Contaminant screening of wastewater with HPLC-IM-qTOF-MS and LC+LC-IM-qTOF-MS using a CCS database

Modulation optimization when using a splitter pump after the first dimension in comprehensive two- dimensional liquid chromatography

The rapid growth in the use of two dimensional liquid chromatography (2D-LC) applied to the analysis of moderately to highly complex mixtures, has been fueled by continuous improvements in performance and robustness of the instrument components, as well as the ease-of-use of software necessary for controlling the 2D-LC instrument hardware, and analysis of the large data files that result from this type of work. This work has focused on the evaluation of the performance of an online full comprehensive mode (LC×LC), when an active modulation is implemented using a flow splitter pump placed after the 1D effluent. Two different types of splitting pumps were evaluated: a binary ultra-high pressure liquid chromatography (UHPLC) pump and a high precision syringe pump. We analyzed the performance (reproducibility in peak area and retention times and the 2D peak dispersion) as a function of the location of the active pump Before or After the modulation valve, and the influence of connecting tubes (based on internal diameter and length) necessary between the interface, waste, and the splitting pump. The effect on the flow direction on the filling and flushing of the injection loops at the modulation valve was also analyzed for each pump. In this study, we demonstrate that flow-splitting LCxLC assembly can be performed using either a UHPLC binary pump or a simple syringe pump. Flow splitting after the first dimension is a straightforward strategy to: (i) independently select the 1D column and flow rates with respect to the second dimension; (ii) consciously dilute the eluate according to the solvent characteristics of the second dimension, thereby avoiding 2D peak distortions; and (iii) adapt the injected amount to the second column according to the relative concentration of the components in a complex sample. However, careful consideration of the system setup is necessary. It is demonstrated how experimental results can be significantly affected in terms of peak broadening and reproducibility if optimization of the interface is not taken into account. In addition, under the optimized conditions, the reproducibility in peak area and dispersion in the 2D dimension were evaluated as a function of the amount of sample transferred in terms of percentage of filled loop.

Formation of halogenated forms of bisphenol A (BPA) in water: Resolving isomers with ion mobility - mass spectrometry and the role of halogenation position in cellular toxicity

Halogenated BPA (XBPA) forms resulting from water chlorination can lead to increased toxicity and different biological effects. While previous studies have reported the occurrence of different XBPAs, analytical limitation have hindered the analysis and differentiation of the many potential isomeric forms. Using online solid-phase extraction - liquid chromatography - ion-mobility - high-resolution mass spectrometry (OSPE-LC-IM-HRMS), we demonstrated a rapid analysis method for the analysis of XBPA forms after water chlorination, with a total analysis time of less than 10 min including extraction and concentration and low detection limits (∼5-80 ng/L range). A multi in-vitro bioassay testing approach for the identified products revealed that cytotoxicity and bioenergetics impacts were largely associated with the presence of halogen atoms at positions 2 or 2' and the overall number of halogens incorporated into the BPA molecule. Different XBPA also showed distinct impacts on oxidative stress, peroxisome proliferator-activated receptor gamma - PPARγ, and inflammatory response. While increased DNA damage was observed for chlorinated water samples (4.14 ± 1.21-fold change), the additive effect of the selected 20 XBPA studied could not explain the increased DNA damage observed, indicating that additional species or synergistic effects might be at play.

Non-target analysis for water characterization: wastewater treatment impact and selection of relevant features

Non-target analyses were conducted to characterize and compare the molecular profiles (UHPLC-HRMS fingerprint) of water samples from a wastewater treatment plant (WWTP). Inlet and outlet samples were collected from three campaigns spaced 6 months apart in order to highlight common trends. A significant impact of the treatment on the sample fingerprints was shown, with a 65-70% abatement of the number of features detected in the effluent, and more polar, smaller and less intense molecules found overall compared to those in WWTP influent waters. Multivariate analysis (PCA) associated with variations of the features between inlets and outlets showed that features appearing or increasing were correlated with effluents while those disappearing or decreasing were correlated with influents. Finally, effluent features considered as relevant to a potentially adverse effect on aqueous media (i.e. those which appeared or increased or slightly varied from the influent) were highlighted. Three hundred seventy-five features common with the 3 campaigns were thus selected and further characterized. For most of them, elementary composition was found to be C, H, N, O (42%) and C, H, N, O, P (18%). Considering the MS2 spectra and several reference MS2 databases, annotations were proposed for 35 of these relevant features. They include synthetic products, pharmaceuticals and metabolites.

Application of Ion Mobility Spectrometry and the Derived Collision Cross Section in the Analysis of Environmental Organic Micropollutants

Ion mobility spectrometry (IMS) is a rapid gas-phase separation technique, which can distinguish ions on the basis of their size, shape, and charge. The IMS-derived collision cross section (CCS) can serve as additional identification evidence for the screening of environmental organic micropollutants (OMPs). In this work, we summarize the published experimental CCS values of environmental OMPs, introduce the current CCS prediction tools, summarize the use of IMS and CCS in the analysis of environmental OMPs, and finally discussed the benefits of IMS and CCS in environmental analysis. An up-to-date CCS compendium for environmental contaminants was produced by combining CCS databases and data sets of particular types of environmental OMPs, including pesticides, drugs, mycotoxins, steroids, plastic additives, per- and polyfluoroalkyl substances (PFAS), polycyclic aromatic hydrocarbons (PAHs), polychlorinated biphenyls (PCBs), and polybrominated diphenyl ethers (PBDEs), as well as their well-known transformation products. A total of 9407 experimental CCS values from 4170 OMPs were retrieved from 23 publications, which contain both drift tube CCS in nitrogen (DTCCSN2) and traveling wave CCS in nitrogen (TWCCSN2). A selection of publicly accessible and in-house CCS prediction tools were also investigated; the chemical space covered by the training set and the quality of CCS measurements seem to be vital factors affecting the CCS prediction accuracy. Then, the applications of IMS and the derived CCS in the screening of various OMPs were summarized, and the benefits of IMS and CCS, including increased peak capacity, the elimination of interfering ions, the separation of isomers, and the reduction of false positives and false negatives, were discussed in detail. With the improvement of the resolving power of IMS and enhancements of experimental CCS databases, the practicability of IMS in the analysis of environmental OMPs will continue to improve.

Collision Cross Section Prediction Based on Machine Learning

Ion mobility-mass spectrometry (IM-MS) is a powerful separation technique providing an additional dimension of separation to support the enhanced separation and characterization of complex components from the tissue metabolome and medicinal herbs. The integration of machine learning (ML) with IM-MS can overcome the barrier to the lack of reference standards, promoting the creation of a large number of proprietary collision cross section (CCS) databases, which help to achieve the rapid, comprehensive, and accurate characterization of the contained chemical components. In this review, advances in CCS prediction using ML in the past 2 decades are summarized. The advantages of ion mobility-mass spectrometers and the commercially available ion mobility technologies with different principles (e.g., time dispersive, confinement and selective release, and space dispersive) are introduced and compared. The general procedures involved in CCS prediction based on ML (acquisition and optimization of the independent and dependent variables, model construction and evaluation, etc.) are highlighted. In addition, quantum chemistry, molecular dynamics, and CCS theoretical calculations are also described. Finally, the applications of CCS prediction in metabolomics, natural products, foods, and the other research fields are reflected.

Exploratory data fusion of untargeted multimodal LC-HRMS with annotation by LCMS-TOF-ion mobility: White wine case study

Applied sciences have increased focus on omics studies which merge data science with analytical tools. These studies often result in large amounts of data produced and the objective is to generate meaningful interpretations from them. This can sometimes mean combining and integrating different datasets through data fusion techniques. The most strategic course of action when dealing with products of unknown profile is to use exploratory approaches. For omics, this means using untargeted analytical methods and exploratory data analysis techniques. The current study aimed to perform data fusion on untargeted multimodal (negative and positive mode) liquid chromatography-high-resolution mass spectrometry data using multiple factor analysis. The data fusion results were interpreted using agglomerative hierarchical clustering on biplot projections. The study reduced the thousands of spectral signals processed to less than a hundred features (a primary parameter combination of retention time and mass-to-charge ratios, RT_m/z). The correlations between cluster members (samples and features from) were calculated and the top 10% highly correlated features were identified for each cluster. These features were then tentatively identified using secondary parameters (drift time, ion mobility constant and collision cross-section values) from the ion mobility spectra. These ion mobility (secondary) parameters can be used for future studies in wine chemical analysis and added to the growing list of annotated chemical signals in applied sciences.

Enhancing spectral quality in complex environmental matrices: Supporting suspect and non-target screening in zebra mussels with ion mobility

Identification of bioaccumulating contaminants of emerging concern (CECs) via suspect and non-target screening remains a challenging task. In this study, ion mobility separation with high-resolution mass spectrometry (IM-HRMS) was used to investigate the effects of drift time (DT) alignment on spectrum quality and peak annotation for screening of CECs in complex sample matrices using data independent acquisition (DIA). Data treatment approaches (Binary Sample Comparison) and prioritisation strategies (Halogen Match, co-occurrence of features in biota and the water phase) were explored in a case study on zebra mussel (Dreissena polymorpha) in Lake Mälaren, Sweden's largest drinking water reservoir. DT alignment evidently improved the fragment spectrum quality by increasing the similarity score to reference spectra from on average (±standard deviation) 0.33 ± 0.31 to 0.64 ± 0.30 points, thus positively influencing structure elucidation efforts. Thirty-two features were tentatively identified at confidence level 3 or higher using MetFrag coupled with the new PubChemLite database, which included predicted collision cross-section values from CCSbase. The implementation of predicted mobility data was found to support compound annotation. This study illustrates a quantitative assessment of the benefits of IM-HRMS on spectral quality, which will enhance the performance of future screening studies of CECs in complex environmental matrices.

Ion mobility mass spectrometry in the omics era: Challenges and opportunities for metabolomics and lipidomics

Researchers worldwide are taking advantage of novel, commercially available, technologies, such as ion mobility mass spectrometry (IM-MS), for metabolomics and lipidomics applications in a variety of fields including life, biomedical, and food sciences. IM-MS provides three main technical advantages over traditional LC-MS workflows. Firstly, in addition to mass, IM-MS allows collision cross-section values to be measured for metabolites and lipids, a physicochemical identifier related to the chemical shape of an analyte that increases the confidence of identification. Second, IM-MS increases peak capacity and the signal-to-noise, improving fingerprinting as well as quantification, and better defining the spatial localization of metabolites and lipids in biological and food samples. Third, IM-MS can be coupled with various fragmentation modes, adding new tools to improve structural characterization and molecular annotation. Here, we review the state-of-the-art in IM-MS technologies and approaches utilized to support metabolomics and lipidomics applications and we assess the challenges and opportunities in this growing field.

Different ion mobility-mass spectrometry coupling techniques to promote metabolomics

Metabolomics has become increasingly popular in recent years for many applications ranging from clinical diagnosis, human health to biotechnological questioning. Despite technological advances, metabolomic studies are still currently limited by the difficulty of identifying all metabolites, a class of compounds with great chemical diversity. Although lengthy chromatographic analyses are often used to obtain comprehensive data, many isobar and isomer metabolites still remain unresolved, which is a critical point for the compound identification. Currently, ion mobility spectrometry is being explored in metabolomics as a way to improve metabolome coverage, analysis throughput and isomer separation. In this review, all the steps of a typical workflow for untargeted metabolomics are discussed considering the use of an ion mobility instrument. An overview of metabolomics is first presented followed by a brief description of ion mobility instrumentation. The ion mobility potential for complex mixture analysis is discussed regarding its coupling with a mass spectrometer alone, providing gas-phase separation before mass analysis as well as its combination with different separation platforms (conventional hyphenation but also multidimensional ion mobility couplings), offering multidimensional separation. Various instrumental and analytical conditions for improving the ion mobility separation are also described. Finally, data mining, including software packages and visualization approaches, as well as the construction of ion mobility databases for the metabolite identification are examined.

Uncovering PFAS and Other Xenobiotics in the Dark Metabolome Using Ion Mobility Spectrometry, Mass Defect Analysis, and Machine Learning

The identification of xenobiotics in nontargeted metabolomic analyses is a vital step in understanding human exposure. Xenobiotic metabolism, transformation, excretion, and coexistence with other endogenous molecules, however, greatly complicate the interpretation of features detected in nontargeted studies. While mass spectrometry (MS)-based platforms are commonly used in metabolomic measurements, deconvoluting endogenous metabolites from xenobiotics is also often challenged by the lack of xenobiotic parent and metabolite standards as well as the numerous isomers possible for each small molecule m/z feature. Here, we evaluate a xenobiotic structural annotation workflow using ion mobility spectrometry coupled with MS (IMS-MS), mass defect filtering, and machine learning to uncover potential xenobiotic classes and species in large metabolomic feature lists. Xenobiotic classes examined included those of known high toxicities, including per- and polyfluoroalkyl substances (PFAS), polycyclic aromatic hydrocarbons (PAHs), polychlorinated biphenyls (PCBs), polybrominated diphenyl ethers (PBDEs), and pesticides. Specifically, when the workflow was applied to identify PFAS in the NIST SRM 1957 and 909c human serum samples, it greatly reduced the hundreds of detected liquid chromatography (LC)-IMS-MS features by utilizing both mass defect filtering and m/z versus IMS collision cross sections relationships. These potential PFAS features were then compared to the EPA CompTox entries, and while some matched within specific m/z tolerances, there were still many unknowns illustrating the importance of nontargeted studies for detecting new molecules with known chemical characteristics. Additionally, this workflow can also be utilized to evaluate other xenobiotics and enable more confident annotations from nontargeted studies.

Cyclic Peptide Protomer Detection in the Gas Phase: Impact on CCS Measurement and Fragmentation Patterns

With the recent improvements in ion mobility resolution, it is now possible to separate small protomeric tautomers, called protomers. In larger molecules above 1000 Da such as peptides, a few studies suggest that protomers do exist as well and may contribute to their gas-phase conformational heterogeneity. In this work, we observed a CCS distribution that can be explained by the presence of protomers of surfactin, a small lipopeptide with no basic site. Following preliminary density functional theoretical calculations, several protonation sites in the gas phase were energetically favorable in positive ionization mode. Experimentally, at least three near-resolved IM peaks were observed in positive ionization mode, while only one was detected in negative ionization mode. These results were in good agreement with the DFT predictions. CID breakdown curve analysis after IM separation showed different inflection points (CE₅₀) suggesting that different intramolecular interactions were implied in the stabilization of the structures of surfactin. The fragment ratio observed after collision-induced fragmentation was also different, suggesting different ring-opening localizations. All these observations support the presence of protomers on the cyclic peptide moieties of the surfactin. These data strongly suggest that protomeric tautomerism can still be observed on molecules above 1000 Da if the IM resolving power is sufficient. It also supports that the proton localization involves a change in the 3D structure that can affect the experimental CCS and the fragmentation channels of such peptides.

Differentiation of industrial hemp strains by their cannabinoid and phenolic compounds using LC × LC-HRMS

Cannabis is an ancient plant that has been used for therapeutic and recreational purposes. Nowadays, industrial hemp, a variety with low concentration of the psychoactive cannabinoid Δ⁹-tetrahydrocannabinol (THC) and high concentration of non-psychoactive cannabinoids, is getting more and more interest in the food, pharmaceutical, and cosmetic industry. However, cannabis not only contains cannabinoids as bioactive components but also other metabolites like terpenes and phenolic compounds, and the content of these interesting secondary metabolites greatly differs with the genetic variety of the plant. Due to the huge complexity of composition of the cannabis matrix, in this work, a comprehensive two-dimensional liquid chromatography (LC × LC) method has been developed as a very power separation technique coupling a pentafluorophenyl (PFP) and a C18 in the first and second dimensions. Two industrial hemp strains (cookie and gelato) were analyzed to determine the difference in their content of cannabinoids and phenolic compounds. To do this, a new demodulation process was applied for the first time to transform 2D raw data into 1D data which allowed carrying out the chemometric analysis needed to determine the statistical differences between the hemp strains. The cookie strain presented a total of 41 cannabinoid markers, while the gelato strain presented more representative phenolic compounds, in total 24 phenolic compounds were detected as potential markers of this sample. These differences in the chemical composition could determine the industrial destiny of the different hemp strains.

On-line 2D-RPLC x RPLC - HRMS to assess wastewater treatment in a pharmaceutical plant

Pharmaceutical effluents are complex media containing hundreds of compounds including active ingredients, intermediate products and unknown impurities. Bringing an industrial wastewater treatment plant (WWTP) into compliance with European directives requires a thorough analysis of the effluent. In this study, we demonstrate how online comprehensive two-dimensional liquid chromatography (on-line LC × LC) hyphenated to high resolution mass spectrometry (HRMS) can be a powerful analytical methodology to monitoring the outlet water, by analysing the content of known molecules while characterizing unknown compounds. Reversed phase liquid chromatography (RPLC) was used in both dimensions, with a penta-fluoro-phenyl silica-based column at neutral pH in the first dimension (¹D) and a C18 column at acidic pH in the second one (²D). The conditions were optimized for a total analysis time of 60 min. The variability of both retention times and peak areas was evaluated. The average standard deviation on retention times was found to be less than 0.1 s in ²D. The relative standard deviation on peak area was about 7% for run-to-run analysis. This analytical approach, applied to the pharmaceutical effluents before (inlet) and after (outlet) wastewater treatment permitted to detect 240 compounds. These included 27 priority pharmaceutical products, 8 of which were of very high priority and their concentrations could be compared to target values. The comparison of 2D-LC and 1D-LC approaches clearly highlights the power of on-line RPLC x RPLC technique, which allows both targeted quantitative analysis and non-targeted qualitative analysis of pharmaceutical effluents.

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.

Comprehensive strategy for pesticide residue analysis in cocoa beans through qualitative and quantitative approach

Multiresidue quantitative and qualitative screening method for the analysis of pesticide residues in dried cocoa beans was validated and applied to imported and domestic cocoa beans samples. The quantitative method comprises of 15 pesticides while the screening method covers 110 pesticides of different chemical classes. The method was based on modified QuEChERS (Quick Easy Cheap Efficient Rugged Safe) extraction and detection using triple quadrupole (QQQ-MS) and ion mobility quadrupole time of flight mass spectrometry (IMS-QTOF). The method was quantitatively validated in terms of linearity, limit of quantification (LOQ), specificity, selectivity, accuracy, and precision. On the other hand, screening detection limits were established for 110 pesticides. Finally, the optimized strategy was successfully applied for the routine analysis of pesticide residues in 137 cocoa bean samples and 32% of the total samples were found positive for ametryn, chlorpyrifos, isoprocarb, and metalaxyl.

Target, suspect and non-target screening analysis from wastewater treatment plant effluents to drinking water using collision cross section values as additional identification criterion

The anthropogenic entry of organic micropollutants into the aquatic environment leads to a potential risk for drinking water resources and the drinking water itself. Therefore, sensitive screening analysis methods are needed to monitor the raw and drinking water quality continuously. Non-target screening analysis has been shown to allow for a more comprehensive investigation of drinking water processes compared to target analysis alone. However, non-target screening is challenging due to the many features that can be detected. Thus, data processing techniques to reduce the high number of features are necessary, and prioritization techniques are important to find the features of interest for identification, as identification of unknown substances is challenging as well. In this study, a drinking water production process, where drinking water is supplied by a water reservoir, was investigated. Since the water reservoir provides surface water, which is anthropogenically influenced by wastewater treatment plant (WWTP) effluents, substances originating from WWTP effluents and reaching the drinking water were investigated, because this indicates that they cannot be removed by the drinking water production process. For this purpose, ultra-performance liquid chromatography coupled with an ion-mobility high-resolution mass spectrometer (UPLC-IM-HRMS) was used in a combined approach including target, suspect and non-target screening analysis to identify known and unknown substances. Additionally, the role of ion-mobility-derived collision cross sections (CCS) in identification is discussed. To that end, six samples (two WWTP effluent samples, a surface water sample that received the effluents, a raw water sample from a downstream water reservoir, a process sample and the drinking water) were analyzed. Positive findings for a total of 60 substances in at least one sample were obtained through quantitative screening. Sixty-five percent (15 out of 23) of the identified substances in the drinking water sample were pharmaceuticals and transformation products of pharmaceuticals. Using suspect screening, further 33 substances were tentatively identified in one or more samples, where for 19 of these substances, CCS values could be compared with CCS values from the literature, which supported the tentative identification. Eight substances were identified by reference standards. In the non-target screening, a total of ten features detected in all six samples were prioritized, whereby metoprolol acid/atenolol acid (a transformation product of the two β-blockers metoprolol and atenolol) and 1,3-benzothiazol-2-sulfonic acid (a transformation product of the vulcanization accelerator 2-mercaptobenzothiazole) were identified with reference standards. Overall, this study demonstrates the added value of a comprehensive water monitoring approach based on UPLC-IM-HRMS analysis.

Recent applications of ion mobility spectrometry in natural product research

Ion mobility spectrometry (IMS) is a rapid separation technique capable of extracting complementary structural information to chromatography and mass spectrometry (MS). IMS, especially in combination with MS, has experienced inordinate growth in recent years as an analytical technique, and elicited intense interest in many research fields. In natural product analysis, IMS shows promise as an additional tool to enhance the performance of analytical methods used to identify promising drug candidates. Potential benefits of the incorporation of IMS into analytical workflows currently used in natural product analysis include the discrimination of structurally similar secondary metabolites, improving the quality of mass spectral data, and the use of mobility-derived collision cross-section (CCS) values as an additional identification criterion in targeted and untargeted analyses. This review aims to provide an overview of the application of IMS to natural product analysis over the last six years. Instrumental aspects and the fundamental background of IMS will be briefly covered, and recent applications of the technique for natural product analysis will be discussed to demonstrate the utility of the technique in this field.

Rapid Characterization of Per- and Polyfluoroalkyl Substances (PFAS) by Ion Mobility Spectrometry-Mass Spectrometry (IMS-MS)

Per- and polyfluoroalkyl substances (PFAS) are an ensemble of persistent organic pollutants of global interest because of their associations with adverse health outcomes. Currently, environmental PFAS pollution is prolific as a result of the widespread manufacturing of these compounds and their chemical persistence. In this work, we demonstrate the advantages of adding ion mobility spectrometry (IMS) separation to existing LC-MS workflows for PFAS analysis. Using a commercially available drift tube IMS-MS, we characterized PFAS species and isomeric content in both analytical standards and environmental water samples. Molecular trendlines based on intrinsic mass and structural relationships were also explored for individual PFAS subclasses (e.g. PFSA, PFCA, etc.). Results from rapid IMS-MS analyses provided a link between mass and collision cross sections (CCS) for specific PFAS families and are linked to compositional differences in molecular structure. In addition, CCS values provide additional confidence of annotating prioritized features in untargeted screening studies for potential environmental pollutants. Results from this study show that the IMS separation provides novel information to support traditional LC-MS PFAS analyses and will greatly benefit the evaluation of unknown pollutants in future environmental studies.

Application of the new at-column dilution (ACD) modulator for the two-dimensional RP×HILIC analysis of Buddleja davidii

The focus of this study was the analysis of the complex chemical composition from different parts of Buddleja davidii, whose species are commonly known as ornamental plants and herbal medicines in many countries. As an herbal medicine, it has been utilized for stroke treatments, headache, wound healing, neurological disorder, etc. However, the understanding of its chemical matrices is still insufficient. Therefore, an online two-dimensional reversed phase liquid chromatography x hydrophilic interaction liquid chromatography (RPLCxHILIC) system coupled with mass spectrometry was applied for further detailed investigation of the chemical constituents in Buddleja dividii. In this two-dimensional liquid chromatography (2D-LC) method, a new at-column dilution (ACD) modulator was introduced in the 2D-LC system to solve the incompatibility problem of the mobile phase between two dimensions, which resulted in a 2D-LC analysis with high orthogonality. For the root extract, as one of the analyzed samples, the optimization of the ¹D and ²D gradients was carried out carefully. With this new modulator, much better peak separation and better peak shape were achieved compared to two-dimensional liquid chromatography system using a traditional standard (TS) modulator. With a similar approach, the other four parts of Buddleja davidii were well separated. Comparing the different analyzed parts, flowers and leaves showed the most complex profiles. MS and MS/MS data were obtained successfully, which demonstrated the potential of the proposed RPLCxHILIC-MS system in the constituents’ analysis of herbal medicine. However, due to the lack of reported reference information, 24 compounds could be tentatively identified.

Fundamentals of Ion Mobility-Mass Spectrometry for the Analysis of Biomolecules

Ion mobility-mass spectrometry (IM-MS) combines complementary size- and mass-selective separations into a single analytical platform. This chapter provides context for both the instrumental arrangements and key application areas that are commonly encountered in bioanalytical settings. New advances in these high-throughput strategies are described with description of complementary informatics tools to effectively utilize these data-intensive measurements. Rapid separations such as these are especially important in systems, synthetic, and chemical biology in which many small molecules are transient and correspond to various biological classes for integrated omics measurements. This chapter highlights the fundamentals of IM-MS and its applications toward biomolecular separations and discusses methods currently being used in the fields of proteomics, lipidomics, and metabolomics.

Utilizing Drift Tube Ion Mobility Spectrometry for the Evaluation of Metabolites and Xenobiotics

Metabolites and xenobiotics are small molecules with a molecular weight that often falls below 600 Da. Over the last few decades, multiple small molecule databases have been curated listing structures, masses, and fragmentation spectra possible in metabolomic and exposomic measurements. To date only a small portion of the spectra in these databases are experimentally derived due to the high expense of obtaining, synthesizing, and analyzing standards. A vast majority of spectra have thus been created using theoretical programs to fit the available experimental data. The errors associated with theoretical data have however caused problems with current small molecule identifications, and accurate quantitation as searching the databases using just one or two analysis dimensions (i.e., chromatography retention times and mass spectrometry (MS) m/z values) results in numerous annotations for each experimental feature. Additional analysis dimensions are therefore needed to better annotate and identify small molecules. Drift tube ion mobility spectrometry coupled with MS (DTIMS-MS) is a promising technique to address this challenge as it is able to perform rapid structural evaluations of small molecules in complex matrices by assessing the collision cross section values for each in addition to their m/z values. The use of IMS in conjunction with other separation techniques such as gas or liquid chromatography and MS has therefore enabled more accurate identifications for the small molecules present in complex biological and environmental samples. Here, we present a review of relevant parameter considerations for DTIMS application with emphasis on xenobiotics and metabolomics isomer separations.

Qualitative and quantitative use of micropollutants as source and process indicators. A review

Nowadays, micropollutants such as pharmaceuticals, pesticides and personal care products can be found ubiquitously in the anthropogenically influenced water cycle. As micropollutants have virtually no natural background concentrations they are significantly more sensitive in detecting processes and flow paths than classic inorganic tracers and indicators and at the same time they are often highly source specific. Therefore, using micropollutants as environmental indicators for anthropogenic activities is a common and frequently applied method today. As they interact in many ways with environmental matrices they can be used for source apportionment as well as to estimate flow paths and residence times in waterbodies. This review gives a systematic overview over the large variety of micropollutants used as indicators in the aquatic environment over the last decades together with the prerequisites on their use. Their application is subdivided into their qualitative (compound presence or absence) and quantitative (volume flows) use and shows the numerous possibilities from gaining basic information on the water regime up to advanced applications such as wastewater-based epidemiology.

Quantitative screening and prioritization based on UPLC-IM-Q-TOF-MS as an alternative water sample monitoring strategy

Suspect and non-target screening based on the use of high-resolution mass spectrometry (HRMS) has become more common in water analysis over the past years. However, this only yields lists of features or suspects without quantitative information. To expand the use of HRMS data to a quantitative screening, we have developed and validated a simple and fast method for more than 140 micropollutants using ultra high-performance liquid chromatography coupled to traveling wave ion mobility quadrupole time-of-flight mass spectrometry (UPLC-IM-Q-TOF-MS). Positive findings from suspect and non-target screening can be prioritized and identified by reference standards. The quantitative screening is then performed by additional measurement of calibration standards. This is carried out by means of direct injection and external calibration, without consideration of matrix effects. For all substances, limits of quantification (LOQs) of less or equal than 100 ng/L are achieved. The calibration is carried out in a range of 100 to 1000 ng/L and the results are reported as concentration ranges, in which the concentration of the analyte in the sample is to be expected. All substances were evaluated using quadratic regressions. For the verification of the accuracy, different water matrices (drinking water, groundwater, and surface water) were spiked with five concentration levels (50 ng/L, 300 ng/L, 500 ng/L, 800 ng/L, and 2000 ng/L) and indicate that for the drinking water and groundwater sample, 97% correct assignments were found, whereas for the surface water sample, 88% correct assignments were achieved. A comparative study of water samples of various matrices was accomplished using the quantitative screening analysis method and validated target methods by means of three UPLC tandem mass spectrometry (MS/MS) methods and two gas chromatography (GC) coupled to MS and MS/MS methods. A total of 510 data could be compared, which showed a good match of both approaches in more than 80% of the results. As an alternative strategy for the monitoring of water samples by UPLC-IM-Q-TOF-MS, this method provides quantitative information about target components, besides tentatively or identified substances from suspect or non-target screening. Depending on the resulting concentration range and reporting requirements, validated target methods can be further used for the previously detected targets. Graphical abstract.

Semi-quantitative non-target analysis of water with liquid chromatography/high-resolution mass spectrometry: How far are we?

Combining high-resolution mass spectrometry (HRMS) with liquid chromatography (LC) has considerably increased the capability of analytical chemistry. Among others, it has stimulated the growth of the non-target analysis, which aims at identifying compounds without their preceding selection. This approach is already widely applied in various fields, such as metabolomics, proteomics, etc. The applicability of LC/HRMS-based non-target analysis in environmental analyses, such as water studies, would be beneficial for understanding the environmental fate of polar pollutants and evaluating the health risks exposed by the new emerging contaminants. During the last five to seven years the use of LC/HRMS-based non-target analysis has grown rapidly. However, routine non-target analysis is still uncommon for most environmental monitoring agencies and environmental scientists. The main reasons are the complicated data processing and the inability to provide quantitative information about identified compounds. The latter shortcoming follows from the lack of standard substances, considered so far as the soul of each quantitative analysis for the newly discovered pollutants. To overcome this, non-target analyses could be combined with semi-quantitation. This Perspective aims at describing the current methods for non-target analysis, the possibilities and challenges of standard substance-free semi-quantitative analysis, and proposes tools to join these two fields together.

Ion Mobility Spectrometry and the Omics: Distinguishing Isomers, Molecular Classes and Contaminant Ions in Complex Samples

Ion mobility spectrometry (IMS) is a widely used analytical technique providing rapid gas phase separations. IMS alone is useful, but its coupling with mass spectrometry (IMS-MS) and various front-end separation techniques has greatly increased the molecular information achievable from different omic analyses. IMS-MS analyses are specifically gaining attention for improving metabolomic, lipidomic, glycomic, proteomic and exposomic analyses by increasing measurement sensitivity (e.g. S/N ratio), reducing the detection limit, and amplifying peak capacity. Numerous studies including national security-related analyses, disease screenings and environmental evaluations are illustrating that IMS-MS is able to extract information not possible with MS alone. Furthermore, IMS-MS has shown great utility in salvaging molecular information for low abundance molecules of interest when high concentration contaminant ions are present in the sample by reducing detector suppression. This review highlights how IMS-MS is currently being used in omic analyses to distinguish structurally similar molecules, isomers, molecular classes and contaminant ions.

ISiCLE: A Quantum Chemistry Pipeline for Establishing in Silico Collision Cross Section Libraries

High-throughput, comprehensive, and confident identifications of metabolites and other chemicals in biological and environmental samples will revolutionize our understanding of the role these chemically diverse molecules play in biological systems. Despite recent technological advances, metabolomics studies still result in the detection of a disproportionate number of features that cannot be confidently assigned to a chemical structure. This inadequacy is driven by the single most significant limitation in metabolomics, the reliance on reference libraries constructed by analysis of authentic reference materials with limited commercial availability. To this end, we have developed the in silico chemical library engine (ISiCLE), a high-performance computing-friendly cheminformatics workflow for generating libraries of chemical properties. In the instantiation described here, we predict probable three-dimensional molecular conformers (i.e., conformational isomers) using chemical identifiers as input, from which collision cross sections (CCS) are derived. The approach employs first-principles simulation, distinguished by the use of molecular dynamics, quantum chemistry, and ion mobility calculations, to generate structures and chemical property libraries, all without training data. Importantly, optimization of ISiCLE included a refactoring of the popular MOBCAL code for trajectory-based mobility calculations, improving its computational efficiency by over 2 orders of magnitude. Calculated CCS values were validated against 1983 experimentally measured CCS values and compared to previously reported CCS calculation approaches. Average calculated CCS error for the validation set is 3.2% using standard parameters, outperforming other density functional theory (DFT)-based methods and machine learning methods (e.g., MetCCS). An online database is introduced for sharing both calculated and experimental CCS values ( metabolomics.pnnl.gov ), initially including a CCS library with over 1 million entries. Finally, three successful applications of molecule characterization using calculated CCS are described, including providing evidence for the presence of an environmental degradation product, the separation of molecular isomers, and an initial characterization of complex blinded mixtures of exposure chemicals. This work represents a method to address the limitations of small molecule identification and offers an alternative to generating chemical identification libraries experimentally by analyzing authentic reference materials. All code is available at github.com/pnnl .

Validating an ion mobility spectrometry-quadrupole time of flight mass spectrometry method for high-throughput pesticide screening

The utility of adding ion mobility (IM) to quadrupole time of flight mass spectrometry (IM-QTOF MS) for highly effective analysis of multiple pesticides in complex matrices was evaluated. Based on an in-house IM-MS database, the identification was performed through the match of the protonated ion ([M + H]+) and the CCS value. Moreover, the structural confirmation was achieved by using the accurate masses of [M + H]+ with its fragment ions, and the reference CCS value. The method did not require chromatographic separation and the analysis time of each measurement cycle is 1.6 min. The "cleaned" IM-MS spectra afforded by the drift time filtration improved the reliability of structural confirmation. As a result, the limit of detection (LOD) of 92% of test pesticides under the APCI mode and 58% of test pesticides under the ESI mode spiked in scallion was not more than 20 ng mL-1. In the analysis of practical samples, the identification of pyrimethanil was confirmed in celery, and benalaxyl and tebuconazole were identified as false positives in scallion. The time-saving, extended-scope and high-throughput method described in this work is capable of determining multiple pesticide residues in complex matrices with high sensitivity for monitoring applications.

Comprehensive Three-Dimensional LC × LC × Ion Mobility Spectrometry Separation Combined with High-Resolution MS for the Analysis of Complex Samples

Comprehensive two-dimensional liquid chromatography (LC × LC) and ion mobility spectrometry-mass spectrometry (IMS-MS) are increasingly being used to address challenges associated with the analysis of highly complex samples. In this work, we evaluate the potential of the combination of these techniques in the form of a comprehensive three-dimensional LC × LC × IMS separation system. As application, hydrophilic interaction chromatography (HILIC) × reversed phase LC (RP-LC) × IMS-high-resolution MS (HR-MS) was used to analyze a range of phenolic compounds, including hydrolyzable and condensed tannins, flavonoids, and phenolic acids in several natural products. A protocol for the extraction and visualization of the four-dimensional data obtained using this approach was developed. We show that the combination of HILIC, RP-LC, and IMS offers excellent separation of complex phenolic samples in three dimensions. Benefits associated with the incorporation of IMS include improved MS sensitivity and mass-spectral data quality. IMS also provided separation of trimeric procyanidin isomeric species that could not be differentiated by HILIC × RP-LC or HR-MS. On the traveling wave IMS (TWIMS) system used here, both IMS separation performance and the extent of second dimension (²D) undersampling depend on the upper mass scan limit, which might present a limitation for the analysis of larger molecular ions. The performance of the LC × LC × IMS system was characterized in terms of practical peak capacity and separation power, using established theory and taking undersampling and orthogonality into account. An average increase in separation performance by a factor of 13 was found for the samples analyzed here when IMS was incorporated into the HILIC × RP-LC-MS workflow.

Increasing Compound Identification Rates in Untargeted Lipidomics Research with Liquid Chromatography Drift Time-Ion Mobility Mass Spectrometry

Unknown metabolites represent a bottleneck in untargeted metabolomics research. Ion mobility-mass spectrometry (IM-MS) facilitates lipid identification because it yields collision cross section (CCS) information that is independent from mass or lipophilicity. To date, only a few CCS values are publicly available for complex lipids such as phosphatidylcholines, sphingomyelins, or triacylglycerides. This scarcity of data limits the use of CCS values as an identification parameter that is orthogonal to mass, MS/MS, or retention time. A combination of lipid descriptors was used to train five different machine learning algorithms for automatic lipid annotations, combining accurate mass ( m/ z), retention time (RT), CCS values, carbon number, and unsaturation level. Using a training data set of 429 true positive lipid annotations from four lipid classes, 92.7% correct annotations overall were achieved using internal cross-validation. The trained prediction model was applied to an unknown milk lipidomics data set and allowed for class 3 level annotations of most features detected in this application set according to Metabolomics Standards Initiative (MSI) reporting guidelines.

Recent trends in water analysis triggering future monitoring of organic micropollutants

Water analysis has been an important area since the beginning of analytical chemistry. The focus though has shifted substantially: from minerals and the main constituents of water in the time of Carl Remigius Fresenius to a multitude of, in particular, organic compounds at concentrations down to the sub-nanogram per liter level nowadays. This was possible only because of numerous innovations in instrumentation in recent decades, drivers of which are briefly discussed. In addition to the high demands on sensitivity, high throughput by automation and short analysis times are major requirements. In this article, some recent developments in the chemical analysis of organic micropollutants (OMPs) are presented. These include the analysis of priority pollutants in whole water samples, extension of the analytical window, in particular to encompass highly polar compounds, the trend toward more than one separation dimension before mass spectrometric detection, and ways of coping with unknown analytes by suspect and nontarget screening approaches involving high-resolution mass spectrometry. Furthermore, beyond gathering reliable concentration data for many OMPs, the question of the relevance of such data for the aquatic system under scrutiny is becoming ever more important. To that end, effect-based analytics can be used and may become part of future routine monitoring, mostly with a focus on adverse effects of OMPs in specific test systems mimicking environmental impacts. Despite advances in the field of water analysis in recent years, there are still many challenges for further analytical research. Graphical abstract Recent trends in water analysis of organic micropollutants that open new opportunities in future water monitoring. HRMS high-resolution mass spectrometry, PMOC persistent mobile organic compounds.

Adding a new separation dimension to MS and LC-MS: What is the utility of ion mobility spectrometry?

Ion mobility spectrometry is an analytical technique known for more than 100 years, which entails separating ions in the gas phase based on their size, shape, and charge. While ion mobility spectrometry alone can be useful for some applications (mostly security analysis for detecting certain classes of narcotics and explosives), it becomes even more powerful in combination with mass spectrometry and high-performance liquid chromatography. Indeed, the limited resolving power of ion mobility spectrometry alone can be tackled when combining this analytical strategy with mass spectrometry or liquid chromatography with mass spectrometry. Over the last few years, the hyphenation of ion mobility spectrometry to mass spectrometry or liquid chromatography with mass spectrometry has attracted more and more interest, with significant progresses in both technical advances and pioneering applications. This review describes the theoretical background, available technologies, and future capabilities of these techniques. It also highlights a wide range of applications, from small molecules (natural products, metabolites, glycans, lipids) to large biomolecules (proteins, protein complexes, biopharmaceuticals, oligonucleotides).

Advancing the large-scale CCS database for metabolomics and lipidomics at the machine-learning era

Metabolomics and lipidomics aim to comprehensively measure the dynamic changes of all metabolites and lipids that are present in biological systems. The use of ion mobility-mass spectrometry (IM-MS) for metabolomics and lipidomics has facilitated the separation and the identification of metabolites and lipids in complex biological samples. The collision cross-section (CCS) value derived from IM-MS is a valuable physiochemical property for the unambiguous identification of metabolites and lipids. However, CCS values obtained from experimental measurement and computational modeling are limited available, which significantly restricts the application of IM-MS. In this review, we will discuss the recently developed machine-learning based prediction approach, which could efficiently generate precise CCS databases in a large scale. We will also highlight the applications of CCS databases to support metabolomics and lipidomics.

Nontarget Screening with High Resolution Mass Spectrometry in the Environment: Ready to Go?

The vast, diverse universe of organic pollutants is a formidable challenge for environmental sciences, engineering, and regulation. Nontarget screening (NTS) based on high resolution mass spectrometry (HRMS) has enormous potential to help characterize this universe, but is it ready to go for real world applications? In this Feature article we argue that development of mass spectrometers with increasingly high resolution and novel couplings to both liquid and gas chromatography, combined with the integration of high performance computing, have significantly widened our analytical window and have enabled increasingly sophisticated data processing strategies, indicating a bright future for NTS. NTS has great potential for treatment assessment and pollutant prioritization within regulatory applications, as highlighted here by the case of real-time pollutant monitoring on the River Rhine. We discuss challenges for the future, including the transition from research toward solution-centered and robust, harmonized applications.