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:In this report, we explored the benefits of cyclic ion mobility (cIM) mass spectrometry in the analysis of isomeric post-transcriptional modifications of RNA. Standard methyl-cytidine samples were initially utilized to test the ability to correctly distinguish different structures sharing the same elemental composition and thus molecular mass. Analyzed individually, the analytes displayed characteristic arrival times (tD ) determined by the different positions of the modifying methyl groups onto the common cytidine scaffold. Analyzed in mixture, the widths of the respective signals resulted in significant overlap that initially prevented their resolution on the tD scale. The separation of the four isomers was achieved by increasing the number of passes through the cIM device, which enabled to fully differentiate the characteristic ion mobility behaviors associated with very subtle structural variations. The placement of the cIM device between the mass-selective quadrupole and the time-of-flight analyzer allowed us to perform gas-phase activation of each of these ion populations, which had been first isolated according to a common mass-to-charge ratio and then separated on the basis of different ion mobility behaviors. The observed fragmentation patterns confirmed the structures of the various isomers thus substantiating the benefits of complementing unique tD information with specific fragmentation data to reach more stringent analyte identification. These capabilities were further tested by analyzing natural mono-nucleotide mixtures obtained by exonuclease digestion of total RNA extracts. In particular, the combination of cIM separation and post-mobility dissociation allowed us to establish the composition of methyl-cytidine and methyl-adenine components present in the entire transcriptome of HeLa cells. For this reason, we expect that this technique will benefit not only epitranscriptomic studies requiring the determination of identity and expression levels of RNA modifications, but also metabolomics investigations involving the analysis of natural extracts that may possibly contain subsets of isomeric/isobaric species.
Project description:BACKGROUND:Retinoblastoma is an ocular neoplastic cancer caused primarily due to the mutation/deletion of RB1 gene. Due to the rarity of the disease very limited information is available on molecular changes in primary retinoblastoma. High throughput analysis of retinoblastoma transcriptome is available however the proteomic landscape of retinoblastoma remains unexplored. In the present study we used high resolution mass spectrometry-based quantitative proteomics to identify proteins associated with pathogenesis of retinoblastoma. METHODS:We used five pooled normal retina and five pooled retinoblastoma tissues to prepare tissue lysates. Equivalent amount of proteins from each group was trypsin digested and labeled with iTRAQ tags. The samples were analyzed on Orbitrap Velos mass spectrometer. We further validated few of the differentially expressed proteins by immunohistochemistry on primary tumors. RESULTS:We identified and quantified a total of 3587 proteins in retinoblastoma when compared with normal adult retina. In total, we identified 899 proteins that were differentially expressed in retinoblastoma with a fold change of ?2 of which 402 proteins were upregulated and 497 were down regulated. Insulin growth factor 2 mRNA binding protein 1 (IGF2BP1), chromogranin A, fetuin A (ASHG), Rac GTPase-activating protein 1 and midkine that were found to be overexpressed in retinoblastoma were further confirmed by immunohistochemistry by staining 15 independent retinoblastoma tissue sections. We further verified the effect of IGF2BP1 on cell proliferation and migration capability of a retinoblastoma cell line using knockdown studies. CONCLUSIONS:In the present study mass spectrometry-based quantitative proteomic approach was applied to identify proteins differentially expressed in retinoblastoma tumor. This study identified the mitochondrial dysfunction and lipid metabolism pathways as the major pathways to be deregulated in retinoblastoma. Further knockdown studies of IGF2BP1 in retinoblastoma cell lines revealed it as a prospective therapeutic target for retinoblastoma.
Project description:High resolution mass spectrometry (HRMS) was successfully applied to elucidate the structure of a polyfluorinated polyether (PFPE)-based formulation. The mass spectrum generated from direct injection into the MS was examined by identifying the different repeating units manually and with the aid of an instrument data processor. Highly accurate mass spectral data enabled the calculation of higher-order mass defects. The different plots of MW and the nth-order mass defects (up to n = 3) could aid in assessing the structure of the different repeating units and estimating their absolute and relative number per molecule. The three major repeating units were -C2H4O-, -C2F4O-, and -CF2O-. Tandem MS was used to identify the end groups that appeared to be phosphates, as well as the possible distribution of the repeating units. Reversed-phase HPLC separated of the polymer molecules on the basis of number of nonpolar repeating units. The elucidated structure resembles the structure in the published manufacturer technical data. This analytical approach to the characterization of a PFPE-based formulation can serve as a guide in analyzing not just other PFPE-based formulations but also other fluorinated and non-fluorinated polymers. The information from MS is essential in studying the physico-chemical properties of PFPEs and can help in assessing the risks they pose to the environment and to human health. Graphical Abstract ?.
Project description:The genome sequencing of H37Rv strain of Mycobacterium tuberculosis was completed in 1998 followed by the whole genome sequencing of a clinical isolate, CDC1551 in 2002. Since then, the genomic sequences of a number of other strains have become available making it one of the better studied pathogenic bacterial species at the genomic level. However, annotation of its genome remains challenging because of high GC content and dissimilarity to other model prokaryotes. To this end, we carried out an in-depth proteogenomic analysis of the M. tuberculosis H37Rv strain using Fourier transform mass spectrometry with high resolution at both MS and tandem MS levels. In all, we identified 3176 proteins from Mycobacterium tuberculosis representing ~80% of its total predicted gene count. In addition to protein database search, we carried out a genome database search, which led to identification of ~250 novel peptides. Based on these novel genome search-specific peptides, we discovered 41 novel protein coding genes in the H37Rv genome. Using peptide evidence and alternative gene prediction tools, we also corrected 79 gene models. Finally, mass spectrometric data from N terminus-derived peptides confirmed 727 existing annotations for translational start sites while correcting those for 33 proteins. We report creation of a high confidence set of protein coding regions in Mycobacterium tuberculosis genome obtained by high resolution tandem mass-spectrometry at both precursor and fragment detection steps for the first time. This proteogenomic approach should be generally applicable to other organisms whose genomes have already been sequenced for obtaining a more accurate catalogue of protein-coding genes.
Project description:Serratia marcescens (S. marcescens), an opportunistic human pathogen, has been identified as a major cause of nosocomial infection and outbreaks. The purpose of this analysis is to examine the S. marcescens (ATCC 13880) protein profile using a high resolution mass spectrometry (MS). S. marcescens ATCC 13880 strain was grown in Luria-Bertani broth and the protein extracted underwent trypsin digestion followed by simple reverse phase liquid chromatography fractionation. Peptide fractions were then analyzed using Orbitrap Fusion Mass Spectrometry and raw MS data was processed using Proteome Discoverer software. The proteomic study identified 2,541 unique protein groups, corresponding to approximately 54% of the measured protein-coding genes. Bioinformatics analysis of these identified proteins demonstrated their involvement in biological processes such as cell wall organization, caperone-mediated protein folding and ATP binding. To our knowledge, this is the first high-performance S.marcescens proteomics analysis (ATCC 13880). These novel observations provide a key baseline molecular profile of the S. marcescens proteome which will prove to be helpful for the future research in understanding the host-pathogen interactions during infection, elucidating the mechanism of multidrug resistance and for developing novel diagnostic markers or vaccine for the disease.
Project description:Biochemical evidence is vital for accurate genome annotation. The integration of experimental data collected at the proteome level using high resolution mass spectrometry allows for improvements in genome annotation by providing evidence for novel gene models, while validating or modifying others. Here we report the results of a proteogenomic analysis of a reference strain of Mycobacterium smegmatis (mc2155), a fast growing model organism for the pathogenic Mycobacterium tuberculosis, the causative agent for Tuberculosis. By integrating high throughput LC/MS/MS proteomic data with genomic six frame translation and ab initio gene prediction databases, a total of 2887 ORFs were identified, including 2810 ORFs annotated to a Reference protein, and 63 ORFs not previously annotated to a Reference protein. Further, the translational start site (TSS) was validated for 558 Reference proteome gene models, while upstream translational evidence was identified for 81. In addition, N-terminus derived peptide identifications allowed for downstream TSS modification of a further 24 gene models. We validated the existence of 6 previously described interrupted coding sequences at the peptide level, and provide evidence for 4 novel frameshift positions. Analysis of peptide posterior error probability (PEP) scores indicate high-confidence novel peptide identifications and indicate that the genome of M. smegmatis is not yet fully annotated.
Project description:The application of metabolic phenotyping to epidemiological studies involving thousands of biofluid samples presents a challenge for the selection of analytical platforms that meet the requirements of high-throughput precision analysis and cost-effectiveness. Here direct infusion-nanoelectrospray (DI-nESI) was compared with an ultra-performance liquid chromatography (UPLC)-high-resolution mass spectrometry (HRMS) method for metabolic profiling of an exemplary set of 132 human urine samples from a large epidemiological cohort. Both methods were developed and optimized to allow the simultaneous collection of high-resolution urinary metabolic profiles and quantitative data for a selected panel of 35 metabolites. The total run time for measuring the sample set in both polarities by UPLC-HRMS was 5 days compared with 9 h by DI-nESI-HRMS. To compare the classification ability of the two MS methods, we performed exploratory analysis of the full-scan HRMS profiles to detect sex-related differences in biochemical composition. Although metabolite identification is less specific in DI-nESI-HRMS, the significant features responsible for discrimination between sexes were mostly the same in both MS-based platforms. Using the quantitative data, we showed that 10 metabolites have strong correlation (Pearson's r > 0.9 and Passing-Bablok regression slope of 0.8-1.3) and good agreement assessed by Bland-Altman plots between UPLC-HRMS and DI-nESI-HRMS and thus can be measured using a cheaper and less sample- and time-consuming method. A further twenty metabolites showed acceptable correlation between the two methods with only five metabolites showing weak correlation (Pearson's r < 0.4) and poor agreement due to the overestimation of the results by DI-nESI-HRMS.