Toxicology studies can take advantage of omics approaches to better understand the phenomena underlying the phenotypic alterations induced by different types of exposure to certain toxicants. Nevertheless, in order to analyse the data generated from multifactorial omics studies, dedicated data analysis tools are needed. In this work, we propose a new workflow comprising both factor deconvolution and data integration from multiple analytical platforms. As a case study, 3D neural cell cultures were exposed to trimethyltin (TMT) and the relevance of the culture maturation state, the exposure duration, as well as the TMT concentration were simultaneously studied using a metabolomic approach combining four complementary analytical techniques (reversed-phase LC and hydrophilic interaction LC, hyphenated to mass spectrometry in positive and negative ionization modes). The ANOVA multiblock OPLS (AMOPLS) method allowed us to decompose and quantify the contribution of the different experimental factors on the outcome of the TMT exposure. Results showed that the most important contribution to the overall metabolic variability came from the maturation state and treatment duration. Even though the contribution of TMT effects represented the smallest observed modulation among the three factors, it was highly statistically significant. The MetaCore™ pathway analysis tool revealed TMT-induced alterations in biosynthetic pathways and in neuronal differentiation and signaling processes, with a predominant deleterious effect on GABAergic and glutamatergic neurons. This was confirmed by combining proteomic data, increasing the confidence on the mechanistic understanding of such a toxicant exposure.
Keywords: AMOPLS; metabolomics; multifactorial experiments; multiplatform omics; pathway analysis; proteomics; toxicology; trimethyltin.