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Special Feature: Tutorial Received: 3 November 2010 Accepted: 12 January 2011 Published online in Wiley Online Library: 2011 (wileyonlinelibrary.com) DOI 10.1002/jms.1895 Selected reaction monitoring applied to proteomics Sebastien Gallien, Elodie Duriez and Bruno Domon∗ Selected reaction monitoring (SRM) performed on triple quadrupole mass spectrometers has been the reference quantitative technique to analyze small molecules for several decades. It is now emerging in proteomics as the ideal tool to complement shotgun qualitative studies; targeted SRM quantitative analysis offers high selectivity, sensitivity and a wide dynamic range. However, SRM applied to proteomics presents singularities that distinguish it from small molecules analysis. This review is an overview of SRM technology and describes the specificities and the technical aspects of proteomics experiments. Ongoing developments aiming at increasing multiplexing capabilities of SRM are discussed; they dramatically improve its throughput c 2011 John Wiley & Sons, Ltd. and extend its field of application to directed or supervised discovery experiments. Copyright Keywords: selected reaction monitoring; proteomics; quantification; triple quadrupole mass spectrometer; multiplexing Introduction 298 Over the past decade, LC–MS/MS-based proteomics has emerged as the most effective method to study complex proteomes. In this approach, the proteins representing a proteome or a subset thereof are enzymatically digested to generate peptides, which in turn are analyzed by liquid chromatography coupled to mass spectrometry. This shotgun approach is a powerful tool to identify proteins in complex biological samples as exemplified by a wealth of publications.[1,2] However, it is not optimal for a systematic quantification of these proteins because of the stochastic nature and the limited sensitivity of the approach; most often, only relative quantification of the abundant components is performed. Thus, alternative MS approaches based on selected reaction monitoring (SRM) have emerged to precisely and quantitatively analyze complex biological samples. SRM is not a new technique per se but its application to proteomics emerged during the past decade. The technique was introduced in the late 1970s[3,4] along with the development of the first triple quadrupole mass spectrometers[5] and has been for 30 years a reference quantitative technique to analyze small molecules,[6,7] notably in clinical applications.[8] The hypothesis-driven nature of such experiments overcomes the bias towards most abundant components. The analysis targets specific subsets of analytes, peptides as surrogates for the proteins of interest, and in this instance it is performed by isolating, within the mass spectrometer, ions corresponding to the molecule of interest. These ions, in the case of peptides, doubly or triply protonated (i.e. charged) molecular species, are then fragmented and a few specific fragments are monitored for detection and quantification purposes. This particular mode of operation of the triple quadrupole instrument yields the high level of selectivity and sensitivity and the wide dynamic range of the analysis. Already realized and still ongoing developments have allowed to multiplex the analytes and measure larger sets of peptides, opening new avenues in terms of productivity[9] and experimental scope. The most common SRM application in proteomics is the precise quantification using isotopically labeled reference peptides. The stable isotope dilution (SID) concept has a long history in J. Mass. Spectrom. 2011, 46, 298–312 quantitative mass spectrometry and is the reference method.[10,11] It was applied to peptides in 1983[12] and to protein digest in 1996[13] using fast atom bombardment (FAB) mass spectrometry. Any mass spectrometer type can in principle take advantage of SID, but only triple quadrupole instruments capable of SRM can fully exploit the MS/MS potential. The combination of SID approach and SRM technique (SID–SRM) is the golden standard for absolute quantification, and pertinent applications include trypsinized proteins from membrane preparation,[14] whole-cell lysates[15] and bodily fluids including serum, plasma, urine and synovia.[16 – 19] The targeted LC–MS analysis of proteins by SRM has singularities that distinguish it from the method used for small molecules. The most striking one is that in the case of proteins, the method is indirect and requires several intermediate steps to generate the peptides and select the appropriate surrogate analytes. The second one is the complexity and the dynamic range of proteomic samples, for instance in bodily fluids, incommensurate with those of low molecular mass samples typically analyzed even in the case of metabolite studies. These particularities have to be taken into account when designing a proteomic SRM experiment. A specific point is the nature of the matrix: in SRM analysis of small molecules, the analytes of interest represent a small fraction (ppm or ppb) of the total sample amount but analyte and matrix have different chemical natures and the up-front separation step is able to effectively separate the analytes from other components. In contrast, in the case of proteomics, both the analytes and the background have the same chemical composition (peptides), which represents the main challenge in terms of interferences, ion suppression effects and thus limit of detection (LOD). These particularities have prompted numerous improvements at the ∗ Correspondence to: Bruno Domon, Luxembourg Clinical Proteomics center (LCP), Centre de Recherche Public de la Santé, 1 B rue Thomas Edison, L-1445 Strassen, Luxembourg. E-mail: [email protected] Luxembourg Clinical Proteomics center (LCP), Centre de Recherche Public de la Santé, 1 B rue Thomas Edison, L-1445 Strassen, Luxembourg c 2011 John Wiley & Sons, Ltd. Copyright Selected reaction monitoring applied to proteomics Source MS-1 CID MS-2 Fixed Fixed Fixed Fixed m/z (Q1) CE (Q2) time m/z (Q3) Figure 1. Principle of the selected reaction monitoring performed on a triple quadruple mass spectrometer. The precursor ion selected by the first mass filter (Q1) enters the collision cell (Q2) where it undergoes collision-induced dissociation. One fragment ions is then selected by the second mass filter (Q2). Multiple precursor/fragment ion pairs can be monitored sequentially within a measurement cycle. Adapted from Ref. [2]. instrument control level as well as at the quantification strategy level. This review provides an overview of the SRM technology; it describes the specificities and the technical aspects of proteomic experiments, focusing on quantification. The last section will discuss ongoing developments regarding the multiplexing capabilities. Proteomic Applications – a Historical Perspective J. Mass. Spectrom. 2011, 46, 298–312 Specificities of SRM Applied to Proteomics SRM analysis of small molecules differs from proteomic applications in which peptides analyzed are surrogates for the targeted proteins. Consequently, proteomics requires a more complex workflow, including an experimental design step to select the peptides suited for LC–MS measurements. The specificities and the technical aspects of a proteomic SRM assay include five main steps, illustrated in Fig. 2: (1) considering a specific biological or clinical question, definition of the set of proteins of interest; (2) for c 2011 John Wiley & Sons, Ltd. Copyright wileyonlinelibrary.com/journal/jms 299 The SRM technique was first described in the late 1970s; initially in the context of ‘mass-analyzed ion kinetic energy’ (MIKE) experiment performed on sector instruments.[4] The SRM technique becomes routine when implemented on triple quadrupole mass spectrometers, first introduced by Enke and Yost.[5] When a triple quadrupole instrument is operated in SRM mode, the first and the third quadrupole serve as mass filters to specifically select predefined m/z values corresponding to the precursor ion and a specific fragment ion of that precursor, whereas the second quadrupole is used as collision cell (Fig. 1). Initially, this mode of operation was called single reaction monitoring or multiple reaction monitoring depending on whether one precursor/fragment ion pair (transition) was monitored, or a series of transitions were measured iteratively. These two terms were replaced by the unique term SRM to avoid the ambiguity between the number of transitions monitored and the number of stages used in the mass spectrometry analysis (MSn ).[85] The SRM mode is characterized by a high sensitivity enabling to detect low amount of the targeted analytes even in a complex matrix, such as those of protein digests. In addition, its targeted nature, its high selectivity achieved by two stages of mass filtering and its wide dynamic range make SRM ideal for quantitative proteomics, especially when combined with SID strategies for absolute quantification purposes. The SID strategies for peptide quantification were first introduced by Desiderio using FAB-MS and 18 O-incorporated standard peptide in order to determine the amount of endogenous enkephalin in thalamus extract.[12] This first promising result represents the cornerstone of absolute quantification of proteins, as demonstrated by the analysis of the enzymatic digest of a purified protein by Barr and co-workers using continuous-flow FAB-MS and synthesized stable isotope-labeled peptides to determine the absolute amount of apolipoprotein A-1.[13] Later on, the strategy was applied in conjunction with SRM to membrane proteins characterization, including the G proteincoupled receptor rhodopsin.[14] This method was also used to quantify low abundance proteins in more complex samples.[15] More recently, SID–SRM analysis was applied to the quantification of low-abundance proteins in clinical biological fluids such as serum, plasma or synovial fluid, which represent an even more challenging task due to the complexity and large dynamic range of proteins present in such media. The first application of this type was the quantification of C-reactive protein, a diagnostic biomarker of rheumatoid arthritis, in serum after depletion of three abundant proteins.[16] The measurements performed by SRM were compared with results from immunoassays, which appeared to be closely correlated. Similarly, prostate-specific antigen was also directly measured in non-depleted serum.[17] These examples demonstrated the potential of SID–SRM analysis to quantify some known protein biomarkers in biological fluids provided they are within the dynamic range of detection. The method is generally effective in clinical proteomics as illustrated by a study of Liao et al.,[18] which aimed at identifying new protein biomarkers reflecting disease severity in rheumatoid arthritis. They developed a two-step proteomic approach in which biomarker discovery is performed in synovial fluid using shotgun LC–MS/MS. Putative biomarkers were then confirmed in serum using SID–SRM analyses. In spite of the limited scale of the study, this work paved the way for the biomarker development pipeline, nowadays a benchmark in clinical proteomics.[20] Subsequent developments have focused on one hand on exploiting the ability to quantify proteins in a multiplexed manner[19,21] and on the other hand on increasing SRM sensitivity by sample fractionation, including immunoaffinity depletion, multidimensional fractionation or affinity enrichment techniques.[22] These studies have demonstrated the feasibility of analyzing biomarkers in bodily fluids. Minimal processing of blood samples, such as depletion of most abundant proteins, allows reaching a good precision (coefficients of variation below 10%) and a sensitivity down to the µg/ml.[19] More elaborated sample preparations, such as glycocapture[21] or SISCAPA[22] which dramatically reduce the background, allow measurements as low as ng/ml. Numerous applications of the SRM technique have recently been reported.[21,23 – 33] S. Gallien, E. Duriez and B. Domon - Biological / clinical question - Proteomic or genomic experiment, literature mining Definition of protein set - Sequence uniqueness - LC attributes (reversed phase) - MS properties Selection of peptides - Sensitivity (intense fragments) - Selectivity (interferences) Selection of transitions - Experimental measurements to assess background interferences Validation of transitions - Ionization source - Collision conditions Optimization of transitions Figure 2. Workflow of a SRM-based proteomic experiment. Firstly, the set of proteins of interest is defined considering the biological or clinical question of the study. Secondly, for each protein, a set of ‘best representing’ peptides is determined on the basis of their uniqueness and performance in LC–MS analysis. Thirdly, transitions maximizing sensitivity and selectivity are selected. Finally, transitions are validated to assure that the detected signals correspond truly to the targeted peptides. An additional step of transition optimization is optionally included in the workflow to increase sensitivity of the quantification. each protein, the determination of the set of peptides representing each protein; (3) selection of the transitions that maximize sensitivity and selectivity of the experiment; (4) experimental validation of the transitions, whenever possible in biological background and (5) if precise quantification and high sensitivity are required, additional optimization of transitions. Definition of the proteins of interest The first step in the design of a SRM analysis is the definition of target proteins. A hypothesis-driven proteomic experiment typically aims at answering a specific biological or clinical question, e.g. the analysis of a pathway, a protein network or the evaluation of a set of biomarkers associated with a disease. The protein subset of immediate interest defines the actual SRM experiment, and results from previously acquired knowledge from -omics study and scientific literature mining represent additional resources to design the actual experiment. Ultimately, proteomics aims at analyzing a whole proteome in one experiment, which remains a long shot even with this technology. Determination of the peptides to be analyzed 300 For each protein to be included in an SRM analysis, a set of tryptic peptides resulting from the enzymatic digestion of the sample is selected. Most proteomic studies use trypsin as proteolytic enzyme, which yields analytes well suited for LC–MS analysis, i.e. typically peptides containing 8–25 amino acids, accommodating the m/z range of the quadrupole analyzer. Typically, a few representative tryptic peptides for each protein are targeted to infer its presence in a sample and to quantify it. However, their selection is not always a trivial step. The targeted peptides, often called proteotypic, need to fulfill some very stringent criteria, namely, having an amino acid sequence uniquely associated with the proteins of interest, and being consistently observed in LC–MS wileyonlinelibrary.com/journal/jms analyses, which is often correlated to good ionization efficiency (i.e. detectability). The sequence uniqueness of a peptide is determined through in silico mapping of the amino acid sequence onto the entire protein space (i.e. the proteome under investigation). In order to be systematically observed in a mass spectrometric analysis, peptides require intrinsic properties: good ionization efficiency and a mass-to-charge ratio within the practical mass range of the instrument. Moreover, in a quantitative experimental workflow, other factors have to be considered. The first one is the sample preparation; in this step peptides should be fully recovered and soluble after digestion. Peptides presenting a missed cleavage (incomplete digestion product) or degradation will translate in inconsistent results. The second factor is the chromatographic behavior of the analytes: hydrophilic peptides are poorly retained on the stationary phase, while very hydrophobic peptides present tailing effects, will elute late, or may even stick on the column. Poor chromatographic behavior may also contribute to an increased chemical background. Peptide selection based on experimental evidences Current practice to assess consistent peptide detection is based on observations in initial discovery experiments from individual laboratories compiled in proteomic data repositories. Such databases represent a tremendous resource as their volume is growing steadily; it includes PeptideAtlas,[34] GPM Proteomics Database,[35] PRIDE.[36] The selection of experimentally observed peptides relies on the number of their observations, as the spectral count that is an indicator of the abundance of proteins in a specific data set. For instance, PeptideAtlas provides an Empirical Proteotypic Score (EPS) reflecting the number of samples in which a given peptide was observed. c 2011 John Wiley & Sons, Ltd. Copyright J. Mass. Spectrom. 2011, 46, 298–312 Selected reaction monitoring applied to proteomics Peptide selection based on prediction In the absence of experimental data or sparse data sets, for instance low abundant proteins poorly represented in repositories, computational approaches can be used. They allow predicting the physico-chemical properties of peptides to select the best responding peptides. Several such tools have been published, including ESP predictor,[37] PeptideSieve,[38] PepFly,[39] STEPP[40] and others.[41,42] These prediction tools are trained in a first step with existing data sets to determine the most relevant physico-chemical properties to predict the LC and MS behavior of the peptides generated by proteolytic digestions of proteins of interest. Although very helpful, at present, neither approach is sufficient to define an optimal peptide set, in particular if signature peptides of a given candidate have not been observed in discovery experiments. As a matter of fact, the observability of a peptide in discovery experiments is largely related to the richness of the fragmentation pattern and the ability of the search engine to reliably assign the amino acid sequence. In contrast, peptides with fewer fragments, e.g. short amino acid sequences or the presence of proline residues often yield fewer but more intense signals. Such peptides may not be reported in discovery experiment but still represent good analytes for an SRM experiment. Furthermore, peptides containing amino acids prone to chemical modifications can bias the quantification as they might occur under different forms; it includes cysteine alkylation, methionine oxidation, asparagine deamidation and N-terminal cyclization of glutamic acid. Similarly, peptides susceptible to undergo post-translational modifications (glycosylation, phosphorylation, etc.) might lead to bias as they can be present in various forms. Unless it is the explicit purpose of the assay to quantify potentially modified peptides (using adequate sample preparation), such peptides should be selected carefully. This in fact raises the more general question of the representativity of a peptide for a given protein. A single peptide only defines a small portion of a protein, irrespective of any chemical modification, proteolytic event or splice variants that can occur for this protein. If the use of a single proteotypic peptide as a surrogate of the protein might be sufficient within the context of a screening experiment (see Multiplexing of Analytes Section), it is certainly unsatisfactory for a reliable absolute quantification.[43] Ideally, several peptides distributed across the full sequence should be selected for a given protein. For proteins existing under different forms (e.g. isoforms), the deliberate selection of the peptides should cover both conserved and variable domains, to quantify the variants. Selection of SRM transitions J. Mass. Spectrom. 2011, 46, 298–312 Validation of SRM transitions In spite of the increased specificity provided by the two-stage mass selection of triple quadrupole instruments, each transition selected for a specific peptide should be evaluated in the context of the actual biological matrix to account for unspecific contributions of the fragment ions deriving from co-eluting species with similar properties. Figure 3 illustrates a typical case. Evaluating the profile of the SRM traces is a simple mean to verify the selectivity of a given transition. The cross-correlation method based on linear regression described[47,48] can be applied to assess the co-elution of traces. The graphical representation allows visualization of the data, and the resulting metrics (slope, correlation coefficient) permit the objective assessment of the co-elution (Fig. 3(A)). When isotopically labeled reference peptides are available, checking the co-elution of the traces of the native peptides (see Specificities of SRM Applied to Proteomics Section) with those of their labeled counterparts (Fig. 3(B)) represents the ultimate validation method. In this instance, the relative intensities measured for a native peptide and for its labeled counterpart should be identical for each transition and represent an additional metrics enabling to assess the purity of the signal related to each transition and to detect potential interferences from the background impacting its measurement. This method, also used to determine LOD, is nevertheless limited to experiments aiming at the quantification c 2011 John Wiley & Sons, Ltd. Copyright wileyonlinelibrary.com/journal/jms 301 Once the set of peptides best representing the proteins of interest determined, it is critical to select transition ions that maximize sensitivity and specificity of the SRM experiment. While sensitivity is related to the signal intensity of a transition, specificity is associated with interferences from co-eluting species that fall within the mass selection windows of Q1 and Q3 analyzers. The current practice is to select the two or three most intense transitions to build an SRM assay. As previously mentioned, in the absence of physical reference peptides, the selection of the transitions relies on MS/MS spectra from discovery experiments. The data (MS/MS spectra) are typically obtained on ion trap or quadrupole-time of flight instruments and stored, without curation, in reference spectra repositories (PeptideAtlas,[34] GPM Proteomics Database,[35] PRIDE[36] ). More recently, systematic efforts were undertaken to develop more standardized and curated repositories such as SRMAtlas[44] that contains optimized SRM transitions. In the process, primary signal, related to the precursor ions, is critical to maximize sensitivity. The m/z value of the first analyzer should thus be set in order to select the most intense mass-to-charge ratio of the targeted peptide. Although charge state distribution of analytes is not completely independent of experimental conditions, experimental LC–MS data available for these analytes are very helpful to determine their dominant massto-charge ratio. Ab initio prediction, required in the absence of experimental data, will favor doubly charged precursor ions unless the peptide sequence contains a histidine residue promoting triply charged ions. The m/z value set for the second analyzer cannot directly be deduced from previous experimental data because the relative intensities of fragment ions depend on the type of instrument used and operating parameters. Although fragmentation patterns have similarities between instruments and between mass spectrometer parameters, relative ion intensities depend on the different modes of collision-induced dissociation, e.g. ion traps versus quadrupole collision cell. For instance, b-type fragments of higher m/z are less extensively represented in the triple quadrupole mass spectra.[45] The calculated m/z values of y-ions resulting from easy cleavages, such as those resulting from fragmentation N-terminal to a proline residue or C-terminal to glutamate or aspartate residues, can be selected. In order to maximize selectivity, fragment ions with m/z values higher than those of the precursor ion are preferred. Fragment ions not resulting from fragmentation of the peptide backbone (i.e. water loss from precursor or side chain fragmentations) do not provide additional information and thus lack specificity. To overcome some of the difficulties related to the selection process of SRM transitions, software tools have recently emerged and enable to expedite the development of SRM methods (see Cham Mead et al.[46] for a review). S. Gallien, E. Duriez and B. Domon NVNDVIAPAFVK SRM transition 643.86 m/z 632.38 m/z Intens. 5 x10 2.0 ? Peak 2 Peak 1 1.5 1.0 0.5 0.0 (A) 10 20 30 40 Peak1 100 90 80 70 60 50 40 30 20 10 0 Relative intensity of transitions (%) Relative intensity (%) 0 averaged 561.34 632.38 745.467 1073.60 50 100 90 80 70 60 50 40 30 20 10 0 0 27 27.5 28 28.5 29 29.5 30 30.5 31 Retention time (min) 10 20 30 40 50 60 70 Relative intensity of the averaged trace (%) SRM transition (m/z) NVNDVIAPAFVK SRM transition 643.86 m/z 632.38 m/z Peak 3 NVNDVIAPAFVK(heavypeptide) SRM transition 647.86 m/z 640.39 m/z Relative intensity (%) Peak 1 100 Relative intensity (%) Peak 2 (B) Time [min] 100 Peak1 y7 (745.46) 80 60 40 20 y5 (561.34) y6 (632.38) y10 (1073.60) 0 y7 (753.47) Peak 3 80 60 40 20 y5 (569.35) y6 (640.39) y10 (1081.61) 0 Peak1 : Full tandem mass spectrum (C) Relative Abundance 745.64 100 95 90 85 80 75 70 65 60 55 50 45 40 35 30 25 20 15 10 5 0 632.38 -Comparison with reference 161.04 spectra 308.14 178.31 1073.68 379.32 64.33 222.17 844.73 393.07534.47 435.22 718.42 959.51 787.95 891.89 200 (D) -Sequence database searching 561.37 400 600 m/z 800 1186.13 1256.61 1000 1200 Peak1 : Composite tandem mass spectrum (8 transitions) y7 100 Relative intensity (%) 90 80 Comparison with full MS/MS 70 reference spectra from: 60 -Previous experiment 50 -Data repository 40 y6 30 y10 y5 20 y8 10 y3 y9 y4 0 393.25 464.29 561.34 632.38 745.46 844.53 959.561073.60 m/z 302 Figure 3. Specificity of SRM measurements. Monitoring one transition of the peptide NVNDVIAPAFVK leads to the observation of peaks 1 and 2 during LC–SRM analysis. In this example, different means allowed assigning peak 1 as corresponding to the peptide NVNDVIAPAFVK. These validation methods also provide purity assessment of measured signals. (A) Co-elution of SRM traces of a given analyte. The ‘averaged’ trace was obtained from all the transitions monitored for the given peptide. In the right panel, the intensity of each transition was correlated against the intensity of the ‘averaged’ trace and linear regression was conducted for each transition. The deviation of data points from regression line measured by the correlation coefficient allows assessing the co-elution of the traces. (B) In the presence of isotopically labeled reference peptides, co-elution of SRM trace pairs and comparison of SRM transition intensity ratios. Monitoring the corresponding transitions of the isotopically labeled peptide leads to the observation of a unique peak (peak 3) co-eluted with peak 1. When several transitions are monitored for peptide pairs (light/heavy), relative intensities for each transition pair should be identical. (C and D) The acquisition of a full tandem mass spectrum (SRM-triggered MS/MS acquisition) or a composite tandem mass spectrum (e.g. eight transitions monitored) provides a fingerprint of the analyte measured in the experiment that can be compared with the reference spectrum of the targeted peptide. wileyonlinelibrary.com/journal/jms c 2011 John Wiley & Sons, Ltd. Copyright J. Mass. Spectrom. 2011, 46, 298–312 Selected reaction monitoring applied to proteomics of a limited number of analytes, mainly because of the costs related to pure isotopically labeled peptides. The identity of the targeted peptides is confirmed by the acquisition of their full tandem mass spectrum in order to c have a complete fragmentation pattern as fingerprint (Fig. 3). This is achieved by acquiring full MS/MS spectra automatically triggered by one SRM transition.[49] Such measurements disrupt the quantification process, as significant time is required to collect a full spectrum. The problem is particularly obvious with classical quadrupole instruments that suffer from slow acquisition speed in scanning mode (Q3), which interrupts the typical cycle of a SRM experiment, and results in loss of sensitivity thus compromising quantification if large numbers of analytes are measured. The issue is less acute with triple quadrupole-linear ion trap instrument[50] because full spectra can be acquired in a shorter time frame. These instruments were developed on the basis of a triple quadrupole mass spectrometer except that the Q3 can operate either as a conventional quadrupole mass filter or a linear ion trap. This results in an increased sensitivity in full tandem mass mode but remains intrinsically less sensitive than SRM acquisition. A new method to confirm the identity of targeted peptides has been introduced recently[51] : A composite MS/MS spectrum is generated by measuring multiple fragment ions (eight to ten ions) for each peptide, instead of recording a full MS/MS spectrum (Fig. 3(D)). The composite MS/MS spectrum is reconstructed from peak area intensities for all SRM transitions monitored for one peptide. The similarity between the reconstructed spectrum and the library spectra is then evaluated using a spectral matching scoring routine. Monitoring a high number of transitions for each peptide, typically between 8 and 10 transitions, can be performed effectively in a data-dependent SRM mode, switching from conventional quantification mode to acquire punctually multiple transitions, while maintaining an acceptable cycle time.[51] This new technique called intelligent selected reaction monitoring is described in detail in Improved Selectivity of SRM Measurements Section. Optimization of SRM transitions J. Mass. Spectrom. 2011, 46, 298–312 CE = a m +b z as shown in Fig. 4(C). The linear equations predict the collision energies generating the most intense b-fragments and yfragments under different conditions. In a first approximation, these parameters (slope, intercept) are generic for a specific instrument type, but fine tuning for each specific triple quadrupole spectrometer can provide increased sensitivity. Peptide Quantification Using SRM When operated to gather qualitative information, the triple quadrupole mass spectrometer scans a wide m/z range to generate full MS/MS spectra essential for compound identification. Overall, only a small fraction of the total scanning time is spent on measuring specific fragments of ions. On the other hand, the precision required for quantification increases with the square root of the number of ions measured,[54] and thus the time devoted to measure one specific ion. Thus, increased precision can be obtained in SRM as nearly 100% of the time is devoted to measure targeted analytes. Strategies Ultimately, an SRM experiment needs to precisely quantify a large set of target proteins in complex biological samples, which c 2011 John Wiley & Sons, Ltd. Copyright wileyonlinelibrary.com/journal/jms 303 To perform proper quantification, high sensitivity is desired, thus each SRM transition is maximized by tuning acquisition parameters of the mass spectrometer. The signal intensity is determined by the combination of peptide ionization efficiency, its transfer into the analyzer and its dissociation into, ideally, a few intense fragments. The parameters associated with the ionization process and ion optics are critical; for instance, at too low extraction voltage: peptides are not efficiently transferred, whereas at high interface voltage, peptides may undergo fragmentation in the ion source. Usually, optimal conditions are determined by using a set of reference compounds spanning the m/z range of the instrument. Tuning of the fragmentation conditions for each peptide can further increase the signal response. In contrast to the CID performed on ion-trap instruments, fragmentation on triple quadrupole instruments is more sensitive to experimental conditions, including parameters such as collision energy, nature and pressure of the collision gas. Thus, the fragmentation patterns are instrument specific; this point is illustrated in Fig. 4(A) by selecting typical SRM peptides, i.e. doubly charged tryptic sequences comprising 10–16 amino acids. The systematic acquisition of fragmentation patterns under various collision conditions on a triple quadrupole instrument and the derived pseudo-breakdown curves[52] provide a mean to rigorously determine the optimal collision energies. Basically, the global optimum corresponds to maximum intensities observed for the high mass y-fragments, which usually dominate the MS/MS spectra. Typically, optimal collision conditions for doubly and triply charged peptides range between 20 and 40 V; and most of the singly charged high mass y-ions appear to have very similar behavior. At low collision energies, the spectrum is dominated by unfragmented precursor ions, and fragments resulting from facile cleavages such as fragmentation N-terminal to a proline residue, while at very high collision energies, the fragment ions (mainly y-ions) undergo secondary dissociation yielding low mass y-ions or internal fragments. As recently documented, b-ions are generated at collision energies lower than y-type ions of similar m/z.[45] The prominence of b-ions in the low m/z range reflects their lower stability and their facile decomposition. The fragmentation patterns are also affected by the nature of collision gas and its pressure. As illustrated in Fig. 4(B) for three doubly charged peptides, optimal collision energy is dependent on the gas pressure. Different nature of collision gas may also affect the optimal collision energy as the energy resulting of collisions between neutral gas molecules and peptide ions that can be converted into internal energy, is directly related to the mass of the collision gas.[53] Consequently, these instrumental parameters need to be carefully monitored and controlled to ensure the inter- and intralaboratory reproducibility of fragmentation patterns and thus quantitative SRM experiments. In practice, apart from exceptional cases like the need to measure transitions for peptides very difficult-to-fragment under classical conditions, the nature and the pressure of the collision gas are kept unchanged on a platform, and the collision energy is adjusted for each targeted peptide. As discussed above, for a defined set of instrumental parameters, the optimal collision energy has a fair broad range, which is directly related to the m/z value of the peptide. It can be estimated by a linear function such as S. Gallien, E. Duriez and B. Domon (A) 40 Relative abundance (%) 35 30 secondary y-fragments Low mass y-ions high mass y-ions 25 N-ter Pro cleavage -> y-ions b-ions 20 Precursor 15 10 5 0 0 10 20 30 40 50 60 Collision Energy (V) (C) 50 (B) 40 GILFVGSGVSGGEEGAR[HeavyR] y-type ions_1.5mTorr GISNEGQNASIK[HeavyK] Optimal collision energy (V) Optimized collision energy (V) y-type ions_1mtorr y-type ions_1.2mTorr ELGQSGVDTYLQTK[HeavyK] 30 20 10 b-type ions_1mTorr 40 y = 0.043x - 2.052; R2 = 0.95 b-type ions_1.2mTorr b-type ions_1.5mTorr 2 y = 0.036x - 0.157; R = 0.94 y = 0.033x + 1.842; R2 = 0.90 y = 0.036x - 1.405; R2 = 0.83 y = 0.030x + 0.432; R2 = 0.87 30 y = 0.023x + 4.374; R2 = 0.80 20 0 1mTorr 1.2mTorr 1.5mTorr 1mTorr 1.2mTorr 1.5mTorr y-type ions 10 400 500 600 b-type ions 700 800 900 1000 1100 Precursor m/z Type of ion_argon pressure Figure 4. Parameters affecting CID fragmentation. (A) Influence of collision energy. Typical pseudo-breakdown curves obtained on peptides showing relative signal intensities of precursor and selected fragment ions as a function of collision energy. (B) Influence of gas pressure. Collision energy generating the most intense y-type and b-type fragments according to the pressure of argon (1, 1.2 and 1.5 mTorr) for three doubly charged peptides (GILFVGSGVSGGEEGAR, ELGQSGVDTYLQTK and GISNEGQNASIK). (C) Linear regression: 17 doubly charged peptides were measured to predict collision energies generating the most intense b-fragments and y-fragments at three different pressures of argon (1, 1.2 and 1.5 mTorr). 304 requires shorter dwell times to maintain an acceptable cycle time (see Multiplexing of Analytes Section).The determination of relative changes in protein concentrations is often the first stage of a proteomics study; for instance, this is the case in comparing concentrations in healthy and disease samples to detect and qualify potential biomarkers. Such analyses rely on measuring peptide ions in individual samples based on their absolute signal intensity after proper normalization, often referred to as label-free method.[55] Based on the first principle, the quantification in mass spectrometry relies on the linear relationship existing between signal intensity and the analyte concentration, provided that the instrument is operating in its linear dynamic range. The slope of the regression line, called response factor, is analyte specific. The determination of inter-sample relative changes of analyte concentrations does not require the knowledge of their response factors as the signal intensities directly reflect concentration, once proper normalization is performed to compensate for injection errors and ionization conditions. Alternatively, relative quantification can be performed using a SID approach based on labeling: one reference sample, wileyonlinelibrary.com/journal/jms for example, from a healthy control, is labeled with heavy stable isotopes (either metabolically[56] or chemically[57] or enzymatically[58] ) and spiked into the samples from a perturbed state (labeled using the normal reagent). Relative quantification is then achieved by comparing signal intensities of the stable-isotope labeled peptides from the control sample and of their unlabeled counterparts in the perturbed sample. This method overcomes the issues of signal fluctuation associated with analyte ionization and matrix effects leading to ion suppression/enhancement. To be effective, the method requires the use of isotopic labels that do not affect chromatographic properties of the peptides. Thus, 15 N, 13 C and 18 O isotope incorporation is preferred as it does not induce significant retention time shifts of isotopically labeled peptides in contrast to deuterium labeling, which exhibits different hydrophobicity and thus different retention on C18 columns. Even if to date only few studies using ICAT and mTRAQ in conjunction with analyses by SRM have been reported,[59,60] the technique is generally applicable to such relative quantitative analyses. As just highlighted, quantitative MS measurements are by nature relative. If reference peptides (e.g. synthetic isotopically c 2011 John Wiley & Sons, Ltd. Copyright J. Mass. Spectrom. 2011, 46, 298–312 Selected reaction monitoring applied to proteomics labeled homologs) are added as internal standards into the test sample in well-defined concentrations, the absolute amount of the corresponding targeted endogenous peptides can be determined precisely, and thus indirectly the amount of the associated protein.[15] The SRM technique is ideally suited for precise quantification, and under specific conditions for absolute quantification; most commonly it is applied to a limited number of analytes as high purity reference material is necessary. Evaluation of the analytical performance J. Mass. Spectrom. 2011, 46, 298–312 Implications for biological samples The ionization efficiency of a given analyte depends on its environment during this process. The presence of co-eluted components during a LC–MS analysis will affect the ionization process. Matrix effects are well documented for small molecules[68,69] and are c 2011 John Wiley & Sons, Ltd. Copyright wileyonlinelibrary.com/journal/jms 305 Quantification combining isotopically labeled reference peptides and LC–SRM typically exhibits a linear response over four orders of magnitude, but factors such as the complexity of samples and matrix effects can reduce the actual LOD of individual peptides (see Multiplexing of Analytes Section). Because the concentrations of the different proteins to be quantified in a given sample are usually spanning several orders of magnitude, an equimolar mixture of all the isotopically labeled reference peptides may lead to signal ratios too high for certain targeted peptides and very low for others. This would prevent an accurate quantification, and thus dilution series are required. Clinical guidelines state that internal standards must be added into the samples to be analyzed at concentrations close to the one of the endogenous targeted peptides.[61] Assay validation is necessary over a wide dynamic range to ensure that the peptide concentrations commonly encountered lie within the linear dynamic range to allow a single-point calibration. The standard procedure to determine the linearity and the limits of detection (LOD) and quantification (LOQ) of an assay consists in generating dilution curves, for example, by adding various amounts of the isotopically labeled reference peptides to a series of aliquots of the sample to be analyzed. Alternatively, multiple dilutions of the sample and subsequent addition of a constant amount of the isotopically labeled reference peptides were suggested,[24,31] which is less desirable due to changes in the background. Ideally, but limited to certain significant applications, in which a blank sample matrix devoid of all targeted endogenous proteins is available, assay linearity and LOD/LOQ can be determined by adding standard proteins to aliquots of blank sample matrix and internal standards.[17,27,28] In any case, linear regression analysis is performed on the observed peak area ratios (endogenous/internal standard) versus peptide concentration ratios (endogenous/internal standard) to generate a calibration curve for each peptide and to determine its response factor, using graphical or statistical methods. Furthermore, linearity determines the highest measurable concentration within the specified conditions. The lower limit of detection (LLOD, often referred to as LOD) and the LOQ are defined, respectively, as the concentration level at which the analyte can be reliably detected in the sample under consideration and as the level at which the analyte can be detected and measured with sufficient precision. Several methods are used to determine LOD and LOQ.[62 – 64] The simplest one consists in calculating LOD and LOQ for a given analyte as the amount of this analyte providing a signal corresponding to the mean value of repeated blank sample measurements +3 and +10 standard deviations, respectively. Another important characteristic of a quantitative assay is the recovery of the peptides, which ensures that the experimentally determined concentrations reflect the actual amounts of the proteins present in the sample. The recovery is assessing the trueness of an assay, i.e. the difference between the average measured value of various samples and the true concentration; trueness reflects bias, due to systematic error but does not take into account random experimental errors reflected in the coefficient of variation (CV). The precision of an assay is evaluated by repeating or reproducing the experiments, i.e. replicating multiple measurements under exactly the same or different experimental conditions, respectively. A recent report by Addona et al.[28] illustrates the multi-site assessment of the precision and reproducibility of SRM measurements of proteins in plasma. In this study, inter-laboratory variability in detecting known amounts of ten peptides in a complex digest was in the range of 10%. Based on target peptides generated by digestion of standard proteins spiked into the tryptic plasma digest, CVs showed value below 15% for peptides at a concentration near their LOQ. This study stresses the importance of the sample preparation: performing digestions independently at each site after spiking intact target proteins into non-digested plasma samples resulted in significantly higher interlaboratory variability with nearly 20% CVs and a much reduced peptide recovery. To overcome some of the issues sometimes associated with the addition of multiple internal standards of well-defined purity, artificial concatemers of isotopically labeled reference peptides (from one or several proteins) have been proposed instead of individual isotopically labeled reference peptides as internal standards. In addition to expanding the range of accessible proteotypic peptides (e.g. hydrophobic peptides) and increasing the scale of protein quantification, this method, called QconCAT,[65] decreases the potential bias due to protein digestion as the standards are added beforehand (Fig. 5). However, it has been noted that QconCAT constructs are typically digested at higher rates than native proteins.[66] Finally, spiking a stable isotopelabeled form of the full-length proteins will account for digestion bias, and that isotopically labeled proteins may thus represent ideal standards. Such references, called Protein Standard Absolute Quantification (PSAQ),[67] are introduced at the first step of sample preparation and thus take into account bias of pre-fractionation and of the whole sample preparation process (Fig. 5). It should, however, be noted that, in spite of decreasing potential bias encountered when using AQUA peptides, high-quality QconCAT and PSAQ standards, which are well soluble and highly purified, are often obtained with more difficulties. This panel of methods is well-suited to generate SRM assays but it is not sufficient to ensure the reliability of the results. Additional considerations have to be taken into account during the SRM method development and data evaluation. Reliable quantification requires at least two surrogate peptides that yield consistent results for a given protein. A larger number of peptides (three or more) spanning, if possible, the whole protein sequence, will validate the results. Conversely, the observation of outliers can attest the occurrence of co- and post-translational events or incomplete digestion. In the same lines, at least three transitions per peptide have to be monitored to ensure sufficient selectivity and to detect possible interferences from the background. When interferences occur in one or more transitions to a significant extent, another set of transitions should be selected for quantification to ensure proper LOD/LOQ. S. Gallien, E. Duriez and B. Domon Figure 5. Strategies used for absolute quantification. (A) Three types of internal standard can be used. Labeled proteins (PSAQ) are added before fractionation; labeled concatenates (QconCAT) are added before digestion; labeled peptides (AQUA) are added into peptide digests. (B) Estimation of losses and recoveries that can be expected with the three types of internal standards. The areas represented with the use of labeled concatenates (QconCAT) and labeled peptides (AQUA), respectively, filled with horizontal black lines or vertical red lines, indicates the windows within which recoveries are likely to lie with these types of internal standards. 306 often referred to as ion suppression if the signal is reduced or ion enhancement if the signal is increased for the same nominal concentration of analyte. These phenomena can dramatically affect the performance of mass spectrometry in terms of accuracy, repeatability, linearity of the response (signal vs concentration) and thus the LOD and LOQ. The chromatographic separation carried out upstream of the mass spectrometer is the first basic step to limit ion suppression by reducing background interferences in measurements. However, a single separation dimension is often not sufficient to handle very complex biological samples. The problem is particularly dramatic in blood plasma samples because protein concentrations are spanning over ten orders of magnitude. While the LOD for samples processed with minimal fractionation lies in the µg/ml range, LODs in the low ng/ml range can be reached if a drastic reduction of the sample complexity is performed. Several sample preparation methods have been developed to deal with the complexity and large dynamic range of complex biological samples, including depletion of the most abundant proteins, enrichment of a subproteome (e.g. glycoproteins or phosphoproteins) or enrichment of the proteins of interest. The removal of the most abundant proteins is a commonly used strategy to enhance the detection of targeted proteins in clinical samples. For this purpose, different strategies such as albumin precipitation,[70] size exclusion fractionation[71,72] or immunodepletion[73,74] have been developed. wileyonlinelibrary.com/journal/jms The use of immunoaffinity depletion leads to an improvement of the LOD: a LOD of a few hundreds ng/ml can be reached on plasma proteins.[19,30] On the other hand, immunoaffinity depletion can be more effective when combined with other fractionation/enrichment methods. For instance, the combination of immunodepletion with strong cation exchange chromatography prior to LC–SRM analysis can significantly improve the LOD of low abundant plasma proteins (low ng/ml range, which constitutes at least a 100-fold increasing compared with a direct plasma analysis).[30] However, in clinical studies, which require a large number of samples to be analyzed, the fractionation and sample preparation steps need to be simplified to maintain throughput. One approach to reduce the complexity of samples is to isolate a specific subpopulation of the proteome in order to keep only a few peptides as surrogates of the protein in the sample. For instance, Stahl-Zeng et al. applied a new approach to isolate and quantify Nlinked glycopeptides in plasma with a LOQ at low ng/ml.[21] Other isolation methods target N-terminal peptides[75,76] or peptides containing rare amino acids, e.g. tryptophan or cysteine residues, or a post-translational modification such as a phosphorylation or a glycosylation.[77] Another approach to detect and quantify low abundant proteins consists in the specific enrichment of the proteins of interest such as in the method developed by Anderson et al. that relies on the use of anti-peptide antibodies to enrich specifically targeted peptides.[22,78] This method yields a 100- to 1000-fold enrichment c 2011 John Wiley & Sons, Ltd. Copyright J. Mass. Spectrom. 2011, 46, 298–312 Selected reaction monitoring applied to proteomics Sensitivity 6000 Sensitivity Scale Selectivity Scale Quantification mode Selectivity Intensity [counts] 5000 Screening mode 4000 cycle time: 1s 3000 2000 1000 0 30.9 10 31.0 SRM screening mode 31.2 31.3 31.4 5000 8 6 Shotgun proteomics 4 31.1 Retention time [min] 6000 SRM quantification mode Intensity [counts] -Log (Concentration) 12 4000 cycle time: 2s 3000 2000 1000 2 10 100 Complexity 1000 10000 [# Proteins] Figure 6. SRM applications. The trade-off between sensitivity, selectivity, scale and analysis speed depends on the experiment purpose. Quantification mode is dedicated to the precise quantification of a fewer analytes. It requires high sensitivity and selectivity obtained to the detriment of the number of peptides analyzed (scale). Screening mode is used for the detection and the relative quantification of a large number of peptides which leads to a decrease in sensitivity and selectivity. of antigen peptides and can be coupled with stable isotope standards. 0 30.9 31.0 31.1 31.2 31.3 31.4 Retention time [min] 6000 5000 Intensity [counts] 1 4000 cycle time: 5s 3000 2000 1000 Multiplexing of Analytes 0 30.9 Over the past few years, the SRM technology has rapidly developed to broaden its field of application. While initially limited to the quantification of a relative small number of peptides in biological samples, the new scheduling capabilities have enabled large screens allowing the detection and quantification of large sets of analytes (several hundreds). The overall aim of targeted experiments has shifted accordingly. The current trend attempts to detect very large sets of peptides (as surrogate probe for specific proteins) and estimate their abundance; i.e. detection of relative changes between samples. In this manner, SRM is being used as a directed discovery tool to screen for putative biomarkers screening and to investigate pathways or protein networks (Fig. 6). The critical parameters driving a large-scale SRM experiment are: the number of peptides to be analyzed, the number of transitions measured for each peptide and the dwell time of each transition. All together they define the cycle time, expressed as cycle time = nb analytes × nb transitions × dwell time J. Mass. Spectrom. 2011, 46, 298–312 31.1 31.2 31.3 31.4 Retention time [min] Figure 7. Precision of quantification is directly dependent of cycle time. With chromatographic peak width of approximately 20 s, a cycle time around 2 s reflects precisely the elution profile. A faster cycle time (1 s) does not bring significant improvements, whereas a long cycle time (5 s) is no sufficient for a precise quantification. If transition or peptide-specific dwell times are used, in particular to monitor low abundant ions, the cycle time adjusted for variable dwell times is expressed as cycle time = n (transitioni × dwell timei ) i=1 i.e. that a summation is performed over all transitions of all peptides. Considering the dynamic of an LC–MS experiment, with chromatographic peak widths typically ranging between 15 and 30 s depending on the chromatographic conditions, the cycle time should be adjusted in a way that eight to ten data points are collected across the elution profile (see Fig. 7). In any case, typical c 2011 John Wiley & Sons, Ltd. Copyright wileyonlinelibrary.com/journal/jms 307 assuming a constant dwell time for all transitions. 31.0 S. Gallien, E. Duriez and B. Domon Conventional LC-SRM 0 4 8 12 Time-scheduled LC-SRM 16 min 0 4 8 12 16 min 10 peptides during all the LC-MS run Additional peptides monitored thanks to the «scheduling» Time range in which 8 peptides are monitored in the samecycle times Figure 8. Time-scheduled LC–SRM. Comparison of conventional and time-scheduled LC–SRM analysis. In a time-scheduled LC–SRM analysis, a time constraint is added to schedule the expected transitions within a defined time window enclosing the retention time of the peptides. Time-scheduled LC–SRM significantly increases the total number of peptides that can be measured in a single analysis. Table 1. Typical parameters used for a time-scheduled SRM experiment Parameters LC-run time (min) Time window (min) nb transitions/peptide Default dwell time (ms) Preset cycle time (s) nb peptides analyzed/window nb peptides analyzed/run Time-scheduled SRM 60 4 2 20 2 50 750 60 2 2 20 2 50 1500 60 4 3 20 2 33 500 60 2 3 20 2 33 1000 60 2 3 10 3 100 3000 cycle times will range between 1 and 3 s and a minimal dwelltime of 10–15 ms should be set to keep a satisfying sensitivity (acceptable signal-to-noise ratio). Often, a trade-off between the number of transitions measured for each analyte and the dwelltime is required if a large set of peptides is analyzed. Time-scheduled SRM 308 Recent developments in data acquisition techniques have enabled the analysis of a larger number of peptides by using the LC elution time as an additional constraint to monitor the transitions of a specific peptide during the corresponding time window. In practice, it means that the instrument method is divided into time segments (typically 2–4 min), during which only subsets of peptides are targeted. This particular acquisition mode, called timescheduled SRM, increases the number of peptides monitored in one LC–MS analysis[21] while keeping the same sampling rate (cycle time) and the same degree of sensitivity without compromising on dwell time (see Fig. 8 and Table 1). Table 1 shows some typical parameters used in a time-scheduled LC–SRM experiment. To fully exploit the potential of the technique, the chromatographic gradients have also to be set accordingly. In this purpose, LC separation does not only aim at keeping satisfying signal-to-noise ratios of SRM measurements by separating interferences from the compounds of interest, but also at separating, at least partially, the targeted peptides from each other to allow their successive analysis. Highly multiplexed LC–SRM experiments are typically performed with 60 min gradients. The analysis of a smaller number wileyonlinelibrary.com/journal/jms of peptides can in principle be performed much faster (two to three times). However, a minimal separation has to be maintained, even when only a few peptides are targeted, to reduce background interferences and to satisfy acceptable signal-to-noise ratio of the measurements. Control and reproducibility of LC runs In large-scale SRM experiments, the control of the conditions and the reproducibility of the HPLC separations are critical. If elution times are properly calibrated and can be reproduced from experiments to experiments, it allows narrowing down the time window used to monitor the transitions of a specific peptide. Consequently, the LC–MS run can be divided into a higher number of time segments, which dramatically increases the number of peptides analyzed during the entire LC–MS run. For instance, a two-fold decrease in the retention time window theoretically allows a two-fold increase in the number of peptides analyzed during a LC run as illustrated in Table 1. Such experiments require an accurate control of the chromatography conditions, and the standardization of the elution time. It is typically performed using a set of reference peptides covering the entire elution range. Improved selectivity of SRM measurements The intelligent selected reaction monitoring (i-SRM) method has been introduced very recently to furthermore increase the number of peptides monitored.[51] Combined with time-scheduled SRM, this method consists in switching between two modes of operation: one being compound-specific, where only a few primary transitions are monitored (typically two primary transitions), and the second being data-dependent, where both primary and additional secondary transitions (typically six secondary transitions) are measured. The principle of the i-SRM acquisition method is illustrated in Fig. 9. Only the two primary transitions are used for the quantification and are monitored continuously in the predefined elution time window. The acquisition of secondary transitions is performed punctually and is triggered by the primary transitions. The composite tandem mass spectra generated by acquiring multiple transitions in parallel are used for transition validation instead of full-scan tandem mass spectra or continuous acquisition of a high number of SRM transitions for each peptide, c 2011 John Wiley & Sons, Ltd. Copyright J. Mass. Spectrom. 2011, 46, 298–312 Selected reaction monitoring applied to proteomics A Composite spectrum Dynamic exclusion Delay Duplicate composite spectrum (optional) GISNEGQNASI K 14.99 B 15.07 D Primary events Primary transitions 725.4 y7 y10 1055.5 15.07 C 14.92 Secondary events E Secondary transitions y3 355.2 ++ y10 y5 y6 540.3 528.3 14.8 15.1 668.4 725.4 y8 854.4 y9 1055.5 968.5 15.4 Figure 9. Second generation of SRM method. (A) i-SRM logic. Two primary transitions of a given peptide are monitored continuously and trigger a data-dependent event if both signals exceed a preset threshold. A pre-trigger delay is applied to ensure that data-dependent acquisition is performed when the peak is reaching its apex. A dynamic exclusion is also included in the method to trigger the secondary event only once or twice for each peptide. (B) Primary i-SRM events. (C) Data-dependent (secondary)-SRM events. (D) Ion intensities of a primary i-SRM event. (E) Ion intensities of a secondary i-SRM event. Reprinted from Ref. [51] with permission. Figure 10. SRM experiment with increased resolution of precursor ion. Three transitions (yellow, black and blue traces) were monitored for the peptide LTILEELR. These transitions were measured with a mass selection window of 0.7 Da in the left panel and 0.2 Da in the right panel. The decrease in background obtained for each transition by narrowing down the mass selection window can even be further improved applying a Boolean operation ‘AND’ on the transitions. The selectivity and the signal-to-noise ratio of the resulting signal were clearly improved as illustrated by the red traces. which require a significant acquisition time. Thus, most of the time is preserved to analyze a higher number of peptides. Outlook J. Mass. Spectrom. 2011, 46, 298–312 c 2011 John Wiley & Sons, Ltd. Copyright wileyonlinelibrary.com/journal/jms 309 As discussed in the section on quantification, sample complexity can dramatically affect the analytical performances, because of ion suppression effects and interferences affecting the LOD. The complexity of proteomic samples also impacts the selectivity of LC–SRM experiments. The frequency of the co-eluting interferences falling within the mass windows and tolerances of Q1 and Q3 settings increases with sample complexity leading to a greater likelihood of false positives. There is an imminent need for mass spectrometry methods improving the specificity of SRM analysis. A technique has recently emerged[79,80] and showed great potential to reduce chemical/endogenous background noise associated with the sample matrix.[81] This technique, called FAIMS S. Gallien, E. Duriez and B. Domon (high-field asymmetric waveform ion mobility spectrometry), exploits differences in ion mobility of ions at very high electric fields, to separate them in the millisecond timescale after LC separation and prior to their introduction into the vacuum region of a mass spectrometer. Even if it does not prevent from matrix effects during the ionization process, FAIMS allows separating, at least partially, ions of interest from co-eluted interferences.[82] The significant residence time (in the 100 ms range) for the FAIMS system, i.e. the time required for ions to travel through the FAIMS device, is a drawback that can limit the number of targeted peptides in one LC–FAIMS–SRM as illustrated in the calculation: nb peptides = cycle time [nb transitions × dwell time] + residence time However, when several peptides share the same optimal value of compensation voltage, acquisition methods can be designed in a way allowing adding the residence time only once in the calculation for this set of peptides, and thus reducing this negative impact. In the context of SRM analysis of complex proteomic samples, the control of the mass selection (Q1) window of the precursor ion is a critical parameter that allows increasing the selectivity of SRM transitions. It improves the signal-to-noise ratio and thus the LOD (sensitivity) by reducing biochemical background due to co-eluting substances. Figure 10 illustrates the selectivity improvement of a SRM experiment when selecting precursors by narrowing the mass selection window from 0.7 to 0.2 Da. Another method termed multiple reaction monitoring cubed (MRM3 ) has recently been proposed to improve the selectivity of SRM experiments.[83] This method relies on the use of a signature of multiple second-generation product ions produced by two stages of CID fragmentation to detect and quantify the targeted analytes. This method was proven to decrease by three- to five-fold the LOD and quantification of five proteins spiked in a serum sample. However, this technique is limited to hybrid triple quadrupole linear ion trap instruments that only have the capability to trap, fragment and analyze the ion fragments of the primary product ions. Furthermore, large screen experiments may not be possible with this technique because of the long duty cycle of the acquisition (up to 350 ms). In addition to these hardware developments, selectivity improvement can also be achieved thanks to computational approaches. For example, a method developed by Sherman et al.[84] consists in determining in silico the SRM transitions that are the most specific for the targeted peptides within the considered proteome. This method is based on the simulation of all transitions potentially observable when analyzing the digest of this proteome. Such computationally assisted designing of SRM experiments combined with the integration of the knowledge available in proteomic spectral libraries is likely to have widespread impact in future SRM-based studies. Acknowledgements 310 This work was supported by a PEARL grant from the Fonds National de la Recherche Luxembourg (FNR). We are grateful to Dr. S Peterman for critical reading of the manuscript. 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