Project description:Our previous studies identified an increase in the levels of the metabolite 1,5-anhydroglucitol (1,5-AG) in the plasma of patients with newly diagnosed B-ALL by untargeted metabolomics detection.Except for the direct influence of 1,5-AG on leukemia cells, the effect on macrophages is still unclear.We reported the application of RNA sequencing to determine the transcriptional response of murine macrophage Raw 264.7 cells in response to stimulate with 1,5-AG conditions.
Project description:Human iPSCs and NSCs were engineered by AAVS1 and/or C13 safe-harbor TALENs which mediated targeted integration of various reporter genes at single or dual safe-harbor loci. Multiple clones of targeted human iPSCs were used to compare with parental untargeted NCRM5 iPSCs. Polyclonal targeted human NSCs were used to compare with their parental untargeted NCRM1NSCs or H9NSCs. Total RNA obtained from targeted human iPSCs or NSCs compared to untargeted control iPSCs or NSCs.
Project description:A novel one-dimensional on-line pH gradient-eluted strong cation exchange (SCX)-nano-ESI-MS/MS method was developed for protein identification and tested with mixture of six standard proteins, total lysate of HuH7 and N2a cells, as well as membrane fraction of N2a cells. This method utilized an on-line nano-flow SCX column in a nano-LC system coupled with a nano-electrospray high-resolution mass spectrometer. Protein digests were separated on a nano-flow SCX column with a pH gradient and directly introduced into a mass spectrometer through nano-electrospray ionization. SCXLC-MS/MS showed identification capability for higher proportion of basic peptides compared to RPLC-MS/MS method, especially for histidine-containing peptides. Our SCXLC-MS/MS method is an excellent alternative method to the RPLC-MS/MS method for analysis of standard proteins, total cell and membrane proteomes.
Project description:Raw untargeted metabolomics profiled by Metabolon Inc. for 540 samples from healthy individuals. Files include sample names and run details which can be matched to their metagenomic sequencing samples from PRJEB11532 and PRJEB17643. Information regarding metabolite metadata is also available, including
Project description:The goal of this study is to compare the RNA expression profile of wild-type C. elegans nematodes to mutants defective in the synthesis of the biogenic amine neurotransmitters dopamine, serotonin, tyramine, and octopamine in day 2 adults.
Project description:Metabolic reprogramming in cancer and immune cells occurs to support their increasing energy needs in biological tissues. Here we propose Single Cell SPAtially resolved METabolic (scSpaMet) framework for joint protein-metabolite profiling of single immune and cancer cells in male human tissues by incorporating untargeted spatial metabolomics and targeted multiplexed protein imaging in a single pipeline. We utilized the scSpaMet pipeline to profile cell types and spatial metabolomic maps of 19507, 31156, and 8215 single cells in human lung cancer, tonsil and endometrium tissues, respectively. ScSpaMet analysis revealed cell type-dependent metabolite profiles and local metabolite competition of neighboring single cells in human tissues. Deep learning-based joint embedding revealed unique metabolite states within cell types. Trajectory inference showed metabolic patterns along cell differentiation paths. Here we show scSpaMet’s ability to quantify and visualize the cell-type specific and spatially resolved metabolic-protein mapping as an emerging tool for systems-level understanding of tissue biology.
Project description:Our study shows reduced expression of Trace-Amine Associated Receptors (TAARs) in the olfactory epithelium of mice in which Taar elements 1 and 2 (TE1+2) are deleted in cis.
Project description:The interplay between pathogens and hosts has been studied for decades using targeted approaches such as the analysis of mutants and host immunological responses. Although much has been learned from such studies, they focus on individual pathways and fail to reveal the global effects of infection on the host. To alleviate this issue, high-throughput methods such as transcriptomics and proteomics have been used to study host-pathogen interactions. Recently, metabolomics was established as a new method to study changes in the biochemical composition of host tissues. We report a metabolomics study of Salmonella enterica serovar Typhimurium infection. We used Fourier Transform Ion Cyclotron Resonance Mass Spectrometry with Direct Infusion to reveal that dozens of host metabolic pathways are affected by Salmonella in a murine infection model. In particular, multiple host hormone pathways are disrupted. Our results identify unappreciated effects of infection on host metabolism and shed light on mechanisms used by Salmonella to cause disease, and by the host to counter infection. Female C57BL/6 mice were infected with Salmonella enterica serovar Typhimurium SL1344 cells by oral gavage. Feces and livers were collected and metabolites extracted using acetonitrile. For experiments with feces, samples were collected from 4 mice before and after infection. For liver experiments, 11 uninfected and 11 infected mice were used. Samples were combined into 3 groups of 3-4 mice each, resulting in the analysis of 3 group samples of uninfected and 3 of infected mice. Extracts were infused into a 12-T Apex-Qe hybrid quadrupole-FT-ICR mass spectrometer equipped with an Apollo II electrospray ionization source, a quadrupole mass filter and a hexapole collision cell. Raw mass spectrometry data were processed as described elsewhere (Han et al. 2008. Metabolomics. 4:128-140 [PMID 19081807]). To identify differences in metabolite composition between uninfected and infected samples, we filtered the list of masses for metabolites which were present on one set of samples but not the other. Additionally, we calculated the ratios between averaged intensities of metabolites from uninfected and infected mice. To assign possible metabolite identities, monoisotopic neutral masses of interest were queried against MassTrix (http://masstrix.org). Masses were searched against the Mus musculus database within a mass error of 3 ppm. Data were analyzed by unpaired t tests with 95% confidence intervals.