Ultra-high-performance liquid chromatography high-resolution mass spectrometry variants for metabolomics research

Characterization and optimization of magnetic molecularly imprinted nanofibers for determination of sunitinib in human serum and capsule samples

This article reports a spectrofluorometric method for the determination of sunitinib (STB) drug based on molecularly imprinted nanofibers fabricated by the electrospinning method and modified by magnetic nanoparticles as sorbent. The characterization of magnetic molecularly imprinted nanofibers (MMINs) was carried out using X-ray diffraction (XRD), scanning electron microscope (SEM), and transmission electron microscopy (TEM), which confirmed the successful synthesis of MMINs with well-distributed magnetite nanoparticles. Drug adsorption and desorption were optimized and important parameters such as sample pH, nanofiber mass, adsorption and desorption time, eluent solvent and sample volume were analyzed. The results demonstrated that the MMINs act as a selective sorbent for STB and can be readily collected through an external magnetic field. Methanol was used as the best eluent solvent for STB desorption from MNIN. A linear correlation was observed between the STB concentrations and fluorescence intensities in the range of 0.01-15.0 mg L-1. The detection limit for this method was 0.002 mg L-1. The relative standard deviation (RSD) of 2.6 % for 1.0 mg L-1 and 1.1 % for 10 mg L-1 of STB (n = 3) were obtained, which indicates that the developed method is precise in determining STB. Human serum and capsule analysis show the applicability of the proposed sensor for real samples.

Simultaneous detection of multiple aging characteristic components in oil-paper insulation using sensitive Raman technology and microfluidics

The aging characteristic components of oil-paper insulation reflect the aging status of the power equipment. In this study, we designed a novel microfluidic chip capable of automatic and rapid extraction of aging components from insulating oil. Combined with Raman spectroscopy technology, it enables simultaneous detection of various aging components. By optimizing the microfluidic chip structural and adopting an optical window encapsulation, it eliminates interference from the Polydimethylsiloxane (PDMS). Measurements and analyses were carried out on multiple oil samples containing three aging products (furfural, acetone, and methanol). The results indicate that this novel microfluidic chip facilitates simultaneous detection of multiple components, significantly improving the detection sensitivity of complex oil. The detection limits for furfural, acetone, and methanol in insulating oil are 0.43 mg/L, 1.04 mg/L, and 2.31 mg/L, respectively. This provides a new approach for the online detection of oil-paper insulation equipment.

Machine learning powered CN-coordinated cobalt nanoparticles embedded cellulosic nanofibers to assess meat quality via clenbuterol monitoring

The World Anti-Doping Agency (WADA) has prohibited the use of clenbuterol (CLN) because it induces anabolic muscle growth while potentially causing adverse effects such as palpitations, anxiety, and muscle tremors. Thus, it is vital to assess meat quality because, athletes might have positive test for CLN even after consuming very low quantity of CLN contaminated meat. Numerous materials applied for CLN monitoring faced potential challenges like sluggish ion transport, non-uniform ion/molecule movement, and inadequate electrode surface binding. To overcome these shortcomings, herein we engineered bimetallic zeolitic imidazole framework (BM-ZIF) derived N-doped porous carbon embedded Co nanoparticles (CN-CoNPs), dispersed on conductive cellulose acetate-polyaniline (CP) electrospun nanofibers for sensitive electrochemical monitoring of CLN. Interestingly, the smartly designed CN-CoNPs wrapped CP (CN-CoNPs-CP) electrospun nanofibers offers rapid diffusion of CLN molecules to the sensing interface through amine and imine groups of CP, thus minimizing the inhomogeneous ion transportation and inadequate electrode surface binding. Additionally, to synchronize experiments, machine learning (ML) algorithms were applied to optimize, predict, and validate voltametric current responses. The ML-trained sensor demonstrated high selectivity, even amidst interfering substances, with notable sensitivity (4.7527 μA/μM/cm2), a broad linear range (0.002-8 μM), and a low limit of detection (1.14 nM). Furthermore, the electrode exhibited robust stability, retaining 98.07% of its initial current over a 12-h period. This ML-powered sensing approach was successfully employed to evaluate meat quality in terms of CLN level. To the best of our knowledge, this is the first study of using ML powered system for electrochemical sensing of CLN.

The marriage between stable isotope ecology and plant metabolomics - new perspectives for metabolic flux analysis and the interpretation of ecological archives

Even though they share many thematical overlaps, plant metabolomics and stable isotope ecology have been rather separate fields mainly due to different mass spectrometry demands. New high-resolution bioanalytical mass spectrometers are now not only offering high-throughput metabolite identification but are also suitable for compound- and intramolecular position-specific isotope analysis in the natural isotope abundance range. In plant metabolomics, label-free metabolic pathway and metabolic flux analysis might become possible when applying this new technology. This is because changes in the commitment of substrates to particular metabolic pathways and the activation or deactivation of others alter enzyme-specific isotope effects. This leads to differences in intramolecular and compound-specific isotope compositions. In plant isotope ecology, position-specific isotope analysis in plant archives informed by metabolic pathway analysis could be used to reconstruct and separate environmental impacts on complex metabolic processes. A technology-driven linkage between the two disciplines could allow us to extract information on environment-metabolism interaction from plant archives such as tree rings but also within ecosystems. This would contribute to a holistic understanding of how plants react to environmental drivers, thus also providing helpful information on the trajectories of the vegetation under the conditions to come.

Toward the apoplast metabolome: Establishing a leaf apoplast collection approach suitable for metabolomics analysis

The leaf apoplast contains several compounds that play important roles in the regulation of different physiological processes in plants. However, this compartment has been neglected in several experimental and modelling studies, which is mostly associated to the difficulty to collect apoplast washing fluid (AWF) in sufficient amount for metabolomics analysis and as free as possible from symplastic contamination. Here, we established an approach based in an infiltration-centrifugation technique that use little leaf material but allows sufficient AWF collection for gas chromatography mass spectrometry (GC-MS)-based metabolomics analysis in both tobacco and Arabidopsis. Up to 54 metabolites were annotated in leaf and apoplast samples from both species using either 20% (v/v) methanol (20% MeOH) or distilled deionized water (ddH2O) as infiltration fluids. The use of 20% MeOH increased the yield of the AWF collected but also the level of symplastic contamination, especially in Arabidopsis. We propose a correction factor and recommend the use of multiple markers such as MDH activity, protein content and conductivity measurements to verify the level of symplastic contamination in MeOH-based protocols. Neither the concentration of sugars nor the level of primary metabolites differed between apoplast samples extracted with ddH2O or 20% MeOH. This indicates that ddH2O can be preferentially used, given that it is a non-toxic and highly accessible infiltration fluid. The infiltration-centrifugation-based approach established here uses little leaf material and ddH2O as infiltration fluid, being suitable for GC-MS-based metabolomics analysis in tobacco and Arabidopsis, with great possibility to be extended for other plant species and tissues.

Promises and challenges of crop translational genomics

Crop translational genomics applies breeding techniques based on genomic datasets to improve crops. Technological breakthroughs in the past ten years have made it possible to sequence the genomes of increasing numbers of crop varieties and have assisted in the genetic dissection of crop performance. However, translating research findings to breeding applications remains challenging. Here we review recent progress and future prospects for crop translational genomics in bringing results from the laboratory to the field. Genetic mapping, genomic selection and sequence-assisted characterization and deployment of plant genetic resources utilize rapid genotyping of large populations. These approaches have all had an impact on breeding for qualitative traits, where single genes with large phenotypic effects exert their influence. Characterization of the complex genetic architectures that underlie quantitative traits such as yield and flowering time, especially in newly domesticated crops, will require further basic research, including research into regulation and interactions of genes and the integration of genomic approaches and high-throughput phenotyping, before targeted interventions can be designed. Future priorities for translation include supporting genomics-assisted breeding in low-income countries and adaptation of crops to changing environments.

Multi-omic analysis identifies metabolic biomarkers for the early detection of breast cancer and therapeutic response prediction

Reliable blood-based tests for identifying early-stage breast cancer remain elusive. Employing single-cell transcriptomic sequencing analysis, we illustrate a close correlation between nucleotide metabolism in the breast cancer and activation of regulatory T cells (Tregs) in the tumor microenvironment, which shows distinctions between subtypes of patients with triple-negative breast cancer (TNBC) and non-TNBC, and is likely to impact cancer prognosis through the A2AR-Treg pathway. Combining machine learning with absolute quantitative metabolomics, we have established an effective approach to the early detection of breast cancer, utilizing a four-metabolite panel including inosine and uridine. This metabolomics study, involving 1111 participants, demonstrates high accuracy across the training, test, and independent validation cohorts. Inosine and uridine prove predictive of the response to neoadjuvant chemotherapy (NAC) in patients with TNBC. This study deepens our understanding of nucleotide metabolism in breast cancer development and introduces a promising non-invasive method for early breast cancer detection and predicting NAC response in patients with TNBC.

LC-MS analysis of chiral amino acids in human urine reveals D-amino acids as potential biomarkers for colorectal cancer

Colorectal cancer (CRC) is a common malignant tumor in the gastrointestinal tract. Changes in amino acid metabolites have been implicated in tumorigenesis and disease progression. Biomarkers on the basis of chiral amino acids, especially D-amino acids, have not been established for early diagnosis of CRC. Quantification of chiral amino acids, especially very low concentrations of endogenous D-amino acids, is technically challenging. We report here the quantification of L- and D-amino acids in urine samples collected from 115 CRC patients and 155 healthy volunteers, using an improved method. The method of chiral labeling, liquid chromatography, and tandem mass spectrometry enabled separation and detection of 28 amino acids (14 L-amino acids, 13 D-amino acids and Gly). Orthogonal partial least squares discriminant analysis identified 14 targeted variables among these chiral amino acids that distinguished the CRC from the healthy controls. Binary logistic regression analysis revealed that D-α-aminobutyric acid (D-AABA), L-alanine (L-Ala), D-alanine (D-Ala), D-glutamine (D-Gln) and D-serine (D-Ser) could be potential biomarkers for CRC. A receiver operating characteristic curve analysis of combined multi-variables contributed to an area under the curve (AUC) of 0.995 with 98.3 % sensitivity and 96.8 % specificity. A model constructed with D-AABA, D-Ala, D-Gln, and D-Ser achieved an AUC of 0.988, indicating important contributions of D-amino acids to the association with CRC. Further analysis also demonstrated that the metabolic aberration was associated with age and the development of CRC, D-methionine (D-Met) was decreased in CRC patients with age over 50, and D/L-Gln in patients at stage IV was higher than patients at stage I. This study provides the signature of D-amino acids in urine samples and offers a promising strategy for developing non-invasive diagnosis of CRC.

Analyzing the differences and correlations between key metabolites and dominant microorganisms in different regions of Daqu based on off-target metabolomics and high-throughput sequencing

Daqu is usually produced in an open environment, which makes its quality unstable. The microbial community of Daqu largely determines its quality. Therefore, in order to improve the fermentation characteristics of Daqu, samples were collected from Jinsha County (MT1), Xishui County (MT2), and Maotai Town (MT3) in Guizhou Province to explore the microbial diversity of Daqu and its impact on Daqu's metabolites.Off-target metabolomics was used to detect metabolites, and high-throughput sequencing was used to detect microorganisms. Metabolomics results revealed that MT1 and MT2 had the highest relative fatty acid content, whereas MT3 had the highest organooxygen compound content. Principal component analysis and partial least squares discriminant analysis revealed significant differences in the metabolites among the three groups, followed by the identification of 33 differential metabolites (key metabolites) filtered using the criteria of variable importance in projection >1 and p < 0.001. According to the microbiological results, Proteobacteria was the dominant bacteria phylum in three samples. Gammaproteobacteria was the dominant class in MT1(26.84 %) and MT2(36.54 %), MT3 is Alphaproteobacteria(25.66 %). And Caulobacteraceae was the dominant family per the abundance analysis, MTI was 24.32 %, MT2 and MT3 were 33.71 % and 24.40 % respectively. Three samples dominant fungi phylum were Ascomycota, and dominant fungi family were Thermoascaceae. Pseudomonas showed a significant positive connection with various fatty acyls, according to correlation analyses between dominant microorganisms (genus level) and key metabolites. Fatty acyls and Thermomyces showed a positive correlation, but Thermoascus had the reverse relation. These findings offer a theoretical framework for future studies on the impact of metabolites on Baijiu quality during fermentation.

Validation of two LCHRMS methods for large-scale untargeted metabolomics of serum samples: Strategy to establish method fitness-for-purpose

Untargeted metabolomics by LCHRMS is a powerful tool to enhance our knowledge of pathophysiological processes. Whereas validation of a bioanalytical method is customary in most analytical chemistry fields, it is rarely performed for untargeted metabolomics. This study aimed to establish and validate an analytical platform for a long-term, clinical metabolomics study. Sample preparation was performed with an automated liquid handler and four analytical methods were developed and evaluated. The validation study spanned three batches with twelve runs using individual serum samples and various quality control samples. Data was acquired with untargeted acquisition and only metabolites identified at level 1 were evaluated. Validation parameters were set to evaluate key performance metrics relevant for the intended application: reproducibility, repeatability, stability, and identification selectivity, emphasizing dataset intrinsic variance. Concordance of semi-quantitative results between methods was evaluated to identify potential bias. Spearman rank correlation coefficients (rs) were calculated from individual serum samples. Of the four methods tested, two were selected for validation. A total of 47 and 55 metabolites (RPLC-ESI+- and HILIC-ESI--HRMS, respectively) met specified validation criteria. Quality assurance involved system suitability testing, sample release, run release, and batch release. The median repeatability and within-run reproducibility as coefficient of variation% for metabolites that passed validation on RPLC-ESI+- and HILIC-ESI--HRMS were 4.5 and 4.6, and 1.5 and 3.8, respectively. Metabolites that passed validation on RPLC-ESI+-HRMS had a median D-ratio of 1.91, and 89 % showed good signal intensity after ten-fold dilution. The corresponding numbers for metabolites with the HILIC-ESI--HRMS method was 1.45 and 45 %, respectively. The rs median ({range}) for metabolites that passed validation on RPLC-ESI+- was 0.93 (N = 9 {0.69-0.98}) and on HILIC-ESI--HRMS was 0.93 (N = 22 {0.55-1.00}). The validated methods proved fit-for-purpose and the laboratory thus demonstrated its capability to produce reliable results for a large-scale, untargeted metabolomics study. This validation not only bolsters the reliability of the assays but also significantly enhances the impact and credibility of the hypotheses generated from the studies. Therefore, this validation study serves as a benchmark in the documentation of untargeted metabolomics, potentially guiding future endeavors in the field.

Techniques, Databases and Software Used for Studying Polar Metabolites and Lipids of Gastrointestinal Parasites

Gastrointestinal parasites (GIPs) are organisms known to have coevolved for millennia with their mammalian hosts. These parasites produce small molecules, peptides, and proteins to evade or fight their hosts' immune systems and also to protect their host for their own survival/coexistence. The small molecules include polar compounds, amino acids, lipids, and carbohydrates. Metabolomics and lipidomics are emerging fields of research that have recently been applied to study helminth infections, host-parasite interactions and biochemicals of GIPs. This review comprehensively discusses metabolomics and lipidomics studies of the small molecules of GIPs, providing insights into the available tools and techniques, databases, and analytical software. Most metabolomics and lipidomics investigations employed LC-MS, MS or MS/MS, NMR, or a combination thereof. Recent advancements in artificial intelligence (AI)-assisted software tools and databases have propelled parasitomics forward, offering new avenues to explore host-parasite interactions, immunomodulation, and the intricacies of parasitism. As our understanding of AI technologies and their utilisation continue to expand, it promises to unveil novel perspectives and enrich the knowledge of these complex host-parasite relationships.

Spatio-temporal plant hormonomics: from tissue to subcellular resolution

Due to technological advances in mass spectrometry, significant progress has been achieved recently in plant hormone research. Nowadays, plant hormonomics is well established as a fully integrated scientific field focused on the analysis of phytohormones, mainly on their isolation, identification, and spatiotemporal quantification in plants. This review represents a comprehensive meta-study of the advances in the phytohormone analysis by mass spectrometry over the past decade. To address current trends and future perspectives, Web of Science data were systematically collected and key features such as mass spectrometry-based analyses were evaluated using multivariate data analysis methods. Our findings showed that plant hormonomics is currently divided into targeted and untargeted approaches. Both aim to miniaturize the sample, allowing high-resolution quantification to be covered in plant organs as well as subcellular compartments. Therefore, we can study plant hormone biosynthesis, metabolism, and signalling at a spatio-temporal resolution. Moreover, this trend has recently been accelerated by technological advances such as fluorescence-activated cell sorting or mass spectrometry imaging.

Advances in mass spectrometry imaging for plant metabolomics-Expanding the analytical toolbox

Mass spectrometry imaging (MSI) has become increasingly popular in plant science due to its ability to characterize complex chemical, spatial, and temporal aspects of plant metabolism. Over the past decade, as the emerging and unique features of various MSI techniques have continued to support new discoveries in studies of plant metabolism closely associated with various aspects of plant function and physiology, spatial metabolomics based on MSI techniques has positioned it at the forefront of plant metabolic studies, providing the opportunity for far higher resolution than was previously available. Despite these efforts, profound challenges at the levels of spatial resolution, sensitivity, quantitative ability, chemical confidence, isomer discrimination, and spatial multi-omics integration, undoubtedly remain. In this Perspective, we provide a contemporary overview of the emergent MSI techniques widely used in the plant sciences, with particular emphasis on recent advances in methodological breakthroughs. Having established the detailed context of MSI, we outline both the golden opportunities and key challenges currently facing plant metabolomics, presenting our vision as to how the enormous potential of MSI technologies will contribute to progress in plant science in the coming years.

Mathematical model of ion chronogram from in-tube solid-phase microextraction device coupled with mass spectrometry and optimization framework

A mathematical description and experimental outputs exhibited that an ion chronogram from an in-tube solid-phase microextraction (SPME) device linked with mass spectrometry (in-tube-SPME-MS) generally appears as a right-skew unimodal signal with a heavy right tail. Analogous to liquid chromatography coupled with mass spectrometry (LC-MS), in-tube-SPME-MS can utilize the area under its produced ion chronogram for regression analysis and has been shown to be a potential approach for fast quantification of analyte. Different level of unimodity of signal in the ion chronogram could positively or negatively affect the choice of the area used for quantification and finally impact on analysis sensitivity and time efficiency of in-tube-SPME-MS. In the paper, we showed that different in-tube SPME design choices and elution experimental setups produce ion chronograms with controllable varying unimodal peak shape patterns. An improved mathematical model was built based on the plate theory of chromatography and the Van Deemter equation to quantitatively describe the elution process from in-tube-SPME device. A computer simulation was implemented to predict ion chronograms and the results were compared with experimental ion chronograms to show the effectiveness of the model. An optimization framework was further presented based on the model to identify optimal device designs (length and diameter of device) and experimental parameters (flow rate) to track targeted ion chronograms with "desired" peak shape patterns. Empirical elution experiments with the in-tube SPME devices adopting optimized geometric parameters and optimal experimental setups confirmed the consistency between the experimental ion chronograms and the numerical simulations to a certain level.

Retention time dataset for heterogeneous molecules in reversed-phase liquid chromatography

Quantitative structure-property relationships have been extensively studied in the field of predicting retention times in liquid chromatography (LC). However, making transferable predictions is inherently complex because retention times are influenced by both the structure of the molecule and the chromatographic method used. Despite decades of development and numerous published machine learning models, the practical application of predicting small molecule retention time remains limited. The resulting models are typically limited to specific chromatographic conditions and the molecules used in their training and evaluation. Here, we have developed a comprehensive dataset comprising over 10,000 experimental retention times. These times were derived from 30 different reversed-phase liquid chromatography methods and pertain to a collection of 343 small molecules representing a wide range of chemical structures. These chromatographic methods encompass common LC setups for studying the retention behavior of small molecules. They offer a wide range of examples for modeling retention time with different LC setups.

Analysis types and quantification methods applied in UHPLC-MS metabolomics research: a tutorial

BACKGROUND

Different types of analytical methods, with different characteristics, are applied in metabolomics and lipidomics research and include untargeted, targeted and semi-targeted methods. Ultra High Performance Liquid Chromatography-Mass Spectrometry is one of the most frequently applied measurement instruments in metabolomics because of its ability to detect a large number of water-soluble and lipid metabolites over a wide range of concentrations in short analysis times. Methods applied for the detection and quantification of metabolites differ and can either report a (normalised) peak area or an absolute concentration.

AIM OF REVIEW

In this tutorial we aim to (1) define similarities and differences between different analytical approaches applied in metabolomics and (2) define how amounts or absolute concentrations of endogenous metabolites can be determined together with the advantages and limitations of each approach in relation to the accuracy and precision when concentrations are reported.

KEY SCIENTIFIC CONCEPTS OF REVIEW

The pre-analysis knowledge of metabolites to be targeted, the requirement for (normalised) peak responses or absolute concentrations to be reported and the number of metabolites to be reported define whether an untargeted, targeted or semi-targeted method is applied. Fully untargeted methods can only provide (normalised) peak responses and fold changes which can be reported even when the structural identity of the metabolite is not known. Targeted methods, where the analytes are known prior to the analysis, can also report fold changes. Semi-targeted methods apply a mix of characteristics of both untargeted and targeted assays. For the reporting of absolute concentrations of metabolites, the analytes are not only predefined but optimized analytical methods should be developed and validated for each analyte so that the accuracy and precision of concentration data collected for biological samples can be reported as fit for purpose and be reviewed by the scientific community.

Application of Metabolomics across the Spectrum of Pulmonary and Critical Care Medicine

In recent years, metabolomics, the systematic study of small-molecule metabolites in biological samples, has yielded fresh insights into the molecular determinants of pulmonary diseases and critical illness. The purpose of this article is to orient the reader to this emerging field by discussing the fundamental tenets underlying metabolomics research, the tools and techniques that serve as foundational methodologies, and the various statistical approaches to analysis of metabolomics datasets. We present several examples of metabolomics applied to pulmonary and critical care medicine to illustrate the potential of this avenue of research to deepen our understanding of pathophysiology. We conclude by reviewing recent advances in the field and future research directions that stand to further the goal of personalizing medicine to improve patient care.

Evaluation and comparison of liquid chromatography/high-resolution mass spectrometry platforms for the separation and characterization of ginsenosides from the leaves of Panax ginseng

The measurement of data repeatability in small-molecule metabolites acquired within and among different liquid chromatography-mass spectrometry (LC-MS) platforms is crucial for data sharing or data transfer in natural products research. This work was designed to investigate and evaluate the separation and detection performance of three commercial high-resolution LC-MS platforms (e.g., Agilent 6550 QTOF, Waters Vion IM-QTOF, and Thermo Scientific Orbitrap Exploris 120) using 68 ginsenoside references and the extract of Panax ginseng leaf. The retention time (tR), measured on these three platforms (under the same chromatography condition), showed good stability in different concentration tests, and within/among different instruments for both intra-day and inter-day precision examinations. Correlation in tR of ginsenosides was also highly determined on these three platforms. In spite of the different mass analyzers involved, these three platforms gave the accurate mass determination ability, especially enhanced resolution gained because of the ion mobility (IM) separation facilitated by IM-quadrupole time-of-flight. The current study has systematically evaluated the separation and MS detection performance enabled by three high-resolution LC-MS platforms taking ginsenosides as the template, and the reported findings can benefit the researchers for the selection of analytical platforms and the purpose of data sharing or data transfer.

Metabolites and metabolic pathway analysis of sulfadimidine in carp (Cyprinus carpio) based on UHPLC-Q-orbitrap HRMS

Sulfadimidine (SM2) is an N-substituted derivative of p-aminobenzenesulfonyl structure. This study aimed to analyze the metabolism of SM2 in carp (Cyprinus carpio). The carps were fed with SM2 at a dose of 200 mg/(kg · bw) and then killed. The blood, muscle, liver, kidney, gill, other guts, and carp aquaculture water samples were collected. The UHPLC-Q-Exactive Plus Orbitrap-MS was adopted for determining the metabolites of SM2 in the aforementioned samples. Twelve metabolites, which were divided into metabolites in vivo and metabolites in vitro, were identified using Compound Discoverer software. The metabolic pathways in vivo of SM2 in carp included acetylation, hydroxylation, glucoside conjugation, glycine conjugation, carboxylation, glucuronide conjugation, reduction, and methylation. The metabolic pathways in vitro included oxidation and acetylation. This study clarified the metabolites and metabolic pathways of SM2 in carp and provided a reference for further pharmacodynamic evaluation and use in aquaculture.

Metabolomics and Microbial Metabolism: Toward a Systematic Understanding

Over the past decades, our understanding of microbial metabolism has increased dramatically. Metabolomics, a family of techniques that are used to measure the quantities of small molecules in biological samples, has been central to these efforts. Advances in analytical chemistry have made it possible to measure the relative and absolute concentrations of more and more compounds with increasing levels of certainty. In this review, we highlight how metabolomics has contributed to understanding microbial metabolism and in what ways it can still be deployed to expand our systematic understanding of metabolism. To that end, we explain how metabolomics was used to (a) characterize network topologies of metabolism and its regulation networks, (b) elucidate the control of metabolic function, and (c) understand the molecular basis of higher-order phenomena. We also discuss areas of inquiry where technological advances should continue to increase the impact of metabolomics, as well as areas where our understanding is bottlenecked by other factors such as the availability of statistical and modeling frameworks that can extract biological meaning from metabolomics data.

Optimization and validation of metabolomics methods for feline urine and serum towards application in veterinary medicine

BACKGROUND

Metabolomics is an emerging and powerful technology that offers a comprehensive view of an organism's physiological status. Although widely applied in human medicine, it is only recently making its introduction in veterinary medicine. As a result, validated metabolomics protocols in feline medicine are lacking at the moment. Since biological interpretation of metabolomics data can be misled by the extraction method used, species and matrix-specific optimized and validated metabolomic protocols are sorely needed.

RESULTS

Systematic optimization was performed using fractional factorial experiments for both serum (n = 57) and urine (n = 24), evaluating dilution for both matrices, and aliquot and solvent volume, protein precipitation time and temperature for serum. For the targeted (n = 76) and untargeted (n = 1949) validation of serum respectively, excellent instrumental, intra-assay and inter-day precision were observed (CV ≤ 15% or 30%, respectively). Linearity deemed sufficient both targeted and untargeted (R2 ≥ 0.99 or 0.90, respectively). An appropriate targeted recovery between 70 and 130% was achieved. For the targeted (n = 69) and untargeted (n = 2348) validation of the urinary protocol, excellent instrumental and intra-assay precision were obtained (CV ≤ 15% or 30%, respectively). Subsequently, the discriminative ability of our metabolomics methods was confirmed for feline chronic kidney disease (CKD) by univariate statistics (n = 41 significant metabolites for serum, and n = 55 for urine, p-value<0.05) and validated OPLS-DA models (R2(Y) > 0.95, Q2(Y) > 0.65, p-value<0.001 for both matrices).

SIGNIFICANCE

This study is the first to present an optimized and validated wholistic metabolomics methods for feline serum and urine using ultra-high performance liquid chromatography coupled to quadrupole-Orbitrap high-resolution mass spectrometry. This robust methodology opens avenues for biomarker panel selection and a deeper understanding of feline CKD pathophysiology and other feline applications.

Stable Isotope Tracing Analysis in Cancer Research: Advancements and Challenges in Identifying Dysregulated Cancer Metabolism and Treatment Strategies

Metabolic reprogramming is a hallmark of cancer, driving the development of therapies targeting cancer metabolism. Stable isotope tracing has emerged as a widely adopted tool for monitoring cancer metabolism both in vitro and in vivo. Advances in instrumentation and the development of new tracers, metabolite databases, and data analysis tools have expanded the scope of cancer metabolism studies across these scales. In this review, we explore the latest advancements in metabolic analysis, spanning from experimental design in stable isotope-labeling metabolomics to sophisticated data analysis techniques. We highlight successful applications in cancer research, particularly focusing on ongoing clinical trials utilizing stable isotope tracing to characterize disease progression, treatment responses, and potential mechanisms of resistance to anticancer therapies. Furthermore, we outline key challenges and discuss potential strategies to address them, aiming to enhance our understanding of the biochemical basis of cancer metabolism.

Multiomics and biotechnologies for understanding and influencing cadmium accumulation and stress response in plants

Cadmium (Cd) is one of the most toxic heavy metals faced by plants and, additionally, via the food chain, threatens human health. It is principally dispersed through agro-ecosystems via anthropogenic activities and geogenic sources. Given its high mobility and persistence, Cd, although not required, can be readily assimilated by plants thereby posing a threat to plant growth and productivity as well as animal and human health. Thus, breeding crop plants in which the edible parts contain low to zero Cd as safe food stuffs and harvesting shoots of high Cd-containing plants as a route for decontaminating soils are vital strategies to cope with this problem. Recently, multiomics approaches have been employed to considerably enhance our understanding of the mechanisms underlying (i) Cd toxicity, (ii) Cd accumulation, (iii) Cd detoxification and (iv) Cd acquisition tolerance in plants. This information can be deployed in the development of the biotechnological tools for developing plants with modulated Cd tolerance and detoxification to safeguard cellular and genetic integrity as well as to minimize food chain contamination. The aim of this review is to provide a current update about the mechanisms involved in Cd uptake by plants and the recent developments in the area of multiomics approach in terms of Cd stress responses, as well as in the development of Cd tolerant and low Cd accumulating crops.

Prediction of mass spectrometry ionization efficiency based on COSMO-RS and machine learning algorithms

Non-targeted analysis of high-resolution mass spectrometry (MS) can identify thousands of compounds, which also gives a huge challenge to their quantification. The aim of this study is to investigate the impact of mass spectrometry ionization efficiency on various compounds in food at different solvent ratios and to develop a predictive model for mass spectrometry ionization efficiency to enable non-targeted quantitative prediction of unknown compounds. This study covered 70 compounds in 14 different mobile phase ratio environments in positive ion mode to analyze the rules of the matrix effect. With the organic phase ratio from low to high, most compounds changed by 1.0 log units in log IE. The addition of formic acid enhanced the signal but also promoted the matrix effect, which often occurred in compounds with strong ionization capacity. It was speculated that the matrix effect was mainly in the form of competitive charge and charged droplet' gasification sites during MS detection. Subsequently, we present a log IE prediction method built using the COSMO-RS software and the artificial neural network (ANN) algorithm to address this difficulty and overcome the shortcomings of previous models, which always ignore the matrix effect. This model was developed following the principles of QSAR modeling recommended by the Organization for Economic Cooperation and Development (OECD). Furthermore, we validated this approach by predicting the log IE of 70 compounds, including those not involved in the log IE model development. The results presented demonstrate that the method we put forward has an excellent prediction accuracy for log IE (R2pred = 0.880), which means that it has the potential to predict the log IE of new compounds without authentic standards.

Application of the Hydrophilic Interaction Liquid Chromatography (HILIC-MS) Novel Protocol to Study the Metabolic Heterogeneity of Glioblastoma Cells

Glioblastoma is a highly malignant brain tumor consisting of a heterogeneous cellular population. The transformed metabolism of glioblastoma cells supports their growth and division on the background of their milieu. One might hypothesize that the transformed metabolism of a primary glioblastoma could be well adapted to limitations in the variety and number of substrates imported into the brain parenchyma and present it their microenvironment. Additionally, the phenotypic heterogeneity of cancer cells could promote the variations among their metabolic capabilities regarding the utilization of available substrates and release of metabolic intermediates. With the aim to identify the putative metabolic footprint of different types of glioblastoma cells, we exploited the possibility for separation of polar and ionic molecules present in culture media or cell lysates by hydrophilic interaction liquid chromatography (HILIC). The mass spectrometry (MS) was then used to identify and quantify the eluted compounds. The introduced method allows the detection and quantification of more than 150 polar and ionic metabolites in a single run, which may be present either in culture media or cell lysates and provide data for polaromic studies within metabolomics. The method was applied to analyze the culture media and cell lysates derived from two types of glioblastoma cells, T98G and U118. The analysis revealed that even both types of glioblastoma cells share several common metabolic aspects, and they also exhibit differences in their metabolic capability. This finding agrees with the hypothesis about metabolic heterogeneity of glioblastoma cells. Furthermore, the combination of both analytical methods, HILIC-MS, provides a valuable tool for metabolomic studies based on the simultaneous identification and quantification of a wide range of polar and ionic metabolites-polaromics.

Bioactive components and mechanisms of Pu-erh tea in improving levodopa metabolism in rats through COMT inhibition

Catechol-O-methyltransferase (COMT) plays a central role in the metabolic inactivation of endogenous neurotransmitters and xenobiotic drugs and hormones having catecholic structures. Its inhibitors are used in clinical practice to treat Parkinson's disease. In this study, a fluorescence-based visualization inhibitor screening method was developed to assess the inhibition activity on COMT both in vitro and in living cells. Following the screening of 94 natural products, Pu-erh tea extract exhibited the most potent inhibitory effect on COMT with an IC50 value of 0.34 μg mL-1. In vivo experiments revealed that Pu-erh tea extract substantially hindered COMT-mediated levodopa metabolism in rats, resulting in a significant increase in levodopa levels and a notable decrease in 3-O-methyldopa in plasma. Subsequently, the chemical components of Pu-erh tea were analyzed using UHPLC-Q-Exactive Orbitrap HRMS, identifying 24 major components. Among them, epigallocatechin gallate, gallocatechin gallate, epicatechin gallate, and catechin gallate exhibited potent inhibition of COMT activity with IC50 values from 93.7 nM to 125.8 nM and were the main bioactive constituents in Pu-erh tea responsible for its COMT inhibition effect. Inhibition kinetics analyses and docking simulations revealed that these compounds competitively inhibit COMT-mediated O-methylation at the catechol site. Overall, this study not only explained how Pu-erh tea catechins inhibit COMT, suggesting Pu-erh tea as a potential dietary intervention for Parkinson's disease, but also introduced a new strategy for discovering COMT inhibitors more effectively.

Solid phase microextraction device coupled with miniature mass spectrometry and mathematical model of its ion chronogram

Modern solid phase microextraction (SPME) device linked with mass spectrometry (SPME-MS) has evolved from producing ion chronogram as flat noisy signal to as unimodal-like signal. We designed a SPME device, which is closer in morphology to LC column, linked it with a miniature mass spectrometer (SPME-Mini MS), and proposed a mathematical model that elution of compound from the SPME device is equivalent to overlay of elution of the compound from the infinite LC columns with the lengths between 0 and the length of the device and it can generate an ion chronogram as right-skew unimodal signal. Rhodamine B as analyte was used for experimental verification and its unimodal signal was used to fit the parameters of a computer simulation program based on the model. The experimental results and simulations empirically cross-confirmed that SPME-Mini MS can generate ion chronogram as clean right-skew unimodal signal. Furthermore, the SPME-Mini MS system was used for quantitative analysis of psychotropic drugs (i.e. risperidone and aripiprazole) in artificial urine. The results preliminarily demonstrated that the system can utilize area under unimodal signal for quantitative analysis and has potential to be applied for on-site, fast and accurate quantification of drugs and other compounds.

Computational methods for processing and interpreting mass spectrometry-based metabolomics

Metabolomics has emerged as an indispensable tool for exploring complex biological questions, providing the ability to investigate a substantial portion of the metabolome. However, the vast complexity and structural diversity intrinsic to metabolites imposes a great challenge for data analysis and interpretation. Liquid chromatography mass spectrometry (LC-MS) stands out as a versatile technique offering extensive metabolite coverage. In this mini-review, we address some of the hurdles posed by the complex nature of LC-MS data, providing a brief overview of computational tools designed to help tackling these challenges. Our focus centers on two major steps that are essential to most metabolomics investigations: the translation of raw data into quantifiable features, and the extraction of structural insights from mass spectra to facilitate metabolite identification. By exploring current computational solutions, we aim at providing a critical overview of the capabilities and constraints of mass spectrometry-based metabolomics, while introduce some of the most recent trends in data processing and analysis within the field.

Study on tissue distribution, metabolite profiling, and excretion of [14C]-labeled flonoltinib maleate in rats

Flonoltinib Maleate (FM) is a dual-target inhibitor that selectively suppresses Janus kinase 2/FMS-like tyrosine kinase 3 (JAK2/FLT3), which is currently in phase I/IIa clinical trial in China for the treatment of myeloproliferative neoplasms (MPNs). In this research, we used [14C]-labeled FM (14C-FM) to investigate the distribution, metabolism, and excretion of FM in rats using High-Performance Liquid Chromatography coupled with High-Resolution Mass Spectrometry/Radioactivity Monitoring (HPLC-HRMS/RAM) and liquid scintillation counter. The results revealed that FM displayed widespread distribution in rats. Furthermore, FM demonstrated rapid clearance without any observed risk of organ toxicity attributed to accumulation. Profiling of FM metabolites in rat plasma, feces, urine, and bile identified a total of 17 distinct metabolites, comprising 7 phase I metabolites and 10 phase II metabolites. The major metabolic reactions involved oxygenation, dealkylation, methylation, sulfation, glucuronidation and glutathione conjugation. Based on these findings, a putative metabolic pathway of FM in rats was proposed. The overall recovery rate in the excretion experiment ranged from 93.04 % to 94.74 %. The results indicated that FM undergoes extensive hepatic metabolism in SD rats, with the majority being excreted through bile as metabolites and ultimately eliminated via feces. A minor fraction of FM (<10 %) was excreted through renal excretion in the form of urine. Integration of the current results with previous pharmacokinetic investigations of FM in rats and dogs enables a comprehensive elucidation of the in vivo ADME processes and characteristics of FM, thereby establishing a solid foundation for subsequent clinical investigations of FM.

Climate-Affected Australian Tropical Montane Cloud Forest Plants: Metabolomic Profiles, Isolated Phytochemicals, and Bioactivities

The Australian Wet Tropics World Heritage Area (WTWHA) in northeast Queensland is home to approximately 18 percent of the nation's total vascular plant species. Over the past century, human activity and industrial development have caused global climate changes, posing a severe and irreversible danger to the entire land-based ecosystem, and the WTWHA is no exception. The current average annual temperature of WTWHA in northeast Queensland is 24 °C. However, in the coming years (by 2030), the average annual temperature increase is estimated to be between 0.5 and 1.4 °C compared to the climate observed between 1986 and 2005. Looking further ahead to 2070, the anticipated temperature rise is projected to be between 1.0 and 3.2 °C, with the exact range depending on future emissions. We identified 84 plant species, endemic to tropical montane cloud forests (TMCF) within the WTWHA, which are already experiencing climate change threats. Some of these plants are used in herbal medicines. This study comprehensively reviewed the metabolomics studies conducted on these 84 plant species until now toward understanding their physiological and metabolomics responses to global climate change. This review also discusses the following: (i) recent developments in plant metabolomics studies that can be applied to study and better understand the interactions of wet tropics plants with climatic stress, (ii) medicinal plants and isolated phytochemicals with structural diversity, and (iii) reported biological activities of crude extracts and isolated compounds.

Combinatory data-independent acquisition and parallel reaction monitoring method for revealing the lipid metabolism biomarkers of coronary heart disease and its comorbidities

Disorders of lipid metabolism are a common cause of coronary heart disease (CHD) and its comorbidities. In this study, ultra-performance liquid chromatography-high-resolution mass spectrometry in data-independent acquisition (DIA) mode was applied to collect abundant tandem mass spectrometry data, which provided valuable information for lipid annotation. For the lipid isomers that could not be completely separated by chromatography, parallel reaction monitoring (PRM) mode was used for quantification. A total of 223 plasma lipid metabolites were annotated, and 116 of them were identified for their fatty acyl chain composition and location. In addition, 152 plasma lipids in patients with CHD and its comorbidities were quantitatively analyzed. Multivariate statistical analysis and metabolic pathway analysis demonstrated that glycerophospholipid and sphingolipid metabolism deserved more attention for CHD. This study proposed a method combining DIA and PRM for high-throughput characterization of plasma lipids. The results also improved our understanding of metabolic disorders of CHD and its comorbidities, which can provide valuable suggestions for medical intervention.

Selected Ion Monitoring for Orbitrap-Based Metabolomics

Orbitrap mass spectrometry in full scan mode enables the simultaneous detection of hundreds of metabolites and their isotope-labeled forms. Yet, sensitivity remains limiting for many metabolites, including low-concentration species, poor ionizers, and low-fractional-abundance isotope-labeled forms in isotope-tracing studies. Here, we explore selected ion monitoring (SIM) as a means of sensitivity enhancement. The analytes of interest are enriched in the orbitrap analyzer by using the quadrupole as a mass filter to select particular ions. In tissue extracts, SIM significantly enhances the detection of ions of low intensity, as indicated by improved signal-to-noise (S/N) ratios and measurement precision. In addition, SIM improves the accuracy of isotope-ratio measurements. SIM, however, must be deployed with care, as excessive accumulation in the orbitrap of similar m/z ions can lead, via space-charge effects, to decreased performance (signal loss, mass shift, and ion coalescence). Ion accumulation can be controlled by adjusting settings including injection time and target ion quantity. Overall, we suggest using a full scan to ensure broad metabolic coverage, in tandem with SIM, for the accurate quantitation of targeted low-intensity ions, and provide methods deploying this approach to enhance metabolome coverage.

LC-QTOF/MS-based non-targeted metabolomics to explore the toxic effects of di(2-ethylhexyl) phthalate (DEHP) on Brassica chinensis L

Di(2-ethylhexyl) phthalate (DEHP) is a widely used plasticizer known to pose health risks to humans upon exposure. Recognizing the toxic nature of DEHP, our study aimed to elucidate the response mechanisms in Brassica chinensis L. (Shanghai Qing) when subjected to varying concentrations of DEHP (2 mg kg-1, 20 mg kg-1, and 50 mg kg-1), particularly under tissue stress. The findings underscored the substantial impact of DEHP treatment on the growth of Brassica chinensis L., with increased DEHP concentration leading to a notable decrease in chlorophyll levels and alterations in the content of antioxidant enzyme activities, particularly superoxide dismutase (SOD) and peroxidase (POD). Moreover, elevated DEHP concentrations correlated with increased malondialdehyde (MDA) levels. Our analysis detected a total of 507 metabolites in Brassica chinensis L., with 331 in shoots and 176 in roots, following DEHP exposure. There was a significant difference in the number of metabolites in shoots and roots, with 79 and 64 identified, respectively (VIP > 1, p < 0.05). Metabolic pathway enrichment in Brassica chinensis L. shoots revealed significant perturbations in valine, leucine, and isoleucine biosynthesis and degradation, aminoacyl-tRNA, and glucosinolate biosynthesis. In the roots of Brassica chinensis L., varying DEHP levels exerted a substantial impact on the biosynthesis of zeatin, ubiquinone terpenoids, propane, piperidine, and pyridine alkaloids, as well as glutathione metabolic pathways. Notably, DEHP's influence was more pronounced in the roots than in the shoots, with higher DEHP concentrations affecting a greater number of metabolic pathways. This experimental study provides valuable insights into the molecular mechanisms underlying DEHP-induced stress in Brassica chinensis L., with potential implications for human health and food safety.

Overexpression of SLIM1 transcription factor accelerates vegetative development in Arabidopsis thaliana

The transcription factor Sulfur Limitation 1 (SLIM1) belongs to the plant-specific Ethylene Insenstive3-Like transcription factor family and is known to coordinate gene expression in response to sulfur deficiency. However, the roles of SLIM1 in nutrient-sufficient conditions have not been characterized. Employing constitutive SLIM1 overexpression (35S::SLIM1) and CRISPR/Cas9 mutant plants (slim1-cr), we identified several distinct phenotypes in nutrient-sufficient conditions in Arabidopsis thaliana. Overexpression of SLIM1 results in plants with approximately twofold greater rosette area throughout vegetative development. 35S::SLIM1 plants also bolt earlier and exhibit earlier downregulation of photosynthesis-associated genes and earlier upregulation of senescence-associated genes than Col-0 and slim1-cr plants. This suggests that overexpression of SLIM1 accelerates development in A. thaliana. Genome-wide differential gene expression analysis relative to Col-0 at three time points with slim1-cr and two 35S::SLIM1 lines allowed us to identify 1,731 genes regulated directly or indirectly by SLIM1 in vivo.

Generic and accurate prediction of retention times in liquid chromatography by post-projection calibration

Retention time predictions from molecule structures in liquid chromatography (LC) are increasingly used in MS-based targeted and untargeted analyses, providing supplementary evidence for molecule annotation and reducing experimental measurements. Nevertheless, different LC setups (e.g., differences in gradient, column, and/or mobile phase) give rise to many prediction models that can only accurately predict retention times for a specific chromatographic method (CM). Here, a generic and accurate method is present to predict retention times across different CMs, by introducing the concept of post-projection calibration. This concept builds on the direct projections of retention times between different CMs and uses 35 external calibrants to eliminate the impact of LC setups on projection accuracy. Results showed that post-projection calibration consistently achieved a median projection error below 3.2% of the elution time. The ranking results of putative candidates reached similar levels among different CMs. This work opens up broad possibilities for coordinating retention times between different laboratories and developing extensive retention databases.

Fine-Scale Characterization of Plant Diterpene Glycosides Using Energy-Resolved Untargeted LC-MS/MS Metabolomics Analysis

Plant diterpene glycosides are essential for diverse physiological processes. Comprehensive structural characterization proved to be a challenge due to variations in glycosylation patterns, diverse aglycone structures, and the absence of comprehensive reference databases. In this study, a method for fine-scale characterization was proposed based on energy-resolved (ER) untargeted LC-MS/MS metabolomics analysis using steviol glycosides as a demonstration. Energy-dependent fragmentation patterns were unveiled by a series of model compounds. Distinct glycosylation sites were discerned by leveraging varying fragmentation energies for the precursor ions. The sugar moiety linkage at C19OOH (R1) exhibited facile and intact cleavage at low collision energies, while the sugar moiety at C13-OH (R2) demonstrated consecutive cleavage with increasing energy. Aglycone ions exhibited a higher relative intensity at NCE 50, with relative intensities ranging from 95% to 100%. Subsequently, aglycone candidates, R1 sugar composition, and R2 sugar sequence were deduced through ER-MS/MS analysis. The developed method was applied to Stevia rebaudiana leaves. A total of 91 diterpene glycosides were unambiguously identified, including 16 steviol glycosides with novel acetylglycosylation patterns. This method offers a rapid alternative for glycan analysis and the structural differentiation of isomers. The developed method enhances the understanding of diterpene glycosides in plants, providing a reliable tool for the in-depth characterization of complex metabolite profiles.

Metabolomics-driven investigation of plant defense response against pest and pathogen attack

The advancement of metabolomics has assisted in the identification of various bewildering characteristics of the biological system. Metabolomics is a standard approach, facilitating crucial aspects of system biology with absolute quantification of metabolites using minimum samples, based on liquid/gas chromatography, mass spectrometry and nuclear magnetic resonance. The metabolome profiling has narrowed the wide gaps of missing information and has enhanced the understanding of a wide spectrum of plant-environment interactions by highlighting the complex pathways regulating biochemical reactions and cellular physiology under a particular set of conditions. This high throughput technique also plays a prominent role in combined analyses of plant metabolomics and other omics datasets. Plant metabolomics has opened a wide paradigm of opportunities for developing stress-tolerant plants, ensuring better food quality and quantity. However, despite advantageous methods and databases, the technique has a few limitations, such as ineffective 3D capturing of metabolites, low comprehensiveness, and lack of cell-based sampling. In the future, an expansion of plant-pathogen and plant-pest response towards the metabolite architecture is necessary to understand the intricacies of plant defence against invaders, elucidation of metabolic pathway operational during defence and developing a direct correlation between metabolites and biotic stresses. Our aim is to provide an overview of metabolomics and its utilities for the identification of biomarkers or key metabolites associated with biotic stress, devising improved diagnostic methods to efficiently assess pest and pathogen attack and generating improved crop varieties with the help of combined application of analytical and molecular tools.

Lessons and Applications of Omics Research in Diabetes Epidemiology

PURPOSE OF REVIEW

Recent advances in genomic technology and molecular techniques have greatly facilitated the identification of disease biomarkers, advanced understanding of pathogenesis of different common diseases, and heralded the dawn of precision medicine. Much of these advances in the area of diabetes have been made possible through deep phenotyping of epidemiological cohorts, and analysis of the different omics data in relation to detailed clinical information. In this review, we aim to provide an overview on how omics research could be incorporated into the design of current and future epidemiological studies.

RECENT FINDINGS

We provide an up-to-date review of the current understanding in the area of genetic, epigenetic, proteomic and metabolomic markers for diabetes and related outcomes, including polygenic risk scores. We have drawn on key examples from the literature, as well as our own experience of conducting omics research using the Hong Kong Diabetes Register and Hong Kong Diabetes Biobank, as well as other cohorts, to illustrate the potential of omics research in diabetes. Recent studies highlight the opportunity, as well as potential benefit, to incorporate molecular profiling in the design and set-up of diabetes epidemiology studies, which can also advance understanding on the heterogeneity of diabetes. Learnings from these examples should facilitate other researchers to consider incorporating research on omics technologies into their work to advance the field and our understanding of diabetes and its related co-morbidities. Insights from these studies would be important for future development of precision medicine in diabetes.

An assessment of AcquireX and Compound Discoverer software 3.3 for non-targeted metabolomics

We used the Exploris 240 mass spectrometer for non-targeted metabolomics on Saccharomyces cerevisiae strain BY4741 and tested AcquireX software for increasing the number of detectable compounds and Compound Discoverer 3.3 software for identifying compounds by MS2 spectral library matching. AcquireX increased the number of potentially identifiable compounds by 50% through six iterations of MS2 acquisition. On the basis of high-scoring MS2 matches made by Compound Discoverer, there were 483 compounds putatively identified from nearly 8000 candidate spectra. Comparisons to 20 amino acid standards, however, revealed instances whereby compound matches could be incorrect despite strong scores. Situations included the candidate with the top score not being the correct compound, matching the same compound at two different chromatographic peaks, assigning the highest score to a library compound much heavier than the mass for the parent ion, and grouping MS2 isomers to a single parent ion. Because the software does not calculate false positive and false discovery rates at these multiple levels where such errors can propagate, we conclude that manual examination of findings will be required post software analysis. These results will interest scientists who may use this platform for metabolomics research in diverse disciplines including medical science, environmental science, and agriculture.

Comparison of Amine-Modified Polymeric Stationary Phases for Polar Metabolomic Analysis Based on Unified-Hydrophilic Interaction/Anion Exchange Liquid Chromatography/High-Resolution Mass Spectrometry (Unified-HILIC/AEX/HRMS)

In metabolomic analysis, one of the most commonly used techniques to support the detection sensitivity and quantitation of mass spectrometry is combining it with liquid chromatography. Recently, we developed a method that enables comprehensive single-run measurement of hydrophilic metabolites using unified-hydrophilic interaction/anion exchange liquid chromatography/high-resolution mass spectrometry (unified-HILIC/AEX/HRMS) with a polymer-based mixed amines column (Gelpack GL-HilicAex). However, the importance of stationary phase functional groups and mobile phase conditions for the separation mechanisms and sensitive detection in unified-HILIC/AEX/HRMS is not yet fully understood. This study aimed to understand the importance of the mobile and stationary phases in unified-HILIC/AEX/HRMS. Two different alkali-resistant polymer-based amines-modified columns (Gelpack GL-HilicAex, primary, secondary, tertiary, and quaternary amine-modified polyglycerol dimethacrylate gel; Asahipak NH2P-50 2D, secondary amine-modified polyvinyl alcohol gel) and two eluents (acetonitrile and ammonium bicarbonate solution, pH 9.8) were used for comparative validation. A comparison of mobile phase conditions using both columns confirmed that the two-step separation from HILIC to AEX characteristic of unified-HILIC/AEX requires a linear gradient condition from acetonitrile to nearly 50% water and AEX with up to 40 mM bicarbonate ions. We found that when alkali-resistant hydrophilic polymer packing materials are modified with amines, unified-HILIC/AEX separation can be reproduced if at least one secondary amine associated with the amine series is present in the stationary phase. Furthermore, the difference in sensitivity in the HILIC and AEX modes owing to the different columns indicates the need for further improvements in the mobile phase composition and stationary phase.

Phytocompounds and Regulation of Flavonoids in In Vitro-Grown Safflower Plant Tissue by Abiotic Elicitor CdCl2

In this study, a Gas chromatography-mass spectrometry (GC-MS) investigation of embryogenic callus and somatic embryo regenerated shoots of Carthamus tinctorius revealed the presence of a variety of sugars, sugar acids, sugar alcohols, fatty acids, organic acids, and amino acids of broad therapeutic value. The in vitro developed inflorescence contained a wide range of active compounds. In embryogenic calluses, important flavonoids like naringenin, myricetin, kaempferol, epicatechin gallate, rutin, pelargonidin, peonidin, and delphinidin were identified. To augment the synthesis of active compounds, the effect of cadmium chloride (CdCl2) elicitation was tested for various treatments (T1-T4) along with a control (T0). Varying concentrations of CdCl2 [0.05 mM (T1), 0.10 mM (T2), 0.15 mM (T3), and 0.20 mM (T4)] were added to the MS medium, and flavonoid accumulation was quantified through ultra-high-pressure liquid chromatography-tandem mass spectroscopy (UHPLC-MS/MS). The flavonoids naringenin, kaempferol, epicatechin gallate, pelargonidin, cyanidin, and delphinidin increased by 6.7-, 1.9-, 3.3-, 2.1-, 1.9-, and 4.4-fold, respectively, at T3, whereas quercetin, myricetin, rutin, and peonidin showed a linear increase with the increase in CdCl2 levels. The impacts of stress markers, i.e., ascorbate peroxidase (APX), catalase (CAT), and superoxide dismutase (SOD), on defense responses in triggering synthesis were also evaluated. The maximum APX and SOD activity was observed at T3, while CAT activity was at its maximum at T2. The impact of elicitor on biochemical attributes like protein, proline, sugar, and malondialdehyde (MDA) content was investigated. The maximum protein, proline, and sugar accumulation was noted at high elicitor dose T4, while the maximum MDA content was noted at T3. These elevated levels of biochemical parameters indicated stress in culture, and the amendment of CdCl2 in media thus could be a realistic approach for enhancing secondary metabolite synthesis in safflower.

High-resolution mass spectrometry-based non-targeted metabolomics reveals toxicity of naphthalene on tall fescue and intrinsic molecular mechanisms

Polycyclic aromatic hydrocarbons (PAHs) are ubiquitous at relatively high concentrations by atmospheric deposition, and they are threatening to the environment. In this study, the toxicity of naphthalene on tall fescue and its potential responding mechanism was first studied by integrating approaches. Tall fescue seedlings were exposed to 0, 20, and 100 mg L-1 naphthalene in a hydroponic environment for 9 days, and toxic effects were observed by the studies of general physiological studies, chlorophyll fluorescence, and root morphology. Additionally, Ultra Performance Liquid Chromatography - Electrospray Ionization - High-Resolution Mass Spectrometry (UPLC-ESI-HRMS) was used to depict metabolic profiles of tall fescue under different exposure durations of naphthalene, and the intrinsic molecular mechanism of tall fescue resistance to abiotic stresses. Tall fescue shoots were more sensitive to the toxicity of naphthalene than roots. Low-level exposure to naphthalene inhibited the electron transport from the oxygen-evolving complex (OEC) to D1 protein in tall fescue shoots but induced the growth of roots. Naphthalene induced metabolic change of tall fescue roots in 12 h, and tall fescue roots maintained the level of sphingolipids after long-term exposure to naphthalene, which may play important roles in plant resistance to abiotic stresses.

Biochemical characterization of Euphorbia resinifera floral cyathia

Euphorbia resinifera O. Berg is a plant endemic to the Northern and Central regions of Morocco known since the ancient Roman and Greek times for secreting a poisonous latex containing resiniferatoxin. However, E. resinifera pseudo-inflorescences called cyathia are devoid of laticifers and, therefore, do not secrete latex. Instead, they exudate nectar that local honey bees collect and craft into honey. Honey and cyathium water extracts find a broad range of applications in the traditional medicine of Northern Africa as ointments and water decoctions. Moreover, E. resinifera monofloral honey has received the Protected Geographic Indication certification for its outstanding qualities. Given the relevance of E. resinifera cyathia for bee nutrition, honey production, and the health benefit of cyathium-derived products, this study aimed to screen metabolites synthesized and accumulated in its pseudo-inflorescences. Our analyses revealed that E. resinifera cyathia accumulate primary metabolites in considerable abundance, including hexoses, amino acids and vitamins that honey bees may collect from nectar and craft into honey. Cyathia also synthesize volatile organic compounds of the class of benzenoids and terpenes, which are emitted by flowers pollinated by honey bees and bumblebees. Many specialized metabolites, including carotenoids, flavonoids, and polyamines, were also detected, which, while protecting the reproductive organs against abiotic stresses, also confer antioxidant properties to water decoctions. In conclusion, our analyses revealed that E. resinifera cyathia are a great source of antioxidant molecules and a good food source for the local foraging honeybees, revealing the central role of the flowers from this species in mediating interactions with local pollinators and the conferral of medicinal properties to plant extracts.

Molecular formula assignment of dissolved organic matter by ultra-performance liquid chromatography quadrupole time-of-flight mass spectrometry using two non-targeted data processing approaches: A case study from recirculating aquaculture systems

BACKGROUND

The accumulation of dissolved organic matter (DOM) poses an issue in the management of the water quality from recirculating aquaculture systems (RAS), but its characterization is often not detailed enough to understand the DOM transformations in RAS. In this study, we investigated the application of two distinct non-targeted data processing approaches using ultra-performance liquid chromatography (UPLC) with quadrupole time-of-flight mass spectrometry (QTOF-MS) and two software with different algorithmic designs: PetroOrg and Progenesis QI to accurately characterize the molecular composition of DOM in RAS by UPLC-QTOF-MS.

RESULTS

The UPLC-QTOF-MS resolution in combination with PetroOrg and Progenesis QI software successfully assigned 912 and 106 unique elemental compositions, respectively, including compounds containing carbon, hydrogen, and oxygen (CHO) and nitrogen-containing CHO compounds (CHON), in the DOM samples from RAS. The results of these two distinct data processing approaches were consistent with the list of DOM formulas from RAS identified by higher resolution mass spectrometry techniques confirming their reliability. PetroOrg approach revealed only compositional information in the DOM samples from RAS, while Progenesis QI in addition to identifying new elemental compositions, increased their chemical space by giving information about their polarity and their possible key structures. DOM samples from RAS were found to be rich in unsaturated CHO compounds, with tentatively key structures of terpenoids with medium polarity indicating natural origins in their composition. The analysis also revealed probable structures of sucrose fatty acid esters and polyethylene glycol, indicating anthropogenic sources.

SIGNIFICANCE AND NOVELTY

The combination of these two non-targeted data processing approaches significantly improves the characterization of the complex mixture of DOM from RAS by UPLC-QTOF-MS reporting for the first time accurate DOM results in terms of its composition, while proposing its key structures. The presented methods can also be used to analyze different DOM samples with other HRMS techniques and software.

Bioinspired Fluorine Labeling for 19F NMR-Based Plasma Amine Profiling

Metabolite profiling serves as a powerful tool that advances our understanding of biological systems, disease mechanisms, and environmental interactions. In this study, we present an approach employing 19F-nuclear magnetic resonance (19F NMR) spectroscopy for plasma amine profiling. This method utilizes a highly efficient and reliable fluorine-labeling reagent, 3,5-difluorosalicylaldehyde, which effectively emulates pyridoxal phosphate, facilitating the formation of Schiff base compounds with primary amines. The fluorine labeling allows for distinct resolution of 19F NMR signals from amine mixtures, leading to precise identification and quantification of amine metabolites in human plasma. This advancement offers valuable tools for furthering metabolomics research.

Enhancing Metabolome Annotation by Electron Impact Excitation of Ions from Organics-Molecular Networking

Liquid chromatography-high-resolution mass spectrometry (LC-HRMS) is widely used in untargeted metabolomics, but large-scale and high-accuracy metabolite annotation remains a challenge due to the complex nature of biological samples. Recently introduced electron impact excitation of ions from organics (EIEIO) fragmentation can generate information-rich fragment ions. However, effective utilization of EIEIO tandem mass spectrometry (MS/MS) is hindered by the lack of reference spectral databases. Molecular networking (MN) shows great promise in large-scale metabolome annotation, but enhancing the correlation between spectral and structural similarity is essential to fully exploring the benefits of MN annotation. In this study, a novel approach was proposed to enhance metabolite annotation in untargeted metabolomics using EIEIO and MN. MS/MS spectra were acquired in EIEIO and collision-induced dissociation (CID) modes for over 400 reference metabolites. The study revealed a stronger correlation between the EIEIO spectra and metabolite structure. Moreover, the EIEIO spectral network outperformed the CID spectral network in capturing structural analogues. The annotation performance of the structural similarity network for untargeted LC-MS/MS was evaluated. For the spiked NIST SRM 1950 human plasma, the annotation coverage and accuracy were 72.94 and 74.19%, respectively. A total of 2337 metabolite features were successfully annotated in NIST SRM 1950 human plasma, which was twice that of LC-CID MS/MS. Finally, the developed method was applied to investigate prostate cancer. A total of 87 significantly differential metabolites were annotated. This study combining EIEIO and MN makes a valuable contribution to improving metabolome annotation.

Comprehensive Single-Platform Lipidomic/Metabolomic Analysis Using Supercritical Fluid Chromatography-Mass Spectrometry

Supercritical fluid chromatography (SFC) is a rapidly expanding technique in the analysis of nonpolar to moderately polar substances and, more recently, also in the analysis of compounds with higher polarity. Herein, we demonstrate a proof of concept for the application of a commercial SFC instrument with electrospray ionization-mass spectrometry (MS) detection as a platform for the comprehensive analysis of metabolites with the full range of polarities, from nonpolar lipids up to highly polar metabolites. The developed single-platform SFC-MS lipidomic/metabolomic method is based on two consecutive injections of lipid and polar metabolite extracts from biphase methyl tert-butyl ether extraction using a diol column and two different gradient programs of methanol-water-ammonium formate modifier. Detailed development of the method focused mainly on the pressure limits of the system, the long-term repeatability of results, and the chromatographic performance, including optimization of the flow rate program, modifier composition and gradient, and injection solvent selection. The developed method enabled fast and comprehensive analysis of lipids and polar metabolites from plasma within a 24 min cycle with two injections using a simple analytical platform based on a single instrument, column, and mobile phase. Finally, the results from SFC-MS analysis of polar metabolites were compared with widely established liquid chromatography MS analysis in metabolomics. The comparison showed different separation selectivity of metabolites using both methods and overall lower sensitivity of the SFC-MS due to the higher flow rate and worse chromatographic performance.

Chemical profiles, differentiation, and quality evaluation of Radix et Rhizoma Thalictri Foliolosi based on LC-MS

Radix et Rhizoma Thalictri Foliolosi (RRTF) belongs to one of the alkaloid-rich traditional Chinese medicines in Ranunculaceae, which possesses anti-inflammatory, anti-tumor, and several other pharmacological activities. However, due to lack of research on chemical composition, serious confusion in the origin, and ambiguity in pharmacological mechanisms, it is quite urgent to establish quality control standards based on modern research and to increase the widespread usage. Aiming to clarify the differential compounds among three species of RRTF (TFD, TFB, and TCW), targeted and untargeted acquisition strategies based on high resolution mass spectrometry were established. Plant metabolomics analysis and multivariate statistical analysis were accomplished to screen out differential markers which were answerable for categorizing different species of RRTF. A network pharmacology analysis was further performed to predict the bioactive constituents and pharmacological mechanisms. Moreover, multi-components quantitative analysis under multiple reaction monitoring mode and multiple logistic regression analysis were conducted to estimate the rationality of the quality markers (Q-markers). Ultimately, the targeted alkaloid detection list was built as premise relying on alkaloid cleavage pathway, and a total 87 compounds were identified. The 25 representative differential metabolites were screened out successfully and divided into three categories to differentiate TFD, TFB, and TCW. 14 active components and 25 presumptive targets of RRTF were found to play a central role according to network pharmacology analysis. The abundance of screened 12 Q-marker showed significant differences in the three varieties. In conclusion, the study systematically investigated the material basis of RRTF, distinguished and evaluated the quality of RRTF effectively, and predicted its pharmacodynamic material basis.

RefMetaPlant: a reference metabolome database for plants across five major phyla

Plants are unique with tremendous chemical diversity and metabolic complexity, which is highlighted by estimates that green plants collectively produce metabolites numbering in the millions. Plant metabolites play crucial roles in all aspects of plant biology, like growth, development, stress responses, etc. However, the lack of a reference metabolome for plants, and paucity of high-quality standard compound spectral libraries and related analytical tools, have hindered the discovery and functional study of phytochemicals in plants. Here, by leveraging an advanced LC-MS platform, we generated untargeted mass spectral data from >150 plant species collected across the five major phyla. Using a self-developed computation protocol, we constructed reference metabolome for 153 plant species. A 'Reference Metabolome Database for Plants' (RefMetaPlant) was built to encompass the reference metabolome, integrated standard compound mass spectral libraries for annotation, and related query and analytical tools like 'LC-MS/MS Query', 'RefMetaBlast' and 'CompoundLibBlast' for searches and profiling of plant metabolome and metabolite identification. Analogous to a reference genome in genomic research, RefMetaPlant provides a powerful platform to support plant genome-scale metabolite analysis to promote knowledge/data sharing and collaboration in the field of metabolomics. RefMetaPlant is freely available at https://www.biosino.org/RefMetaDB/.

Diagnosis of Esophageal Squamous Cell Carcinoma by High-Performance Serum Metabolic Fingerprints: A Retrospective Study

Esophageal squamous cell carcinoma (ESCC) is a highly prevalent and aggressive malignancy, and timely diagnosis of ESCC contributes to an increased cancer survival rate. However, current detection methods for ESCC mainly rely on endoscopic examination, limited by a relatively low participation rate. Herein, ferric-particle-enhanced laser desorption/ionization mass spectrometry (FPELDI MS) is utilized to record the serum metabolic fingerprints (SMFs) from a retrospective cohort (523 non-ESCC participants and 462 ESCC patients) to build diagnostic models toward ESCC. The PFELDI MS achieved high speed (≈30 s per sample), desirable reproducibility (coefficients of variation < 15%), and high throughput (985 samples with ≈124 200 data points for each spectrum). Desirable diagnostic performance with area-under-the-curves (AUCs) of 0.925-0.966 is obtained through machine learning of SMFs. Further, a metabolic biomarker panel is constructed, exhibiting superior diagnostic sensitivity (72.2-79.4%, p < 0.05) as compared with clinical protein biomarker tests (4.3-22.9%). Notably, the biomarker panel afforded an AUC of 0.844 (95% confidence interval [CI]: 0.806-0.880) toward early ESCC diagnosis. This work highlighted the potential of metabolic analysis for accurate screening and early detection of ESCC and offered insights into the metabolic characterization of diseases including but not limited to ESCC.