Non-targeted screening workflows for gas chromatography–high-resolution mass spectrometry analysis and identification of biomagnifying contaminants in biota samples

Abstract

The health of key species in the Baltic region has been impaired by exposure to anthropogenic hazardous substances (AHSs), which accumulate in organisms and are transferred through food chains. There is, thus, a need for comprehensive characterization of the occurrence and accumulation of AHSs in the ecosystem. In this study, we use a non-target screening (NTS) approach for this purpose. A major challenge in NTS of biological samples is the removal of matrix components such as lipids that may interfere with the detection and identification of compounds of interest. Here, we combine gel permeation chromatography with Florisil® column fractionation to achieve sufficient lipid removal for gas chromatography–high-resolution mass spectrometry analysis using electron ionization (EI) and electron capture negative ion chemical ionization (ECNI). In addition, we present new data processing workflows designed to systematically find and identify frequently occurring and biomagnifying AHSs, including known, emerging, and new contaminants. Using these workflows, we discovered a wide range of contaminants in tissue samples from blue mussels, fish, and marine mammals, and calculated their biomagnification factors (BMFs). Compounds with BMFs above 1 for herring and at least one marine mammal included legacy chlorinated pollutants (polychlorinated biphenyls, DDTs, chloro-benzenes/cyclohexanes, chlordanes, toxaphenes, dieldrin), polybrominated diphenyl ethers (PBDEs), and brominated biphenyls. However, there were also several halogenated natural products (halogenated methoxylated brominated diphenyl ethers, 1′-methyl-1,2′-bipyrroles, 1,1′-dimethyl-2,2′-bipyrroles, and the halogenated monoterpene mixed halogenated compound 1) as well as the novel flame retardant Dechlorane 602 and several polycyclic aromatic hydrocarbons, terpenoids, and steroids. The legacy pollutants exhibited the expected biomagnification behavior, demonstrating the utility of the unguided data processing workflow.

Biomagnification and Temporal Trends of New and Emerging Dechloranes and Related Transformation Products in Baltic Sea Biota

“…were determined gravimetrically. 7 Bulk lipids were removed by high-resolution gel permeation chromatography (HR-GPC), as described in Table S2 . Contaminant fractions were collected, concentrated, and subjected to a second round of HR-GPC.…”

“…Contaminant fractions were collected, concentrated, and subjected to a second round of HR-GPC. Samples were further fractionated using a Florisil column ( Table S2 ), 7 and the first three fractions ( n -hexane, 15% dichloromethane in n -hexane, and 50% dichloromethane in n -hexane) were retained. Volumetric standard ( 13 C 12 –CB-188; 3 ng) was added to each fraction, and their volumes were reduced to 0.3 mL.…”

“…They were then subjected to GC-HRMS using an Agilent 7250 (Santa Clara, CA, USA) system operating in electron ionization (EI) or methane electron capture chemical ionization (ECNI) mode, as previously described. 7 EI was used for Mirex and its TPs, and ECNI for the other compounds ( Table S3 ). Suspect screening was performed using extracted ion chromatograms of C 5 Cl 6 fragment ions ( m / z 271.8096) of Mirex and monohydro-Mirex, molecular ions of Dec602, Dec603, Dec604, DP, and CP, and molecular ions of the associated mono/di/trihydro-TPs, mono/dicarbonyl-TPs, and monohydroxy-TPs.…”

“…The study presented here was triggered by the discovery of Dechlorane 602 (Dec602) in Baltic Sea biota and the realization that it biomagnifies (with BMFs of 3.0 to 20) in several top predators. 7 A literature search revealed the occurrence of additional dechloranes in aquatic biota including Dechlorane (Mirex), Dechlorane 603 (Dec603), Dechlorane 604 (Dec604), syn -/ anti -Dechlorane Plus (syn-/anti-DP), and Chlordene Plus (CP). 10 It also showed that these compounds undergo dechlorination (forming mono- or dihydro-analogues) 11 − 15 and oxidation (forming carbonyls).…”

Biological studies with comprehensive GCxGC-HRMS screening: Exploring the human sweat volatilome

“…One of the main reasons is that large-sample batch data processing of GCxGC-HRMS data is in its infancy. There are several levels of identification and verification steps needed for such a data-driven approach which were recently summarized by Stefanuto et al 1 and Schymanski et al 2 It is often concluded that the automation of non-target analysis and subsequent suspect-screening remains an elusive process. This process can be intricate due to small deviations in retention times, suboptimal peak shapes, intensive background, and the diversity of chromatographic features among samples (e.g., large qualitative differences between samples), which makes the development of an efficient approach for use with large batches of samples challenging.…”