Electrospray Ionization and heterogeneous matrix effects in Liquid Chromatography-Mass Spectrometry-based meta-metabolomics: a biomarker or a suppressed ion?

RATIONALE Correct biomarkers determination in metabolomics is crucial for unbiased conclusions and reliable applications. However, this determination is subject to several drifts, e.g. matrix effects and ion suppression in Liquid Chromatography-Mass Spectrometry-based approaches. This phenomenon provokes critical issues for biomarkers determination, particularly during comparative studies dealing with samples exhibiting heterogeneous complexities. METHODS Occurrence of the issue was coincidentally noticed when studying the environmental impact of a complex bioinsecticide: Bacillus thuringiensis israelensis. Studied samples consisted of insecticide-spiked sediments and untreated control sediments. QuEChERS extractions followed by LC/ESI-Q/ToF analyses were performed on sediments after 15 days of incubation. Meta-metabolomes containing pesticide xenometabolites and sediments' endometabolites were in-depth analyzed using XCMS-based computational data preprocessing. Multivariate statistical analyses (PCA, OPLS-DA) and raw data crosschecks were performed to search for environmental biomarkers. RESULTS Multivariate analyses and raw data crosschecks led to the selection of 9 metabolites as biomarker candidates. However, when exploring mass spectra, co-elutions were noticed between 7 of these metabolites and multi-charged macromolecules originating from the pesticide. Provoked false positives were thus suspected due to a potential ion suppression exclusively occurring in the spiked samples. A dilution-based approach was then applied. It confirmed 5 metabolites as suppressed ions. CONCLUSIONS Ion suppression should be considered as a critical issue for biomarkers determination when comparing heterogeneous metabolic profiles. Raw chromatograms and mass spectra crosscheck is mandatory to reveal potential ion suppressions in such cases. The dilution is a suitable approach to filtrate reliable biomarker candidates before their identification and absolute quantification.