Project description:Glycosylation is one of the most common and important post-translational modifications. Quantitative analysis of intact N-glycopeptides is critical to understand the role of protein glycosylation in physiological and pathological processes. In this work, we developed a novel approach called methylamine stable isotope labeling (MeSIL) to relatively quantify intact N-glycopeptides through one step isotopic labeling. Isotopic methylamine was employed to label both the sialic acid residues of glycans and the carboxyl groups on the peptide moiety, and followed by the mixing of samples for mass spectrometric analysis. The relative abundance of intact N-glycopeptides between two samples was obtained by comparing the signal of the particular peak pairs with a 3*N Da mass shift (where N is an integer correlating with the number of carboxylic acids within the glycopeptides). Additionally, the number of sialylated glycopeptides can be distinguished simultaneously through the mass difference after labeling. The MeSIL str
Project description:Recent developments in mass spectrometry-based single-cell proteomics (SCP) have resulted in dramatically improved sensitivity, yet the relatively low measurement throughput remains a limitation. Isobaric and isotopic labeling methods have been separately applied to SCP to increase throughput through multiplexing. Here we combined both forms of labeling to achieve multiplicative scaling for higher throughput. Two-plex stable isotope labeling of amino acids in cell culture and isobaric Tandem Mass Tag labeling enabled up to 32 single cells to be analyzed in a single LC-MS analysis, in addition to reference and negative control channels. A custom nested nanowell chip was used for nanoliter sample processing to minimize sample losses. Using a 145-min total LC-MS cycle time, ~280 single cells were analyzed per day, which could be increased to ~700 samples per day with a high-duty-cycle multicolumn LC system producing the same active gradient. The labeling efficiency and achievable proteome coverage were characterized for multiple analysis conditions. Despite some decrease in coverage, this method readily differentiated four different cell types and thus proves suitable for, e.g., rapid cell typing.
Project description:Studying the flow of chemical moieties through the complex set of metabolic reactions that happen in the cell is essential to understanding the alterations in homeostasis that occur in disease. Recently, LC/MS-based untargeted metabolomics and isotopically labeled metabolites have been used to facilitate the unbiased mapping of labeled moieties through metabolic pathways. However, due to the complexity of the resulting experimental data sets few computational tools are available for data analysis. Here we introduce geoRge, a novel computational approach capable of analyzing untargeted LC/MS data from stable isotope-labeling experiments. geoRge is written in the open language R and runs on the output structure of the XCMS package, which is in widespread use. As opposed to the few existing tools, which use labeled samples to track stable isotopes by iterating over all MS signals using the theoretical mass difference between the light and heavy isotopes, geoRge uses unlabeled and labeled biologically equivalent samples to compare isotopic distributions in the mass spectra. Isotopically enriched compounds change their isotopic distribution as compared to unlabeled compounds. This is directly reflected in a number of new m/z peaks and higher intensity peaks in the mass spectra of labeled samples relative to the unlabeled equivalents. The automated untargeted isotope annotation and relative quantification capabilities of geoRge are demonstrated by the analysis of LC/MS data from a human retinal pigment epithelium cell line (ARPE-19) grown on normal and high glucose concentrations mimicking diabetic retinopathy conditions in vitro. In addition, we compared the results of geoRge with the outcome of X(13)CMS, since both approaches rely entirely on XCMS parameters for feature selection, namely m/z and retention time values. geoRge is available as an R script at https://github.com/jcapelladesto/geoRge.
Project description:Natural products are an essential source of bioactive compounds. Isotopic labeling is an effective way to identify natural products that incorporate a specific precursor; however, this approach is limited by the availability of isotopically enriched precursors. We used an inverse stable isotopic labeling approach to identify natural products by growing bacteria on a 13C-carbon source and then identifying 12C-precursor incorporation by mass spectrometry. We applied this approach to methylotrophs, ecologically important bacteria predicted to have significant yet underexplored biosynthetic potential. We demonstrate that this method identifies N-acyl homoserine lactone quorum sensing signals produced by diverse methylotrophs grown on three different one-carbon compounds. We then apply this approach to simultaneously detect five previously unidentified signals produced by a methylotroph and link these compounds to their synthases. We envision that this method can be used to identify other natural product classes synthesized by methylotrophs and other organisms that grow on relatively inexpensive 13C-carbon sources.
Project description:Phospholipids (PLs), one of the lipid categories, are not only the primary building blocks of cellular membranes, but also can be split to produce products that function as second messengers in signal transduction and play a pivotal role in numerous cellular processes, including cell growth, survival, and motility. Here, we present an integrated novel method that combines a fast and robust TMS-diazomethane-based phosphate derivatization and isotopic labeling strategy, which enables simultaneous profiling and relative quantification of PLs from biological samples. Our results showed that phosphate methylation allows fast and sensitive identification of the six major PL classes, including their lysophospholipid counterparts, under positive ionization mode. The isotopic labeling of endogenous PLs was achieved by deuterated diazomethane, which was generated through acid-catalyzed hydrogen/deuterium (H/D) exchange and methanolysis of TMS-diazomethane during the process of phosphate derivatization. The measured H/D ratios of unlabeled and labeled PLs, which were mixed in known proportions, indicated that the isotopic labeling strategy is capable of providing relative quantitation with adequate accuracy, reproducibility, and a coefficient of variation of 9.1%, on average. This novel method offers unique advantages over existing approaches and presents a powerful tool for research of PL metabolism and signaling.
Project description:Although stable isotopic labeling has found widespread use in the proteomics field, its application to carbohydrate quantification has been limited. Herein we report the design, synthesis, and application of a novel series of compounds that allow for the incorporation of isotopic variation within glycan structures. The novel feature of the compounds is the ability to incorporate the isotopes in a controlled manner, allowing for the generation of four tags that vary only in their isotopic content. This allows for the direct comparisons of three samples or triplicate measurements with an internal standard within one mass spectral analysis. Quantitation of partially depolymerized glycosaminoglycan mixtures, as well as N-linked glycans released from fetuin, is used to demonstrate the utility of the tetraplex tagging strategy.
Project description:107 BMDMs per group (Sirt3 WT and Sirt3 K223R)were seeded in 10cm plates and incubated in RPMI-1640 cell culture medium with 10% FBS. Prior to isotopic labeling, the medium was replaced with RPMI-1640 without glutamine for 4 hrs. Then stable-isotope labeled analog 4 mM (U-13C5) glutamine (Cambridge Isotope) was added together with IL-4 (20ng/ml) for 4 hrs.
Project description:The unique biology of a neoplasm is reflected by its distinct molecular profile compared with normal tissue. To understand tumor development better, we have undertaken a quantitative proteomic search for abnormally expressed proteins in colonic tumors from Apc(Min/+) (Min) mice. By raising pairs of Min and wild-type mice on diets derived from natural-abundance or (15)N-labeled algae, we used metabolic labeling to compare protein levels in colonic tumor versus normal tissue. Because metabolic labeling allows internal control throughout sample preparation and analysis, technical error is minimized as compared with in vitro labeling. Several proteins displayed altered expression, and a subset was validated via stable isotopic dilution using synthetic peptide standards. We also compared gene and protein expression among tumor and nontumor tissue, revealing limited correlation. This divergence was especially pronounced for species showing biological change, highlighting the complementary perspectives provided by transcriptomics and proteomics. Our work demonstrates the power of metabolic labeling combined with stable isotopic dilution as an integrated strategy for the identification and validation of differentially expressed proteins using rodent models of human disease.