Metabolomic- and Molecular Networking-Based Exploration of the Chemical Responses Induced in Citrus sinensis Leaves Inoculated with Xanthomonas citri.

Citrus canker, caused by the bacterium Xanthomonas citri subsp. citri (X. citri), is a plant disease affecting Citrus crops worldwide. However, little is known about defense compounds in Citrus. Here, we conducted a mass spectrometry-based metabolomic approach to obtain an overview of the chemical responses of Citrus leaves to X. citri infection. To facilitate result interpretation, the multivariate analyses were combined with molecular networking to identify biomarkers. Metabolite variations among untreated and X. citri-inoculated Citrus samples under greenhouse conditions highlighted induced defense biomarkers. Notably, the plant tryptophan metabolism pathway was activated, leading to the accumulation of N-methylated tryptamine derivatives. This finding was subsequently confirmed in symptomatic leaves in the field. Several tryptamine derivatives showed inhibitory effects in vitro against X. citri. This approach has enabled the identification of new chemically related biomarker groups and their dynamics in the response of Citrus leaves to Xanthomonas infection.

New insights into the conversion of electropherograms to the effective electrophoretic mobility scale.

CE-MS is increasingly gaining momentum as an analytical tool in metabolomics, due to its ability to obtain information about the most polar elements in biological samples. This has been helped by improvements of robustness in peak identification by means of mobility-scale representations of the electropherograms (mobilograms). As a necessary step toward facilitating the use of CE-MS for untargeted metabolomics data, the authors previously developed and introduced ROMANCE, a software automating mobilogram generation for large untargeted datasets through a simple and self-contained user interface. Herein, we introduce a new version of ROMANCE including new features such as compatibility with other types of data (targeted MS data and 2D UV-Vis absorption-like electropherograms), and the much needed additional flexibility in the transformation parameters (including field ramping and the use of secondary markers), more measurement conditions (depending on detection and integration modes), and most importantly tackling the issue of quantitative peak conversion. First, we present a review of the current theoretical framework with regard to peak characterization, and we develop new formulas for multiple marker peak area corrections, for anticipating peak position precision, and for assessing peak shape distortion. Then, the new version of the software is presented and validated experimentally. We contrast the multiple marker mobility transformations with previous results, finding increased peak position precision, and finally we showcase an application to actual untargeted metabolomics data.

Gaining Insights Into Metabolic Networks Using Chemometrics and Bioinformatics: Chronic Kidney Disease as a Clinical Model.

Because of its ability to generate biological hypotheses, metabolomics offers an innovative and promising approach in many fields, including clinical research. However, collecting specimens in this setting can be difficult to standardize, especially when groups of patients with different degrees of disease severity are considered. In addition, despite major technological advances, it remains challenging to measure all the compounds defining the metabolic network of a biological system. In this context, the characterization of samples based on several analytical setups is now recognized as an efficient strategy to improve the coverage of metabolic complexity. For this purpose, chemometrics proposes efficient methods to reduce the dimensionality of these complex datasets spread over several matrices, allowing the integration of different sources or structures of metabolic information. Bioinformatics databases and query tools designed to describe and explore metabolic network models offer extremely useful solutions for the contextualization of potential biomarker subsets, enabling mechanistic hypotheses to be considered rather than simple associations. In this study, network principal component analysis was used to investigate samples collected from three cohorts of patients including multiple stages of chronic kidney disease. Metabolic profiles were measured using a combination of four analytical setups involving different separation modes in liquid chromatography coupled to high resolution mass spectrometry. Based on the chemometric model, specific patterns of metabolites, such as N-acetyl amino acids, could be associated with the different subgroups of patients. Further investigation of the metabolic signatures carried out using genome-scale network modeling confirmed both tryptophan metabolism and nucleotide interconversion as relevant pathways potentially associated with disease severity. Metabolic modules composed of chemically adjacent or close compounds of biological relevance were further investigated using carbon transfer reaction paths. Overall, the proposed integrative data analysis strategy allowed deeper insights into the metabolic routes associated with different groups of patients to be gained. Because of their complementary role in the knowledge discovery process, the association of chemometrics and bioinformatics in a common workflow is therefore shown as an efficient methodology to gain meaningful insights in a clinical context.

Algorithms to optimize multi-column chromatographic separations of proteins.

The goal of this work was to provide a technical solution for the automated optimization of multi-column systems for protein separation and fractionation. Both algorithm and a software that can be downloaded are provided. In this algorithm, the length and order of the individual column segments can be considered. Various solutions are provided by the algorithm, including i) to obtain uniform peak distribution, ii) to park the different species at the inlet of the individual column segments, and iii) to elute all species as a single peak. Two representative examples are presented, showing the possibility to obtain uniform selectivity between monoclonal antibody (mAb) sub-units, and the on-column fractioning of intact mAbs.

Network principal component analysis: a versatile tool for the investigation of multigroup and multiblock datasets.

MOTIVATION: Complex data structures composed of different groups of observations and blocks of variables are increasingly collected in many domains, including metabolomics. Analysing these high-dimensional data constitutes a challenge, and the objective of this article is to present an original multivariate method capable of explicitly taking into account links between data tables when they involve the same observations and/or variables. For that purpose, an extension of standard principal component analysis called NetPCA was developed. RESULTS: The proposed algorithm was illustrated as an efficient solution for addressing complex multigroup and multiblock datasets. A case study involving the analysis of metabolomic data with different annotation levels and originating from a chronic kidney disease (CKD) study was used to highlight the different aspects and the additional outputs of the method compared to standard PCA. On the one hand, the model parameters allowed an efficient evaluation of each group's influence to be performed. On the other hand, the relative relevance of each block of variables to the model provided decisive information for an objective interpretation of the different metabolic annotation levels. AVAILABILITY AND IMPLEMENTATION: NetPCA is available as a Python package with NumPy dependencies. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Capillary Electrophoresis-Mass Spectrometry at Trial by Metabo-Ring: Effective Electrophoretic Mobility for Reproducible and Robust Compound Annotation.

Capillary zone electrophoresis-mass spectrometry (CE-MS) is a mature analytical tool for the efficient profiling of (highly) polar and ionizable compounds. However, the use of CE-MS in comparison to other separation techniques remains underrepresented in metabolomics, as this analytical approach is still perceived as technically challenging and less reproducible, notably for migration time. The latter is key for a reliable comparison of metabolic profiles and for unknown biomarker identification that is complementary to high resolution MS/MS. In this work, we present the results of a Metabo-ring trial involving 16 CE-MS platforms among 13 different laboratories spanning two continents. The goal was to assess the reproducibility and identification capability of CE-MS by employing effective electrophoretic mobility (µeff) as the key parameter in comparison to the relative migration time (RMT) approach. For this purpose, a representative cationic metabolite mixture in water, pretreated human plasma, and urine samples spiked with the same metabolite mixture were used and distributed for analysis by all laboratories. The µeff was determined for all metabolites spiked into each sample. The background electrolyte (BGE) was prepared and employed by each participating lab following the same protocol. All other parameters (capillary, interface, injection volume, voltage ramp, temperature, capillary conditioning, and rinsing procedure, etc.) were left to the discretion of the contributing laboratories. The results revealed that the reproducibility of the µeff for 20 out of the 21 model compounds was below 3.1% vs 10.9% for RMT, regardless of the huge heterogeneity in experimental conditions and platforms across the 13 laboratories. Overall, this Metabo-ring trial demonstrated that CE-MS is a viable and reproducible approach for metabolomics.

Implementation of liquid chromatography-high resolution mass spectrometry methods for untargeted metabolomic analyses of biological samples: A tutorial.

Untargeted metabolomics is now widely recognized as a useful tool for exploring metabolic changes taking place in biological systems under different conditions. By its nature, this is a highly interdisciplinary field of research, and mastering all of the steps comprised in the pipeline can be a challenging task, especially for those researchers new to the topic. In this tutorial, we aim to provide an overview of the most widely adopted methods of performing LC-HRMS-based untargeted metabolomics of biological samples. A detailed protocol is provided in the Supplementary Information for rapidly implementing a basic screening workflow in a laboratory setting. This tutorial covers experimental design, sample preparation and analysis, signal processing and data treatment, and, finally, data analysis and its biological interpretation. Each section is accompanied by up-to-date literature to guide readers through the preparation and optimization of such a workflow, as well as practical information for avoiding or fixing some of the most frequently encountered pitfalls.

Choosing an Optimal Sample Preparation in Caulobacter crescentus for Untargeted Metabolomics Approaches.

Untargeted metabolomics aims to provide a global picture of the metabolites present in the system under study. To this end, making a careful choice of sample preparation is mandatory to obtain reliable and reproducible biological information. In this study, eight different sample preparation techniques were evaluated using Caulobacter crescentus as a model for Gram-negative bacteria. Two cell retrieval systems, two quenching and extraction solvents, and two cell disruption procedures were combined in a full factorial experimental design. To fully exploit the multivariate structure of the generated data, the ANOVA multiblock orthogonal partial least squares (AMOPLS) algorithm was employed to decompose the contribution of each factor studied and their potential interactions for a set of annotated metabolites. All main effects of the factors studied were found to have a significant contribution on the total observed variability. Cell retrieval, quenching and extraction solvent, and cell disrupting mechanism accounted respectively for 27.6%, 8.4%, and 7.0% of the total variability. The reproducibility and metabolome coverage of the sample preparation procedures were then compared and evaluated in terms of relative standard deviation (RSD) on the area for the detected metabolites. The protocol showing the best performance in terms of recovery, versatility, and variability was centrifugation for cell retrieval, using MeOH:H2O (8:2) as quenching and extraction solvent, and freeze-thaw cycles as the cell disrupting mechanism.

Impact of particle size gradients on the apparent efficiency of chromatographic columns.

In this paper, the benefits of using columns packed with particles of decreasing size (particle size gradient) in liquid chromatography was investigated from a theoretical point of view. It is indeed well known that such columns may be useful in gradient elution, since the decrease of particle size along the chromatographic column can provide extra peak focusing effect. In the present contribution, several parameters (i.e., mobile phase gradient steepness, retention times and operating pressures) were considered and the kinetic performance of various types of columns packed with particle size gradient were evaluated. In the best case, about 15-20% gain in efficiency can be expected at a given retention time when utilizing a particle size gradient, compared to constant particle size. Conversely, when fixing efficiency, the analysis time can be decreased by about 15% with an optimal particle size gradient. However, it is also important to keep in mind that a too large a particle size gradient can result in lower efficiencies than a column packed with monodisperse packing. We have introduced the gd value, which is a dimensionless measure of the particle size gradient steepness that measures the relative variation of particle diameter throughout the column with respect to the average. We finally observed that gd=0.3-0.4 provides the highest gain under practically useful conditions.

DynaStI: A Dynamic Retention Time Database for Steroidomics.

: Steroidomics studies face the challenge of separating analytical compounds with very similar structures (i.e., isomers). Liquid chromatography (LC) is commonly used to this end, but the shared core structure of this family of compounds compromises effective separations among the numerous chemical analytes with comparable physico-chemical properties. Careful tuning of the mobile phase gradient and an appropriate choice of the stationary phase can be used to overcome this problem, in turn modifying the retention times in different ways for each compound. In the usual workflow, this approach is suboptimal for the annotation of features based on retention times since it requires characterizing a library of known compounds for every fine-tuned configuration. We introduce a software solution, DynaStI, that is capable of annotating liquid chromatography-mass spectrometry (LC-MS) features by dynamically generating the retention times from a database containing intrinsic properties of a library of metabolites. DynaStI uses the well-established linear solvent strength (LSS) model for reversed-phase LC. Given a list of LC-MS features and some characteristics of the LC setup, this software computes the corresponding retention times for the internal database and then annotates the features using the exact masses with predicted retention times at the working conditions. DynaStI (https://dynasti.vital-it.ch) is able to automatically calibrate its predictions to compensate for deviations in the input parameters. The database also includes identification and structural information for each annotation, such as IUPAC name, CAS number, SMILES string, metabolic pathways, and links to external metabolomic or lipidomic databases.

A scoring approach for multi-platform acquisition in metabolomics.

Since the ultimate goal of untargeted metabolomics is the analysis of the broadest possible range of metabolites, some new metrics have to be used by researchers to evaluate and select different analytical strategies when multi-platform analyses are considered. In this context, we aimed at developing a scoring approach allowing to compare the performance of different LC-MS conditions for metabolomics studies. By taking into account both chromatographic and MS attributes of the analytes' peaks (i.e. retention, signal-to-noise ratio, peak intensity and shape), the newly proposed score reflects the potential of a set of LC-MS operating conditions to provide useful analytical information for a given compound. A chemical library containing 597 metabolites was used as a benchmark to apply this approach on two RPLC and three HILIC methods hyphenated to high resolution mass spectrometry (HRMS) in positive and negative ionization modes. The scores not only allowed to evaluate each analytical platform, but also to optimize the number of analytical methods needed for the analysis of metabolomics samples. As a result, the most informative combination of three LC methods and ionization modes was found, leading to a coverage of nearly 95% of the detected compounds. It was therefore demonstrated that the overall performance reached with three selected methods was almost equivalent to the performance reached when five LC-MS conditions were used.

Apparent efficiency of serially coupled columns in gradient elution liquid chromatography: Extension to the combination of any column formats.

It was shown earlier that in serially coupled column systems, not only retention (selectivity) but peak width as well can change and the apparent peak width depends on the order of the columns. A generic equation has been developed to describe the apparent efficiency of serially coupled columns in gradient elution liquid chromatography mode. In a previous work, the theoretical basis for a two-column system was developed and by extending and generalizing this model, it is now possible to predict the apparent peak width for the combination of any column segments in any formats (columns possessing various efficiencies and lengths). (Identical retention and selectivity have been assumed to develop this model.) At the end, column order can be a useful parameter to set the desired efficiency of coupled systems.

Apparent efficiency of serially coupled columns in isocratic and gradient elution modes.

The goal of this work was to understand the variation of apparent efficiency when serially coupling columns with identical stationary phase chemistries, but with differences in their kinetic performance. For this purpose, a mathematical treatment was developed both for isocratic and gradient modes to assess the change in plate numbers and peak widths when coupling arbitrary several columns. To validate the theory, experiments were also carried out using various mixtures of compounds, on columns packed with different particle sizes, to mimic highly efficient (new, not used) and poorly efficient columns (used one with many injections). Excellent agreement was found between measured and calculated peak widths. The average error in prediction was about 5% (which may be explained by the additional volume of the coupling tubes). In isocratic mode, the plate numbers are not additive when the coupled columns possess different efficiencies, and a limiting plate count value can be calculated depending on the efficiency and length of the individual columns. Theoretical efficiency limit can also be determined assuming one column in the row with infinite efficiency. In gradient elution mode, the columns' order has a role (non-symmetrical system). When the last column has high enough efficiency, the gradient band compression effect may outperform the competing band broadening caused by dispersive and diffusive processes (peak sharpening). Therefore, in gradient mode, the columns should generally be sequentially placed according to their increasing efficiency.

ROMANCE: A new software tool to improve data robustness and feature identification in CE-MS metabolomics.

The use of capillary electrophoresis coupled to mass spectrometry (CE-MS) in metabolomics remains an oddity compared to the widely adopted use of liquid chromatography. This technique is traditionally regarded as lacking the reproducibility to adequately identify metabolites by their migration times. The major reason is the variability of the velocity of the background electrolyte, mainly coming from shifts in the magnitude of the electroosmotic flow and from the suction caused by electrospray interfaces. The use of the effective electrophoretic mobility is one solution to overcome this issue as it is a characteristic feature of each compound. To date, such an approach has not been applied to metabolomics due to the complexity and size of CE-MS data obtained in such studies. In this paper, ROMANCE (RObust Metabolomic Analysis with Normalized CE) is introduced as a new software for CE-MS-based metabolomics. It allows the automated conversion of batches of CE-MS files with minimal user intervention. ROMANCE converts the x-axis of each MS file from the time into the effective mobility scale and the resulting files are already pseudo-aligned, present normalized peak areas and improved reproducibility, and can eventually follow existing metabolomic workflows. The software was developed in Scala, so it is multi-platform and computationally-efficient. It is available for download under a CC license. In this work, the versatility of ROMANCE was demonstrated by using data obtained in the same and in different laboratories, as well as its application to the analysis of human plasma samples.

Evolution in the design of a low sheath-flow interface for CE-MS and application to biological samples.

Although several interfaces for CE-MS hyphenation are commercially available, the development of new versatile, simple and yet efficient and sensitive alternatives remains an important field of research. In a previous work, a simple low sheath-flow interface was developed from inexpensive parts. This interface features a design easy to build, maintain, and adapt to particular needs. The present work introduces an improved design of the previous interface. By reducing the diameter of the separation capillary and the emitter, a smaller Taylor cone is spontaneously formed, minimizing the zone dispersion while the analytes go through the interface and leading to less peak broadening associated to the ESI process. Numerical modeling allowed studying the mixing and diffusion processes taking place in the Taylor cone. The analytical performance of this new interface was tested with pharmaceutically relevant molecules and endogenous metabolites. The interface was eventually applied to the analysis of neural cell culture samples, allowing the identification of a panel of neurotransmission-related molecules. An excellent migration time repeatability was obtained (intra-day RSD <0.5% for most compounds, and <3.0% for inter-day precision). Most metabolites showed S/N ratios >10 with an injected volume of 6.7 nL of biological extract.

Evaluation of a new low sheath-flow interface for CE-MS.

The current trend for increasing technical complexity in the field of CE-ESI-MS interfaces has incited for more accessible alternatives. In this work, a simple low sheath-flow ESI interface operating in the submicroliter nanospray regime without nebulizing gas assistance was evaluated. The use of sheath liquid enabled improving the ionization of the analytes, while the absence of nebulizing gas minimized sample dilution and loss of efficiency. After a rapid qualification, the effect of main operational parameters such as sheath liquid composition and flow rate, working distance and ESI potential was studied. Simulation of the mixing processes inside the Taylor cone proved its size to be of utmost importance in band broadening processes. As a proof of concept, the interface was eventually applied to a set of representative basic drugs analyzed by CE-TOF/MS. Limits of detection reached the 25-100 ppb range with suitable robustness and repeatability results. This design has demonstrated good performance while being simple and accessible to the user.