Protomer Formation Can Aid the Structural Identification of Caffeine Metabolites

The structural annotation of isomeric metabolites remains a key challenge in untargeted electrospray ionization/high-resolution mass spectrometry (ESI/HRMS) metabolomic analysis. Many metabolites are polyfunctional compounds that may form protomers in electrospray ionization sources and therefore yield multiple peaks in ion mobility spectra. Protomer formation is strongly structure-specific. Here, we explore the possibility of using protomer formation for structural elucidation in metabolomics on the example of caffeine, its eight metabolites, and structurally related compounds. It is observed that two-thirds of the studied compounds formed high- and low-mobility species in high-resolution ion mobility. Structures in which proton hopping was hindered by a methyl group at the purine ring nitrogen (position 3) yielded structure-indicative fragments with collision-induced dissociation (CID) for high- and low-mobility ions. For compounds where such a methyl group was not present, a gas-phase equilibrium could be observed for tautomeric species with two-dimensional ion mobility. We show that the protomer formation and the gas-phase properties of the protomers can be related to the structure of caffeine metabolites and facilitate the identification of the structural isomers

The NORMAN Association and the European Partnership for Chemicals Risk Assessment (PARC): let’s cooperate! [Commentary]

The Partnership for Chemicals Risk Assessment (PARC) is currently under development as a joint research and innovation programme to strengthen the scientific basis for chemical risk assessment in the EU. The plan is to bring chemical risk assessors and managers together with scientists to accelerate method development and the production of necessary data and knowledge, and to facilitate the transition to next-generation evidence-based risk assessment, a non-toxic environment and the European Green Deal. The NORMAN Network is an independent, well-established and competent network of more than 80 organisations in the field of emerging substances and has enormous potential to contribute to the implementation of the PARC partnership. NORMAN stands ready to provide expert advice to PARC, drawing on its long experience in the development, harmonisation and testing of advanced tools in relation to chemicals of emerging concern and in support of a European Early Warning System to unravel the risks of contaminants of emerging concern (CECs) and close the gap between research and innovation and regulatory processes. In this commentary we highlight the tools developed by NORMAN that we consider most relevant to supporting the PARC initiative: (i) joint data space and cutting-edge research tools for risk assessment of contaminants of emerging concern; (ii) collaborative European framework to improve data quality and comparability; (iii) advanced data analysis tools for a European early warning system and (iv) support to national and European chemical risk assessment thanks to harnessing, combining and sharing evidence and expertise on CECs. By combining the extensive knowledge and experience of the NORMAN network with the financial and policy-related strengths of the PARC initiative, a large step towards the goal of a non-toxic environment can be taken

Towards a comprehensive characterisation of the human internal chemical exposome: Challenges and perspectives

The holistic characterisation of the human internal chemical exposome using high-resolution mass spectrometry (HRMS) would be a step forward to investigate the environmental AE tiology of chronic diseases with an unprecedented precision. HRMS-based methods are currently operational to reproducibly profile thousands of endogenous metabolites as well as externally-derived chemicals and their biotransformation products in a large number of biological samples from human cohorts. These approaches provide a solid ground for the discovery of unrecognised biomarkers of exposure and metabolic effects associated with many chronic diseases. Nevertheless, some limitations remain and have to be overcome so that chemical exposomics can provide unbiased detection of chemical exposures affecting disease susceptibility in epidemiological studies. Some of these limitations include (i) the lack of versatility of analytical techniques to capture the wide diversity of chemicals; (ii) the lack of analytical sensitivity that prevents the detection of exogenous (and endogenous) chemicals occurring at (ultra) trace levels from restricted sample amounts, and (iii) the lack of automation of the annotation/identification process. In this article, we discuss a number of technological and methodological limitations hindering applications of HRMS-based methods and propose initial steps to push towards a more comprehensive characterisation of the internal chemical exposome. We also discuss other challenges including the need for harmonisation and the difficulty inherent in assessing the dynamic nature of the internal chemical exposome, as well as the need for establishing a strong international collaboration, high level networking, and sustainable research infrastructure. A great amount of research, technological development and innovative bio-informatics tools are still needed to profile and characterise the "invisible" (not profiled), "hidden" (not detected) and "dark" (not annotated) components of the internal chemical exposome and concerted efforts across numerous research fields are paramount

The NORMAN Association and the European Partnership for Chemicals Risk Assessment (PARC): let’s cooperate! [Commentary]

The Partnership for Chemicals Risk Assessment (PARC) is currently under development as a joint research and innovation programme to strengthen the scientific basis for chemical risk assessment in the EU. The plan is to bring chemical risk assessors and managers together with scientists to accelerate method development and the production of necessary data and knowledge, and to facilitate the transition to next-generation evidence-based risk assessment, a non-toxic environment and the European Green Deal. The NORMAN Network is an independent, well-established and competent network of more than 80 organisations in the field of emerging substances and has enormous potential to contribute to the implementation of the PARC partnership. NORMAN stands ready to provide expert advice to PARC, drawing on its long experience in the development, harmonisation and testing of advanced tools in relation to chemicals of emerging concern and in support of a European Early Warning System to unravel the risks of contaminants of emerging concern (CECs) and close the gap between research and innovation and regulatory processes. In this commentary we highlight the tools developed by NORMAN that we consider most relevant to supporting the PARC initiative: (i) joint data space and cutting-edge research tools for risk assessment of contaminants of emerging concern; (ii) collaborative European framework to improve data quality and comparability; (iii) advanced data analysis tools for a European early warning system and (iv) support to national and European chemical risk assessment thanks to harnessing, combining and sharing evidence and expertise on CECs. By combining the extensive knowledge and experience of the NORMAN network with the financial and policy-related strengths of the PARC initiative, a large step towards the goal of a non-toxic environment can be taken

Performance comparison of electrospray ionization and atmospheric pressure chemical ionization in untargeted and targeted liquid chromatography/mass spectrometry based metabolomics analysis of grapeberry metabolites

Electrospray ionization (ESI) and atmospheric pressure chemical ionization (APCI) are both used to generate ions for the analysis of metabolites by liquid chromatography/mass spectrometry (LC/MS). We compared the performance of these methods for the analysis of Corvina grapevine berry methanolic extracts, which are complex mixtures of diverse metabolites

Maatriksefektid elektropihustusionisatsiooniallikas vedelikkromatograafilisel massispektromeetrilisel analüüsil

Väitekirja elekrooniline versioon ei sisalda publikatsioone.Maatriksefektid tekivad ESI ionisatsiooniallikas analüüdi ionisatsioonil kui analüüdiga samal ajal elueerub vedelikkromatograafist kaasa mõni maatriksi komponent. Enamasti väljendub maatriksefekt analüüdi ionisatsiooniefektiivsuse languses – st kromatogrammil nähtav analüüdi piik proovi korral on väiksem kui sama kontsentratsiooniga analüüdi standardlahuse puhul. Selline fenomen viib alandatud analüüsitulemuseni. Seetõttu on oluline üritada maatriksefekti vähendada või arvesse võtta. Käesolevas töös esitatakse viis lähenemisviisi maatriksefekti vastu võitlemiseks. Kaks neist annavad võimaluse maatriksefekti vähendamiseks – prooviettevalmistuse optimeerimine ning ESI/MS parameetrite optimeerimine. Kaks meetodit võimaldavad maatriksefekti arvesse võtta – maatriksefekti arvestamine analüüsitulemuse määramatuse koosseisus ning korrigeerimine taustaioonide signaalide kaudu. Samuti esitatakse kombinatsiooniline meetod – ekstrapoleeriv lahjendus. Kõik lähenemisviisid on valideeritud ja nende rakendatavus testitud reaalsetel proovidel. Viiest lähenemisviisist kolm on kirjeldatud esmakordselt.The matrix effect occurs in ESI ionization source in LC/ESI/MS analyses. The ionization efficiency of the analyte changes (usually decreases) due to co-eluting matrix compounds. Therefore underestimated analysis results are observed. In order to improve the accuracy of LC/ESI/MS measurements matrix effect should be reduced (preferably eliminated) or accounted for. In this work altogether five approaches are proposed for combating the matrix effect on the example of pesticide analyses in fruits and vegetables. Two methods – improvement of sample preparation and optimization of ESI/MS parameters – are used to reduce matrix effect. Accounting for matrix effect in the uncertainty of the analytical result and correcting of the analysis result via background ion signals are introduced as methods of accounting for matrix effect. As a combinational method extrapolative dilution is introduced. All the approaches are validated and tested on real samples. Three of the five approaches are presented for the first time

Machine Learning for Absolute Quantification of Unidentified Compounds in Non-Targeted LC/HRMS

LC/ESI/HRMS is increasingly employed for monitoring chemical pollutants in water samples, with non-targeted analysis becoming more common. Unfortunately, due to the lack of analytical standards, non-targeted analysis is mostly qualitative. To remedy this, models have been developed to evaluate the response of compounds from their structure, which can then be used for quantification in non-targeted analysis. Still, these models rely on tentatively known structures while for most detected compounds, a list of structural candidates, or sometimes only exact mass and retention time are identified. In this study, a quantification approach was developed, where LC/ESI/HRMS descriptors are used for quantification of compounds even if the structure is unknown. The approach was developed based on 92 compounds analyzed in parallel in both positive and negative ESI mode with mobile phases at pH 2.7, 8.0, and 10.0. The developed approach was compared with two baseline approaches— one assuming equal response factors for all compounds and one using the response factor of the closest eluting standard. The former gave a mean prediction error of a factor of 29, while the latter gave a mean prediction error of a factor of 1300. In the machine learning-based quantification approach developed here, the corresponding prediction error was a factor of 10. Furthermore, the approach was validated by analyzing two blind samples containing 48 compounds spiked into tap water and ultrapure water. The obtained mean prediction error was lower than a factor of 6.0 for both samples. The errors were found to be comparable to approaches using structural information

Benchmarking of the quantification approaches for the non-targeted screening of micropollutants and their transformation products in groundwater

A wide range of micropollutants can be monitored with non-targeted screening; however, the quantification of the newly discovered compounds is challenging. Transformation products (TPs) are especially problematic because analytical standards are rarely available. Here, we compared three quantification approaches for non-target compounds that do not require the availability of analytical standards. The comparison is based on a unique set of concentration data for 341 compounds, mainly pesticides, pharmaceuticals, and their TPs in 31 groundwater samples from Switzerland. The best accuracy was observed with the predicted ionization efficiency-based quantification, the mean error of concentration prediction for the groundwater samples was a factor of 1.8, and all of the 74 micropollutants detected in the groundwater were quantified with an error less than a factor of 10. The quantification of TPs with the parent compounds had significantly lower accuracy (mean error of a factor of 3.8) and could only be applied to a fraction of the detected compounds, while the mean performance (mean error of a factor of 3.2) of the closest eluting standard approach was similar to the parent compound approach.ISSN:1618-2650ISSN:1618-264