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:Single-cell measurements aid our understanding of chemically heterogeneous systems such as the brain. Lipids are one of the least studied chemical classes, and their cell-to-cell heterogeneity remains largely unexplored. We adapted microscopy-guided single-cell profiling using matrix-assisted laser desorption/ionization ion cyclotron resonance mass spectrometry to profile the lipid composition of over 30?000 individual rat cerebellar cells. We detected 520 lipid features, many of which were found in subsets of cells; Louvain clustering identified 101 distinct groups that can be correlated to neuronal and astrocytic classifications and lipid classes. Overall, the two most common lipids found were [PC(32:0)+H]+ and [PC(34:1)+H]+, which were present within 98.9 and 89.5% of cells, respectively; lipid signals present in <1% of cells were also detected, including [PC(34:1)+K]+ and [PG(40:2(OH))+Na]+. These results illustrate the vast lipid heterogeneity found within rodent cerebellar cells and hint at the distinct functional consequences of this heterogeneity.
Project description:Matrix-assisted ionization (MAI) is demonstrated to be a robust and sensitive analytical method capable of analyzing proteins such as cholera toxin B-subunit and pertussis toxin mutant from conditions containing relatively high amounts of inorganic salts, buffers, and preservatives without the need for prior sample clean-up or concentration. By circumventing some of the sample preparation steps, MAI simplifies and accelerates the analytical workflow for biological samples in complex media. The benefits of multiply charged ions characteristic of electrospray ionization (ESI) and the robustness of matrix-assisted laser desorption/ionization (MALDI) can be obtained from a single method, making it well suited for analysis of proteins and other biomolecules at ultra-high resolution as demonstrated on an Orbitrap Fusion where protein subunits were resolved for which MALDI-time-of-flight failed. MAI results are compared with those obtained with ESI, MALDI, and laserspray ionization methods and fundamental commonalities discussed.
Project description:MicroRNAs (miRNAs) are small single-stranded non-coding RNAs that post-transcriptionally regulate gene expression, and play key roles in the regulation of a variety of cellular processes and in disease. New tools to analyze miRNAs will add understanding of the physiological origins and biological functions of this class of molecules. In this study, we investigate the utility of high resolution mass spectrometry for the analysis of miRNAs through proof-of-concept experiments. We demonstrate the ability of mass spectrometry to resolve and separate miRNAs and corresponding 3' variants in mixtures. The mass accuracy of the monoisotopic deprotonated peaks from various miRNAs is in the low ppm range. We compare fragmentation of miRNA by collision-induced dissociation (CID) and by higher-energy collisional dissociation (HCD) which yields similar sequence coverage from both methods but additional fragmentation by HCD versus CID. We measure the linear dynamic range, limit of detection, and limit of quantitation of miRNA loaded onto a C18 column. Lastly, we explore the use of data-dependent acquisition of MS/MS spectra of miRNA during online LC-MS and demonstrate that multiple charge states can be fragmented, yielding nearly full sequence coverage of miRNA on a chromatographic time scale. We conclude that high resolution mass spectrometry allows the separation and measurement of miRNAs in mixtures and a standard LC-MS setup can be adapted for online analysis of these molecules.
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:Obtaining fast screening information on molecular composition of a tissue sample is of great importance for a disease biomarkers search and for online surgery control. In this study, high resolution mass spectrometry analysis of eutopic and ectopic endometrium tissues (90 samples) is done using direct tissue spray mass spectrometry in both positive and negative ion modes. The most abundant peaks in the both ion modes are those corresponding to lipids. Species of three lipid classes are observed, phosphatidylcholines (PC), sphingomyelins (SM) and phosphoethanolamines (PE). Direct tissue analysis gives mainly information on PC and SM lipids (29 species) in positive ion mode and PC, SM and PE lipids (50 species) in negative ion mode which gives complementary data for endometriosis foci differentiation. The biggest differences were found for phospholipids with polyunsaturated acyls and alkils. Although, tissue spray shows itself as appropriate tool for tissue investigation, caution should be paid to the interpretation of mass spectra because of their higher complexity with more possible adducts formation and multiple interferences must be taken into account. The present work extends the application of direct tissue analysis for the rapid differentiation between endometriotic tissues of different foci.
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:We have coupled 2D-NMR and infusion FT-ICR-MS with computer-assisted assignment to profile 13C-isotopologues of glycerophospholipids (GPL) directly in crude cell extracts, resulting in very high information throughput of >3000 isobaric molecules in a few minutes. A mass accuracy of better than 1 ppm combined with a resolution of 100,000 at the measured m/z was required to distinguish isotopomers from other GPL structures. Isotopologue analysis of GPLs extracted from LCC2 breast cancer cells grown on [U-13C]-glucose provided a rich trove of information about the biosynthesis and turnover of the GPLs. The isotopologue intensity ratios from the FT-ICR-MS were accurate to approximately 1% or better based on natural abundance background, and depended on the signal-to-nose ratio. The time course of incorporation of 13C from [U-13C]-glucose into a particular phosphatidylcholine was analyzed in detail, to provide a quantitative measure of the sizes of glycerol, acetyl CoA and total GPL pools in growing LCC2 cells. Independent and complementary analysis of the positional 13C enrichment in the glycerol and fatty acyl chains obtained from high resolution 2D NMR was used to verify key aspects of the model. This technology enables simple and rapid sample preparation, has rapid analysis, and is generally applicable to unfractionated GPLs of almost any head group, and to mixtures of other classes of metabolites.
Project description:Native mass spectrometry (MS) involves the analysis and characterization of macromolecules, predominantly intact proteins and protein complexes, whereby as much as possible the native structural features of the analytes are retained. As such, native MS enables the study of secondary, tertiary, and even quaternary structure of proteins and other biomolecules. Native MS represents a relatively recent addition to the analytical toolbox of mass spectrometry and has over the past decade experienced immense growth, especially in enhancing sensitivity and resolving power but also in ease of use. With the advent of dedicated mass analyzers, sample preparation and separation approaches, targeted fragmentation techniques, and software solutions, the number of practitioners and novel applications has risen in both academia and industry. This review focuses on recent developments, particularly in high-resolution native MS, describing applications in the structural analysis of protein assemblies, proteoform profiling of─among others─biopharmaceuticals and plasma proteins, and quantitative and qualitative analysis of protein-ligand interactions, with the latter covering lipid, drug, and carbohydrate molecules, to name a few.
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.