Untargeted Comprehensive Two-Dimensional Liquid Chromatography Coupled with High-Resolution Mass Spectrometry Analysis of Rice Metabolome Using Multivariate Curve Resolution

Determination of trace chelating carboxylic acids in rice by green extraction combined with liquid chromatography-mass spectrometry analysis and its application in the evaluation of old and new rice

RATIONALE

Accurate identification of old rice samples from new ones benefits their market circulation and consumers. However, the current detection methods are still not satisfactory because of their insufficient accuracy or (and) time-consuming process.

METHODS

Chelating carboxylic acids (CCAs) were selectively extracted from rice, by stirring with chelating resin and a dilute Na2CO3 solution. The green analytical chemistry guidelines for sample preparation were investigated by using the green chemistry calculator AGREE prep. The extractant was determined by liquid chromatography-mass spectrometry (LC/MS), and statistical analysis of the analytical data was carried out to evaluate the significance of the difference by ChiPlot.

RESULTS

The limit of quantitation for the CCAs is in the range of 1 to 50 ng/mL, with a reasonable reproducibility. The CCAs in 23 rice samples were determined within a wide concentration range from 0.03 to 1174 μg/g. Intriguingly, the content of citric acid, malonic acid, α-ketoglutaric acid and cis-aconite acid in new rice was each found to be distinctively higher than that in old rice by several times. Even mixtures of old and new rice were found to show much difference in the concentration of citric acid and malic acid.

CONCLUSION

A green analytical method has been developed for the simultaneous determination of CCAs by LC/MS analysis, and the identification of old rice samples from new ones was easily carried out according to their CCA content for the first time. The results indicated that the described method has powerful potential for the accurate identification of old rice samples from new ones.

Applications of chromatographic methods in metabolomics: A review

Chromatography is a robust and reliable separation method that can use various stationary phases to separate complex mixtures commonly seen in metabolomics. This review examines the types of chromatography and stationary phases that have been used in targeted or untargeted metabolomics with methods such as mass spectrometry (MS) and nuclear magnetic resonance (NMR) spectroscopy. General considerations for sample pretreatment and separations in metabolomics are considered, along with the various supports and separation formats for chromatography that have been used in such work. The types of liquid chromatography (LC) that have been most extensively used in metabolomics will be examined, such as reversed-phase liquid chromatography and hydrophilic liquid interaction chromatography. In addition, other forms of LC that have been used in more limited applications for metabolomics (e.g., ion-exchange, size-exclusion, and affinity methods) will be discussed to illustrate how these techniques may be utilized for new and future research in this field. Multidimensional LC methods are also discussed, as well as the use of gas chromatography and supercritical fluid chromatography in metabolomics. In addition, the roles of chromatography in NMR- vs. MS-based metabolomics are considered. Applications are given within the field of metabolomics for each type of chromatography, along with potential advantages or limitations of these separation methods.

Food authentication, current issues, analytical techniques, and future challenges: A comprehensive review

Food authentication and contamination are significant concerns, especially for consumers with unique nutritional, cultural, lifestyle, and religious needs. Food authenticity involves identifying food contamination for many purposes, such as adherence to religious beliefs, safeguarding health, and consuming sanitary and organic food products. This review article examines the issues related to food authentication and food fraud in recent periods. Furthermore, the development and innovations in analytical techniques employed to authenticate various food products are comprehensively focused. Food products derived from animals are susceptible to deceptive practices, which can undermine customer confidence and pose potential health hazards due to the transmission of diseases from animals to humans. Therefore, it is necessary to employ suitable and robust analytical techniques for complex and high-risk animal-derived goods, in which molecular biomarker-based (genomics, proteomics, and metabolomics) techniques are covered. Various analytical methods have been employed to ascertain the geographical provenance of food items that exhibit rapid response times, low cost, nondestructiveness, and condensability.

Enhanced efficiency of MS/MS all-ion fragmentation for non-targeted analysis of trace contaminants in surface water using multivariate curve resolution and data fusion

Data-independent acquisition-all-ion fragmentation (DIA-AIF) mode of mass spectrometry can facilitate wide-scope non-target analysis of contaminants in surface water due to comprehensive spectral identification. However, because of the complexity of the resulting MS2 AIF spectra, identifying unknown pollutants remains a significant challenge, with a significant bottleneck in translating non-targeted chemical signatures into environmental impacts. The present study proposes to process fused MS1 and MS2 data sets obtained from LC-HRMS/MS measurements in non-targeted AIF workflows on surface water samples using multivariate curve resolution-alternating least squares (MCR-ALS). This enables straightforward assignment between precursor ions obtained from resolved MS1 spectra and their corresponding MS2 spectra. The method was evaluated for two sets of tap water and surface water contaminated with 14 target chemicals as a proof of concept. The data set of surface water samples consisting of 3506 MS1 and 2170 MS2 AIF mass spectral features was reduced to 81 components via a fused MS1-MS2 MCR model that describes at least 98.8% of the data. Each component summarizes the distinct chromatographic elution of components together with their corresponding MS1 and MS2 spectra. MS2 spectral similarity of more than 82% was obtained for most target chemicals. This highlights the potential of this method for unraveling the composition of MS/MS complex data in a water environment. Ultimately, the developed approach was applied to the retrospective non-target analysis of an independent set of surface water samples.

Secondary Ion Mass Spectral Imaging of Metals and Alloys

Secondary Ion Mass Spectrometry (SIMS) is an outstanding technique for Mass Spectral Imaging (MSI) due to its notable advantages, including high sensitivity, selectivity, and high dynamic range. As a result, SIMS has been employed across many domains of science. In this review, we provide an in-depth overview of the fundamental principles underlying SIMS, followed by an account of the recent development of SIMS instruments. The review encompasses various applications of specific SIMS instruments, notably static SIMS with time-of-flight SIMS (ToF-SIMS) as a widely used platform and dynamic SIMS with Nano SIMS and large geometry SIMS as successful instruments. We particularly focus on SIMS utility in microanalysis and imaging of metals and alloys as materials of interest. Additionally, we discuss the challenges in big SIMS data analysis and give examples of machine leaning (ML) and Artificial Intelligence (AI) for effective MSI data analysis. Finally, we recommend the outlook of SIMS development. It is anticipated that in situ and operando SIMS has the potential to significantly enhance the investigation of metals and alloys by enabling real-time examinations of material surfaces and interfaces during dynamic transformations.

Optimization of Parameters for ROI Data Compression for Nontargeted Analyses Using LC-HRMS

Nontargeted analyses of low-concentration analytes in the information-rich data collected by liquid chromatography with high-resolution mass spectrometry detection can be challenging to accomplish in an efficient and comprehensive manner. The aim of this study is to demonstrate a workflow involving targeted parameter optimization for entire chromatograms using region of interest (ROI) data compression uncoupled from a subsequent tile-based Fisher ratio (F-ratio) analysis, a supervised discovery-based method, for the discovery of low-concentration analytes. Soil samples spiked with 18 pesticides at nominal concentrations ranging from 0.1 to 50 ppb for a total of six sample classes served as challenging samples to demonstrate the overall workflow. Optimization of two parameters proved to be the most critical for ROI data compression: the signal threshold parameter and the admissible mass deviation parameter. The parameter optimization method workflow we introduce is based upon spiking known analytes into a representative sample and determining the number of detectable spikes and the Δppm for various combinations of the signal threshold and admissible mass deviation, where Δppm is the absolute value of the difference between the theoretical m/z and the ROI m/z. Once optimal parameters are determined providing the lowest average Δppm and the greatest number of detectable analytes, the optimized parameters can be utilized for the intended analysis. Herein, tile-based F-ratio analysis was performed on the ROI compressed data of all spiked soil samples first by applying ROI parameters recommended in the literature, referred to herein as the initial ROI parameters, and finally by the combination of the two optimized parameters. Using the initial ROI parameters, three pesticides were discovered, whereas all 18 spiked pesticides were discovered by optimizing both ROI parameters.

Quantification strategies for two-dimensional liquid chromatography datasets using regions of interest and multivariate curve resolution approaches

In this work, three chemometrics-based approaches are compared for quantification purposes when using two-dimensional liquid chromatography (LC×LC-MS), taking as a study case the quantification of amino acids in commercial drug mixtures. Although the approaches have been already used for one-dimensional gas or liquid chromatography, the main novelty of this work is the demonstration of their applicability to LC×LC-MS datasets. Besides, steps such as peak alignment and modelling, commonly applied in this type of data analysis, are not required with the approaches proposed here. In a first step, regions of interest (ROI) strategy is used for the spectral compression of the LC×LC-MS datasets. Then the first strategy consists of building a calibration curve from the areas obtained in this ROI compression step. Alternatively, the ROI intensity matrices can be used as input for a second analysis step employing the multivariate curve resolution alternating least squares (MCR-ALS) method. The main benefit of MCR-ALS is the resolution of elution and spectral profiles for each of the analytes in the mixture, even in the case of strong coelutions and high signal overlapping. Classical MCR-ALS based calibration curve from the peak areas resolved only applying non-negativity constraints (second strategy) is compared to the results obtained when an area correlation constraint is imposed during the ALS optimization (third strategy). All in all, similar quantification results were achieved by the three approaches but, especially in prediction studies, the more accurate quantification is obtained when the calibration curve is built from the peak areas obtained with MCR-ALS when the area correlation constraint is imposed.

Current state-of-the-art of separation methods used in LC-MS based metabolomics and lipidomics

Metabolomics deals with the large-scale analysis of metabolites, belonging to numerous compound classes and showing an extremely high chemical diversity and complexity. Lipidomics, being a subcategory of metabolomics, analyzes the cellular lipid species. Both require state-of-the-art analytical methods capable of accessing the underlying chemical complexity. One of the major techniques used for the analysis of metabolites and lipids is Liquid Chromatography-Mass Spectrometry (LC-MS), offering both different selectivities in LC separation and high sensitivity in MS detection. Chromatography can be divided into different modes, based on the properties of the employed separation system. The most popular ones are Reversed-Phase (RP) separation for non- to mid-polar molecules and Hydrophilic Interaction Liquid Chromatography (HILIC) for polar molecules. So far, no single analysis method exists that can cover the entire range of metabolites or lipids, due to the huge chemical diversity. Consequently, different separation methods have been used for different applications and research questions. In this review, we explore the current use of LC-MS in metabolomics and lipidomics. As a proxy, we examined the use of chromatographic methods in the public repositories EBI MetaboLights and NIH Metabolomics Workbench. We extracted 1484 method descriptions, collected separation metadata and generated an overview on the current use of columns, eluents, etc. Based on this overview, we reviewed current practices and identified potential future trends as well as required improvements that may allow us to increase metabolite coverage, throughput or both simultaneously.

Metabolomics of Chlorophylls and Carotenoids: Analytical Methods and Metabolome-Based Studies

Chlorophylls and carotenoids are two families of antioxidants present in daily ingested foods, whose recognition as added-value ingredients runs in parallel with the increasing number of demonstrated functional properties. Both groups include a complex and vast number of compounds, and extraction and analysis methods evolved recently to a modern protocol. New methodologies are more potent, precise, and accurate, but their application requires a better understanding of the technical and biological context. Therefore, the present review compiles the basic knowledge and recent advances of the metabolomics of chlorophylls and carotenoids, including the interrelation with the primary metabolism. The study includes material preparation and extraction protocols, the instrumental techniques for the acquisition of spectroscopic and spectrometric properties, the workflows and software tools for data pre-processing and analysis, and the application of mass spectrometry to pigment metabolomics. In addition, the review encompasses a critical description of studies where metabolomics analyses of chlorophylls and carotenoids were developed as an approach to analyzing the effects of biotic and abiotic stressors on living organisms.

Systematic Evaluation of Liquid Chromatography (LC) Column Combinations for Application in Two-Dimensional LC Metabolomic Studies

In comparison to proteomics, the application of two-dimensional liquid chromatography (2D LC) in the field of metabolomics is still premature. One reason might be the elevated chemical complexity and the associated challenge of selecting proper separation conditions in each dimension. As orthogonality of dimensions is a major issue, the present study aimed for the identification of successful stationary phase combinations. To determine the degree of orthogonality, first, six different metrics, namely, Pearson's correlation coefficient (1 - |R|), the nearest-neighbor distances (H̅_NND), the "asterisk equations" (A_O), and surface coverage by bins (SC_G), convex hulls (SC_CH), and α-convex hulls (SC_αH), were critically assessed by 15 artificial 2D data sets, and a systematic parameter optimization of α-convex hulls was conducted. SG_G, SC_αH with α = 0.1, and H̅_NND generated valid results with sensitivity toward space utilization and data distribution and, therefore, were applied to pairs of experimental retention time sets obtained for >350 metabolites, selected to represent the chemical space of human urine. Normalized retention data were obtained for 23 chromatographic setups, comprising reversed-phase (RP), hydrophilic interaction liquid chromatography (HILIC), and mixed-mode separation systems with an ion exchange (IEX) contribution. As expected, no single LC setting provided separation of all considered analytes, but while conventional RP×HILIC combinations appeared rather complementary than orthogonal, the incorporation of IEX properties into the RP dimension substantially increased the 2D potential. Eventually, one of the most promising column combinations was implemented for an offline 2D LC time-of-flight mass spectrometry analysis of a lyophilized urine sample. Targeted screening resulted in a total of 164 detected metabolites and confirmed the outstanding coverage of the 2D retention space.

Strategies to circumvent the solvent strength mismatch problem in online comprehensive two-dimensional liquid chromatography

On-line comprehensive two-dimensional liquid chromatography is a powerful technique for the separation of highly complex samples. Due to the addition of the second dimension of separation, impressive peak capacities can be obtained within a reasonable analysis time compared to one-dimensional liquid chromatography. In online comprehensive two-dimensional liquid chromatography, the separation power is maximized by selecting two separation dimensions as orthogonal as possible, which most often requires the combination of different mobile phases and stationary phases. The online transfer of a given solvent from the first dimension to the second dimension may cause severe injection effects in the second dimension, mostly due to solvent strength mismatch. Those injection effects may include peak broadening, peak distortion, peak splitting or breakthrough phenomenon. They are often found to reduce significantly the peak capacity and the peak intensity. To overcome such effects, arising specifically in online comprehensive two-dimensional liquid chromatography, different methods have been developed over the years. In this review, we focused on the most recently reported ones. A critical discussion, supported by a theoretical approach, gives an overview of their advantages and drawbacks.

Untargeted metabolomics of prostate cancer zwitterionic and positively charged compounds in urine

Prostate cancer, a leading cause of cancer-related deaths worldwide, principally occurs in over 50-year-old men. Nowadays there is urgency to discover biomarkers alternative to prostate-specific antigen, as it cannot discriminate patients with benign prostatic hyperplasia from clinically significant forms of prostatic cancer. In the present paper, 32 benign prostatic hyperplasia and 41 prostatic cancer urine samples were collected and analyzed. Polar and positively charged metabolites were therein investigated using an analytical platform comprising an up to 40-fold analyte enrichment step by graphitized carbon black solid-phase extraction, HILIC separation, and untargeted high-resolution mass spectrometry analysis. These classes of compounds are often neglected in common metabolomics experiments even though previous studies reported their significance in cancer biomarker discovery. The complex metabolomics big datasets, generated by the UHPLC-HRMS, were analyzed with the ROIMCR procedure, based on the selection of the MS regions of interest data and their analysis by the Multivariate Curve-Resolution Alternating Least Squares chemometrics method. This approach allowed the resolution and tentative identification of the metabolites differentially expressed by the two data sets. Among these, amino acids and carnitine derivatives were tentatively identified highlighting the importance of the proposed methodology for cancer biomarker research.

Mass spectrometry based untargeted metabolomics for plant systems biology

Untargeted metabolomics enables the identification of key changes to standard pathways, but also aids in revealing other important and possibly novel metabolites or pathways for further analysis. Much progress has been made in this field over the past decade and yet plant metabolomics seems to still be an emerging approach because of the high complexity of plant metabolites and the number one challenge of untargeted metabolomics, metabolite identification. This final and critical stage remains the focus of current research. The intention of this review is to give a brief current state of LC–MS based untargeted metabolomics approaches for plant specific samples and to review the emerging solutions in mass spectrometer hardware and computational tools that can help predict a compound's molecular structure to improve the identification rate.

"Force iteration molecular designing" strategy for the systematic characterization and discovery of new protostane triterpenoids from Alisma Rhizoma by UHPLC/LTQ-Orbitrap-MS

Comprehensive analysis and identification of chemical components are of great significance for evaluating the efficacy and safety of herbal medicines, as well as for drug exploitation and development. Here we developed a "force iteration molecular designing" strategy, by combing a database-based in-house software for a precursor ion list (PIL) and PIL-triggered collision-induced dissociation-MS² and high-energy C-trap dissociation-MS² (PIL-CID/MS²-HCD/MS²) on an LTQ-Orbitrap mass spectrometer, aiming for the systematic characterization and discovery of new protostane triterpenoids (PTs) from Alisma Rhizoma (AR). AR was a well-known herbal remedy widely used for diarrhea, but its systematic characterization and comparison between two botanical origins have not been reported. Firstly, in-house software was developed based on force iteration, to generate a PIL that contains 483 accurate precursor ions. Secondly, to facilitate the acquisition of rich fragments and diagnostic ions sufficient for the structural elucidation of different types of PTs, a hybrid data acquisition method, namely PIL-CID/MS²-HCD/MS², was generated. Thirdly, a total of 473 PTs were rapidly characterized from two botanical origins of AR according to an established four-step interpretation method, and the common constituents were 277 with ratio 70% (277/395) and 78% (277/355) in the rhizome of Alisma plantago-aquatica and A. orientale, respectively. Finally, two new PTs were isolated and unambiguously identified by NMR verifying the feasibility of this combined data acquisition strategy. This integrated strategy could improve the efficiency in the detection of new compounds in a single run and is practical to comprehensively characterize the complex components in herbal medicines.

Analysis of the molecular heterogeneity of poly(lactic acid)/poly(butylene succinate-co-adipate) blends by hyphenating size exclusion chromatography with nuclear magnetic resonance and infrared spectroscopy

The compositional and stereochemical heterogeneity of copolymers are key molecular metrics, and their knowledge is of pivotal importance for evidence based material development. Yet, while it is state of the art to determine these parameters for many petroleum based polymers, little insight exists in that regard for bio-based materials. Towards this end, size exclusion chromatography (SEC) was hyphenated with nuclear magnetic resonance spectroscopy (NMR) in an offline manner and a blend of poly(lactic acid) (PLA) and poly(butylene succinate-co-adipate) (PBSA) investigated. Thus, the microstructural heterogeneity could be shown with regard to tacticity of the PLA and regioregularity of the PBSA component. The results show, that the highest molar mass fraction differs in stereochemical composition from the others. It may be assumed that this is the result of misinsertions with regard to stereochemistry occurring during the catalytic polymerization of the lactide. While the content of both constituent polymers along the molar mass axis could be well studied using a univariate analysis of the infrared (IR) spectra, this method failed to profile the adipate and succinate content individually. For this purpose, SEC was coupled to IR spectroscopy in online mode and the spectra were evaluated by a multivariate protocol. Thus, the content of each monomer along the molar mass distribution could be mapped with high chromatographic resolution.

Analytical Chemistry in the 21st Century: Challenges, Solutions, and Future Perspectives of Complex Matrices Quantitative Analyses in Biological/Clinical Field

Nowadays, the challenges in analytical chemistry, and mostly in quantitative analysis, include the development and validation of new materials, strategies and procedures to meet the growing need for rapid, sensitive, selective and green methods. In this context, considering the constantly updated International Guidelines, constant innovation is mandatory both in the pre-treatment procedures and in the instrumental configurations to obtain reliable, true, and reproducible information. In this context, additionally to the classic plasma (or serum) matrices, biopsies, whole blood, and urine have seen an increase in the works that also consider non-conventional matrices. Obviously, all these studies have shown that there is a correlation between the blood levels and those found in the new matrix, in order to be able to correlate and compare the results in a robust way and reduce any bias problems. This review provides an update of the most recent developments currently in use in the sample pre-treatment and instrument configurations in the biological/clinical fields. Furthermore, the review concludes with a series of considerations regarding the role and future developments of Analytical Chemistry in light of the forthcoming challenges and new goals to be achieved.

Integrating Two-Dimensional Gas and Liquid Chromatography-Mass Spectrometry for Untargeted Colorectal Cancer Metabolomics: A Proof-of-Principle Study

Untargeted metabolomics is expected to lead to a better mechanistic understanding of diseases and thus applications of precision medicine and personalized intervention. To further increase metabolite coverage and achieve high accuracy of metabolite quantification, the present proof-of-principle study was to explore the applicability of integration of two-dimensional gas and liquid chromatography-mass spectrometry (GC × GC-MS and 2DLC-MS) platforms to characterizing circulating polar metabolome extracted from plasma collected from 29 individuals with colorectal cancer in comparison with 29 who remained cancer-free. After adjustment of multiple comparisons, 20 metabolites were found to be up-regulated and 8 metabolites were found to be down-regulated, which pointed to the dysregulation in energy metabolism and protein synthesis. While integrating the GC × GC-MS and 2DLC-MS data can dramatically increase the metabolite coverage, this study had a limitation in analyzing the non-polar metabolites. Given the small sample size, these results need to be validated with a larger sample size and with samples collected prior to diagnostic and treatment. Nevertheless, this proof-of-principle study demonstrates the potential applicability of integration of these advanced analytical platforms to improve discrimination between colorectal cancer cases and controls based on metabolite profiles in future studies.

Recent applications of chemometrics in one- and two-dimensional chromatography

The proliferation of increasingly more sophisticated analytical separation systems, often incorporating increasingly more powerful detection techniques, such as high-resolution mass spectrometry, causes an urgent need for highly efficient data-analysis and optimization strategies. This is especially true for comprehensive two-dimensional chromatography applied to the separation of very complex samples. In this contribution, the requirement for chemometric tools is explained and the latest developments in approaches for (pre-)processing and analyzing data arising from one- and two-dimensional chromatography systems are reviewed. The final part of this review focuses on the application of chemometrics for method development and optimization.

Evaluation of LC-MS and LC×LC-MS in analysis of zebrafish embryo samples for comprehensive lipid profiling

In this study, both conventional one-dimensional liquid chromatography (1DLC) and comprehensive two-dimensional liquid chromatography (2DLC) coupled to a high-resolution time-of-flight mass spectrometer (HR-TOF MS) were used for full-scale lipid characterization of lipid extracts from zebrafish embryos. We investigated the influence on annotated lipids and different separation mechanisms (HILIC, C18, and PFP), and their different orders arranged in the first and the second dimensions. As a result, the number of lipid species annotated by conventional one-dimensional LC-MS was between 212 and 448. In contrast, the number of individual lipids species annotated by C18×HILIC, HILIC×C18, and HILIC×PFP were 1784, 1059, and 1123, respectively. Therefore, it was evident that the performance of comprehensive 2DLC, especially the C18×HILIC method, considerably exceeded 1DLC. Interestingly, a comparison of the HILIC×C18 and C18×HILIC approaches showed, under the optimized conditions, similar orthogonality, but the effective separation power of the C18×HILIC was much higher. A comparison of the HILIC×C18 and the HILIC×PFP methods demonstrated that the HILIC×PFP separation had superior orthogonality with a small increase on its effective peak capacity, indicating that the HILIC×PFP combination maybe a promising platform for untargeted lipidomics in complex samples. Finally, from the comprehensive lipid profiling respective, the C18×HILIC was selected for further studies.

Independent components analysis (ICA) at the "cocktail-party" in analytical chemistry

Independent components analysis (ICA) is a probabilistic method, whose goal is to extract underlying component signals, that are maximally independent and non-Gaussian, from mixed observed signals. Since the data acquired in many applications in analytical chemistry are mixtures of component signals, such a method is of great interest. In this article recent ICA applications for quantitative and qualitative analysis in analytical chemistry are reviewed. The following experimental techniques are covered: fluorescence, UV-VIS, NMR, vibrational spectroscopies as well as chromatographic profiles. Furthermore, we reviewed ICA as a preprocessing tool as well as existing hybrid ICA-based multivariate approaches. Finally, further research directions are proposed. Our review shows that ICA is starting to play an important role in analytical chemistry, and this will definitely increase in the future.

Applications of metabolomics in assessing ecological effects of emerging contaminants and pollutants on plants

Metabolomics, the global profiling of metabolite composition, is a powerful technique that can be applied to answer a diverse set of research questions concerning effects of toxicants on organisms. It has recently emerged as a tool to understand complex environmental perturbations in biological systems, especially at sub-lethal concentrations. Organisms can be affected by different stressors such as xenobiotics or increase in concentration of natural compounds such as nitrogen, phosphorous, and sulfur. Metabolomics has facilitated a better understanding of the effects of these perturbations on organisms such as plants, animals, and humans providing phenotypic and biological information in a high throughput manner. In this review, we will discuss recent applications of metabolomics to study the ecological effects of different environmental perturbations, including nanoparticles, pharmaceuticals and personal care products, pesticides, as well as the changes in natural compounds found in the environment with a focus on plant systems.

Expanding phytoremediation to the realms of known and unknown organic chemicals of concern

Recent advancements in analytical chemistry and data analyses via high-resolution mass spectrometry (HRMS) are evolving scientific understanding of the potential totality of organic chemical exposure and pollutant risk. This review addresses the importance of HRMS approaches, namely suspect screening and nontarget chemical analyses, to the realm of phytoremediation. These analytical approaches are not without caveats and constraints, but they provide an opportunity to understand in greater totality how plant-based technologies contribute, mitigate, and reduce organic chemical exposure across scales of experimental and system-level studies. These analytical tools can enlighten the complexity and efficacy of plant-contaminant system design and expand our understanding of biogenic and anthropogenic chemicals at work in phytoremediation systems. Advances in data analytics from biological sciences, such as metabolomics, are crucial to HRMS analysis. This review provides an overview of targeted, suspect screening, and nontarget HRMS approaches, summarizes the expanding knowledge of regulated and unregulated organic chemicals in the environment, addresses requisite HRMS instrumentation, analysis cost, uncertainty, and data processing techniques, and offers potential bridges of HRMS analyses to phytoremediation research and application.

Quantitative analysis and resolution of pharmaceuticals in the environment using multivariate curve resolution-alternating least squares (MCR-ALS)

The study presents the application of multivariate curve resolution alternating least squares (MCR-ALS) with a correlation constraint for simultaneous resolution and quantification of ketoprofen, naproxen, paracetamol and caffeine as target analytes and triclosan as an interfering component in different water samples using UV-Vis spectrophotometric data. A multivariate regression model using the partial least squares regression (PLSR) algorithm was developed and calculated. The MCR-ALS results were compared with the PLSR obtained results. Both models were validated on external sample sets and were applied to the analysis of real water samples. Both models showed comparable and satisfactory results with the relative error of prediction of real water samples in the range of 1.70-9.75 % and 1.64-9.43 % for MCR-ALS and PLSR, resp. The obtained results show the potential of MCR-ALS with correlation constraint to be applied for the determination of different pharmaceuticals in complex environmental matrices.

ROIMCR: a powerful analysis strategy for LC-MS metabolomic datasets

BACKGROUND

The analysis of LC-MS metabolomic datasets appears to be a challenging task in a wide range of disciplines since it demands the highly extensive processing of a vast amount of data. Different LC-MS data analysis packages have been developed in the last few years to facilitate this analysis. However, most of these strategies involve chromatographic alignment and peak shaping and often associate each "feature" (i.e., chromatographic peak) with a unique m/z measurement. Thus, the development of an alternative data analysis strategy that is applicable to most types of MS datasets and properly addresses these issues is still a challenge in the metabolomics field.

RESULTS

Here, we present an alternative approach called ROIMCR to: i) filter and compress massive LC-MS datasets while transforming their original structure into a data matrix of features without losing relevant information through the search of regions of interest (ROIs) in the m/z domain and ii) resolve compressed data to identify their contributing pure components without previous alignment or peak shaping by applying a Multivariate Curve Resolution-Alternating Least Squares (MCR-ALS) analysis. In this study, the basics of the ROIMCR method are presented in detail and a detailed description of its implementation is also provided. Data were analyzed using the MATLAB (The MathWorks, Inc., www.mathworks.com ) programming and computing environment. The application of the ROIMCR methodology is described in detail, with an example of LC-MS data generated in a lipidomic study and with other examples of recent applications.

CONCLUSIONS

The methodology presented here combines the benefits of data filtering and compression based on the searching of ROI features, without the loss of spectral accuracy. The method has the benefits of the application of the powerful MCR-ALS data resolution method without the necessity of performing chromatographic peak alignment or modelling. The presented method is a powerful alternative to other existing data analysis approaches that do not use the MCR-ALS method to resolve LC-MS data. The ROIMCR method also represents an improved strategy compared to the direct applications of the MCR-ALS method that use less-powerful data compression strategies such as binning and windowing. Overall, the strategy presented here confirms the usefulness of the ROIMCR chemometrics method for analyzing LC-MS untargeted metabolomics data.

An initial estimation method using cosine similarity for multivariate curve resolution: application to NMR spectra of chemical mixtures

Mixture spectra is decomposed into pure spectra without prior knowledge, and the MCR calculation refines the spectra and provides the concentrations.

Simultaneously targeted and untargeted multicomponent characterization of Erzhi Pill by offline two-dimensional liquid chromatography/quadrupole-Orbitrap mass spectrometry

Large-scale targeted and untargeted metabolites characterization can be achieved by feat of different liquid chromatography/mass spectrometry (LC-MS) platforms by multiple MS experiments or using data-independent acquisition followed by precursor-product ions matching based on certain algorithms. The resulting insufficiency in efficiency and availability greatly restricts the applicability of these strategies in large-scale profiling and identification of various metabolites. A strategy simultaneously enabling both the targeted and untargeted metabolites characterization is established on a Q Exactive hybrid quadrupole-Orbitrap mass spectrometer, by integrating precursor ions list-triggered data-dependent MS² acquisition (PIL/dd-MS²) of the targeted components and using the "If idle-pick others" (IIPO) function to induce untargeted metabolites fragmentation. A compounds-specific mass defect filter (MDF) algorithm is proposed as a method to generate the PIL. As a proof of concept, this strategy coupled with offline two-dimensional liquid chromatography (2D-LC) was applied to identify the multicomponents of a traditional Chinese medicine formula Erzhi Pill (EZP). A rigid MDF vehicle was elaborated by orthogonal screening of the integer mass and integer mass-dependent dynamic mass defects considering a variation of 20 ppm. The Full MS/dd-MS² method enabling PIL and IIPO exhibited better performance than Full MS/dd-MS² and Targeted SIM/dd-MS² (selected ion monitoring) in respect of the sensitivity in identifying the targeted components and the ability to characterize more untargeted ones. As a consequence, 270 components were separated from EZP, and 146 thereof were selectively characterized. In conclusion, it is a practical, multifaced strategy facilitating the in-depth metabolites profiling and characterization of complex herbal and biological samples.

High-resolution gas chromatography/mass spectrometry metabolomics of non-human primate serum

RATIONALE

Metabolomics analyses using gas chromatography/mass spectrometry (GC/MS)-based metabolomics are heavily impeded by the lack of high-resolution mass spectrometers and limited spectral libraries to complement the excellent chromatography that GC platforms offer, a challenge that is being addressed with the implementation of high-resolution (HR) platforms such as 1D-GC/Orbitrap-MS.

METHODS

We used serum samples from a non-human primate (NHP), a baboon (Papio hamadryas), with suitable quality controls to quantify the chemical space using an advanced HRMS platform for confident metabolite identification and robust quantification to assess the suitability of the platform for routine clinical metabolomics research. In a complementary approach, we also analyzed the same serum samples using two-dimensional gas chromatography/time-of-flight mass spectrometry (2D-GC/TOF-MS) for metabolite identification and quantification following established standard protocols.

RESULTS

Overall, the 2D-GC/TOF-MS (~5000 peaks per sample) and 1D-GC/Orbitrap-MS (~500 peaks per sample) analyses enabled identification and quantification of a total of 555 annotated metabolites from the NHP serum with a spectral similarity score Rsim  ≥ 900 and signal-to-noise (S/N) ratio of >25. A common set of 30 metabolites with HMDB and KEGG IDs was quantified in the serum samples by both platforms where 2D-GC/TOF-MS enabled quantification of a total 384 metabolites (118 HMDB IDs) and 1D-GC/Orbitrap-MS analysis quantification of a total 200 metabolites (47 HMDB IDs). Thus, roughly 30-70% of the peaks remain unidentified or un-annotated across both platforms.

CONCLUSIONS

Our study provides insights into the benefits and limitations of the use of a higher mass resolution and mass accuracy instrument for untargeted GC/MS-based metabolomics with multi-dimensional chromatography in future studies addressing clinical conditions or exposome studies.