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Effect of collision energy optimization on the measurement of peptides by selected reaction monitoring (SRM) mass spectrometry.

ABSTRACT: 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 ).

SUBMITTER: Maclean B

PROVIDER: S-EPMC3005404 | biostudies-literature | 2010 Dec

REPOSITORIES: biostudies-literature

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Effect of collision energy optimization on the measurement of peptides by selected reaction monitoring (SRM) mass spectrometry.

Maclean Brendan B Tomazela Daniela M DM Abbatiello Susan E SE Zhang Shucha S Whiteaker Jeffrey R JR Paulovich Amanda G AG Carr Steven A SA Maccoss Michael J MJ

Analytical chemistry 20101119 24

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 reaso ...[more]

PMID: 21090646

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Project description:BACKGROUND:Multiple sclerosis (MS) is a chronic inflammatory disorder of the central nervous system (CNS). It involves damage to the myelin sheath surrounding axons and to the axons themselves. MS most often presents with a series of relapses and remissions but then evolves over a variable period of time into a slowly progressive form of neurological dysfunction termed secondary progressive MS (SPMS). The reasons for this change in clinical presentation are unclear. The absence of a diagnostic marker means that there is a lag time of several years before the diagnosis of SPMS can be established. At the same time, understanding the mechanisms that underlie SPMS is critical to the development of rational therapies for this untreatable stage of the disease. RESULTS:Using high performance liquid chromatography-coupled mass spectrometry (HPLC); we have established a highly specific and sensitive selected reaction monitoring (SRM) assay. Our multiplexed SRM assay has facilitated the simultaneous detection of surrogate peptides originating from 26 proteins present in cerebrospinal fluid (CSF). Protein levels in CSF were generally ~200-fold lower than that in human sera. A limit of detection (LOD) was determined to be as low as one femtomol. We processed and analysed CSF samples from a total of 22 patients with SPMS, 7 patients with SPMS treated with lamotrigine, 12 patients with non-inflammatory neurological disorders (NIND) and 10 healthy controls (HC) for the levels of these 26 selected potential protein biomarkers. Our SRM data found one protein showing significant difference between SPMS and HC, three proteins differing between SPMS and NIND, two proteins between NIND and HC, and 11 protein biomarkers showing significant difference between a lamotrigine-treated and untreated SPMS group. Principal component analysis (PCA) revealed that these 26 proteins were correlated, and could be represented by four principal components. Overall, we established an efficient platform to develop and verify protein biomarkers in CSF, which can be easily adapted to other proteins of interest related to neurodegenerative diseases. CONCLUSIONS:A highly specific and sensitive multiplex SRM-MS assay was established for development and verification of CSF protein biomarkers in SPMS. Five proteins were found to be expressed significantly differently between the three cohorts, SPMS, NIND and HC and 11 proteins associated with lamotrigine treatment, which we expect will further our current understanding of SPMS disease pathology and/or therapeutic intervention.

Project description:High-density lipoprotein (HDL), a lipid nanoparticle containing many different low abundance proteins, is an attractive target for clinical proteomics because its compositional heterogeneity is linked to its cardioprotective effects. Selected reaction monitoring (SRM) is currently the method of choice for targeted quantification of proteins in such a complex biological matrix. However, model system studies suggest that parallel reaction monitoring (PRM) is more specific than SRM because many product ions can be used to confirm the identity of a peptide. We therefore compared PRM and SRM for their abilities to quantify proteins in HDL, using (15)N-labeled apolipoprotein A-I (HDL's most abundant protein) as the internal standard. PRM and SRM exhibited comparable linearity, dynamic range, precision, and repeatability for protein quantification of HDL. Moreover, the single internal standard protein performed as well as protein-specific peptide internal standards when quantifying 3 different proteins. Importantly, PRM and SRM yielded virtually identical quantitative results for 26 proteins in HDL isolated from 44 subjects. Because PRM requires less method development than SRM and is potentially more specific, our observations indicate that PRM in concert with a single isotope-labeled protein is a promising new strategy for quantifying HDL proteins in translational studies.Biological significanceHDL, a complex matrix composed of lipids and proteins, is implicated in cardioprotection. Its cholesterol content correlates inversely with cardiovascular disease and it is the current metric to assess cardiovascular risk. However, the cholesterol content does not capture HDL's complexity and heterogeneity. Devising metrics that better capture HDL's cardioprotective effects, we developed an optimized method for quantification of HDL proteome, using PRM in concert with a single labeled protein as internal standard. The availability of a method that increases sample throughput without compromising the reproducibility, sensitivity, and accuracy could therefore point to better risk assessment for CVD or other diseases.

Project description:BackgroundSince its inception, proteomics has essentially operated in a discovery mode with the goal of identifying and quantifying the maximal number of proteins in a sample. Increasingly, proteomic measurements are also supporting hypothesis-driven studies, in which a predetermined set of proteins is consistently detected and quantified in multiple samples. Selected reaction monitoring (SRM) is a targeted mass spectrometric technique that supports the detection and quantification of specific proteins in complex samples at high sensitivity and reproducibility. Here, we describe ATAQS, an integrated software platform that supports all stages of targeted, SRM-based proteomics experiments including target selection, transition optimization and post acquisition data analysis. This software will significantly facilitate the use of targeted proteomic techniques and contribute to the generation of highly sensitive, reproducible and complete datasets that are particularly critical for the discovery and validation of targets in hypothesis-driven studies in systems biology.ResultWe introduce a new open source software pipeline, ATAQS (Automated and Targeted Analysis with Quantitative SRM), which consists of a number of modules that collectively support the SRM assay development workflow for targeted proteomic experiments (project management and generation of protein, peptide and transitions and the validation of peptide detection by SRM). ATAQS provides a flexible pipeline for end-users by allowing the workflow to start or end at any point of the pipeline, and for computational biologists, by enabling the easy extension of java algorithm classes for their own algorithm plug-in or connection via an external web site.This integrated system supports all steps in a SRM-based experiment and provides a user-friendly GUI that can be run by any operating system that allows the installation of the Mozilla Firefox web browser.ConclusionsTargeted proteomics via SRM is a powerful new technique that enables the reproducible and accurate identification and quantification of sets of proteins of interest. ATAQS is the first open-source software that supports all steps of the targeted proteomics workflow. ATAQS also provides software API (Application Program Interface) documentation that enables the addition of new algorithms to each of the workflow steps. The software, installation guide and sample dataset can be found in http://tools.proteomecenter.org/ATAQS/ATAQS.html.

Dataset Information

Effect of collision energy optimization on the measurement of peptides by selected reaction monitoring (SRM) mass spectrometry.

Publications

Effect of collision energy optimization on the measurement of peptides by selected reaction monitoring (SRM) mass spectrometry.

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