MRMaid, the web-based tool for designing multiple reaction monitoring (MRM) transitions.
ABSTRACT: Multiple reaction monitoring (MRM) of peptides uses tandem mass spectrometry to quantify selected proteins of interest, such as those previously identified in differential studies. Using this technique, the specificity of precursor to product transitions is harnessed for quantitative analysis of multiple proteins in a single sample. The design of transitions is critical for the success of MRM experiments, but predicting signal intensity of peptides and fragmentation patterns ab initio is challenging given existing methods. The tool presented here, MRMaid (pronounced "mermaid") offers a novel alternative for rapid design of MRM transitions for the proteomics researcher. The program uses a combination of knowledge of the properties of optimal MRM transitions taken from expert practitioners and literature with MS/MS evidence derived from interrogation of a database of peptide identifications and their associated mass spectra. The tool also predicts retention time using a published model, allowing ordering of transition candidates. By exploiting available knowledge and resources to generate the most reliable transitions, this approach negates the need for theoretical prediction of fragmentation and the need to undertake prior "discovery" MS studies. MRMaid is a modular tool built around the Genome Annotating Proteomic Pipeline framework, providing a web-based solution with both descriptive and graphical visualizations of transitions. Predicted transition candidates are ranked based on a novel transition scoring system, and users may filter the results by selecting optional stringency criteria, such as omitting frequently modified residues, constraining the length of peptides, or omitting missed cleavages. Comparison with published transitions showed that MRMaid successfully predicted the peptide and product ion pairs in the majority of cases with appropriate retention time estimates. As the data content of the Genome Annotating Proteomic Pipeline repository increases, the coverage and reliability of MRMaid are set to increase further. MRMaid is freely available over the internet as an executable web-based service at www.mrmaid.info.
Project description:Multiple reaction monitoring mass spectrometry (MRM-MS) is a targeted analysis method that has been increasingly viewed as an avenue to explore proteomes with unprecedented sensitivity and throughput. We have developed a software tool, called MaRiMba, to automate the creation of explicitly defined MRM transition lists required to program triple quadrupole mass spectrometers in such analyses. MaRiMba creates MRM transition lists from downloaded or custom-built spectral libraries, restricts output to specified proteins or peptides, and filters based on precursor peptide and product ion properties. MaRiMba can also create MRM lists containing corresponding transitions for isotopically heavy peptides, for which the precursor and product ions are adjusted according to user specifications. This open-source application is operated through a graphical user interface incorporated into the Trans-Proteomic Pipeline, and it outputs the final MRM list to a text file for upload to MS instruments. To illustrate the use of MaRiMba, we used the tool to design and execute an MRM-MS experiment in which we targeted the proteins of a well-defined and previously published standard mixture.
Project description:BACKGROUND:Multiple reaction monitoring mass spectrometry (MRM-MS) of peptides with stable isotope-labeled internal standards (SISs) is increasingly being used to develop quantitative assays for proteins in complex biological matrices. These assays can be highly precise and quantitative, but the frequent occurrence of interferences requires that MRM-MS data be manually reviewed, a time-intensive process subject to human error. We developed an algorithm that identifies inaccurate transition data based on the presence of interfering signal or inconsistent recovery among replicate samples. METHODS:The algorithm objectively evaluates MRM-MS data with 2 orthogonal approaches. First, it compares the relative product ion intensities of the analyte peptide to those of the SIS peptide and uses a t-test to determine if they are significantly different. A CV is then calculated from the ratio of the analyte peak area to the SIS peak area from the sample replicates. RESULTS:The algorithm identified problematic transitions and achieved accuracies of 94%-100%, with a sensitivity and specificity of 83%-100% for correct identification of errant transitions. The algorithm was robust when challenged with multiple types of interferences and problematic transitions. CONCLUSIONS:This algorithm for automated detection of inaccurate and imprecise transitions (AuDIT) in MRM-MS data reduces the time required for manual and subjective inspection of data, improves the overall accuracy of data analysis, and is easily implemented into the standard data-analysis work flow. AuDIT currently works with results exported from MRM-MS data-processing software packages and may be implemented as an analysis tool within such software.
Project description:Multiple reaction monitoring (MRM) is a highly sensitive method of targeted mass spectrometry (MS) that can be used to selectively detect and quantify peptides based on the screening of specified precursor peptide-to-fragment ion transitions. MRM-MS sensitivity depends critically on the tuning of instrument parameters, such as collision energy and cone voltage, for the generation of maximal product ion signal. Although generalized equations and values exist for such instrument parameters, there is no clear indication that optimal signal can be reliably produced for all types of MRM transitions using such an algorithmic approach. To address this issue, we have devised a workflow functional on both Waters Quattro Premier and ABI 4000 QTRAP triple quadrupole instruments that allows rapid determination of the optimal value of any programmable instrument parameter for each MRM transition. Here, we demonstrate the strategy for the optimizations of collision energy and cone voltage, but the method could be applied to other instrument parameters, such as declustering potential, as well. The workflow makes use of the incremental adjustment of the precursor and product m/z values at the hundredth decimal place to create a series of MRM targets at different collision energies that can be cycled through in rapid succession within a single run, avoiding any run-to-run variability in execution or comparison. Results are easily visualized and quantified using the MRM software package Mr. M to determine the optimal instrument parameters for each transition.
Project description:Selected reaction monitoring (SRM), sometimes called multiple reaction monitoring (MRM), is becoming the tool of choice for targeted quantitative proteomics in the plant science community. Key to a successful SRM experiment is prior identification of the distinct peptides for the proteins of interest and the determination of the so-called transitions that can be programmed into an LC-MS/MS to monitor those peptides. The transition for a given peptide comprises the intact peptide m/z and a high intensity product ion that can be monitored at a characteristic retention time (RT). To aid the design of SRM experiments, several online tools and databases have been produced to help researchers select transitions for their proteins of interest, but many of these tools are limited to the most popular model organisms such as human, yeast, and mouse or require the experimental acquisition of local spectral libraries. In this paper we present MRMaid, a web-based SRM assay design tool whose transitions are generated by mining the millions of identified peptide spectra held in the EBI's PRIDE database. By using data from this large public repository, MRMaid is able to cover a wide range of species that can increase as the coverage of PRIDE grows. In this paper MRMaid transitions for 25 Arabidopsis thaliana proteins are evaluated experimentally, and found capable of quantifying 23 of these proteins. This performance was found to be comparable with the more time consuming approach of designing transitions using locally acquired orbitrap data, indicating that MRMaid is a valuable tool for targeted Arabidopsis proteomics.
Project description:Quantitative MS assays for Igs are compared with existing clinical methods in samples from patients with plasma cell dyscrasias, for example, multiple myeloma (MM).Using LC-MS/MS data, Ig constant region peptides, and transitions were selected for LC-MRM MS. Quantitative assays were used to assess Igs in serum from 83 patients. RNA sequencing and peptide-based LC-MRM are used to define peptides for quantification of the disease-specific Ig.LC-MRM assays quantify serum levels of Igs and their isoforms (IgG1-4, IgA1-2, IgM, IgD, and IgE, as well as kappa (?) and lambda (?) light chains). LC-MRM quantification has been applied to single samples from a patient cohort and a longitudinal study of an IgE patient undergoing treatment, to enable comparison with existing clinical methods. Proof-of-concept data for defining and monitoring variable region peptides are provided using the H929 MM cell line and two MM patients.LC-MRM assays targeting constant region peptides determine the type and isoform of the involved Ig and quantify its expression; the LC-MRM approach has improved sensitivity compared with the current clinical method, but slightly higher inter-assay variability. Detection of variable region peptides is a promising way to improve Ig quantification, which could produce a dramatic increase in sensitivity over existing methods, and could further complement current clinical techniques.
Project description:Despite increasing importance of protein glycosylation, most of the large-scale glycoproteomics have been limited to profiling the sites of N-glycosylation. However, in-depth knowledge of protein glycosylation to uncover functions and their clinical applications requires quantitative glycoproteomics eliciting both peptide and glycan sequences concurrently. Here we describe a novel strategy for the multiplexed quantitative mouse serum glycoproteomics based on a specific chemical ligation, namely, reverse glycoblotting technique, focusing sialic acids and multiple reaction monitoring (MRM). LC-MS/MS analysis of de-glycosylated peptides identified 270 mouse serum peptides (95 glycoproteins) as sialylated glycopeptides, of which 67 glycopeptides were fully characterized by MS/MS analyses in a straightforward manner. We revealed the importance of a fragment ion containing innermost N-acetylglucosamine (GlcNAc) residue as MRM transitions regardless the sequence of the peptides. Versatility of the reverse glycoblotting-assisted MRM assays was demonstrated by quantitative comparison of 25 targeted glycopeptides from 16 proteins between mice with homo and hetero types of diabetes disease model.
Project description:Metabolite profiling by mass spectrometry (MS) is an area of interest for disease diagnostics, biomarker discovery, and therapeutic evaluation. A recently developed approach, multiple reaction monitoring (MRM)-profiling, searches for metabolites with precursor (Prec) and neutral loss (NL) scans in a representative sample and creates a list of ion transitions. These are then used in an MRM method for fast screening of individual samples and discrimination between healthy and diseased. A large variety of functional groups are considered and all signals discovered are recorded in the individual samples, making this a largely unsupervised method. MRM-profiling is described here and then demonstrated with data for over 900 human plasma coronary artery disease (CAD) samples. Representative pooled samples for each condition were interrogated using a library of over a hundred Prec and NL scans on a triple quadrupole MS. The data from the Prec and NL experiments were converted into ion transitions, initially some 1266 transitions. Each ion transition was examined in the individual samples on a time scale of milliseconds per transition, which allows for rapid screening of large sample sets (<5 days for 1000 samples). Use of univariate and multivariate statistics allowed classification of the sample set with high accuracy. The metabolite profiles classified the CAD female, CAD male, and peripheral artery disease (PAD) samples relative to controls with an accuracy of 90%, 78%, and 85%, respectively. The compounds responsible for informative ion transitions were identified by chromatography and high resolution MS; some have been previously reported and found to be associated with coronary artery disease metabolism, indicating that the methodology generates a meaningful metabolite profile while being faster than traditional methodologies.
Project description:Proteomics experiments based on Selected Reaction Monitoring (SRM, also referred to as Multiple Reaction Monitoring or MRM) are being used to target large numbers of protein candidates in complex mixtures. At present, instrument parameters are often optimized for each peptide, a time and resource intensive process. Large SRM experiments are greatly facilitated by having the ability to predict MS instrument parameters that work well with the broad diversity of peptides they target. For this reason, we investigated the impact of using simple linear equations to predict the collision energy (CE) on peptide signal intensity and compared it with the empirical optimization of the CE for each peptide and transition individually. Using optimized linear equations, the difference between predicted and empirically derived CE values was found to be an average gain of only 7.8% of total peak area. We also found that existing commonly used linear equations fall short of their potential, and should be recalculated for each charge state and when introducing new instrument platforms. We provide a fully automated pipeline for calculating these equations and individually optimizing CE of each transition on SRM instruments from Agilent, Applied Biosystems, Thermo-Scientific and Waters in the open source Skyline software tool ( http://proteome.gs.washington.edu/software/skyline ).
Project description:We present a comprehensive workflow for large scale (>1000 transitions/run) label-free LC-MRM proteome assays. Innovations include automated MRM transition selection, intelligent retention time scheduling that improves S/N by twofold, and automatic peak modeling. Improvements to data analysis include a novel Q/C metric, normalized group area ratio, MLR normalization, weighted regression analysis, and data dissemination through the Yale protein expression database. As a proof of principle we developed a robust 90 min LC-MRM assay for mouse/rat postsynaptic density fractions which resulted in the routine quantification of 337 peptides from 112 proteins based on 15 observations per protein. Parallel analyses with stable isotope dilution peptide standards (SIS), demonstrate very high correlation in retention time (1.0) and protein fold change (0.94) between the label-free and SIS analyses. Overall, our method achieved a technical CV of 11.4% with >97.5% of the 1697 transitions being quantified without user intervention, resulting in a highly efficient, robust, and single injection LC-MRM assay.
Project description:Recent technical advances in the field of quantitative proteomics have stimulated a large number of biomarker discovery studies of various diseases, providing avenues for new treatments and diagnostics. However, inherent challenges have limited the successful translation of candidate biomarkers into clinical use, thus highlighting the need for a robust analytical methodology to transition from biomarker discovery to clinical implementation. We have developed an end-to-end computational proteomic pipeline for biomarkers studies. At the discovery stage, the pipeline emphasizes different aspects of experimental design, appropriate statistical methodologies, and quality assessment of results. At the validation stage, the pipeline focuses on the migration of the results to a platform appropriate for external validation, and the development of a classifier score based on corroborated protein biomarkers. At the last stage towards clinical implementation, the main aims are to develop and validate an assay suitable for clinical deployment, and to calibrate the biomarker classifier using the developed assay. The proposed pipeline was applied to a biomarker study in cardiac transplantation aimed at developing a minimally invasive clinical test to monitor acute rejection. Starting with an untargeted screening of the human plasma proteome, five candidate biomarker proteins were identified. Rejection-regulated proteins reflect cellular and humoral immune responses, acute phase inflammatory pathways, and lipid metabolism biological processes. A multiplex multiple reaction monitoring mass-spectrometry (MRM-MS) assay was developed for the five candidate biomarkers and validated by enzyme-linked immune-sorbent (ELISA) and immunonephelometric assays (INA). A classifier score based on corroborated proteins demonstrated that the developed MRM-MS assay provides an appropriate methodology for an external validation, which is still in progress. Plasma proteomic biomarkers of acute cardiac rejection may offer a relevant post-transplant monitoring tool to effectively guide clinical care. The proposed computational pipeline is highly applicable to a wide range of biomarker proteomic studies.