Mechanistic Understanding of the Discrepancies between Common Peak Picking Algorithms in Liquid Chromatography–Mass Spectrometry-Based Metabolomics

Synthesis and Toxicity Evaluation of p-Phenylenediamine-Quinones

N-(1,3-Dimethylbutyl)-N'-phenyl-p-phenylenediamine-quinone (6PPD-Q), the tire rubber-derived transformation product of 6PPD, was recently discovered to cause the acute mortality of coho salmon (Oncorhynchus kisutch). Aiming to identify potential replacement antiozonants for 6PPD that do not produce toxic quinones, seven PPD-quinones with distinct side chains were synthesized to investigate their structure-related toxicities in vivo using rainbow trout (Oncorhynchus mykiss). While 6PPD-Q exerted high toxicity (96 h LC50 = 0.35 μg/L), toxicity was not observed for six other PPD-quinones despite their similar structures. The fish tissue concentrations of 6PPD-Q after sublethal exposure (0.29 μg/L) were comparable to the other PPD-quinones, which indicated that bioaccumulation levels were not the reason for the selective toxicity of 6PPD-Q. Hydroxylated PPD-quinones were detected as the predominant metabolites in fish tissue. Interestingly, a single major aromatic hydroxylation metabolite was detected for the alternate PPD-quinones, but two abundant OH-6PPD-Q isomers were detected for 6PPD-Q. MS2 spectra confirmed that hydroxylation occurred on the alkyl side chain for one isomer. The structurally selective toxicity of 6PPD-Q was also observed in a coho salmon (CSE-119) cell line, which further supports its intrinsic toxicity. This study reported the selective toxicity of 6PPD-Q and pinpointed the possibility for other PPDs to be applied as potential substitutes of 6PPD.

Prioritization strategies for non-target screening in environmental samples by chromatography - High-resolution mass spectrometry: A tutorial

Non-target screening (NTS) using chromatography coupled to high-resolution mass spectrometry (HRMS), has become fundamental for detecting and prioritizing chemicals of emerging concern (CECs) in complex environmental matrices. The vast number of generated features (m/z, retention time, and intensity) necessitate effective prioritization strategies to identify environmentally and toxicologically relevant CECs. Since compound identification remains a major bottleneck in NTS, prioritization is critical to focus identification efforts where they matter most. This tutorial presents seven prioritization strategies: (1) Target and suspect screening for identifying known or suspected compounds using reference libraries. (2) Data quality filtering to apply quality control measures to reduce noise and the number of false positives. (3) Chemistry-driven prioritization using HRMS data properties to prioritize specific compound classes (e.g., halogenated substances, transformation products). (4) Process-driven - using spatial, temporal, or process-based comparisons (pre- and post-technical processes) to identify key features. (5) Effect-Directed Analysis (EDA) and Virtual Effect-Directed Analysis (vEDA) prioritization to link chemical features to biological effects. (6) Prediction-based prioritization such as quantitative structure-property relationships (QSPR) and machine learning to estimate risk or concentration levels, and (7) Pixel- or tile-based analysis where the chromatographic image (2D data) is used to pin-point regions of interest or for comparison of larger sample sets. By integrating these prioritization strategies, this tutorial provides a structured foundation to evaluate both identified and unidentified features, prioritize high-risk compounds, and advance environmental risk assessment and regulatory decision-making.

QuanFormer: A Transformer-Based Precise Peak Detection and Quantification Tool in LC-MS-Based Metabolomics

In metabolomic analysis based on liquid chromatography coupled with mass spectrometry, detecting and quantifying intricate objects is a massive job. Current peak picking methods still cause high rates of incorrectly picked peaks to influence the reliability and reproducibility of results. To address these challenges, we developed QuanFormer, a deep learning method based on object detection designed to accurately quantify peak signals. Our algorithm combines the feature extraction capabilities of convolutional neural networks (CNNs) with the global computation capability of Transformer architecture. Data training in QuanFormer by using nearly 20,000 annotated regions-of-interest (ROIs) ensures unique prediction via bipartite matching, achieving 96.5% of the average precision value on the test set. Even without retraining, QuanFormer achieves over 90% accuracy in distinguishing true from false peaks. Performance was further analyzed using visualization techniques applied to the encoder and decoder layers. We also demonstrated that QuanFormer could correct retention time shifts for peak alignment and generally surpass the existing methods, including MZmine 3 and PeakDetective, to obtain a larger number of picked peaks and higher accurate quantification. Finally, we also carried out metabolomic analysis in a clinical cohort of breast cancer patients and utilized QuanFormer to detect and quantify the potential biomarkers. QuanFormer is open-source and available at https://github.com/LinShuhaiLAB/QuanFormer.

Toward Automated Preprocessing of Untargeted LC-MS-Based Metabolomics Feature Lists from Human Biofluids

Maximizing the extraction of true, high-quality, nonredundant features from biofluids analyzed via LC-MS systems is challenging. Here, the R packages IPO and AutoTuner were used to optimize XCMS parameter settings for the retrieval of metabolite or lipid features in both ionization modes from either faecal or urine samples from two cohorts (n = 621). The feature lists obtained were compared with those where the parameter values were selected manually. Three categories were used to compare feature lists: 1) feature quality through removing false positives, 2) tentative metabolite identification using the Human Metabolome Database (HMDB) and 3) feature utility such as analyzing the proportion of features within intensity threshold bins. Furthermore, a PCA-based approach to feature filtering using QC samples and variable loadings was also explored under this category. Overall, more features were observed after automated selection of parameter values for all data sets (1.3- to 3.7-fold), which propagated through comparative exercises. For example, a greater number of features (on average 51 vs 45%) had a coefficient of variation (CV) < 30%. Additionally, there was a significant increase (7.6-10.4%) in the number of faecal metabolites that could be tentatively annotated, and more features were present in higher intensity threshold bins. Considering the overlap across all three categories, a greater number of features were also retained. Automated approaches that guide selection of optimal parameter values for preprocessing are important to decrease the time invested for this step, while taking advantage of the wealth of data that LC-MS systems provide.

Simultaneous determination of free biliverdin and free bilirubin in serum: A comprehensive LC-MS approach

BACKGROUND

Prognosis, diagnosis, and treatment of several diseases strongly rely on the sensitive, selective, and accurate determination of specific biomarkers in relevant biological samples. Free biliverdin and free bilirubin represent important new biomarkers of oxidative stress, however, the lack of suitable analytical methods for their determination has hindered progress in biomedical and clinical research.

RESULTS

Here, we introduce a first comprehensive approach for robust and simultaneous determination of these bilins in serum using liquid chromatography - mass spectrometry (LC-MS). The developed analytical method exhibits linearity for both analytes within the concentration range of 0.5-100 nM, with limits of detection and quantitation determined at 0.1 nM and 0.5 nM, respectively. Moreover, several analytical pitfalls related to the intrinsic molecular structures of free bilirubin and free biliverdin and their trace concentration levels in biological samples are discussed here in detail for the first time. We have shown that the solubility, chemical stability, and affinity of these bilins to various materials strongly depend on the solvent, pH, and addition of stabilizing and chelating agents. Finally, the validated LC-MS method was successfully applied to the analysis of both bilins in fetus bovine serums, yielding higher free bilirubin/biliverdin ratios compared with previously reported values for human serum.

SIGNIFICANCE

Failure to recognize and address the challenges presented here often leads to substantial analytical errors and consequently biased interpretation of the obtained results. This pertains not only to LC-MS, but also to many other analytical platforms due to the compound-derived sources of error.

Is Nontarget Analysis Ready for Regulatory Application? Influence of Peak-Picking Algorithms on Data Analysis

The use of peak-picking algorithms is an essential step in all nontarget analysis (NTA) workflows. However, algorithm choice may influence reliability and reproducibility of results. Using a real-world data set, the aim of this study was to investigate how different peak-picking algorithms influence NTA results when exploring temporal and/or spatial trends. For this, drinking water catchment monitoring data, using passive samplers collected twice per year across Southeast Queensland, Australia (n = 18 sites) between 2014 and 2019, was investigated. Data were acquired using liquid chromatography coupled to high-resolution mass spectrometry. Peak picking was performed using five different programs/algorithms (SCIEX OS, MSDial, self-adjusting-feature-detection, two algorithms within MarkerView), keeping parameters identical whenever possible. The resulting feature lists revealed low overlap: 7.2% of features were picked by >3 algorithms, while 74% of features were only picked by a single algorithm. Trend evaluation of the data, using principal component analysis, showed significant variability between the approaches, with only one temporal and no spatial trend being identified by all algorithms. Manual evaluation of features of interest (p-value <0.01, log fold change >2) for one sampling site revealed high rates of incorrectly picked peaks (>70%) for three algorithms. Lower rates (<30%) were observed for the other algorithms, but with the caveat of not successfully picking all internal standards used as quality control. The choice is therefore currently between comprehensive and strict peak picking, either resulting in increased noise or missed peaks, respectively. Reproducibility of NTA results remains challenging when applied for regulatory frameworks.