Project description:Stable isotope assisted metabolomics (SIAM) uses stable isotope tracers to support studies of biochemical mechanisms. We report a suite of data analysis algorithms for automatic analysis of SIAM data acquired on a high resolution mass spectrometer. To increase the accuracy of isotopologue assignment, metabolites detected in the unlabeled samples were used as reference metabolites to generate possible isotopologue candidates for analysis of peaks detected in the labeled samples. An iterative linear regression model was developed to deconvolute the overlapping isotopic peaks of isotopologues present in a full MS spectrum, where the threshold for the weight factor was determined by a simulation study assuming different levels of Gaussian white noise contamination. A normalization method enabling isotope ratio-based normalization was implemented to study the difference of isotopologue abundance distribution between sample groups. The developed method can analyze SIAM data acquired by direct infusion MS and LC-MS, and can handle metabolite tracers containing different tracer elements. Analysis of SIAM data acquired from mixtures of known compounds showed that the developed algorithms accurately identify metabolites and quantify stable isotope enrichment. Application of SIAM data acquired from a biological study further demonstrated the effectiveness and accuracy of the developed method for analysis of complex samples.
Project description:BackgroundSecondary-ion mass spectrometry (SIMS) is an important tool for investigating isotopic composition in the chemical and materials sciences, but its use in biology has been limited by technical considerations. Multi-isotope imaging mass spectrometry (MIMS), which combines a new generation of SIMS instrument with sophisticated ion optics, labeling with stable isotopes, and quantitative image-analysis software, was developed to study biological materials.ResultsThe new instrument allows the production of mass images of high lateral resolution (down to 33 nm), as well as the counting or imaging of several isotopes simultaneously. As MIMS can distinguish between ions of very similar mass, such as 12C15N- and 13C14N-, it enables the precise and reproducible measurement of isotope ratios, and thus of the levels of enrichment in specific isotopic labels, within volumes of less than a cubic micrometer. The sensitivity of MIMS is at least 1,000 times that of 14C autoradiography. The depth resolution can be smaller than 1 nm because only a few atomic layers are needed to create an atomic mass image. We illustrate the use of MIMS to image unlabeled mammalian cultured cells and tissue sections; to analyze fatty-acid transport in adipocyte lipid droplets using 13C-oleic acid; to examine nitrogen fixation in bacteria using 15N gaseous nitrogen; to measure levels of protein renewal in the cochlea and in post-ischemic kidney cells using 15N-leucine; to study DNA and RNA co-distribution and uridine incorporation in the nucleolus using 15N-uridine and 81Br of bromodeoxyuridine or 14C-thymidine; to reveal domains in cultured endothelial cells using the native isotopes 12C, 16O, 14N and 31P; and to track a few 15N-labeled donor spleen cells in the lymph nodes of the host mouse.ConclusionMIMS makes it possible for the first time to both image and quantify molecules labeled with stable or radioactive isotopes within subcellular compartments.
Project description:Quantification of cellular deoxyribonucleoside mono- (dNMP), di- (dNDP), triphosphates (dNTPs) and related nucleoside metabolites are difficult due to their physiochemical properties and widely varying abundance. Involvement of dNTP metabolism in cellular processes including senescence and pathophysiological processes including cancer and viral infection make dNTP metabolism an important bioanalytical target. We modified a previously developed ion pairing reversed phase chromatography-mass spectrometry method for the simultaneous quantification and 13C isotope tracing of dNTP metabolites. dNMPs, dNDPs, and dNTPs were chromatographically resolved to avoid mis-annotation of in-source fragmentation. We used commercially available 13C15N-stable isotope labeled analogs as internal standards and show that this isotope dilution approach improves analytical figures of merit. At sufficiently high mass resolution achievable on an Orbitrap mass analyzer, stable isotope resolved metabolomics allows simultaneous isotope dilution quantification and 13C isotope tracing from major substrates including 13C-glucose. As a proof of principle, we quantified dNMP, dNDP and dNTP pools from multiple cell lines. We also identified isotopologue enrichment from glucose corresponding to ribose from the pentose-phosphate pathway in dNTP metabolites.
Project description:Protein deamidation can severely alter the physicochemical characteristics and biological functions of protein therapeutics. Cobratide is a non-addictive analgesic with wide clinical acceptance. However, the Asn residue at position 48 from the N-terminus of the cobratide amino acid sequence (N48) tends to degrade during purification, storage, and transport. This characteristic could severely affect the drug safety and clinical efficacy of cobratide. Traditional methods for quantitating deamidation reported in previous research are characterised by low efficiency and accuracy; the quality control of cobratide via this method is limited. Herein, we developed an improved 18O-labelling method based on the detection of a unique peptide (i.e., the protein fragment of cobratide containing the N48 deamidation hotspot after enzymolysis) using an Orbitrap high-resolution mass spectrometer to quantify deamidated cobratide. The limits of detection and quantification of this method reached 0.02 and 0.025 μM, respectively, and inter- and intra-day precision values of the method were <3%. The accuracy of the 18O-labelling strategy was validated by using samples containing synthesised peptides with a known ratio of deamidation impurities and also by comparing the final total deamidation results with our previously developed capillary electrophoresis method. The recoveries for deamidation (Asp), deamidation isomerisation (iso-Asp), and total deamidation were 101.52 ± 1.17, 102.42 ± 1.82, and 103.55 ± 1.07, respectively. The robustness of the method was confirmed by verifying the chromatographic parameters. Our results demonstrate the applicability of the 18O-labelling strategy for detecting protein deamidation and lay a robust foundation for protein therapeutics studies and drug quality consistency evaluations.
Project description:Most kinases are capable of recognizing and phosphorylating peptides containing short, linear sequence motifs. To measure the activation state of many kinases from the same cell lysate, we created a multiplexed, mass-spectrometry-based in vitro kinase assay. Ninety chemically synthesized peptides derived from well-characterized peptide substrates and in vivo phosphorylation sites with either known or previously unidentified upstream kinases were reacted individually in a plate format with crude cell lysates and ATP. Phosphorylation rates were directly measured based on the addition of 90 same-sequence, site-specific phosphopeptides enriched in stable isotopes to act as ideal quantitative internal standards for analysis by liquid chromatography coupled to tandem mass spectrometry. This approach concurrently measured up to 90 site-specific peptide phosphorylation rates, reporting a diagnostic fingerprint for activated kinase pathways. We applied this unique kinome-activity profiling strategy in a variety of cellular settings, including mitogen stimulation, cell cycle, pharmacological inhibition of pathways, and to a panel of breast cancer cell lines. Finally, we identified the source of activity for a peptide (derived from a PI3K regulatory subunit) from our library. This peptide substrate demonstrated mitotic and tyrosine-specific phosphorylation, which was confirmed to be a novel Src family kinase site in vivo.
Project description:Protein turnover is an important aspect of the regulation of cellular processes for organisms when responding to developmental or environmental cues. The measurement of protein turnover in plants, in contrast to that of rapidly growing unicellular organismal cultures, is made more complicated by the high degree of amino acid recycling, resulting in significant transient isotope incorporation distributions that must be dealt with computationally for high throughput analysis to be practical. An algorithm in R, ProteinTurnover, was developed to calculate protein turnover with transient stable isotope incorporation distributions in a high throughput automated manner using high resolution MS and MS/MS proteomic analysis of stable isotopically labeled plant material. ProteinTurnover extracts isotopic distribution information from raw MS data for peptides identified by MS/MS from data sets of either isotopic label dilution or incorporation experiments. Variable isotopic incorporation distributions were modeled using binomial and beta-binomial distributions to deconvolute the natural abundance, newly synthesized/partial-labeled, and fully labeled peptide distributions. Maximum likelihood estimation was performed to calculate the distribution abundance proportion of old and newly synthesized peptides. The half-life or turnover rate of each peptide was calculated from changes in the distribution abundance proportions using nonlinear regression. We applied ProteinTurnover to obtain half-lives of proteins from enriched soluble and membrane fractions from Arabidopsis roots.
Project description:Stable isotopes are ideal labels for studying biological processes because they have little or no effect on the biochemical properties of target molecules. The NanoSIMS is a tool that can image the distribution of stable isotope labels with up to 50 nm spatial resolution and with good quantitation. This combination of features has enabled several groups to undertake significant experiments on biological problems in the last decade. Combining the NanoSIMS with other imaging techniques also enables us to obtain not only chemical information but also the structural information needed to understand biological processes. This article describes the methodologies that we have developed to correlate atomic force microscopy and backscattered electron imaging with NanoSIMS experiments to illustrate the imaging of stable isotopes at molecular, cellular, and tissue scales. Our studies make it possible to address 3 biological problems: (1) the interaction of antimicrobial peptides with membranes; (2) glutamine metabolism in cancer cells; and (3) lipoprotein interactions in different tissues.
Project description:Label-free quantitation of proteins analyzed by tandem mass spectrometry uses either integrated peak intensity from the parent-ion mass analysis (MS1) or features from fragment-ion analysis (MS2), such as spectral counts or summed fragment-ion intensity. We directly compared MS1 and MS2 quantitation by analyzing human protein standards diluted into Escherichia coli extracts on an Orbitrap mass spectrometer. We found that summed MS2 intensities were nearly as accurate as integrated MS1 intensities, and both outperformed MS2 spectral counting in accuracy and linearity. We compared these results to those obtained from two low-resolution ion-trap mass spectrometers; summed MS2 intensities from LTQ and LTQ Velos instruments were similar in accuracy to those from the Orbitrap. Data from all three instruments are available via ProteomeXchange with identifier PXD000602. Abundance measurements using MS1 or MS2 intensities had limitations, however. While measured protein concentration was on average well-correlated with the known concentration, there was considerable protein-to-protein variation. Moreover, not all human proteins diluted to a mole fraction of 10(-3) or lower were detected, with a strong falloff below 10(-4) mole fraction. These results show that MS1 and MS2 intensities are simple measures of protein abundance that are on average accurate but should be limited to quantitation of proteins of intermediate to higher fractional abundance.
Project description:Background: Mass spectrometry metabolomics-based data-processing approaches have been developed for drug metabolite profiling. However, existing approaches cannot be used to comprehensively identify drug metabolites with high efficacy. Methods: Herein, we propose a two-stage data-processing approach for effective and comprehensive drug metabolite identification. The approach combines dose-response experiments with stable isotope tracing (SIT). Rosiglitazone (ROS), commonly used to treat type 2 diabetes, was employed as a model drug. Results: In the first stage of data processing, 1,071 features exhibited a dose-response relationship among 22,597 features investigated. In the second stage, these 1,071 features were screened for isotope pairs, and 200 features with isotope pairs were identified. In time-course experiments, a large proportion of the identified features (69.5%: 137 out of 200 features) were confirmed to be possible ROS metabolites. We compared the validated features identified using our approach with those identified using a previously reported approach [the mass defect filter (MDF) combined with SIT] and discovered that most of the validated features (37 out of 42) identified using the MDF-SIT combination were also successfully identified using our approach. Of the 143 validated features identified by both approaches, 74 had a proposed structure of an ROS-structure-related metabolite; the other 34 features that contained a specific fragment of ROS metabolites were considered possible ROS metabolites. Interestingly, numerous ROS-structure-related metabolites were identified in this study, most of which were novel. Conclusion: The results reveal that the proposed approach can effectively and comprehensively identify ROS metabolites.