基于质谱的选择反应监测技术相关策略和方法的研究进展

随着蛋白质组学研究的不断深入,基于质谱的选择反应监测技术(SRM)已经成为以发现生物标志物为代表的定向蛋白质组学研究的重要手段.SRM技术根据假设信息,特异性地获取符合假设条件的质谱信号,去除不符合条件的离子信号干扰,从而得到特定蛋白质的定量信息.SRM技术具有更高的灵敏度和精确性、更大的动态范围等优势.该技术可分为实验设计、数据获取和数据分析三个步骤.在这几个步骤中,最重要的是利用生物信息学手段总结当前实验数据的结果,并用机器学习方法和总结的经验规则进行SRM实验的母离子和子离子对的预测.针对数据质控和定量的生物信息学方法研究在提高SRM数据可靠性方面具有重要作用.此外,为方便SRM的研究,本文还收集、汇总了SRM技术相关的软件、工具和数据库资源.随着质谱仪器的不断发展,新的SRM实验策略以及分析方法、计算工具也应运而生.结合更优化的实验策略、方法,采用更精准的生物信息学算法和工具,SRM在未来蛋白质组学的发展中将发挥更加重要的作用.     

An assessment of current bioinformatic solutions for analyzing LC-MS data acquired by selected reaction monitoring technology

Selected reaction monitoring (SRM) is an accurate quantitative technique, typically used for small-molecule mass spectrometry (MS). SRM has emerged as an important technique for targeted and hypothesis-driven proteomic research, and is becoming the reference method for protein quantification in complex biological samples. SRM offers high selectivity, a lower limit of detection and improved reproducibility, compared to conventional shot-gun-based tandem MS (LC-MS/MS) methods. Unlike LC-MS/MS, which requires computationally intensive informatic postanalysis, SRM requires preacquisition bioinformatic analysis to determine proteotypic peptides and optimal transitions to uniquely identify and to accurately quantitate proteins of interest. Extensive arrays of bioinformatics software tools, both web-based and stand-alone, have been published to assist researchers to determine optimal peptides and transition sets. The transitions are oftentimes selected based on preferred precursor charge state, peptide molecular weight, hydrophobicity, fragmentation pattern at a given collision energy (CE), and instrumentation chosen. Validation of the selected transitions for each peptide is critical since peptide performance varies depending on the mass spectrometer used. In this review, we provide an overview of open source and commercial bioinformatic tools for analyzing LC-MS data acquired by SRM.     

Selected reaction monitoring for quantitative proteomics: a tutorial

Systems biology relies on data sets in which the same group of proteins is consistently identified and precisely quantified across multiple samples, a requirement that is only partially achieved by current proteomics approaches. Selected reaction monitoring (SRM)—also called multiple reaction monitoring—is emerging as a technology that ideally complements the discovery capabilities of shotgun strategies by its unique potential for reliable quantification of analytes of low abundance in complex mixtures. In an SRM experiment, a predefined precursor ion and one of its fragments are selected by the two mass filters of a triple quadrupole instrument and monitored over time for precise quantification. A series of transitions (precursor/fragment ion pairs) in combination with the retention time of the targeted peptide can constitute a definitive assay. Typically, a large number of peptides are quantified during a single LC‐MS experiment. This tutorial explains the application of SRM for quantitative proteomics, including the selection of proteotypic peptides and the optimization and validation of transitions. Furthermore, normalization and various factors affecting sensitivity and accuracy are discussed.     

Representation of selected‐reaction monitoring data in the mzQuantML data standard

The mzQuantML data standard was designed to capture the output of quantitative software in proteomics, to support submissions to public repositories, development of visualization software and pipeline/modular approaches. The standard is designed around a common core that can be extended to support particular types of technique through the release of semantic rules that are checked by validation software. The first release of mzQuantML supported four quantitative proteomics techniques via four sets of semantic rules: (i) intensity‐based (MS¹) label free, (ii) MS¹ label‐based (such as SILAC or N¹⁵), (iii) MS² tag‐based (iTRAQ or tandem mass tags), and (iv) spectral counting. We present an update to mzQuantML for supporting SRM techniques. The update includes representing the quantitative measurements, and associated meta‐data, for SRM transitions, the mechanism for inferring peptide‐level or protein‐level quantitative values, and support for both label‐based or label‐free SRM protocols, through the creation of semantic rules and controlled vocabulary terms. We have updated the specification document for mzQuantML (version 1.0.1) and the mzQuantML validator to ensure that consistent files are produced by different exporters. We also report the capabilities for production of mzQuantML files from popular SRM software packages, such as Skyline and Anubis.     

DeepQuanTR: MALDI‐MS‐based label‐free quantification of proteins in complex biological samples

The quantification of changes in protein abundance in complex biological specimens is essential for proteomic studies in basic and applied research. Here we report on the development and validation of the DeepQuanTR software for identification and quantification of differentially expressed proteins using LC‐MALDI‐MS. Following enzymatic digestion, HPLC peptide separation and normalization of MALDI‐MS signal intensities to the ones of internal standards, the software extracts peptide features, adjusts differences in HPLC retention times and performs a relative quantification of features. The annotation of multiple peptides to the corresponding parent protein allows the definition of a Protein Quant Value, which is related to protein abundance and which allows inter‐sample comparisons. The performance of DeepQuanTR was evaluated by analyzing 24 samples deriving from human serum spiked with different amounts of four proteins and eight complex samples of vascular proteins, derived from surgically resected human kidneys with cancer following ex vivo perfusion with a reactive ester biotin derivative. The identification and experimental validation of proteins, which were differentially regulated in cancerous lesions as compared with normal kidney, was used to demonstrate the power of DeepQuanTR. This software, which can easily be used with established proteomic methodologies, facilitates the relative quantification of proteins derived from a wide variety of different samples.     

Applying selected reaction monitoring to targeted proteomics

Selected reaction monitoring (SRM) is a highly selective and sensitive mass spectrometric methodology for precise and accurate quantification of low-abundant proteins in complex mixtures and for characterization of modified peptides, and constitutes the method of choice in targeted proteomics. Owing to its outstanding features, SRM arises as an alternative to antibody-based assays for discovery and validation of clinically relevant biomarkers, a topic that is tackled in this article. Several of the obstacles encountered in SRM experiments, mainly those derived from shared physicochemical properties of peptides (e.g., mass, charge and chromatographic retention time), can compromise selectivity and quantitation. We illustrate how to circumvent these limitations on the basis of using time-scheduled chromatographic approaches and choosing appropriate spectrometric conditions, including the careful selection of the precursor and diagnostic ions.     

选择反应监测技术在蛋白质组学研究中的应用

基于三重四极杆质谱仪的选择反应监测(SRM)技术是一种根据已有信息或理论信息靶向进行质谱信号采集的技术,具有高选择性、高重复性、高灵敏度、宽动态范围等特点,已被广泛应用于蛋白质组学研究,用于生物样本中蛋白质的绝对定量分析.本文对SRM技术的特点、发展过程、在蛋白质组学中的应用现状以及发展前景进行了概述.     

Highly multiplexed targeted proteomics using precise control of peptide retention time

Large-scale proteomics applications using SRM analysis on triple quadrupole mass spectrometers present new challenges to LC-MS/MS experimental design. Despite the automation of building large-scale LC-SRM methods, the increased numbers of targeted peptides can compromise the balance between sensitivity and selectivity. To facilitate large target numbers, time-scheduled SRM transition acquisition is performed. Previously published results have demonstrated incorporation of a well-characterized set of synthetic peptides enabled chromatographic characterization of the elution profile for most endogenous peptides. We have extended this application of peptide trainer kits to not only build SRM methods but to facilitate real-time elution profile characterization that enables automated adjustment of the scheduled detection windows. Incorporation of dynamic retention time adjustments better facilitate targeted assays lasting several days without the need for constant supervision. This paper provides an overview of how the dynamic retention correction approach identifies and corrects for commonly observed LC variations. This adjustment dramatically improves robustness in targeted discovery experiments as well as routine quantification experiments.     

Protein significance analysis in selected reaction monitoring (SRM) measurements

Selected reaction monitoring (SRM) is a targeted mass spectrometry technique that provides sensitive and accurate protein detection and quantification in complex biological mixtures. Statistical and computational tools are essential for the design and analysis of SRM experiments, particularly in studies with large sample throughput. Currently, most such tools focus on the selection of optimized transitions and on processing signals from SRM assays. Little attention is devoted to protein significance analysis, which combines the quantitative measurements for a protein across isotopic labels, peptides, charge states, transitions, samples, and conditions, and detects proteins that change in abundance between conditions while controlling the false discovery rate. We propose a statistical modeling framework for protein significance analysis. It is based on linear mixed-effects models and is applicable to most experimental designs for both isotope label-based and label-free SRM workflows. We illustrate the utility of the framework in two studies: one with a group comparison experimental design and the other with a time course experimental design. We further verify the accuracy of the framework in two controlled data sets, one from the NCI-CPTAC reproducibility investigation and the other from an in-house spike-in study. The proposed framework is sensitive and specific, produces accurate results in broad experimental circumstances, and helps to optimally design future SRM experiments. The statistical framework is implemented in an open-source R-based software package SRMstats, and can be used by researchers with a limited statistics background as a stand-alone tool or in integration with the existing computational pipelines.     

An automated platform for analysis of phosphoproteomic datasets: application to kidney collecting duct phosphoproteins

Large-scale phosphoproteomic analysis employing liquid chromatography-tandem mass spectrometry (LC-MS/MS) often requires a significant amount of manual manipulation of phosphopeptide datasets in the post-acquisition phase. To assist in this process, we have created software, PhosphoPIC (PhosphoPeptide Identification and Compilation), which can perform a variety of useful functions including automated selection and compilation of phosphopeptide identifications from multiple MS levels, estimation of dataset false discovery rate, and application of appropriate cross-correlation (XCorr) filters. In addition, the output files generated by this program are compatible with downstream phosphorylation site assignment using the Ascore algorithm, as well as phosphopeptide quantification via QUOIL. In this report, we utilized this software to analyze phosphoproteins from short-term vasopressin-treated rat kidney inner medullary collecting duct (IMCD). A total of 925 phosphopeptides representing 173 unique proteins were identified from membrane-enriched fractions of IMCD with a false discovery rate of 1.5%. Of these proteins, 106 were found only in the membrane-enriched fraction of IMCD cells and not in whole IMCD cell lysates. These identifications included a number of well-studied ion and solute transporters including ClC-1, LAT4, MCT2, NBC3, and NHE1, all of which contained novel phosphorylation sites. Using a label-free quantification approach, we identified phosphoproteins that changed in abundance with vasopressin exposure including aquaporin-2 (AQP2), Hnrpa3, IP3 receptor 3, and pur-beta.     

Platform for establishing interlaboratory reproducibility of selected reaction monitoring-based mass spectrometry peptide assays

Mass spectrometry (MS) is an attractive alternative to quantification of proteins by immunoassays, particularly for protein biomarkers of clinical relevance. Reliable quantification requires that the MS-based assays are robust, selective, and reproducible. Thus, the development of standardized protocols is essential to introduce MS into clinical research laboratories. The aim of this study was to establish a complete workflow for assessing the transferability and reproducibility of selected reaction monitoring (SRM) assays between clinical research laboratories. Four independent laboratories in North America, using identical triple-quadrupole mass spectrometers (Quantum Ultra, Thermo), were provided with standard protocols and instrumentation settings to analyze unknown samples and internal standards in a digested plasma matrix to quantify 51 peptides from 39 human proteins using a multiplexed SRM assay. The interlaboratory coefficient of variation (CV) was less than 10% for 25 of 39 peptides quantified (12 peptides were not quantified based upon hydrophobicity) and exhibited CVs less than 20% for the remaining peptides. In this report, we demonstrate that previously developed research platforms for SRM assays can be improved and optimized for deployment in clinical research environments.     

Selected Reaction Monitoring Mass Spectrometry for Absolute Protein Quantification

Absolute quantification of target proteins within complex biological samples is critical to a wide range of research and clinical applications. This protocol provides step-by-step instructions for the development and application of quantitative assays using selected reaction monitoring (SRM) mass spectrometry (MS). First, likely quantotypic target peptides are identified based on numerous criteria. This includes identifying proteotypic peptides, avoiding sites of posttranslational modification, and analyzing the uniqueness of the target peptide to the target protein. Next, crude external peptide standards are synthesized and used to develop SRM assays, and the resulting assays are used to perform qualitative analyses of the biological samples. Finally, purified, quantified, heavy isotope labeled internal peptide standards are prepared and used to perform isotope dilution series SRM assays. Analysis of all of the resulting MS data is presented. This protocol was used to accurately assay the absolute abundance of proteins of the chemotaxis signaling pathway within RAW 264.7 cells (a mouse monocyte/macrophage cell line). The quantification of G_i2 (a heterotrimeric G-protein α-subunit) is described in detail.     

Development of a Chip/Chip/SRM platform using digital chip isoelectric focusing and LC-Chip mass spectrometry for enrichment and quantitation of low abundance protein biomarkers in human plasma

Protein biomarkers are critical for diagnosis, prognosis, and treatment of disease. The transition from protein biomarker discovery to verification can be a rate limiting step in clinical development of new diagnostics. Liquid chromatography-selected reaction monitoring mass spectrometry (LC-SRM MS) is becoming an important tool for biomarker verification studies in highly complex biological samples. Analyte enrichment or sample fractionation is often necessary to reduce sample complexity and improve sensitivity of SRM for quantitation of clinically relevant biomarker candidates present at the low ng/mL range in blood. In this paper, we describe an alternative method for sample preparation for LC-SRM MS, which does not rely on availability of antibodies. This new platform is based on selective enrichment of proteotypic peptides from complex biological peptide mixtures via isoelectric focusing (IEF) on a digital ProteomeChip (dPC) for SRM quantitation using a triple quadrupole (QQQ) instrument with an LC-Chip (Chip/Chip/SRM). To demonstrate the value of this approach, the optimization of the Chip/Chip/SRM platform was performed using prostate specific antigen (PSA) added to female plasma as a model system. The combination of immunodepletion of albumin and IgG with peptide fractionation on the dPC, followed by SRM analysis, resulted in a limit of quantitation of PSA added to female plasma at the level of ～1-2.5 ng/mL with a CV of ～13%. The optimized platform was applied to measure levels of PSA in plasma of a small cohort of male patients with prostate cancer (PCa) and healthy matched controls with concentrations ranging from 1.5 to 25 ng/mL. A good correlation (r(2) = 0.9459) was observed between standard clinical ELISA tests and the SRM-based assay. Our data demonstrate that the combination of IEF on the dPC and SRM (Chip/Chip/SRM) can be successfully applied for verification of low abundance protein biomarkers in complex samples.     

TagFinder for the quantitative analysis of gas chromatography--mass spectrometry (GC-MS)-based metabolite profiling experiments

MOTIVATION: Typical GC-MS-based metabolite profiling experiments may comprise hundreds of chromatogram files, which each contain up to 1000 mass spectral tags (MSTs). MSTs are the characteristic patterns of approximately 25-250 fragment ions and respective isotopomers, which are generated after gas chromatography (GC) by electron impact ionization (EI) of the separated chemical molecules. These fragment ions are subsequently detected by time-of-flight (TOF) mass spectrometry (MS). MSTs of profiling experiments are typically reported as a list of ions, which are characterized by mass, chromatographic retention index (RI) or retention time (RT), and arbitrary abundance. The first two parameters allow the identification, the later the quantification of the represented chemical compounds. Many software tools have been reported for the pre-processing, the so-called curve resolution and deconvolution, of GC-(EI-TOF)-MS files. Pre-processing tools generate numerical data matrices, which contain all aligned MSTs and samples of an experiment. This process, however, is error prone mainly due to (i) the imprecise RI or RT alignment of MSTs and (ii) the high complexity of biological samples. This complexity causes co-elution of compounds and as a consequence non-selective, in other words impure MSTs. The selection and validation of optimal fragment ions for the specific and selective quantification of simultaneously eluting compounds is, therefore, mandatory. Currently validation is performed in most laboratories under human supervision. So far no software tool supports the non-targeted and user-independent quality assessment of the data matrices prior to statistical analysis. TagFinder may fill this gap. Strategy: TagFinder facilitates the analysis of all fragment ions, which are observed in GC-(EI-TOF)-MS profiling experiments. The non-targeted approach allows the discovery of novel and unexpected compounds. In addition, mass isotopomer resolution is maintained by TagFinder processing. This feature is essential for metabolic flux analyses and highly useful, but not required for metabolite profiling. Whenever possible, TagFinder gives precedence to chemical means of standardization, for example, the use of internal reference compounds for retention time calibration or quantitative standardization. In addition, external standardization is supported for both compound identification and calibration. The workflow of TagFinder comprises, (i) the import of fragment ion data, namely mass, time and arbitrary abundance (intensity), from a chromatography file interchange format or from peak lists provided by other chromatogram pre-processing software, (ii) the annotation of sample information and grouping of samples into classes, (iii) the RI calculation, (iv) the binning of observed fragment ions of equal mass from different chromatograms into RI windows, (v) the combination of these bins, so-called mass tags, into time groups of co-eluting fragment ions, (vi) the test of time groups for intensity correlated mass tags, (vii) the data matrix generation and (viii) the extraction of selective mass tags supported by compound identification. Thus, TagFinder supports both non-targeted fingerprinting analyses and metabolite targeted profiling. AVAILABILITY: Exemplary TagFinder workspaces and test data sets are made available upon request to the contact authors. TagFinder is made freely available for academic use from http://www-en.mpimp-golm.mpg.de/03-research/researchGroups/01-dept1/Root_Metabolism/smp/TagFinder/index.html.     

ProbID: a probabilistic algorithm to identify peptides through sequence database searching using tandem mass spectral data

With the recent quick expansion of DNA and protein sequence databases, intensive efforts are underway to interpret the linear genetic information of DNA in terms of function, structure, and control of biological processes. The systematic identification and quantification of expressed proteins has proven particularly powerful in this regard. Large-scale protein identification is usually achieved by automated liquid chromatography-tandem mass spectrometry of complex peptide mixtures and sequence database searching of the resulting spectra [Aebersold and Goodlett, Chem. Rev. 2001, 101, 269-295]. As generating large numbers of sequence-specific mass spectra (collision-induced dissociation/CID) spectra has become a routine operation, research has shifted from the generation of sequence database search results to their validation. Here we describe in detail a novel probabilistic model and score function that ranks the quality of the match between tandem mass spectral data and a peptide sequence in a database. We document the performance of the algorithm on a reference data set and in comparison with another sequence database search tool. The software is publicly available for use and evaluation at http://www.systemsbiology.org/research/software/proteomics/ProbID.     

Mass spectrometry-based detection and quantification of plasma glycoproteins using selective reaction monitoring

Mass spectrometry-based targeted proteomics is a rapidly expanding method for quantifying proteins in complex clinical samples such as plasma. In conjunction with the stable isotope dilution method, selected reaction monitoring (SRM) assays provide unparalleled sensitivity and selectivity for detection and quantification. A crucial factor for robust SRM assays is the reduction of interference by lowering the background. This can be achieved by the selective isolation of a subproteome, such as N-glycosylated proteins, from the original sample. The present protocol includes the development and optimization of SRM assays associated with each peptide of interest and the qualification of assays in the biological matrix to establish the limits of detection and quantification. The protocol also describes the enrichment of formerly N-glycosylated peptides relying on periodate oxidation of glycan moieties attached to the proteins, their immobilization on solid supports through hydrazide chemistry, proteolysis and enzymatic release of the formerly N-glycosylated peptides.     

Advancing the sensitivity of selected reaction monitoring-based targeted quantitative proteomics

Selected reaction monitoring (SRM) - also known as multiple reaction monitoring (MRM) - has emerged as a promising high-throughput targeted protein quantification technology for candidate biomarker verification and systems biology applications. A major bottleneck for current SRM technology, however, is insufficient sensitivity for, e.g. detecting low-abundance biomarkers likely present at the low ng/mL to pg/mL range in human blood plasma or serum, or extremely low-abundance signaling proteins in cells or tissues. Herein, we review recent advances in methods and technologies, including front-end immunoaffinity depletion, fractionation, selective enrichment of target proteins/peptides including posttranslational modifications, as well as advances in MS instrumentation which have significantly enhanced the overall sensitivity of SRM assays and enabled the detection of low-abundance proteins at low- to sub-ng/mL level in human blood plasma or serum. General perspectives on the potential of achieving sufficient sensitivity for detection of pg/mL level proteins in plasma are also discussed.     

mProphet: automated data processing and statistical validation for large-scale SRM experiments

Selected reaction monitoring (SRM) is a targeted mass spectrometric method that is increasingly used in proteomics for the detection and quantification of sets of preselected proteins at high sensitivity, reproducibility and accuracy. Currently, data from SRM measurements are mostly evaluated subjectively by manual inspection on the basis of ad hoc criteria, precluding the consistent analysis of different data sets and an objective assessment of their error rates. Here we present mProphet, a fully automated system that computes accurate error rates for the identification of targeted peptides in SRM data sets and maximizes specificity and sensitivity by combining relevant features in the data into a statistical model.     

Modeling and systematic analysis of biomarker validation using selected reaction monitoring

Background

Discovery and validation of protein biomarkers with high specificity is the main challenge of current proteomics studies. Different mass spectrometry models are used as shotgun tools for the discovery of biomarkers. Validation of a set of selected biomarkers from a list of candidates is an important stage in the biomarker identification pipeline. Validation is typically done by triple quadrupole (QQQ) mass spectrometry (MS) running in selected reaction monitoring (SRM) mode. Although the individual modules of this pipeline have been studied, there is little work on integrating the components from a systematic point of view.

Results

This paper analyzes the SRM experiment pipeline in a systematic fashion, by modeling the main stages of the biomarker validation process. The proposed models for SRM and protein mixture are then used to study the effect of different parameters on the final performance of biomarker validation. Sample complexity, purification, peptide ionization, and peptide specificity are among the parameters of the SRM experiment that are studied. We focus on the sensitivity of the SRM pipeline to the working parameters, in order to identify the bottlenecks where time and energy should be spent in designing the experiment.

Conclusions

The model presented in this paper can be utilized to observe the effect of different instrument and experimental settings on biomarker validation by SRM. On the other hand, the model would be beneficial for optimization of the work flow as well as identification of the bottlenecks of the pipeline. Also, it creates the required infrastructure for predicting the performance of the SRM pipeline for a specific setting of the parameters.