Metabolomic and sphingolipidomic profiling of human hepatoma cells exposed to widely used pharmaceuticals.

Pharmaceutical compounds have become one of the main contaminants of emerging concern (CECs) due to their high usage and increased release into the environment. This study aims to assess the effects caused by three widely consumed hepatotoxic pharmaceutical compounds: an antibiotic (amoxicillin), an antiepileptic (carbamazepine), and an antidepressant (trazodone), on human health when indirectly exposed to toxicologically relevant concentrations (30, 15, and 7.5 μM for amoxicillin and carbamazepine, and 4, 2, and 1 μM for trazodone).

Cohort-based strategies as an in-house tool to evaluate and improve phenotyping robustness of LC-MS/MS lipidomics platforms.

In recent years, instrumental improvements have enabled the spread of mass spectrometry-based lipidomics platforms in biomedical research. In mass spectrometry, the reliability of generated data varies for each compound, contingent on, among other factors, the availability of labeled internal standards. It is challenging to evaluate the data for lipids without specific labeled internal standards, especially when dozens to hundreds of lipids are measured simultaneously.

Effects of Indoor Dust Exposure on Lung Cells: Association of Chemical Composition with Phenotypic and Lipid Changes in a 3D Lung Cancer Cell Model.

Indoor dust is a key contributor to the global human exposome in urban areas since the population develops most of its activities in private and public buildings. To gain insight into the health risks associated with this chronic exposure, it is necessary to characterize the chemical composition of dust and understand its biological impacts using reliable physiological models. The present study investigated the biological effects of chemically characterized indoor dust extracts using three-dimensional (3D) lung cancer cell cultures combining phenotypic and lipidomic analyses.

Quantification strategies for two-dimensional liquid chromatography datasets using regions of interest and multivariate curve resolution approaches.

In this work, three chemometrics-based approaches are compared for quantification purposes when using two-dimensional liquid chromatography (LC×LC-MS), taking as a study case the quantification of amino acids in commercial drug mixtures. Although the approaches have been already used for one-dimensional gas or liquid chromatography, the main novelty of this work is the demonstration of their applicability to LC×LC-MS datasets. Besides, steps such as peak alignment and modelling, commonly applied in this type of data analysis, are not required with the approaches proposed here.

Comparison of Multivariate ANOVA-Based Approaches for the Determination of Relevant Variables in Experimentally Designed Metabolomic Studies.

The use of chemometric methods based on the analysis of variances (ANOVA) allows evaluation of the statistical significance of the experimental factors used in a study. However, classical multivariate ANOVA (MANOVA) has a number of requirements that make it impractical for dealing with metabolomics data. For this reason, in recent years, different options have appeared that overcome these limitations.