Streamlined MRM method transfer between instruments assisted with HRMS matching and retention-time prediction

Nitrophenols in the environment: An update on pretreatment and analysis techniques since 2017

Nitrophenols, a versatile intermediate, have been widely used in leather, medicine, chemical synthesis, and other fields. Because these components are widely applied, they can enter the environment through various routes, leading to many hazards and toxicities. There has been a recent surge in the development of simple, rapid, environmentally friendly, and effective techniques for determining these environmental pollutants. This review provides a comprehensive overview of the latest research progress on the pretreatment and analysis methods of nitrophenols since 2017, with a focus on environmental samples. Pretreatment methods include liquid-liquid extraction, solid-phase extraction, dispersive extraction, and microextraction methods. Analysis methods mainly include liquid chromatography-based methods, gas chromatography-based methods, supercritical fluid chromatography. In addition, this review also discusses and compares the advantages/disadvantages and development prospects of different pretreatment and analysis methods to provide a reference for further research.

Assessment of environmental forensic indicator for anthropogenic groundwater contamination via target/suspect/nontarget analysis using HRMS techniques

This study compared target/suspect/nontarget analysis via liquid chromatography-high-resolution mass spectrometry (LC-HRMS) with traditional environmental forensic methods, specifically nitrate and its stable N isotope, in assessing groundwater pollution from livestock manure and agriculture. Using an in-house database of 1471 target and suspects, 35 contaminants (pesticides, veterinary drugs, surfactants) were identified, some uniquely linked to specific pollution sources, such as sulfamethazine and 4-formylaminoantipyrine in manure-affected areas. Pesticides were widespread, typically showing higher intensity in agricultural zones. On the other hand, the results of stable N isotope analysis (δ15N-NO3: 4.8 to 16.4‰) indicated the influence of human activities such as fertilizers, sewage, and manure in all sampling sites, including the control site far from the pollution sources and cannot differentiate the specific sources. The study underscores LC-HRMS's efficacy in different pollution sources, surpassing the limitations of stable N isotope analysis, and provides valuable insights for polluted groundwater source tracking strategies.

An automated toxicity based prioritization framework for fast chemical characterization in non-targeted analysis

Identification of environmental pollutants with harmful effects is commonly conducted by non-targeted analysis (NTA) using liquid chromatography coupled with high-resolution mass spectrometry. Prioritization of possible candidates is important yet challenging because of the large number of candidates from MS acquisitions. We aimed to prioritize candidates to the exposure potential of organic chemicals by their toxicity and identification evidence in the matrix. We have developed an R package application, "NTAprioritization.R", for fast prioritization of suspect lists. In this workflow, the identification levels of candidates were first rated according to spectral matching and retention time prediction. The toxicity levels were rated according to candidates' toxicity of different endpoints or ToxPi score. Finally, the various levels of candidates were identified as Tier 1 - 5 descending in priority. For validation, we used this workflow to identify pollutants in a sludge water sample spiked with 28 environmental pollutants. The workflow reduced the candidate list of over 6,982 candidates to a final list of 2,779 compounds and prioritized them to 5 tiers (Tier 1 - 5), including 21 out of 28 spiked standards. Overall, this study shows the added value of an automated prioritization R package for the fast screening of environmental pollutants based on the NTA method.

Advancements in Oncoproteomics Technologies: Treading toward Translation into Clinical Practice

Proteomics continues to forge significant strides in the discovery of essential biological processes, uncovering valuable information on the identity, global protein abundance, protein modifications, proteoform levels, and signal transduction pathways. Cancer is a complicated and heterogeneous disease, and the onset and progression involve multiple dysregulated proteoforms and their downstream signaling pathways. These are modulated by various factors such as molecular, genetic, tissue, cellular, ethnic/racial, socioeconomic status, environmental, and demographic differences that vary with time. The knowledge of cancer has improved the treatment and clinical management; however, the survival rates have not increased significantly, and cancer remains a major cause of mortality. Oncoproteomics studies help to develop and validate proteomics technologies for routine application in clinical laboratories for (1) diagnostic and prognostic categorization of cancer, (2) real-time monitoring of treatment, (3) assessing drug efficacy and toxicity, (4) therapeutic modulations based on the changes with prognosis and drug resistance, and (5) personalized medication. Investigation of tumor-specific proteomic profiles in conjunction with healthy controls provides crucial information in mechanistic studies on tumorigenesis, metastasis, and drug resistance. This review provides an overview of proteomics technologies that assist the discovery of novel drug targets, biomarkers for early detection, surveillance, prognosis, drug monitoring, and tailoring therapy to the cancer patient. The information gained from such technologies has drastically improved cancer research. We further provide exemplars from recent oncoproteomics applications in the discovery of biomarkers in various cancers, drug discovery, and clinical treatment. Overall, the future of oncoproteomics holds enormous potential for translating technologies from the bench to the bedside.

Prediction of Retention Time and Collision Cross Section (CCSH+, CCSH-, and CCSNa+) of Emerging Contaminants Using Multiple Adaptive Regression Splines

Ultra-high performance liquid chromatography coupled to ion mobility separation and high-resolution mass spectrometry instruments have proven very valuable for screening of emerging contaminants in the aquatic environment. However, when applying suspect or nontarget approaches (i.e., when no reference standards are available), there is no information on retention time (RT) and collision cross-section (CCS) values to facilitate identification. In silico prediction tools of RT and CCS can therefore be of great utility to decrease the number of candidates to investigate. In this work, Multiple Adaptive Regression Splines (MARS) were evaluated for the prediction of both RT and CCS. MARS prediction models were developed and validated using a database of 477 protonated molecules, 169 deprotonated molecules, and 249 sodium adducts. Multivariate and univariate models were evaluated showing a better fit for univariate models to the experimental data. The RT model (R² = 0.855) showed a deviation between predicted and experimental data of ±2.32 min (95% confidence intervals). The deviation observed for CCS data of protonated molecules using the CCS_H model (R² = 0.966) was ±4.05% with 95% confidence intervals. The CCS_H model was also tested for the prediction of deprotonated molecules, resulting in deviations below ±5.86% for the 95% of the cases. Finally, a third model was developed for sodium adducts (CCS_Na, R² = 0.954) with deviation below ±5.25% for 95% of the cases. The developed models have been incorporated in an open-access and user-friendly online platform which represents a great advantage for third-party research laboratories for predicting both RT and CCS data.

The use of pseudo-MRM for a sensitive and selective detection and quantification of polycyclic aromatic compounds by tandem mass spectrometry

RATIONALE

Multiple Reaction Monitoring (MRM) is a sensitive and selective detection mode for target trace-level analysis. However, it requires the fragmentation of labile bonds which are not present in molecules such as Polycyclic Aromatic Hydrocarbons (PAHs) and their heterocyclic derivatives (PANHs, PASHs).

METHODS

We present the application of an alternative tandem mass spectrometry (MS/MS) mode called "pseudo-MRM" for the GCMS/MS analysis of Polycyclic Aromatic Compounds (PACs). This mode is based on the monitoring of transitions with no mass loss between the precursor and the product ion. Pseudo-MRM peak areas were compared with those of classic MRM on three different mass spectrometers: two triple quadrupoles and an ion trap.

RESULTS

For all non-polar PACs studied here (PAHs, PANHs and PASHs), the pseudo-MRM transition was always the most intense. The classic MRM transitions exhibited peak areas 2 to 5 times lower. On the contrary, for the functionalized PACs (oxygenated and nitrated PAHs), classic MRM was favored over pseudo-MRM. These observations were confirmed on two triple quadrupoles (QqQs), and the real-world applicability of pseudo-MRM on QqQs was validated by the successful analysis of Diesel PM. However, a comparison with an ion trap showed that pseudo-MRM was never favored on that instrument, which caused fragmentation of non-polar PACs in MS/MS.

CONCLUSIONS

The results of this study show an important gain in sensitivity when using pseudo-MRM instead of MRM for non-polar PACs on QqQ instruments. The selectivity of MRM is preserved in pseudo-MRM by applying non-zero collision energies to which only these non-polar PACs are resistant, not the isobaric interferences. No interference issue was observed when analyzing Diesel PM, a complex matrix, with our pseudo-MRM method. Therefore, we advise for a broader use of this MS/MS mode for trace-level determination of non-polar PAHs.

A multiplex protein panel assay for severity prediction and outcome prognosis in patients with COVID-19: An observational multi-cohort study

BACKGROUND

Global healthcare systems continue to be challenged by the COVID-19 pandemic, and there is a need for clinical assays that can help optimise resource allocation, support treatment decisions, and accelerate the development and evaluation of new therapies.

METHODS

We developed a multiplexed proteomics assay for determining disease severity and prognosis in COVID-19. The assay quantifies up to 50 peptides, derived from 30 known and newly introduced COVID-19-related protein markers, in a single measurement using routine-lab compatible analytical flow rate liquid chromatography and multiple reaction monitoring (LC-MRM). We conducted two observational studies in patients with COVID-19 hospitalised at Charité - Universitätsmedizin Berlin, Germany before (from March 1 to 26, 2020, n=30) and after (from April 4 to November 19, 2020, n=164) dexamethasone became standard of care. The study is registered in the German and the WHO International Clinical Trials Registry (DRKS00021688).

FINDINGS

The assay produces reproducible (median inter-batch CV of 10.9%) absolute quantification of 47 peptides with high sensitivity (median LLOQ of 143 ng/ml) and accuracy (median 96.8%). In both studies, the assay reproducibly captured hallmarks of COVID-19 infection and severity, as it distinguished healthy individuals, mild, moderate, and severe COVID-19. In the post-dexamethasone cohort, the assay predicted survival with an accuracy of 0.83 (108/130), and death with an accuracy of 0.76 (26/34) in the median 2.5 weeks before the outcome, thereby outperforming compound clinical risk assessments such as SOFA, APACHE II, and ABCS scores.

INTERPRETATION

Disease severity and clinical outcomes of patients with COVID-19 can be stratified and predicted by the routine-applicable panel assay that combines known and novel COVID-19 biomarkers. The prognostic value of this assay should be prospectively assessed in larger patient cohorts for future support of clinical decisions, including evaluation of sample flow in routine setting. The possibility to objectively classify COVID-19 severity can be helpful for monitoring of novel therapies, especially in early clinical trials.

FUNDING

This research was funded in part by the European Research Council (ERC) under grant agreement ERC-SyG-2020 951475 (to M.R) and by the Wellcome Trust (IA 200829/Z/16/Z to M.R.). The work was further supported by the Ministry of Education and Research (BMBF) as part of the National Research Node 'Mass Spectrometry in Systems Medicine (MSCoresys)', under grant agreements 031L0220 and 161L0221. J.H. was supported by a Swiss National Science Foundation (SNSF) Postdoc Mobility fellowship (project number 191052). This study was further supported by the BMBF grant NaFoUniMedCOVID-19 - NUM-NAPKON, FKZ: 01KX2021. The study was co-funded by the UK's innovation agency, Innovate UK, under project numbers 75594 and 56328.

Gut microbial metabolite p-cresol alters biotransformation of bisphenol A: Enzyme competition or gene induction?

Recent studies on pharmaceuticals have revealed the direct and indirect mechanisms that link human gut microbiome to xenobiotic biotransformation. Though environmental contaminants compose a vital portion of xenobiotics and share overlapping biotransformation pathways with gut microbial metabolites, the possible interplay between gut microbiome and biotransformation of environmental contaminants remains obscure. This study utilized bisphenol A (BPA) and p-cresol as model compounds to explore whether gut microbial metabolites could affect environmental phenol metabolism on both in vitro and in vivo models. We have observed some distinct biotransformation behavior, where in vivo mouse examination using 171 & 1972 μg/kg bw p-cresol injection exhibited enhancing effect on BPA metabolism, but p-cresol was found as a strong inhibitor from 10/5 μM in a non-competitive pattern for BPA biotransformation in in vitro models of liver S9 fractions and HepG2 cell line, respectively. A further investigation revealed that the expression of biotransformation enzyme genes including Ugt1a1, Ugt2b1, or Sult1a1 of p-cresol treated mice were dynamically induced. In silico docking approach was also utilized to explore the non-competitive inhibition mechanism by estimating the binding affinity of key enzyme SULT 1A1. Overall, our results provided a novel insight into the biotransformation interaction between gut microbiome and environmental contaminants.

Predicting reversed-phase liquid chromatographic retention times of pesticides by deep neural networks

To be able to predict reversed phase liquid chromatographic (RPLC) retention times of contaminants is an asset in order to solve food contamination issues. The development of quantitative structure-retention relationship models (QSRR) requires selection of the best molecular descriptors and machine-learning algorithms. In the present work, two main approaches have been tested and compared, one based on an extensive literature review to select the best set of molecular descriptors (16), and a second with diverse strategies in order to select among 1545 molecular descriptors (MD), 16 MD. In both cases, a deep neural network (DNN) were optimized through a gridsearch.

The relevant role of ion mobility separation in LC-HRMS based screening strategies for contaminants of emerging concern in the aquatic environment

Ion mobility separation (IMS) coupled to high resolution mass spectrometry (IMS-HRMS) is a promising technique for (non-)target/suspect analysis of micropollutants in complex matrices. IMS separates ionized compounds based on their charge, shape and size facilitating the removal of co-eluting isomeric/isobaric species. Additionally, IMS data can be translated into collision cross-section (CCS) values, which can be used to increase the identification reliability. However, IMS-HRMS for the screening of contaminants of emerging concern (CECs) have been scarcely explored. In this study, the role of IMS-HRMS for the identification of CECs in complex matrices is highlighted, with emphasis on when and with which purpose is of use. The utilization of IMS can result in much cleaner mass spectra, which considerably facilitates data interpretation and the obtaining of reliable identifications. Furthermore, the robustness of IMS measurements across matrices permits the use of CCS as an additional relevant parameter during the identification step even when reference standards are not available. Moreover, an effect on the number of true and false identifications could be demonstrated by including IMS restrictions within the identification workflow. Data shown in this work is of special interest for environmental researchers dealing with the detection of CECs with state-of-the-art IMS-HRMS instruments.

Development and application of sequential window acquisition of all theoretical mass spectra data acquisition modes on ultra-high-performance liquid chromatography triple-quadrupole/time-of-flight mass spectrometry for metabolic profiling of amino acids in human plasma

Accumulating evidence suggests that amino acids are important indicators of nutritional and metabolic status. A high-resolution mass spectrometry method based on sequential window acquisition of all theoretical mass spectra acquisition was developed for the simultaneous determination of 16 amino acids in human plasma. Sample preparation by protein precipitation using a mixture of acetonitrile and formic acid was followed by a BEH Amide column. The superiority of this method was investigated by comparing it to time-of-flight scan and multiple reaction monitoring modes. The limit of detection in sequential window acquisition of all theoretical mass spectra mode for threonine, methionine, histidine, citrulline, and tryptophan is 0.1 ng on the column; for lysine and asparagine is 0.2 ng; for alanine, pyroglutamic acid, leucine, ornithine, and aspartate is 0.5 ng, for arginine is 1.0 ng; for glutamate and serine is 2.0 ng; for glutamine is 10.0 ng. This method was linear in the range 0.8-40 μg/mL for arginine, citrulline, glutamate, histidine, leucine, methionine, pyroglutamic acid, threonine, tryptophan; 2-100 μg/mL for asparagine, aspartate, lysine, ornithine, serine; and 4-200 μg/mL for alanine, glutamine with good accuracy and precision. Significantly different levels in 11 amino acids were observed between childhood and adulthood, representing the growth and development of individuals relating to the level of amino acids.