Project description:The study of urinary phase II sulfate metabolites is central to understanding the role and fate of endogenous and exogenous compounds in biological systems. This study describes a new workflow for the untargeted metabolic profiling of sulfated metabolites in a urine matrix. Analysis was performed using ultra-high-performance liquid chromatography-high resolution tandem mass spectrometry (UHPLC-HRMS/MS) with data dependent acquisition (DDA) coupled to an automated script-based data processing pipeline and differential metabolite level analysis. Sulfates were identified through k-means clustering analysis of sulfate ester derived MS/MS fragmentation intensities. The utility of the method was highlighted in two applications. Firstly, the urinary metabolome of a thoroughbred horse was examined before and after administration of the anabolic androgenic steroid (AAS) testosterone propionate. The analysis detected elevated levels of ten sulfated steroid metabolites, three of which were identified and confirmed by comparison with synthesised reference materials. This included 5α-androstane-3β,17α-diol 3-sulfate, a previously unreported equine metabolite of testosterone propionate. Secondly, the hydrolytic activity of four sulfatase enzymes on pooled human urine was examined. This revealed that Pseudomonas aeruginosa arylsulfatases (PaS) enzymes possessed higher selectivity for the hydrolysis of sulfated metabolites than the commercially available Helix pomatia arylsulfatase (HpS). This novel method provides a rapid tool for the systematic, untargeted metabolic profiling of sulfated metabolites in a urinary matrix.
Project description:Trained sensory panels are regularly used to rate food products but do not allow for data-driven approaches to steer food product development. This study evaluated the potential of a molecular-based strategy by analyzing 27 tomato soups that were enhanced with yeast-derived flavor products using a sensory panel as well as LC-MS and GC-MS profiling. These data sets were used to build prediction models for 26 different sensory attributes using partial least squares analysis. We found driving separation factors between the tomato soups and metabolites predicting different flavors. Many metabolites were putatively identified as dipeptides and sulfur-containing modified amino acids, which are scientifically described as related to umami or having "garlic-like" and "onion-like" attributes. Proposed identities of high-impact sensory markers (methionyl-proline and asparagine-leucine) were verified using MS/MS. The overall results highlighted the strength of combining sensory data and metabolomics platforms to find new information related to flavor perception in a complex food matrix.
Project description:The peptide content of individual mammalian cells is profiled using matrix-assisted laser desorption/ionization (MALDI) time-of-flight mass spectrometry. Both enzymatic and nonenzymatic procedures, including a glycerol cell stabilization method, are reported for the isolation of individual mammalian cells in a manner compatible with MALDI MS measurements. Guided microdeposition of MALDI matrix allows samples to be created with suitable analyte-to-matrix ratios. More than 15 peptides are observed in individual rat intermediate pituitary cells. The combination of accurate mass data, expected cleavages by proteolytic enzymes, and postsource decay sequencing allows identification of 14 of these peptides as pro-opiomelanocortin prohormone-derived molecules. These protocols permit the classification of individual mammalian cells by peptide profile, the elucidation of cell-specific prohormone processing, and the discovery of new signaling peptides on a cell-to-cell basis in a wide variety of mammalian cell types.
Project description:Lymph node metastasis remains a key factor that affects the prognosis of patients with colon cancer. The aim of the present study was to identify and evaluate serum metabolites as biomarkers for the detection of tumor lymph node metastasis and the prediction of patient survival. The present study analyzed the metabolites in the serum of patients with advanced colon cancer both with and without lymph node metastasis. Blood samples from 104 patients with stage T3 colon cancer were collected and analyzed using liquid chromatography-mass spectrometry. The metabolites were structurally confirmed with data from the Human Metabolome Database. The association between the serum metabolites and the clinicopathological characteristics and survival time of patients from the present study was analyzed. Overall, 227 different metabolites were identified in the serum of patients with stage T3 colon cancer with or without lymph node metastasis. Furthermore, 17 of these metabolites may potentially distinguish those patients with lymph node metastasis from those patients without. In addition, five factors, including abscisic acid, calcitroic acid and glucosylsphingosine presence in the serum, age and sex, were identified as independent predictors for lymph node metastasis (P<0.05). Furthermore, three factors, including abscisic acid, calcitroic acid and glucosylsphingosine presence in the serum were independent predictors for patient survival (P<0.05). In conclusion, the serum levels of abscisic acid, calcitroic-acid and glucosylsphingosine may be considered as potential biomarkers to predict the occurrence of lymph node metastasis and the survival time of patients with colon cancer.
Project description:A high-throughput single cell profiling method has been developed for matrix-enhanced-secondary ion mass spectrometry (ME-SIMS) to investigate the lipid profiles of neuronal cells. Populations of cells are dispersed onto the substrate, their locations determined using optical microscopy, and the cell locations used to guide the acquisition of SIMS spectra from the cells. Up to 2,000 cells can be assayed in one experiment at a rate of 6 s per cell. Multiple saturated and unsaturated phosphatidylcholines (PCs) and their fragments are detected and verified with tandem mass spectrometry from individual cells when ionic liquids are employed as a matrix. Optically guided single cell profiling with ME-SIMS is suitable for a range of cell sizes, from Aplysia californica neurons larger than 75 μm to 7-μm rat cerebellar neurons. ME-SIMS analysis followed by t-distributed stochastic neighbor embedding of peaks in the lipid molecular mass range (m/z 700-850) distinguishes several cell types from the rat central nervous system, largely based on the relative proportions of four dominant lipids, PC(32:0), PC(34:1), PC(36:1), and PC(38:5). Furthermore, subpopulations within each cell type are tentatively classified consistent with their endogenous lipid ratios. The results illustrate the efficacy of a new approach to classify single cell populations and subpopulations using SIMS profiling of lipid and metabolite contents. These methods are broadly applicable for high throughput single cell chemical analyses.
Project description:Metabolomic profiling studies aim to provide a comprehensive, quantitative, and dynamic portrait of the endogenous metabolites in a biological system. While contemporary technologies permit routine profiling of many metabolites, intrinsically labile metabolites are often improperly measured or omitted from studies due to unwanted chemical transformations that occur during sample preparation or mass spectrometric analysis. The primary glycolytic metabolite 1,3-bisphosphoglyceric acid (1,3-BPG) typifies this class of metabolites, and, despite its central position in metabolism, has largely eluded analysis in profiling studies. Here we take advantage of the reactive acylphosphate group in 1,3-BPG to chemically trap the metabolite with hydroxylamine during metabolite isolation, enabling quantitative analysis by targeted LC-MS/MS. This approach is compatible with complex cellular metabolome, permits specific detection of the reactive (1,3-) instead of nonreactive (2,3-) BPG isomer, and has enabled direct analysis of dynamic 1,3-BPG levels resulting from perturbations to glucose processing. These studies confirmed that standard metabolomic methods misrepresent cellular 1,3-BPG levels in response to altered glucose metabolism and underscore the potential for chemical trapping to be used for other classes of reactive metabolites.
Project description:We advance mass spectrometry from a cell population-averaging tool to one capable of quantifying the expression of diverse proteins in single embryonic cells. Our instrument combines capillary electrophoresis (CE), electrospray ionization, and a tribrid ultrahigh-resolution mass spectrometer (HRMS) to enable untargeted (discovery) proteomics with ca. 25 amol lower limit of detection. CE-μESI-HRMS enabled the identification of 500-800 nonredundant protein groups by measuring 20 ng, or <0.2% of the total protein content in single blastomeres that were isolated from the 16-cell frog (Xenopus laevis) embryo, amounting to a total of 1709 protein groups identified between n=3 biological replicates. By quantifying ≈150 nonredundant protein groups between all blastomeres and replicate measurements, we found significant translational cell heterogeneity along multiple axes of the embryo at this very early stage of development when the transcriptional program of the embryo has yet to begin.
Project description:The integration of spatial omics technologies can provide important insights into the biology of tissues. Here we combined mass spectrometry imaging-based metabolomics and imaging mass cytometry-based immunophenotyping on a single tissue section to reveal metabolic heterogeneity at single-cell resolution within tissues and its association with specific cell populations such as cancer cells or immune cells. This approach has the potential to greatly increase our understanding of tissue-level interplay between metabolic processes and their cellular components.
Project description:The brain consists of organized ensembles of cells that exhibit distinct morphologies, cellular connectivity, and dynamic biochemistries that control the executive functions of an organism. However, the relationships between chemical heterogeneity, cell function, and phenotype are not always understood. Recent advancements in matrix-assisted laser desorption/ionization mass spectrometry have enabled the high-throughput, multiplexed chemical analysis of single cells, capable of resolving hundreds of molecules in each mass spectrum. We developed a machine learning workflow to classify single cells according to their mass spectra based on cell groups of interest (GOI), e.g., neurons vs astrocytes. Three data sets from various cell groups were acquired on three different mass spectrometer platforms representing thousands of individual cell spectra that were collected and used to validate the single cell classification workflow. The trained models achieved >80% classification accuracy and were subjected to the recently developed instance-based model interpretation framework, SHapley Additive exPlanations (SHAP), which locally assigns feature importance for each single-cell spectrum. SHAP values were used for both local and global interpretations of our data sets, preserving the chemical heterogeneity uncovered by the single-cell analysis while offering the ability to perform supervised analysis. The top contributing mass features to each of the GOI were ranked and selected using mean absolute SHAP values, highlighting the features that are specific to the defined GOI. Our approach provides insight into discriminating the chemical profiles of the single cells through interpretable machine learning, facilitating downstream analysis and validation.
Project description:The exploration of single cells reveals cell heterogeneity and biological principle of cellular metabolism. Although a number of mass spectrometry (MS) based single cell MS (SCMS) techniques have been dedicatedly developed with high efficiency and sensitivity, limitations still exist. In this work, we introduced a microscale multifunctional device, the T-probe, which integrates cellular contents extraction and immediate ionization, to implement online in situ SCMS analysis at ambient conditions with minimal sample preparation. With high sensitivity and reproducibility, the T-probe was employed for MS analysis of single HeLa cells under control and anticancer drug treatment conditions. Intracellular species and xenobiotic metabolites were detected, and changes of cellular metabolic profiles induced by drug treatment were measured. Combining SCMS experiments with statistical data analyses, including Orthogonal Partial Least Squares-Discriminant Analysis (OPLS-DA) and two-sample t-test, we provided biological insights into cellular metabolic response to drug treatment. Online MS/MS analysis was conducted at single cell level to identify species of interest, including endogenous metabolites and the drug compound. Using the T-probe SCMS technique combined with comprehensive data analyses, we provide an approach to understanding cellular metabolism and evaluate chemotherapies at the single cell level.