Braun Georg, Krauss Martin, Spahr Stephanie, Escher Beate I
Department of Cell Toxicology, Helmholtz Centre for Environmental Research - UFZ, Leipzig, Germany.
Department of Exposure Science, Helmholtz Centre for Environmental Research - UFZ, Leipzig, Germany.
Anal Bioanal Chem. 2024 May;416(12):2983-2993. doi: 10.1007/s00216-024-05245-5. Epub 2024 Apr 1.
Liquid chromatography (LC) or gas chromatography (GC) coupled to high-resolution mass spectrometry (HRMS) is a versatile analytical method for the analysis of thousands of chemical pollutants that can be found in environmental and biological samples. While the tools for handling such complex datasets have improved, there are still no fully automated workflows for targeted screening analysis. Here we present an R-based workflow that is able to cope with challenging data like noisy ion chromatograms, retention time shifts, and multiple peak patterns. The workflow can be applied to batches of HRMS data recorded after GC with electron ionization (GC-EI) and LC coupled to electrospray ionization in both negative and positive mode (LC-ESIneg/LC-ESIpos) to perform peak annotation and quantitation fully unsupervised. We used Orbitrap HRMS data of surface water extracts to compare the Automated Target Screening (ATS) workflow with data evaluations performed with the vendor software TraceFinder and the established semi-automated analysis workflow in the MZmine software. The ATS approach increased the overall evaluation performance of the peak annotation compared to the established MZmine module without the need for any post-hoc corrections. The overall accuracy increased from 0.80 to 0.86 (LC-ESIpos), from 0.77 to 0.83 (LC-ESIneg), and from 0.67 to 0.76 (GC-EI). The mean average percentage errors for quantification of ATS were around 30% compared to the manual quantification with TraceFinder. The ATS workflow enables time-efficient analysis of GC- and LC-HRMS data and accelerates and improves the applicability of target screening in studies with a large number of analytes and sample sizes without the need for manual intervention.
液相色谱（LC）或气相色谱（GC）与高分辨率质谱（HRMS）联用，是一种用于分析环境和生物样品中数千种化学污染物的通用分析方法。尽管处理此类复杂数据集的工具已有改进，但针对目标筛选分析仍不存在完全自动化的工作流程。在此，我们展示了一种基于R的工作流程，它能够处理具有挑战性的数据，如有噪声的离子色谱图、保留时间偏移和多种峰型。该工作流程可应用于在气相色谱-电子电离（GC-EI）以及液相色谱分别在正、负离子模式下与电喷雾电离联用（LC-ESIneg/LC-ESIpos）后记录的多批次高分辨质谱数据，以完全无监督地进行峰注释和定量分析。我们使用地表水提取物的轨道阱高分辨质谱数据，将自动目标筛选（ATS）工作流程与使用供应商软件TraceFinder进行的数据评估以及MZmine软件中已确立的半自动分析工作流程进行比较。与已确立的MZmine模块相比，ATS方法在无需任何事后校正的情况下提高了峰注释的整体评估性能。整体准确率从0.80提高到0.86（LC-ESIpos），从0.77提高到0.83（LC-ESIneg），从0.67提高到0.76（GC-EI）。与使用TraceFinder进行手动定量相比，ATS定量的平均百分比误差约为30%。ATS工作流程能够对GC-和LC-HRMS数据进行高效分析，并且在无需人工干预的情况下，加速并提高了在大量分析物和样本量的研究中目标筛选的适用性。
