR editing. However, in order to understand metabolism we must rely on the study of metabolite profiles and metabolic reactions. The purpose of this thesis to contribute to this area, which unites the fields of proteomics and metabolomics. Traditionally, omics data analysis treats variables independently even if it is strongly settled that molecular mechanisms involve the interaction of diverse pathways, therefore data should be analyzed correspondingly. A vast amount of metabolic pathways have been described, together with enzymes that are responsible for metabolite transformations, this information has been assembled in databases that, in turn, can be used to build metabolic networks. In here, we use metabolic networks to predict metabolite dysregulation based on quantitative proteomics profiles. To validate the predictions, it is possible to measure the abundance of metabolites or their flux, namely the rate at which they are transformed, using stable isotope labelling experiments, both measurements can be performed by metabolomics. In this thesis, two different metabolomics-based stable isotope labelling approaches have been developed, one for the study of central carbon metabolites and one for the unbiased detection of deregulated fluxes in other metabolic pathways. These approaches have been tested on different datasets and have proven valuable to obtain remarkable results, unraveling molecular mechanisms in diabetes complications or novel metabolic hallmarks of cancer