Development and application of analytical and chemometric methodology for environmental metabolomic studies based on one-and two-dimensional liquid chromatography coupled to mass spectrometry

Abstract

Chemical exposure to emerging contaminants (ECs) is a major concern nowadays. These ECs have recently become a global environmental threat, and an in-depth characterization of their occurrence and toxic impact is needed. In this context, omic sciences have arisen as powerful tools to shed some light on the biological mechanisms affected by exposure to these chemicals. Particularly, metabolomics and lipidomics can provide a snapshot of what is actually happening at the molecular level, pointing to metabolic pathways affected by the contaminants. New analytical methodologies are required to extract the sought information in more complex biological matrices (from single cells to whole organisms). Hence, a major emphasis has been put on developing multidimensional separations and multiplatform approaches to increase the metabolome coverage. However, these novel approaches bring about massive datasets, and the complexity of the data analysis augments considerably. Therefore, chemometric strategies are a perfect match to get through this bottleneck and provide useful tools to obtain the most from the data collected. In this PhD Thesis, the focus was set on developing analytical protocols, especially using two-dimensional liquid chromatography coupled to mass spectrometry (LC×LC-MS), as well as chemometric data analysis strategies applicable to environmental metabolomic studies. On the one hand, LC×LC-MS methods have been developed for both untargeted and targeted analyses. Active modulation strategies have been also successfully implemented in the multidimensional chromatographic separation of lipids. On the other hand, the Regions Of Interest (ROI) approach for compression and filtering has been validated for LC×LC-MS analyses. Regarding chemometric resolution methods (i.e., which allow obtaining quantitative and qualitative information from the sample constituents), and due to deviations from an ideal trilinear behavior presented by LC×LC datasets, the use of the Multivariate Curve Resolution Alternating Least Squares (MCR-ALS) method has been preferred. Different quantification strategies have been tested based on the Regions Of Interest Multivariate Curve Resolution (ROIMCR) approach. In addition, several multivariate statistical methods based on the analysis of variance (ANOVA) have been compared for metabolomic studies. As a result, a combination of ANOVA-simultaneous component analysis (ASCA) and partial least squares discriminant analysis (PLS-DA) has been selected for statistical analysis and variable (metabolite) selection, respectively. All in all, different metabolomic workflows have been validated for the assessment of emerging contaminants in model biosystems.

Actualmente, las ciencias ómicas han aparecido como herramientas muy útiles para arrojar luz sobre los mecanismos biológicos y rutas metabólicas que se ven afectados debido a las exposiciones a contaminantes emergentes. En concreto, la metabolómica y la lipidómica proporcionan información de lo que está ocurriendo a nivel molecular. Por tanto, se requieren nuevas metodologías que sean capaces de extraer dicha información en matrices cada vez más complejas (desde una única célula a un organismo entero). Esta Tesis doctoral se centra principalmente en el desarrollo de protocolos analíticos basados en el uso de la cromatografía líquida bidimensional acoplada a espectrometría de masas (LC ×LC-MS), así como en el desarrollo de estrategias quimiométricas que permitan su uso en aplicaciones medioambientales. Por un lado, se han optimizado métodos LC×LC-MS para análisis dirigidos y no dirigidos, implementando con éxito estrategias de modulación activa (en el caso de análisis de lípidos). Por otro lado, se ha validado la estrategia de regiones de interés (ROI) para comprimir y filtrar los datos obtenidos con LC×LC-MS. Asimismo, se ha preferido el uso de métodos de resolución multivariante de curvas mediante mínimos cuadrados alternados (MCR-ALS) para la resolución cualitativa y cuantitativa de muestras complejas en el caso de datos de LC×LC, debido a las deviaciones de la trilinealidad encontradas en dichos datos. Por otra parte, se han comparado diferentes estrategias cuantitativas aplicables a datos en LC×LC-MS, todas ellas basadas en el uso del método combinado de regiones de interés y resolución multivariante de curvas (ROIMCR). También se han comparado diversos métodos estadísticos multivariante basados en el análisis de varianza (ANOVA) y su aplicabilidad en estudios metabolómicos. Finalmente, se ha elegido una combinación de análisis estadístico efectuado con ANOVA-análisis de componentes simultáneos (ASCA) y un método de clasificación, análisis discriminante mínimos cuadrados parciales (PLS-DA) para seleccionar las variables (metabolitos) más relevantes. En resumen, se han validado diferentes flujos de trabajo para el estudio metabolómico del efecto de contaminantes emergentes en organismos modelo ambientales.