Chapter 16 Selected Reaction Monitoring Mass Spectrometry: A Methodology Overview H. Alexander Ebhardt Abstract Moving past the discovery phase of proteomics, the term targeted proteomics combines multiple approaches investigating a certain set of proteins in more detail. One such targeted proteomics approach is the combination of liquid chromatography and selected or multiple reaction monitoring mass spectrometry (SRM, MRM). SRM-MS requires prior knowledge of the fragmentation pattern of peptides, as the presence of the analyte in a sample is determined by measuring the m/z values of predefined precursor and fragment ions. Using scheduled SRM-MS, many analytes can robustly be monitored allowing for high-throughput sample analysis of the same set of proteins over many conditions. In this chapter, fundaments of SRM-MS are explained as well as an optimized SRM pipeline from assay generation to data analyzed. Key words Selected reaction monitoring (SRM), Multiple reaction monitoring (MRM), Targeted mass spectrometry

1

Introduction SRM-MS is a mass spectrometry method typically performed on triple-quadrupole instruments taking full advantage of the large dynamic range of quadrupoles. The first quadrupole is used to define the precursor ion mass (Q1) with a specified window, e.g., m/z ± 0.35. All precursor ions passing through the first window are fragmented in the second quadrupole. The third quadrupole (Q3) acts again as a filter with a specific window, e.g., m/z ± 0.35, to allow for specific fragment ion to pass through toward the detector. These dual filters provide a higher sensitivity for monitoring specific analytes as supposed to conventional shotgun experiments. The values of Q1, a particular precursor ion, and Q3, a specific fragment ion, are often referred to as transitions. The intensity of the transition is recorded by a detector (electron multiplier tube) resulting in an ion chromatograms as a function of time and transition measured. Per precursor ion, typically five transitions are

Jesus V. Jorrin-Novo et al. (eds.), Plant Proteomics: Methods and Protocols, Methods in Molecular Biology, vol. 1072, DOI 10.1007/978-1-62703-631-3_16, © Springer Science+Business Media, LLC 2014

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measured, all of which coelute to positively identify the precursor/ peptide of interest [1]. A group of transitions per peptide is termed assay and is instrument specific. Due to the nature of SRM-MS, the ion chromatogram is distinct from conventional shotgun MS/MS spectra, which record all possible fragment ions of a particular precursor ion. Hence, SRM-MS does not depend on a single spectrum for positive identification, but on coeluting transitions [1] (see Notes 1, 2 and 6). Using unscheduled SRM, the number of transitions is defined by the dwell time of each transition and the cycle time. The cycle time is defined as time it takes to measure all transitions listed in the method, the dwell time per transition and some instrument specific set up time to switch the m/z value of the filters. For example if each transition is measured for 25 ms dwell time and instrument set up time is 3 ms, in 2,000 ms approximately 71 transitions can be measured. With 5 transitions per peptide, an unscheduled SRM-MS experiment would allow to monitor 14 peptides. As this number is relatively low, scheduled SRM-MS is employed. For scheduled SRM-MS, prior knowledge of the retention time of a peptide is required (see Note 3). Effectively, a peptide assay is measured only during a 2–4 min window during the LC-SRM-MS run allowing for hundreds of assays to be monitored in a single injection [2] (see Note 5). Another strength of SRM-MS is the robustness of the methodology allowing for consistent measurements of different sample conditions, but also obtaining reliable results across various laboratories with coefficient of variation of less than 20 % [3] (see Note 4). The robustness of measurements extends to quantifying analytes using stable isotope labeled reference standards. Typically, the endogenous analyte is unlabeled (see Note 8), while the exogenous reference peptide is marked using stable isotope labeling methods (see Note 7). As the synthetic reference peptides fragment identically to the endogenous peptides, the same SRM assay applies, albeit with the appropriate mass shift. If the relative order of transitions is not conserved between endogenous and exogenous peptides, interference might be the cause (see Note 2). To guide the reader through the SRM-MS workflow, a flowchart of the SRM-MS method development is shown in Fig. 1 and explained in detail in each section.

2 2.1

Methods Software Tools

There are multiple software tools utilized in a typical SRM-MS workflow. Table 1 gives an overview of the software tools described in this chapter with detailed descriptions included in their respective sections.

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Fig. 1 Flowchart of a SRM-MS project Table 1 List of software tools mentioned in this chapter, including references, and short descriptions Skyline [4]

Windows based client application supporting quantitative SRM-MS method development and extensive analysis of resulting mass spectrometry data

SRM Collider [5]

Web service to detect and avoid shared transitions to increase precision of SRM-MS measurements http://www.srmcollider.org

MQuest/Mprophet [6] Web service to automatically score SRM-MS measurements using decoy transitions and obtain false discovery rates (FDR) http://www.mprophet. org

2.2 SRM Peptide assays 2.2.1 Prior Knowledge of Transitions: Repositories

2.2.2

HCD shotgun

SRM-MS requires prior knowledge on the properties of peptides to be monitored. This prior knowledge includes retention time as well as fragmentation pattern of peptides obtained with a certain mass spectrometer. Either this prior knowledge is deposited in repositories such as MR Maid [7], MASCP Gator [8], Promex [9], PASSEL [10] or has to be determined prior to SRM-MS measurements take place. Outlined below are common ways to obtain this prior knowledge. Conventional low energy collision-induced dissociation (CID) ion-trap fragmentation patterns of peptides of shotgun mass spectrometers are often distinct from CID fragmentation pattern of the same peptide in a beam type triple quadrupole mass spectrometer. For example, the most intense fragment ions in CID mode are not necessarily the most intense fragment ions in a triple quadrupole due to the differences in energy transfer to the peptide found in resonance (ion-trap) versus beam type (quadrupole) [11]. Recently, changes to instrumentation have lead to the possibility of peptide fragmentation in a quadrupole

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with fragments still analyzed in the mass analyzer, e.g., Orbitrap or Time of Flight. This type of fragmentation is also referred to as higher energy collision dissociation (HCD) fragmentation and more closely resembles the fragmentation pattern observed in a triple quadrupole instrument and is therefore the method of choice to obtain SRM assays directly from shotgun experiments [12]. Typically, multiple transitions of a HCD spectrum are selected and the same sample analyzed on a triple quadrupole instrument. The five most intense transitions are then used for a high confidence SRM assay. Obtaining SRM assays from HCD shotgun experiments is very cost efficient. The drawback of this method is that SRM assays are obtained only for peptides that are detectable by shotgun mass spectrometry. If the protein(s) of interest are not in the list of identified peptides, fractionation is necessary. This fractionation can be in form of biological fragmentation of cellular subcomponents, or enrichment of proteins and/or protein complexes using affinity purification mass spectrometry [13]. Another way of fractionation is to separate proteins or peptides on the basis of their physical properties, e.g., first separate peptides by off-gel isoelectric focusing (pI) [14] and then analyze each fraction using shotgun reverse phase LC-MS/MS run. 2.2.3

Synthetic Peptides

As additional fractionation steps are labor intense and require several LC-MS/MS runs, another avenue are chemically synthesized peptides. Very small peptide amounts are already sufficient to generate SRM assays either in HCD shotgun mode or using mass spectrometers with a trapping device in MS1 triggered MS2 mode. The initial peptide spectra are then confirmed in SRM mode in the triple quadrupole instrument. There are several advantages to this method: chemical peptide synthesis up to 25 amino acids is very cost efficient and many peptides can be analyzed simultaneously. As all chemically synthesized peptides are targeted peptides, resources are used efficiently, compared to off-gel pre-fractionation discussed above. Another advantage: peptide sequences can be synthesized and SRM assays obtained for peptides not detected by shotgun mass spectrometry before [15]. The disadvantage of the method lies in the prediction of peptides to be synthesized. There are many selection criteria: peptides already detected in other shotgun experiments; proteotypic peptides; predicted favorable electrospray mass spectrometry properties; no or little theoretical shared transitions; favorable hydrophobic/ hydrophilic properties; fully tryptic peptide [16]. The latter criterion dictates that only a Lys or Arg residue resides at the C-terminus of a peptide. Due to difficulties predicting precise protease cleavage activity, semi-tryptic peptides are typically not considered. Further, once a choice is made to use a certain protease, e.g., trypsin cleaving C-terminal to Lys or Arg, in the future new peptides have to be synthesized if another protease is used, e.g., Asp-N cleaving N-terminal to Asp or Glu. An additional protease digest might be

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necessary in cases where closely related proteins do not result in proteotypic peptides using a single protease. 2.2.4 Synthesized Proteins

3

Many of the disadvantages of non-targeted expeditions tracking down individual peptides in fractionated cell lysates or difficulties in synthetic peptide selection can be overcome by in vitro transcription/translation of the proteins of interest. On the genome scale this approach might be ambitious; however, individual laboratories typically are interested in networks containing hundreds of proteins, making in vitro transcription/translation of proteins for generating SRM assays a viable option [17]. In vitro transcription/translation will generate whole proteins, which can be digested by various proteases; hence, a switch in protease requires only another protein digest. The protease efficiency is considered in the context of the protein, e.g., noncanonical peptides generated are considered for SRM assay development. Peptides of the digested protein are all analyzed in an LC-MS/MS experiment and best performing peptides can be selected for subsequent SRM assay conformation. Best performing peptides are often referred to high fliers and frequently results in 10 times more signal than other peptides of the same protein present in equimolar amounts [17]. The proper choice of high flier peptides can make the difference between detecting the peptide in a complex mixture of whole cell lysates, or not. In vitro transcription/translation systems were described for Escherichia coli [18] and are commercially available. Further, cell free transcription/translation systems are available using wheat germ extract [19, 20]. Already the comparison between prokaryotic and eukaryotic in vitro translated proteins might give insights into possible post-translational modifications (PTMs) of proteins [21]. Unexpected PTMs are one of the causes in silico search algorithms do not attribute a spectrum to a certain peptide. Also, as outlined below, a less complex sample is very valuable tool to determine signal interferences concerning certain SRM transitions. In closing, in vitro transcription/translation of proteins might diverge from conventional large scale SRM assay development [16], however, in a more targeted analysis of the dynamic proteome [22] together with the flexibility of protease treatment [23] favors the whole protein synthesis.

Notes 1. General Considerations for Peptide Selection Besides favorably LC and MS properties of the analyte, the unique nature of the peptide should also be considered. Unique sequences are peptide sequences that are found only

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once in a given proteome and are termed proteotypic peptides. For closely related proteins or splice isoforms, finding proteotypic peptides with favorable LC and MS properties might be challenging. Hence, sometimes statements can be only made for a protein or closely related protein group, but not for splice isoforms of the protein or individual proteins, respectively. Using trypsin, or any other sequence specific endoprotease, the surrounding protein sequence should be checked for rugged ends, which are sequences of repeating recognition amino acids, e.g., KK, KR, RK, or RR in the case of trypsin. If possible, peptides with rugged ends should be avoided as both the rugged end and canonically cleaved peptide may coexist and are hard to predict a priori. Rugged end peptides are not part of typical peptide libraries (mentioned in Subheading 2.2.3). However, when unavoidable, rugged end proteotypic peptides can be used for SRM-MS and transitions are obtained as described above in Subheadings 2.1.1/2.1.2/2.1.3/2.1.4. It is advisable to monitor the canonical and rugged end peptide. 2. Interferences SRM transitions are defined by Q1/Q3 pairs, which represent the m/z of the precursor ion and the targeted fragment ion, respectively. Unlike shotgun MS/MS where whole spectra are recorded, SRM-MS heavily relies on the specificity of these two filters as the electron multiplier detector only records the amount of ions present following the “filtration.” The window, or pore size, of these filters is typically m/z 0.7; tighter windows might increase specificity, but decrease signal intensity. Peptides to be monitored could theoretically share transitions with other peptides. Practically, in whole cell lysates of complex proteomes, shared transitions are observable, especially for short peptides and fragment ions with low sequence information [24]. The most important denominator is the retention time, as these shared transitions only play a role if they coelute during the peak of the targeted peptide. There is a computational tool predicting transition interferences called SRM Collider [25], which bases its calculations on all theoretically possible fragment ions of a given proteome (without any PTM) as well as all peptides ever detected in PeptideAtlas [26] and predicts retention time using SSR-Calc [27]. The output of SRM Collider should be taken into consideration, e.g., if a certain peptide has a long list of shared transitions, its best to choose another peptide. But, if the list of shared transitions is limited or nonexistent, there is no guarantee that the targeted peptide will be free of interferences. After all due diligence, only the actual LC-SRM-MS experiment will determine if the targeted peptide can be measured and quantified. There are two ways to detect interferences in complex samples. Generally, a low complexity sample is compared to a

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Fig. 2 Ion chromatogram of NH2-IPALDLLIK-COOH in SRM-MS on a triple quadrupole (Thermo TSQ Vantage). (a) Shows the synthetically synthesized peptide with y8++ transition as the most intense transition while y4+ is one of the least intense measured transitions. (b) Shows the endogenous measurement of the same peptide, although in a complex cell lysate background. Note that the relative transition order is not conserved between the synthetic less complex peptide and the complex whole cell lysate peptide mixture. Transition y8++ is still the most intense however y4+ is now the second most intense transition. This gain in intensity is most likely due to interference, as not only the relative intensity of a single transition changed, but also the peak shape and retention time

high complexity sample. For example, during SRM assay development the sample complexity is relatively low (as described in Subheadings 2.2.3 and 2.2.4) and the relative order of transitions can be compared to the measurements of the same SRM assay in a high complex sample, ideally using the same mass spectrometer. Another way to detect interferences is to use a finite number of stable isotope labeled reference peptides and peptides originating from whole cell lysate and measure both light and heavy peptide pairs with the same SRM assay. As shown in Fig. 2 there are discrepancies in the relative order of transitions between the low and high complexity measurements due to interference. Transitions with interference should be omitted from quantification and alternative transitions from the peptide’s SRM assay be measured. Further omitted from the SRM transition list should be very small fragment ions, as these carry little sequence information, e.g., a b2 fragment ion is comprised of an NH2-terminus and two amino acids, frequently resulting in a nondiscriminatory m/z value. As a rule of thumb, the smallest fragment ion for SRM transitions should be b4 and y4 [24, 28]. 3. Relative Retention Time Crucial to scheduled SRM-MS are standardized retention times, as the window of recording SRM transitions is limited to the time when the peptide is expected to elute from the column. These retention times can be predicted using in silico approaches, such

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as SSR-Calc [27]. However, there are discrepancies between in silico predicted retention times of peptides and experimentally observed retention times [29]. Further, the retention time of a peptide was already empirically determined during the initial screening phase (see Subheadings 2.2.2/2.2.3/2.2.4), which is a more accurate measure than in silico prediction. In order to universally apply the measured retention times of targeted peptides to various LC-gradients and MS setups, reference peptides are also measured during the same run and relative retention times determined [29]. In detail, the reference retention time peptides span the whole range of the LC-gradient and a linear regression is calculated onto which the targeted peptides are projected. Once these relative values have been established, the same relative retention time peptides can be measured on different LC-MS systems allowing for a robust detection of targeted analytes [29]. Principally there are two ways of establishing relative retention times: using an external standard (multiple vendors sell retention time peptides) or internal standards (using peptides of “housekeeping” proteins). Latter approach has the advantage of not adding any additional peptides to a precious sample, but “housekeeping” proteins might differ depending on tissue type and/or species analyzed. One such implementation of relative retention times is iRT [29]. There, eleven reference peptides were synthesized which do not share any peptide sequence with currently known proteins and elute off a C18 column during the entire gradient due to their diverse hydrophobic properties. Even measuring only seven reference peptides will decrease retention time precision by only 10 %. These retention time peptides are measured with three transitions each and their retention time is monitored (Table 2). 4. Sample Preparation The advantage of SRM-MS lies in the fact that targeted proteomics allows for consistent monitoring of analytes from unfractionated lysate. Hence, special care should be taken to obtain peptides well suited for nano-LC systems allowing for multiple subsequent injections of peptide samples. Depending on the species under investigation, e.g., angiosperms or gymnosperms [30], different lysis protocols will be employed [31, 32]. As traditional detergents used for cell lysis are not compatible with LC-MS in general, specialized protocols were established [33, 34] or specialized LC-MS compatible detergents were developed. One such specialized detergent is Sodium3-[(2-methyl-2-undecyl-1,3-dioxolan-4-yl)methoxy]1-propanes-ulfonate (World patent number 2005116607), also known under the trade mark RapiGest™ SF (Waters

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Table 2 Retention time peptides, their transition and iRT values [29] Peptide sequence

Precursor ion

Fragment ions

iRT

LGGNEQVTR

2+

y8+, y4+, y7+

−28.31

GAGSSEPVTGLDAK

2+

y8+, y6+, y10+

VEATFGVDESNAK

2

+ +

0.23

+

+

+

13.11

+

+

+

b8 , y9 , y6

YILAGVENSK

2

y8 , y6 , y7

22.38

TPVISGGPYEYR

2+

y8+, y9+, y7+

28.99

TPVITGAPYEYR

2+

y8+, y7+, y9+

33.63

DGLDAASYYAPVR

2

ADVTPADFSEWSK GTFIIDPGGVIR GTFIIDPAAVIR LFLQFGAQGSPFLK

+

+

+

+

y7 , y8 , y5

43.28

2+

y9+, y9++, y10+

54.97

2+

y6+, y7+, y8+

71.38

2

+

2

+

+

+

+

y6 , y8 , y7 +

+

86.72 +

y9 , y10 , y4

98.09

Technologies Ireland Ltd). RapiGest™ SF itself is still not compatible with LC-MS, however, RapiGest™ SF can easily be precipitated by lowering the pH of the solution to pH 2–3. In our hands, protein extraction is more than 10 times more efficient using RapiGest™ SF as compared to 6 M urea/0.1 M ammonium bicarbonate lysis buffer alone. 5. LC-SRM-MS Consideration Regarding the liquid chromatography gradient, a shorter gradient, e.g., 35 min linear gradient from 5 to 35 % acetonitrile, is typically used for LC-SRM-MS [35]. The rationale behind a shorter gradient is that for a particular peptide the area under the curve is constant, regardless of the length of the gradient. Hence, a shorter gradient will result in sharper and higher peaks versus a longer gradient with wider peaks or elution profile (which is advantageous for data dependent shotgun MS/ MS or SWATH-MS [36]). As the peak height is crucial for the signal to rise above the noise, it is advantageous to use a shorter gradient in LC-SRM-MS. Another consideration regarding short versus long linear LC gradients is the fact that in scheduled SRM a retention time window has to be defined. The size of the window greatly depends on the length of the gradient, for example a 2 min window of a 30 min linear LC gradient is equivalent to a 6 min window of a 90 min linear LC gradient [29]. Hence, the three times longer gradient does not allow for more transitions per injection to be measured and only lowers the peak intensity.

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As mentioned in the introduction, cycle time is of essence for (un)scheduled SRM. If a targeted peptide elutes during 20 s of the gradient, a 2 s cycle time will result in approximately 10 positive identifications per peptide. Longer cycle times increase dwell time if the number of transitions are kept constant, but also decrease the number of positive identifications during the elution of the peptide. An SRM transition is defined as a pair of m/z values (Q1/ Q3) for precursor ion and fragment ion, respectively. In Q2, the precursor ion is fragmented and the collision energy required for proper fragmentation is a function of the instrument. Typically, the collision energy given for a particular mass spectrometer performs well, but only reflects an average value. One can gain signal increase if the collision energy is optimized [37]. Collision energy optimization is a trade off in terms of how much mass spectrometer time is available, how much time is invested in SRM assay development and how often this optimized collision energy will be used in the future. The resulting optimized collision energy can be unique for each transition. For large scale projects where the targeted list of peptides exceeds 500, we typically do not employ energy optimization and rely on instrument specific collision energy calculations. 6. Data Visualization and Analysis Data visualization, management and analysis tool of choice currently is Skyline, developed in the MacCoss lab, Department of Genome Sciences, University of Washington [4]. Skyline manages an entire SRM-MS project starting with selecting peptides, using spectral libraries, collision energy optimization options [37], importing all current mass spectrometry vendor instrument files, managing retention time calculators [29] and extensive report function. The program is very well documented and a future development includes integration of MQuest and Mprophet [6]. Besides targeted SRM-MS, Skyline is also suited to import shotgun MS/MS [38] and SWATH-MS data [36]. Skyline has extensive reporting function, e.g., height, area of each transition per peptide which can be used for quantification of peptides (Fig. 3) [28, 39]. For protein significance analysis, SRM Stats is a valuable tool using a family of linear mixed effects models [40] to statistically evaluate a wide range of significant parameters typically used in SRM experiments, e.g., isotopically labeled spike-in reference peptides. 7. Stable Isotope Labeled Reference Peptides Stable isotope labeling methods are frequently employed to generate reference peptides for relative or absolute quantification of peptides. Dimethyl labeling in this context a costefficient, simple, but powerful method for quantification [41].

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Fig. 3 Dilution series for absolute quantification of endogenous peptide levels. Shown in filled symbols are two dilution series of externally calibrated stable isotope labeled (13C615N4 Arg) reference peptides NH2-ALYDNVAESPDELSFR-COOH (filled circles) and NH2-FNSLNELVDYHR-COOH (filled triangles). The SRM-MS measurements were carried out using a triple quadrupole (Thermo TSQ Vantage) and each measurement, both the exogenous reference peptide and the endogenous peptide were measured. The concentration of endogenous peptides can be inferred from the calibration curve. Together with the number of cells used to obtain the tryptic peptides, absolute values can be determined

Another popular labeling method are stable isotope amino acids 13C615N2 Lys or 13C615N4 Arg as reference standards for tryptic protein digests. These stable isotope amino acids are favorable, as H to D exchanges affect the C18 reverse phase chromatography of the analytes [42]. On the protein level, 13 C615N2 Lys or 13C615N4 Arg can be used as described in detail by the Brun laboratory [43, 44]. On the peptide level, crude purified 13C615N2 Lys or 13C615N4Arg labeled peptides can be added as reference standard to monitor changes of peptides, and therefore proteins, as a function of perturbation. In case of purified externally calibrated (amino acid analysis) stable isotope labeled reference peptides, absolute values can be obtained if the same amount of endogenous lysate is measured in a dilution series of the reference peptide measuring the endogenous/exogenous peptide pair (Fig. 3) [45]. 8. Concluding Remarks Scheduled SRM-MS is a very powerful mass spectrometry method allowing for reliable measurement of analytes [35]. Future developments will undoubtedly increase the dynamic range of mass spectrometers capable of SRM-MS. A limitation of SRM-MS is the limited number of analytes that can be analyzed per LC-SRM-MS run. Using data-independent acquisition of the peptidome, e.g., SWATH-MS [36], has the promise to monitor the peptidome which ionizes well in electrospray ionization mass spectrometry. Also, the development of speedy

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shotgun mass spectrometers should not completely be ignored [46], which could record entire spectra of targeted peptides given an extensive inclusion list resulting in a targeted shotgun experiment.

Acknowledgments HAE is an International Incoming Marie Curie Fellow (FP7) and would like to thank former and current members of the Aebersold lab, ETH Zürich, Switzerland for fruitful discussions. References 1. Barnidge DR, Dratz EA, Martin T et al (2003) Absolute quantification of the G proteincoupled receptor rhodopsin by LC/MS/MS using proteolysis product peptides and synthetic peptide standards. Anal Chem 75:445–451 2. Stahl-Zeng J, Lange V, Ossola R et al (2007) High sensitivity detection of plasma proteins by multiple reaction monitoring of N-glycosites. Mol Cell Proteomics 6:1809–1817 3. Prakash A, Rezai T, Krastins B et al (2010) Platform for establishing interlaboratory reproducibility of selected reaction monitoringbased mass spectrometry peptide assays. J Proteome Res 9:6678–6688 4. MacLean B, Tomazela DM, Shulman N et al (2010) Skyline: an open source document editor for creating and analyzing targeted proteomics experiments. Bioinformatics 26:966–968. doi:10.1093/bioinformatics/btq054 5. Rost H, Malmstrom L, Aebersold R (2012) A computational tool to detect and avoid redundancy in selected reaction monitoring. Mol Cell Proteomics 11:540–549 6. Reiter L, Rinner O, Picotti P et al (2011) mProphet: automated data processing and statistical validation for large-scale SRM experiments. Nat Methods 8:430–435 7. Cham JA, Bianco L, Barton C et al (2010) MRMaid-DB: a repository of published SRM transitions. J Proteome Res 9:620–625 8. Joshi HJ, Hirsch-Hoffmann M, Baerenfaller K et al (2011) MASCP Gator: an aggregation portal for the visualization of Arabidopsis proteomics data. Plant Physiol 155:259–270 9. Wienkoop S, Staudinger C, Hoehenwarter W et al (2012) ProMEX—a mass spectral reference database for plant proteomics. Front Plant Sci 3:125

10. Farrah T, Deutsch EW, Kreisberg R et al (2012) PASSEL: the PeptideAtlas SRMexperiment library. Proteomics 12:1170–1175 11. Xia Y, Liang X, McLuckey SA (2006) Ion trap versus low-energy beam-type collision-induced dissociation of protonated ubiquitin ions. Anal Chem 78:1218–1227 12. de Graaf EL, Altelaar AF, van Breukelen B et al (2011) Improving SRM assay development: a global comparison between triple quadrupole, ion trap, and higher energy CID peptide fragmentation spectra. J Proteome Res 10:4334–4341 13. Glatter T, Wepf A, Aebersold R et al (2009) An integrated workflow for charting the human interaction proteome: insights into the PP2A system. Mol Syst Biol 5:237 14. Picotti P, Rinner O, Stallmach R et al (2010) High-throughput generation of selected reaction-monitoring assays for proteins and proteomes. Nat Methods 7:43–46 15. Edwards AM, Isserlin R, Bader GD et al (2011) Too many roads not taken. Nature 470:163–165 16. Fusaro VA, Mani DR, Mesirov JP et al (2009) Prediction of high-responding peptides for targeted protein assays by mass spectrometry. Nat Biotechnol 27:190–198 17. Stergachis AB, MacLean B, Lee K et al (2011) Rapid empirical discovery of optimal peptides for targeted proteomics. Nat Methods 8:1041–1043 18. Shimizu Y, Kanamori T, Ueda T (2005) Protein synthesis by pure translation systems. Methods 36:299–304 19. Ge X, Xu J (2012) Cell-free protein synthesis as a promising expression system for recombinant proteins. Methods Mol Biol 824:565–578

SRM-MS Overview 20. Stueber D, Ibrahimi I, Cutler D et al (1984) A novel in vitro transcription-translation system: accurate and efficient synthesis of single proteins from cloned DNA sequences. EMBO J 3:3143–3148 21. Altelaar AF, Navarro D, Boekhorst J et al (2012) Database independent proteomics analysis of the ostrich and human proteome. Proc Natl Acad Sci U S A 109:407–412 22. Lamond AI, Uhlen M, Horning S et al (2012) Advancing cell biology through proteomics in space and time (PROSPECTS). Mol Cell Proteomics 11(O112):017731 23. Taouatas N, Mohammed S, Heck AJ (2011) Exploring new proteome space: combining Lys-N proteolytic digestion and strong cation exchange (SCX) separation in peptide-centric MS-driven proteomics. Methods Mol Biol 753:157–167 24. Sherman J, McKay MJ, Ashman K et al (2009) How specific is my SRM?: the issue of precursor and product ion redundancy. Proteomics 9:1120–1123 25. Rost HL, Malmstrom L, Aebersold R (2012) A computational tool to detect and avoid redundancy in selected reaction monitoring. Mol Cell Proteomics 11:540–549. doi:10.1074/mcp.M111.013045 26. Deutsch EW (2010) The PeptideAtlas project. Methods Mol Biol 604:285–296 27. Spicer V, Yamchuk A, Cortens J et al (2007) Sequence-specific retention calculator. A family of peptide retention time prediction algorithms in reversed-phase HPLC: applicability to various chromatographic conditions and columns. Anal Chem 79:8762–8768 28. Ebhardt HA, Sabido E, Huttenhain R et al (2012) Range of protein detection by selected/ multiple reaction monitoring mass spectrometry in an unfractionated human cell culture lysate. Proteomics 12:1185–1193 29. Escher C, Reiter L, Maclean B et al (2012) Using iRT, a normalized retention time for more targeted measurement of peptides. Proteomics 12:1111–1121 30. Morin RD, Aksay G, Dolgosheina E et al (2008) Comparative analysis of the small RNA transcriptomes of Pinus contorta and Oryza sativa. Genome Res 18:571–584 31. Neilson KA, Mariani M, Haynes PA (2011) Quantitative proteomic analysis of coldresponsive proteins in rice. Proteomics 11:1696–1706 32. Wang W, Vignani R, Scali M et al (2006) A universal and rapid protocol for protein extraction from recalcitrant plant tissues for pro-

33.

34.

35.

36.

37.

38.

39.

40.

41.

42.

43.

44.

221

teomic analysis. Electrophoresis 27:2782–2786 Wisniewski JR, Zougman A, Nagaraj N et al (2009) Universal sample preparation method for proteome analysis. Nat Methods 6:359–362 Bereman MS, Egertson JD, MacCoss MJ (2011) Comparison between procedures using SDS for shotgun proteomic analyses of complex samples. Proteomics 11:2931–2935 Picotti P, Bodenmiller B, Mueller LN et al (2009) Full dynamic range proteome analysis of S. cerevisiae by targeted proteomics. Cell 138:795–806 Gillet LC, Navarro P, Tate S et al (2012) Targeted data extraction of the MS/MS spectra generated by data-independent acquisition: a new concept for consistent and accurate proteome analysis. Mol Cell Proteomics 11, O111016717 Maclean B, Tomazela DM, Abbatiello SE et al (2010) Effect of collision energy optimization on the measurement of peptides by selected reaction monitoring (SRM) mass spectrometry. Anal Chem 82:10116–10124 Schilling B, Rardin MJ, Maclean BX et al (2012) Platform independent and label-free quantitation of proteomic data using MS1 extracted ion chromatograms in skyline. Application to protein acetylation and phosphorylation. Mol Cell Proteomics 11:202–214 Sherrod SD, Myers MV, Li M et al (2012) Label-free quantitation of protein modifications by pseudo-selected reaction monitoring with internal reference peptides. J Proteome Res 11:3467–3479 Chang CY, Picotti P, Huttenhain R et al (2012) Protein significance analysis in selected reaction monitoring (SRM) measurements. Mol Cell Proteomics 11, M111 014662 Kovanich D, Cappadona S, Raijmakers R et al (2012) Applications of stable isotope dimethyl labeling in quantitative proteomics. Anal Bioanal Chem 404:991–1009 Ong SE, Kratchmarova I, Mann M (2003) Properties of 13C-substituted arginine in stable isotope labeling by amino acids in cell culture (SILAC). J Proteome Res 2:173–181 Dupuis A, Hennekinne JA, Garin J et al (2008) Protein Standard Absolute Quantification (PSAQ) for improved investigation of staphylococcal food poisoning outbreaks. Proteomics 8:4633–4636 Huillet C, Adrait A, Lebert D et al (2012) Accurate quantification of cardiovascular biomarkers in

222

H. Alexander Ebhardt

serum using Protein Standard Absolute Quantification (PSAQ) and selected reaction monitoring. Mol Cell Proteomics 11, M111-008235 45. Keshishian H, Addona T, Burgess M et al (2007) Quantitative, multiplexed assays for low abundance proteins in plasma by targeted mass spectrometry and stable isotope

dilution. Mol Cell Proteomics 6:2212–2229 46. Michalski A, Damoc E, Lange O et al (2012) Ultra high resolution linear ion trap Orbitrap mass spectrometer (Orbitrap Elite) facilitates top down LC MS/MS and versatile peptide fragmentation modes. Mol Cell Proteomics 11, O111.013698