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Primary dysmenorrhea is one of the most common reasons for gynecologic visits, but due to the lack of suitable animal models, the pathologic mechanisms and related drug development are limited. Herein, we establish a new mouse model which can mimic the periodic occurrence of primary dysmenorrhea to solve this problem. Non-pregnant female mice were pretreated with estradiol benzoate for 3 consecutive days. After that, mice were injected with oxytocin to simulate menstrual pain on the 4th, 8th, 12th, and 16th days (four estrus cycles). Assessment of the cumulative writhing score, uterine tissue morphology, and uterine artery blood flow and biochemical analysis were performed at each time point. Oxytocin injection induced an equally severe writhing reaction and increased PGF2α accompanied with upregulated expression of COX-2 on the 4th and 8th days. In addition, decreased uterine artery blood flow but increased resistive index (RI) and pulsatility index (PI) were also observed. Furthermore, the metabolomics analysis results indicated that arachidonic acid metabolism; linoleic acid metabolism; glycerophospholipid metabolism; valine, leucine, and isoleucine biosynthesis; alpha-linolenic acid metabolism; and biosynthesis of unsaturated fatty acids might play important roles in the recurrence of primary dysmenorrhea. This new mouse model is able to mimic the clinical characteristics of primary dysmenorrhea for up to two estrous cycles.

Cellular metabolism regulates immune cell activation, differentiation and effector functions, but current metabolic approaches lack single-cell resolution and simultaneous characterization of cellular phenotype. In this study, we developed an approach to characterize the metabolic regulome of single cells together with their phenotypic identity. The method, termed single-cell metabolic regulome profiling (scMEP), quantifies proteins that regulate metabolic pathway activity using high-dimensional antibody-based technologies. We employed mass cytometry (cytometry by time of flight, CyTOF) to benchmark scMEP against bulk metabolic assays by reconstructing the metabolic remodeling of in vitro-activated naive and memory CD8 + T cells. We applied the approach to clinical samples and identified tissue-restricted, metabolically repressed cytotoxic T cells in human colorectal carcinoma. Combining our method with multiplexed ion beam imaging by time of flight (MIBI-TOF), we uncovered the spatial organization of metabolic programs in human tissues, which indicated exclusion of metabolically repressed immune cells from the tumor–immune boundary. Overall, our approach enables robust approximation of metabolic and functional states in individual cells. An antibody-based method enables profiling of metabolic protein expression and regulation in single cells using mass spectrometry.

Mass spectrometry is a predominant experimental technique in metabolomics and related fields, but metabolite structural elucidation remains highly challenging. We report SIRIUS 4 (https://bio.informatik.uni-jena.de/sirius/), which provides a fast computational approach for molecular structure identification. SIRIUS 4 integrates CSI:FingerID for searching in molecular structure databases. Using SIRIUS 4, we achieved identification rates of more than 70% on challenging metabolomics datasets.SIRIUS 4 is a fast and highly accurate tool for molecular structure interpretation from mass-spectrometry-based metabolomics data.

This study was designed to evaluate the chemical fingerprints and the antioxidant, anti-inflammatory and hypolipidemic activity of selected Crepis species collected in Greece, namely, C. commutata, C. dioscoridis, C. foetida, C. heldreichiana, C. incana, C. rubra, and Phitosia crocifolia (formerly known as Crepis crocifolia). For the phytochemical analyses, sample measurements were carried out by using nuclear magnetic resonance (NMR) spectroscopy and liquid chromatography coupled with mass spectrometry (LC-MS). Τhe extracts were evaluated both in vitro (radical scavenging activity: DPPH assay and total phenolic content: Folin–Ciocalteu) and in vivo (paw edema reduction and hypolipidemic activity: experimental mouse protocols). Among the tested extracts, C. incana presented the highest gallic acid equivalents (GAE) (0.0834 mg/mL) and the highest antioxidant activity (IC[sub.50] = 0.07 mg/mL) in vitro, as well as the highest anti-inflammatory activity with 32% edema reduction in vivo. Moreover, in the hypolipidemic protocol, the same extract increased plasma total antioxidant capacity (TAC) by 48.7%, and decreased cholesterol (41.3%) as well as triglycerides (37.2%). According to fractionation of the extract and the phytochemical results, this biological effect may be associated with the rich phenolic composition; caffeoyl tartaric acid derivatives (cichoric and caftaric acid) are regarded as the most prominent bioactive specialized metabolites. The present study contributes to the knowledge regarding the phytochemical and pharmacological profile of Crepis spp.

Metabolism has an essential role in biological systems. Identification and quantitation of the compounds in the metabolome is defined as metabolic profiling, and it is applied to define metabolic changes related to genetic differences, environmental influences and disease or drug perturbations. Chromatography–mass spectrometry (MS) platforms are frequently used to provide the sensitive and reproducible detection of hundreds to thousands of metabolites in a single biofluid or tissue sample. Here we describe the experimental workflow for long-term and large-scale metabolomic studies involving thousands of human samples with data acquired for multiple analytical batches over many months and years. Protocols for serum- and plasma-based metabolic profiling applying gas chromatography–MS (GC-MS) and ultraperformance liquid chromatography–MS (UPLC-MS) are described. These include biofluid collection, sample preparation, data acquisition, data pre-processing and quality assurance. Methods for quality control–based robust LOESS signal correction to provide signal correction and integration of data from multiple analytical batches are also described.

Stable isotope, and in particular (13)C-based flux analysis, is the exclusive approach to experimentally quantify the integrated responses of metabolic networks. Here we describe a protocol that is based on growing microbes on (13)C-labeled glucose and subsequent gas chromatography mass spectrometric detection of (13)C-patterns in protein-bound amino acids. Relying on publicly available software packages, we then describe two complementary mathematical approaches to estimate either local ratios of converging fluxes or absolute fluxes through different pathways. As amino acids in cell protein are abundant and stable, this protocol requires a minimum of equipment and analytical expertise. Most other flux methods are variants of the principles presented here. A true alternative is the analytically more demanding dynamic flux analysis that relies on (13)C-pattern in free intracellular metabolites. The presented protocols take 5-10 d, have been used extensively in the past decade and are exemplified here for the central metabolism of Escherichia coli.

The revival of interest in cancer cell metabolism in recent years has prompted the need for quantitative analytical platforms for studying metabolites from in vivo sources. We implemented a quantitative polar metabolomics profiling platform using selected reaction monitoring with a 5500 QTRAP hybrid triple quadrupole mass spectrometer that covers all major metabolic pathways. The platform uses hydrophilic interaction liquid chromatography with positive/negative ion switching to analyze 258 metabolites (289 Q1/Q3 transitions) from a single 15-min liquid chromatography–mass spectrometry acquisition with a 3-ms dwell time and a 1.55-s duty cycle time. Previous platforms use more than one experiment to profile this number of metabolites from different ionization modes. The platform is compatible with polar metabolites from any biological source, including fresh tissues, cancer cells, bodily fluids and formalin-fixed paraffin-embedded tumor tissue. Relative quantification can be achieved without using internal standards, and integrated peak areas based on total ion current can be used for statistical analyses and pathway analyses across biological sample conditions. The procedure takes ∼12 h from metabolite extraction to peak integration for a data set containing 15 total samples (∼6 h for a single sample).

Background Plants of the genus Taxus have attracted much attention owing to the natural product taxol, a successful anti-cancer drug. T. fuana and T. yunnanensis are two endangered Taxus species mainly distributed in the Himalayas. In our study, an untargeted metabolomics approach integrated with a targeted UPLC-MS/MS method was applied to examine the metabolic variations between these two Taxus species growing in different environments. Results The level of taxol in T. yunnanensis is much higher than that in T. fuana , indicating a higher economic value of T. yunnanensis for taxol production. A series of specific metabolites, including precursors, intermediates, competitors of taxol, were identified. All the identified intermediates are predominantly accumulated in T. yunnanensis than T. fuana , giving a reasonable explanation for the higher accumulation of taxol in T. yunnanensis . Taxusin and its analogues are highly accumulated in T. fuana , which may consume limited intermediates and block the metabolic flow towards taxol. The contents of total flavonoids and a majority of tested individual flavonoids are significantly accumulated in T. fuana than T. yunnanensis , indicating a stronger environmental adaptiveness of T. fuana . Conclusions Systemic metabolic profiling may provide valuable information for the comprehensive industrial utilization of the germplasm resources of these two endangered Taxus species growing in different environments.

Nuclear magnetic resonance (NMR)-based metabolomics has many applications in plant science. Metabolomics can be used in functional genomics and to differentiate plants from different origin, or after different treatments. In this protocol, the following steps of plant metabolomics using NMR spectroscopy are described: sample preparation (freeze drying followed by extraction by ultrasonication with 1:1 CD 3 OD:KH 2 PO 4 buffer in D 2 O), NMR analysis (standard 1 H, J -resolved, 1 H– 1 H correlation spectroscopy (COSY) and heteronuclear multiple bond correlation (HMBC)) and chemometric methods. The main advantage of NMR metabolomic analysis is the possibility of identifying metabolites by comparing NMR data with references or by structure elucidation using two-dimensional NMR. This protocol is particularly suited for the analysis of secondary metabolites such as phenolic compounds (usually abundant in plants), and for primary metabolites (e.g., sugars and amino acids). This procedure is rapid; it takes not more than 30 min for sample preparation (multiple parallel) and a further 10 min for NMR spectrum acquisition.

Background Detection of low abundance metabolites is important for de novo mapping of metabolic pathways related to diet, microbiome or environmental exposures. Multiple algorithms are available to extract m/z features from liquid chromatography-mass spectral data in a conservative manner, which tends to preclude detection of low abundance chemicals and chemicals found in small subsets of samples. The present study provides software to enhance such algorithms for feature detection, quality assessment, and annotation. Results xMSanalyzer is a set of utilities for automated processing of metabolomics data. The utilites can be classified into four main modules to: 1) improve feature detection for replicate analyses by systematic re-extraction with multiple parameter settings and data merger to optimize the balance between sensitivity and reliability, 2) evaluate sample quality and feature consistency, 3) detect feature overlap between datasets, and 4) characterize high-resolution m/z matches to small molecule metabolites and biological pathways using multiple chemical databases. The package was tested with plasma samples and shown to more than double the number of features extracted while improving quantitative reliability of detection. MS/MS analysis of a random subset of peaks that were exclusively detected using xMSanalyzer confirmed that the optimization scheme improves detection of real metabolites. Conclusions xMSanalyzer is a package of utilities for data extraction, quality control assessment, detection of overlapping and unique metabolites in multiple datasets, and batch annotation of metabolites. The program was designed to integrate with existing packages such as apLCMS and XCMS, but the framework can also be used to enhance data extraction for other LC/MS data software.