Retrospective non-target analysis to support regulatory water monitoring: from masses of interest to recommendations via in silico workflows

Abstract

Background Applying non-target analysis (NTA) in regulatory environmental monitoring remains challenging—instead of having exploratory questions, regulators usually already have specific questions related to environmental protection aims. Additionally, data analysis can seem overwhelming because of the large data volumes and many steps required. This work aimed to establish an open in silico workflow to identify environmental chemical unknowns via retrospective NTA within the scope of a pre-existing Swiss environmental monitoring campaign focusing on industrial chemicals. The research question addressed immediate regulatory priorities: identify pollutants with industrial point sources occurring at the highest intensities over two time points. Samples from 22 wastewater treatment plants obtained in 2018 and measured using liquid chromatography–high resolution mass spectrometry were retrospectively analysed by (i) performing peak-picking to identify masses of interest; (ii) prescreening and quality-controlling spectra, and (iii) tentatively identifying priority “known unknown” pollutants by leveraging environmentally relevant chemical information provided by Swiss, Swedish, EU-wide, and American regulators. This regulator-supplied information was incorporated into MetFrag, an in silico identification tool replete with “post-relaunch” features used here. This study’s unique regulatory context posed challenges in data quality and volume that were directly addressed with the prescreening, quality control, and identification workflow developed. Results One confirmed and 21 tentative identifications were achieved, suggesting the presence of compounds as diverse as manufacturing reagents, adhesives, pesticides, and pharmaceuticals in the samples. More importantly, an in-depth interpretation of the results in the context of environmental regulation and actionable next steps are discussed. The prescreening and quality control workflow is openly accessible within the R package Shinyscreen, and adaptable to any (retrospective) analysis requiring automated quality control of mass spectra and non-target identification, with potential applications in environmental and metabolomics analyses. Conclusions NTA in regulatory monitoring is critical for environmental protection, but bottlenecks in data analysis and results interpretation remain. The prescreening and quality control workflow, and interpretation work performed here are crucial steps towards scaling up NTA for environmental monitoring.

Discovering Pesticides and their Transformation Products in Luxembourg Waters using Open Cheminformatics Approaches

“…The features that passed the quality control were then analysed using MetFrag 23 coupled to CompTox 25,34 to achieve tentative identi cations 23 , generally consistent with Lai et al 22 Candidates were retrieved using an (exact mass + 10 ppm) window, where the exact mass settings included the measured ion mass plus adduct species ([M + H] + for positive and [M-H] − for negative mode, automatically detected from the Shinyscreen mode output) for internal correction to neutral mass in MetFrag for candidate retrieval. The InChIKey ltering (default setting) was left on, i.e., candidates that vary only in the stereochemistry are merged in the output, and the highest scoring candidate is considered.…”

“…Pre-screening was performed using Shinyscreen 21 with the following settings for extraction and automatic quality control (explained in greater detail in Lai et al 22 ): coarse precursor m/z error ± 0.5 Da, ne precursor m/z error ± 2.5 ppm, extracted ion chromatogram m/z error ± 0.001 Da, retention time (RT) tolerance ± 0.5 min, an MS1 intensity threshold of 1.0 x 10 5 and an MS2 intensity threshold relative to the MS1 peak intensity of 0.05. Features that ful lled the following four criteria were considered as passing the quality control: 1) MS1 peak intensity > 1x10 5 , 2) presence of MS2 spectrum, 3) alignment of MS1 and MS2 peaks within the RT tolerance, 4) signal to noise ratio > 3.…”

“…35 While MetFrag compares the experimental results with in silico fragmentation results, it also searches the experimental data with online mass spectral records from a public spectral library, MoNA, and presents these outcomes to users via the MoNA spectral similarity scoring term (hereafter "MoNA Score"). Several additional metadata terms were used in the MetFrag calculation (generally consistent with Lai et al 22 ) but were not considered further here, yielding in the end a maximum score of 10 where every scoring term has the same weight (10 scoring terms each with a weight of 1). However, as described below, the MoNA Score became the primary decision-making criterion in this work.…”

“…Thus, a pre-screening step helps to determine which suspects may be present with matching MS1 and MS2 spectra of su cient quality for further data analysis. This step was performed using Shinyscreen (https://git-r3lab.uni.lu/eci/shinyscreen/) 21 , a semiautomated, open-source alternative to vendor software for peak inspection, with built in quality control criteria as described recently by Lai et al 22 Potential suspects with MS1 and MS2 spectra passing the Shinyscreen pre-screening were promoted to further for additional identi cation efforts via MS2 spectra annotation using the open source in silico fragmentation approach MetFrag (https://msbi.ipbhalle.de/MetFrag/). 23 MetFrag combines compound database searching and fragmentation prediction plus other experimental and metadata terms for molecule identi cation using HR-MS2 fragmentation information.…”

“…23 Given a single MS2 spectrum of a suspect and the neutral mass of the parent ion, MetFrag rst selects matching candidates from databases, such as PubChem (https://pubchem.ncbi.nlm.nih.gov/) 24 and CompTox (https://comptox.epa.gov/dashboard/) 25 , before each of the retrieved candidates is fragmented in silico using a bond-disconnection method and ranked using various scoring terms (see methods for further details). 23 For this study, the US Environmental Protection Agency (US EPA) CompTox Chemicals Dashboard was used as the main compound database, consistent with Lai et al 22 , because of its relatively small size (~ 880,000 chemicals), and the extensive environmentally-relevant metadata such as toxicity, exposure, and presence integrated in CompTox from various information sources. 25 The recently-released PubChemLite for Exposomics collection 26 , which demonstrated very good performance particularly for agrochemicals (pesticides) was under development at the time that this work was performed.…”