Selected reaction monitoring-based proteomics: Workflows, potential, pitfalls and future directions

Nature Methods

Volumn 9, Issue 6, 2012, Pages 555-566

  Picotti, Paola ^a   Aebersold, Ruedi ^a,b,c  

^a ETH ZURICH   (Switzerland)

^b Competence Center for Systems Physiology and Metabolic Diseases ^*  (Switzerland)

^c UNIVERSITY OF ZURICH   (Switzerland)

Author keywords

[No Author keywords available]

Indexed keywords

PROTEIN;

ACCURACY; HUMAN; MASS SPECTROMETRY; NONHUMAN; PRIORITY JOURNAL; PROTEIN ANALYSIS; PROTEOMICS; QUANTITATIVE ANALYSIS; REPRODUCIBILITY; REVIEW; SELECTED REACTION MONITORING; TECHNOLOGY;

BIOLOGICAL MARKERS; ISOTOPE LABELING; MASS SPECTROMETRY; METABOLIC NETWORKS AND PATHWAYS; PEPTIDE FRAGMENTS; PROTEIN PROCESSING, POST-TRANSLATIONAL; PROTEOMICS; SENSITIVITY AND SPECIFICITY; SIGNAL TRANSDUCTION; SYSTEMS BIOLOGY;

EID: 84861990380     PISSN: 15487091     EISSN: 15487105     Source Type: Journal    
DOI: 10.1038/nmeth.2015     Document Type: Review

References (117)

1. Kuhn, E. et al. Quantification of C-reactive protein in the serum of patients with rheumatoid arthritis using multiple reaction monitoring mass spectrometry and 13C-labeled peptide standards. Proteomics 4, 1175-1186 (2004).
2. Picotti, P., Bodenmiller, B., Mueller, L.N., Domon, B. & Aebersold, R. Full dynamic range proteome analysis of S. cerevisiae by targeted proteomics. Cell 138, 795-806 (2009). First demonstration of the capability of SRM to detect and quantify proteins over the whole range of cellular concentrations in S. cerevisiae.
3. Anderson, L. & Hunter, C.L. Quantitative mass spectrometric multiple reaction monitoring assays for major plasma proteins. Mol. Cell Proteomics 5, 573-588 (2006). Assessment of the precision and dynamic range of the SRM technique when applied to measuring multiple proteins concomitantly in whole and depleted human plasma.
4. Zweigenbaum, J. & Henion, J. Bioanalytical high-throughput selected reaction monitoring-LC/MS determination of selected estrogen receptor modulators in human plasma: 2000 samples/day. Anal. Chem. 72, 2446-2454 (2000).
5. Yost, R.A. & Enke, C.G. Triple quadrupole mass spectrometry for direct mixture analysis and structural elucidation. Anal. Chem. 51, 231-243 (1979).
6. Yost, R.A. & Enke, C.G. Selected Ion Fragmentation with a Tandem Quadrupole Mass Spectrometer. J. Am. Chem. Soc. 100, 2274-2275 (1978).
7. Kondrat, R.W., McClusky, G.A. & Cooks, R.G. Multiple reaction monitoring in mass spectrometry/mass spectrometry for direct analysis of complex mixtures. Anal. Chem. 14, 2017-2021 (1978).
8. Lange, V., Picotti, P., Domon, B. & Aebersold, R. Selected reaction monitoring for quantitative proteomics: a tutorial. Mol. Syst. Biol. 4, 222 (2008).
9. Picotti, P., Aebersold, R. & Domon, B. The implications of proteolytic background for shotgun proteomics. Mol. Cell Proteomics 6, 1589-1598 (2007).
10. Kuster, B., Schirle, M., Mallick, P. & Aebersold, R. Scoring proteomes with proteotypic peptide probes. Nat. Rev. Mol. Cell Biol. 6, 577-583 (2005).
11. Mallick, P. et al. Computational prediction of proteotypic peptides for quantitative proteomics. Nat. Biotechnol. 25, 125-131 (2007).
12. Huillet, C. et al. Accurate quantification of cardiovascular biomarkers in serum using protein standard absolute quantification (PSAQTM) and selected reaction monitoring. Mol. Cell Proteomics advance online publication, doi:10.1074/mcp.M111.008235 (11 November 2011).
13. Deutsch, E.W., Lam, H. & Aebersold, R. PeptideAtlas: a resource for target selection for emerging targeted proteomics workflows. EMBO Rep. 9, 429-434 (2008).
14. Craig, R., Cortens, J.P. & Beavis, R.C. Open source system for analyzing, validating, and storing protein identification data. J. Proteome Res. 3, 1234-1242 (2004).
15. Shadforth, I., Xu, W., Crowther, D. & Bessant, C. GAPP: a fully automated software for the confident identification of human peptides from tandem mass spectra. J. Proteome Res. 5, 2849-2852 (2006).
16. Vizcaino, J.A. et al. A guide to the Proteomics Identifications Database proteomics data repository. Proteomics 9, 4276-4283 (2009).
17. Falkner, J.A. & Andrews, P.C. Tranche: secure decentralized data storage for the proteomics community. J. Biomol. Tech. 1, 3 (2007).
18. Tabb, D.L. et al. Repeatability and reproducibility in proteomic identifications by liquid chromatography-tandem mass spectrometry. J. Proteome Res. 9, 761-776 (2010).
19. Hermjakob, H. & Apweiler, R. The Proteomics Identifications Database (PRIDE) and the ProteomExchange Consortium: making proteomics data accessible. Expert Rev. Proteomics 3, 1-3 (2006).
20. Fusaro, V.A., Mani, D.R., Mesirov, J.P. & Carr, S.A. Prediction of high-responding peptides for targeted protein assays by mass spectrometry. Nat. Biotechnol. 27, 190-198 (2009).
21. Tang, H. et al. A computational approach toward label-free protein quantification using predicted peptide detectability. Bioinformatics 22, e481-e488 (2006).
22. Webb-Robertson, B.J. et al. A support vector machine model for the prediction of proteotypic peptides for accurate mass and time proteomics. Bioinformatics 26, 1677-1683 (2010).
23. Brownridge, P. et al. Global absolute quantification of a proteome: challenges in the deployment of a QconCAT strategy. Proteomics 11, 2957-2970 (2011).
24. Sherwood, C.A. et al. Correlation between y-type ions observed in ion trap and triple quadrupole mass spectrometers. J. Proteome Res. 8, 4243-4251 (2009).
25. Mead, J.A. et al. MRMaid, the web-based tool for designing multiple reaction monitoring (MRM) transitions. Mol. Cell Proteomics 8, 696-705 (2009).
26. Martin, D.B. et al. MRMer, an interactive open source and cross-platform system for data extraction and visualization of multiple reaction monitoring experiments. Mol. Cell Proteomics 7, 2270-2278 (2008).
27. Sherwood, C.A. et al. MaRiMba: a software application for spectral library-based MRM transition list assembly. J. Proteome Res. 8, 4396-4405 (2009).
28. Lam, H. et al. Building consensus spectral libraries for peptide identification in proteomics. Nat. Methods 5, 873-875 (2008).
29. Picotti, P. et al. A database of mass spectrometric assays for the yeast proteome. Nat. Methods 5, 913-914 (2008).
30. Picotti, P. et al. High-throughput generation of selected reaction-monitoring assays for proteins and proteomes. Nat. Methods 7, 43-46 (2010). This study presents a method based on peptide libraries that allows generating SRM assays for proteins and proteomes at high-throughput and confidence.
31. Lange, V. et al. Targeted quantitative analysis of Streptococcus pyogenes virulence factors by multiple reaction monitoring. Mol. Cell Proteomics 7, 1489-1500 (2008).
32. Maclean, B. et al. Effect of collision energy optimization on the measurement of peptides by selected reaction monitoring (SRM) mass spectrometry. Anal. Chem. 82, 10116-10124 (2010).
33. Kuzyk, M.A. et al. Multiple reaction monitoring-based, multiplexed, absolute quantitation of 45 proteins in human plasma. Mol. Cell Proteomics 8, 1860-1877 (2009).
34. Holstein Sherwood, C.A., Gafken, P.R. & Martin, D.B. Collision energy optimization of b- and y-ions for multiple reaction monitoring mass spectrometry. J. Proteome Res. 10, 231-240 (2011).
35. Sherwood, C.A. et al. Rapid optimization of MRM-MS instrument parameters by subtle alteration of precursor and product m/z targets. J. Proteome Res. 8, 3746-3751 (2009).
36. Stahl-Zeng, J. et al. High sensitivity detection of plasma proteins by multiple reaction monitoring of N-glycosites. Mol. Cell Proteomics 6, 1809-1817 (2007).
37. Bisson, N. et al. Selected reaction monitoring mass spectrometry reveals the dynamics of signaling through the GRB2 adaptor. Nat. Biotechnol. 29, 653-658 (2011). Application of SRM in combination with affinity purification to study the dynamics of protein complexes formed around an adaptor protein involved in multiple signaling pathways in human cells.
38. Kiyonami, R. et al. Increased selectivity, analytical precision, and throughput in targeted proteomics. Mol. Cell Proteomics advance online publication, doi:10.1074/mcp.M110.002931 (27 July 2011).
39. Rinner, O. et al. An integrated mass spectrometric and computational framework for the analysis of protein interaction networks. Nat. Biotechnol. 25, 345-352 (2007).
40. Choi, S., Kim, J., Yea, K., Suh, P.G. & Ryu, S.H. Targeted label-free quantitative analysis of secretory proteins from adipocytes in response to oxidative stress. Anal. Biochem. 401, 196-202 (2010).
41. Gygi, S.P. et al. Quantitative analysis of complex protein mixtures using isotope-coded affinity tags. Nat. Biotechnol. 17, 994-999 (1999).
42. Zhang, Y. et al. Time-resolved mass spectrometry of tyrosine phosphorylation sites in the epidermal growth factor receptor signaling network reveals dynamic modules. Mol. Cell Proteomics 4, 1240-1250 (2005).
43. DeSouza, L.V. et al. Multiple reaction monitoring of mTRAQ-labeled peptides enables absolute quantification of endogenous levels of a potential cancer marker in cancerous and normal endometrial tissues. J. Proteome Res. 7, 3525-3534 (2008).
44. Schmidt, A., Kellermann, J. & Lottspeich, F. A novel strategy for quantitative proteomics using isotope-coded protein labels. Proteomics 5, 4-15 (2005).
45. Ong, S.E. & Mann, M. Stable isotope labeling by amino acids in cell culture for quantitative proteomics. Methods Mol. Biol. 359, 37-52 (2007).
46. Rangiah, K. et al. Differential secreted proteome approach in murine model for candidate biomarker discovery in colon cancer. J. Proteome Res. 8, 5153-5164 (2009).
47. Zhao, Y. et al. Combination of improved (18)O incorporation and multiple reaction monitoring: a universal strategy for absolute quantitative verification of serum candidate biomarkers of liver cancer. J. Proteome Res. 9, 3319-3327 (2010).
48. Gevaert, K. et al. Stable isotopic labeling in proteomics. Proteomics 8, 4873-4885 (2008).
49. Zhang, H. et al. Methods for peptide and protein quantitation by liquid chromatography-multiple reaction monitoring mass spectrometry. Mol. Cell Proteomics advance online publication, doi:10.1074/mcp.M110.006593 (27 February 2011).
50. Gerber, S.A., Rush, J., Stemman, O., Kirschner, M.W. & Gygi, S.P. Absolute quantification of proteins and phosphoproteins from cell lysates by tandem MS. Proc. Natl. Acad. Sci. USA 100, 6940-6945 (2003).
51. Keshishian, H., Addona, T., Burgess, M., Kuhn, E. & Carr, S.A. Quantitative, multiplexed assays for low abundance proteins in plasma by targeted mass spectrometry and stable isotope dilution. Mol. Cell Proteomics 6, 2212-2229 (2007).
52. Proc, J.L. et al. A quantitative study of the effects of chaotropic agents, surfactants, and solvents on the digestion efficiency of human plasma proteins by trypsin. J. Proteome Res. 9, 5422-5437 (2010).
53. Schmidt, C., Lenz, C., Grote, M., Luhrmann, R. & Urlaub, H. Determination of protein stoichiometry within protein complexes using absolute quantification and multiple reaction monitoring. Anal. Chem. 82, 2784-2796 (2010).
54. Elschenbroich, S. et al. In-depth proteomics of ovarian cancer ascites: combining shotgun proteomics and selected reaction monitoring mass spectrometry. J. Proteome Res. 10, 2286-2299 (2011).
55. Beynon, R.J., Doherty, M.K., Pratt, J.M. & Gaskell, S.J. Multiplexed absolute quantification in proteomics using artificial QCAT proteins of concatenated signature peptides. Nat. Methods 2, 587-589 (2005).
56. Dupuis, A., Hennekinne, J.A., Garin, J. & Brun, V. Protein Standard Absolute Quantification (PSAQ) for improved investigation of staphylococcal food poisoning outbreaks. Proteomics 8, 4633-4636 (2008).
57. Mirzaei, H., McBee, J.K., Watts, J. & Aebersold, R. Comparative evaluation of current peptide production platforms used in absolute quantification in proteomics. Mol. Cell Proteomics 7, 813-823 (2008).
58. Holzmann, J., Pichler, P., Madalinski, M., Kurzbauer, R. & Mechtler, K. Stoichiometry determination of the MP1-p14 complex using a novel and cost-efficient method to produce an equimolar mixture of standard peptides. Anal. Chem. 15, 10254-10261 (2009).
59. MacLean, B. et al. Skyline: an open source document editor for creating and analyzing targeted proteomics experiments. Bioinformatics 26, 966-968 (2010).
60. Brusniak, M.Y. et al. ATAQS: A computational software tool for high throughput transition optimization and validation for selected reaction monitoring mass spectrometry. BMC Bioinformatics 12, 78 (2011).
61. Prakash, A. et al. Expediting the development of targeted SRM assays: using data from shotgun proteomics to automate method development. J. Proteome Res. 8, 2733-2739 (2009).
62. Randall, S.A., McKay, M.J. & Molloy, M.P. Evaluation of blood collection tubes using selected reaction monitoring MS: implications for proteomic biomarker studies. Proteomics 10, 2050-2056 (2010).
63. Chang, C.Y., Picotti, P., Huettenhain, R., Heinzelmann-Schwarz, V., Jovanovic, M. & Aebersold, R. et al. Protein significance analysis in selected reaction monitoring (SRM) measurements. Mol. Cell Proteomics advance online publication, doi:10.1074/mcp.M111.014662 (21 December 2011).
64. Spicer, V. et al. 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 (2007).
65. Domon, B. & Aebersold, R. Options and considerations when selecting a quantitative proteomics strategy. Nat. Biotechnol. 28, 710-721 (2010).
66. Pawlak, M. et al. Zeptosens' protein microarrays: a novel high performance microarray platform for low abundance protein analysis. Proteomics 2, 383-393 (2002).
67. Wingren, C. & Borrebaeck, C.A. Progress in miniaturization of protein arrays-a step closer to high-density nanoarrays. Drug Discov. Today 12, 813-819 (2007).
68. Cubitt, A.B. et al. Understanding, improving and using green fluorescent proteins. Trends Biochem. Sci. 20, 448-455 (1995).
69. Ghaemmaghami, S. et al. Global analysis of protein expression in yeast. Nature 425, 737-741 (2003).
70. Newman, J.R. et al. Single-cell proteomic analysis of S. cerevisiae reveals the architecture of biological noise. Nature 441, 840-846 (2006).
71. Kumar, V. Immunofluorescence and enzyme immunomicroscopy methods. J. Immunoassay 21, 235-253 (2000).
72. Bandura, D.R. et al. Mass cytometry: technique for real time single cell multitarget immunoassay based on inductively coupled plasma time-of-flight mass spectrometry. Anal. Chem. 81, 6813-6822 (2009).
73. Zulak, K.G. et al. Targeted proteomics using selected reaction monitoring reveals the induction of specific terpene synthases in a multi-level study of methyl jasmonate-treated Norway spruce (Picea abies). Plant J. 60, 1015-1030 (2009).
74. Costenoble, R. et al. Comprehensive quantitative analysis of central carbon and amino-acid metabolism in Saccharomyces cerevisiae under multiple conditions by targeted proteomics. Mol. Syst. Biol. 7, 464 (2011).
75. Wang, Q. et al. Mutant proteins as cancer-specific biomarkers. Proc. Natl. Acad. Sci. USA 108, 2444-2449 (2011). Demonstration of the applicability of SRM to the measurement of cancer associated mutant proteins in cancer cell lines and clinical specimens.
76. Chen, Y. et al. Quantification of beta-catenin signaling components in colon cancer cell lines, tissue sections, and microdissected tumor cells using reaction monitoring mass spectrometry. J. Proteome Res. 9, 4215-4227 (2010).
77. Hewel, J.A. et al. Synthetic peptide arrays for pathway-level protein monitoring by liquid chromatography-tandem mass spectrometry. Mol. Cell Proteomics 9, 2460-2473 (2010).
78. Jovanovic, M. et al. A quantitative targeted proteomics approach to validate predicted microRNA targets in C. elegans. Nat. Methods 7, 837-842 (2010).
79. Bodenmiller, B. et al. PhosphoPep-a database of protein phosphorylation sites in model organisms. Nat. Biotechnol. 26, 1339-1340 (2008).
80. Gnad, F., Gunawardena, J. & Mann, M. PHOSIDA 2011: the postttranslational modification database. Nucleic Acids Res. 39, D253-D260 (2011).
81. Cox, D.M. et al. Multiple reaction monitoring as a method for identifying protein posttranslational modifications. J. Biomol. Tech. 16, 83-90 (2005).
82. Unwin, R.D. et al. Multiple reaction monitoring to identify sites of protein phosphorylation with high sensitivity. Mol. Cell Proteomics 4, 1134-1144 (2005).
83. Glinski, M. & Weckwerth, W. Differential multisite phosphorylation of the trehalose-6-phosphate synthase gene family in Arabidopsis thaliana: a mass spectrometry-based process for multiparallel peptide library phosphorylation analysis. Mol. Cell Proteomics 4, 1614-1625 (2005).
84. Domanski, D., Murphy, L.C. & Borchers, C.H. Assay development for the determination of phosphorylation stoichiometry using multiple reaction monitoring methods with and without phosphatase treatment: application to breast cancer signaling pathways. Anal. Chem. 82, 5610-5620 (2010).
85. Wolf-Yadlin, A., Hautaniemi, S., Lauffenburger, D.A. & White, F.M. Multiple reaction monitoring for robust quantitative proteomic analysis of cellular signaling networks. Proc. Natl. Acad. Sci. USA 104, 5860-5865 (2007).
86. Darwanto, A. et al. A modified "cross-talk" between histone H2B Lys-120 ubiquitination and H3 Lys-79 methylation. J. Biol. Chem. 285, 21868-21876 (2010). Application of SRM to the concurrent quantification of a set of post-translational modifications of core histone proteins, including acetylation, propionylation, methylation and ubiquitination in a single analysis.
87. Kirkpatrick, D.S. et al. Quantitative analysis of in vitro ubiquitinated cyclin B1 reveals complex chain topology. Nat. Cell Biol. 8, 700-710 (2006).
88. Mirzaei, H. et al. Characterizing the connectivity of poly-ubiquitin chains by selected reaction monitoring mass spectrometry. Mol. Biosyst. 6, 2004-2014 (2010).
89. Held, J.M. et al. Targeted quantitation of site-specific cysteine oxidation in endogenous proteins using a differential alkylation and multiple reaction monitoring mass spectrometry approach. Mol. Cell Proteomics 9, 1400-1410 (2010).
90. Danielson, S.R. et al. Preferentially increased nitration of alpha-synuclein at tyrosine-39 in a cellular oxidative model of Parkinson's disease. Anal. Chem. 81, 7823-7828 (2009).
91. Rifai, N., Gillette, M.A. & Carr, S.A. Protein biomarker discovery and validation: the long and uncertain path to clinical utility. Nat. Biotechnol. 24, 971-983 (2006).
92. Wang, P., Whiteaker, J.R. & Paulovich, A.G. The evolving role of mass spectrometry in cancer biomarker discovery. Cancer Biol. Ther. 8, 1083-1094 (2009).
93. Zhang, F., Bartels, M.J. & Stott, W.T. Quantitation of human glutathione S-transferases in complex matrices by liquid chromatography/tandem mass spectrometry with signature peptides. Rapid Commun. Mass Spectrom. 18, 491-498 (2004).
94. Yocum, A.K. et al. Coupled global and targeted proteomics of human embryonic stem cells during induced differentiation. Mol. Cell Proteomics 7, 750-767 (2008).
95. Addona, T.A. et al. Multi-site assessment of the precision and reproducibility of multiple reaction monitoring-based measurements of proteins in plasma. Nat. Biotechnol. 27, 633-641 (2009). First large-scale study that shows reproducibility and precision of SRM measurements in plasma across multiple laboratories and instrument platforms.
96. Anderson, N.L. & Anderson, N.G. The human plasma proteome: history, character, and diagnostic prospects. Mol. Cell Proteomics 1, 845-867 (2002).
97. Fortin, T. et al. Clinical quantitation of prostate-specific antigen biomarker in the low nanogram/milliliter range by conventional bore liquid chromatography-tandem mass spectrometry (multiple reaction monitoring) coupling and correlation with ELISA tests. Mol. Cell Proteomics 8, 1006-1015 (2009).
98. Keshishian, H. et al. Quantification of cardiovascular biomarkers in patient plasma by targeted mass spectrometry and stable isotope dilution. Mol. Cell Proteomics 8, 2339-2349 (2009).
99. Berna, M.J., Zhen, Y., Watson, D.E., Hale, J.E. & Ackermann, B.L. Strategic use of immunoprecipitation and LC/MS/MS for trace-level protein quantification: myosin light chain 1, a biomarker of cardiac necrosis. Anal. Chem. 79, 4199-4205 (2007).
100. Nicol, G.R. et al. Use of an immunoaffinity-mass spectrometry-based approach for the quantification of protein biomarkers from serum samples of lung cancer patients. Mol. Cell Proteomics 7, 1974-1982 (2008).
101. Berna, M. & Ackermann, B. Increased throughput for low-abundance protein biomarker verification by liquid chromatography/tandem mass spectrometry. Anal. Chem. 81, 3950-3956 (2009).
102. Anderson, N.L. et al. Mass spectrometric quantitation of peptides and proteins using stable isotope standards and capture by anti-peptide antibodies (SISCAPA). J. Proteome Res. 3, 235-244 (2004).
103. Hoofnagle, A.N., Becker, J.O., Wener, M.H. & Heinecke, J.W. Quantification of thyroglobulin, a low-abundance serum protein, by immunoaffinity peptide enrichment and tandem mass spectrometry. Clin. Chem. 54, 1796-1804 (2008).
104. Kuhn, E. et al. Developing multiplexed assays for troponin I and interleukin-33 in plasma by peptide immunoaffinity enrichment and targeted mass spectrometry. Clin. Chem. 55, 1108-1117 (2009). Study that demonstrates the potential of SIS CAPA-coupled SRM for multiplexed quantification of biomarker candidates in plasma in the low ng/ml concentration range.
105. Whiteaker, J.R., Zhao, L., Anderson, L. & Paulovich, A.G. An automated and multiplexed method for high throughput peptide immunoaffinity enrichment and multiple reaction monitoring mass spectrometry-based quantification of protein biomarkers. Mol. Cell. Proteomics 9, 184-196 (2010).
106. Zhang, H., Li, X.J., Martin, D.B. & Aebersold, R. Identification and quantification of N-linked glycoproteins using hydrazide chemistry, stable isotope labeling and mass spectrometry. Nat. Biotechnol. 21, 660-666 (2003).
107. Cima, I. et al. Cancer genetics-guided discovery of serum biomarker signatures for diagnosis and prognosis of prostate cancer. Proc. Natl. Acad. Sci. USA 108, 3342-3347 (2011). Application of SRM to the measurement of a panel of candidate biomarkers for prostate cancer through a set of serum samples from more than 100 individuals.
108. Ahn, Y.H., Lee, J.Y., Kim, Y.S., Ko, J.H. & Yoo, J.S. Quantitative analysis of an aberrant glycoform of TIMP1 from colon cancer serum by L-PHA-enrichment and SISCAPA with MRM mass spectrometry. J. Proteome Res. 8, 4216-4224 (2009).
109. Huttenhain, R., Malmstrom, J., Picotti, P. & Aebersold, R. Perspectives of targeted mass spectrometry for protein biomarker verification. Curr. Opin. Chem. Biol. 13, 518-525 (2009).
110. Hoofnagle, A.N. & Wener, M.H. The fundamental flaws of immunoassays and potential solutions using tandem mass spectrometry. J. Immunol. Methods 347, 3-11 (2009).
111. Gillet, L.C. et al. 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 advance online publication, doi:10.1074/mcp.O111.016717 (18 January 2012).
112. Hossain, M. et al. Enhanced sensitivity for selected reaction monitoring mass spectrometry-based targeted proteomics using a dual stage electrodynamic ion funnel interface. Mol. Cell Proteomics 10, 62-201 (2011).
113. Fortin, T. et al. Multiple reaction monitoring cubed for protein quantification at the low nanogram/milliliter level in nondepleted human serum. Anal. Chem. 81, 9343-9352 (2009).
114. Reiter, L. et al. mProphet: automated data processing and statistical validation for large-scale SRM experiments. Nat. Methods 8, 430-435 (2011).
115. Abbatiello, S.E., Mani, D.R., Keshishian, H. & Carr, S.A. Automated detection of inaccurate and imprecise transitions in peptide quantification by multiple reaction monitoring mass spectrometry. Clin. Chem. 56, 291-305 (2010).
116. Sherman, J., McKay, M.J., Ashman, K. & Molloy, M.P. Unique ion signature mass spectrometry, a deterministic method to assign peptide identity. Mol. Cell Proteomics 8, 2051-2062 (2009).
117. Anderson, N.L. Libraries of specific assays covering whole proteomes: from yeast to man. Clin. Chem. 56, 1521-1522 (2010).