Influence of data acquisition modes and data analysis approaches on non-targeted analysis of phthalate metabolites in human urine

Abstract

Humans are often exposed to phthalates and their alternatives, on account of their widespread use in PVC as plasticizers, which are associated with harmful human effects. While targeted biomonitoring provides quantitative information for exposure assessment, only a small portion of phthalate metabolites has been targeted. This results in a knowledge gap in human exposure to other unknown phthalate compounds and their metabolites. Although the non-targeted analysis (NTA) approach is capable of screening a broad spectrum of chemicals, there is a lack of harmonized workflow in NTA to generate reproducible data within and between different laboratories. The objective of this study was to compare two different NTA data acquisition modes, the data-dependent (DDA) and independent (DIA) acquisition (DDA), as well as two data analysis approaches, based on diagnostic ions and Compound Discoverer software for the prioritization of candidate precursors and identification of unknown compounds in human urine. Liquid chromatography coupled to high-resolution mass spectrometry was used for sample analysis. The combination of three-diagnostic-ion extraction and DDA data acquisition was able to improve data filtering and data analysis for prioritizing phthalate metabolites. With DIA, 25 molecular features were identified in human urine, while 32 molecular features were identified in the same urine samples using DDA data. The number of molecular features identified with level 1 confidence was 11 and 9 using DIA and DDA data, respectively. The study demonstrated that besides sample preparation, the impact of data acquisition must be taken into account when developing a NTA method and a consistent protocol for evaluating such an impact is necessary. Graphical Abstract

Untargeted Plasma Metabolomic Profiling in Patients with Depressive Disorders: A Preliminary Study

“…The last one works only on .raw files generated by Xcalibur software and Orbitrap mass spectrometers but provides automated compound annotation via the mzCloud database [24]. Compound Discoverer was used in various untargeted metabolomics studies for, e.g., compound iden-tification in ethanol extract of plants [25][26][27][28], comparing samples of propolis [29] and lentil seed coats [30], determining the metabolic changes during the browning process [31], the analysis of biological fluid samples [32,33], autism studies, [34] etc.…”