Advances in targeted proteomics and applications to biomedical research

Proteomics

Volumn 16, Issue 15-16, 2016, Pages 2160-2182

  Shi, Tujin ^a   Song, Ehwang ^a   Nie, Song ^a   Rodland, Karin D ^a   Liu, Tao ^a   Qian, Wei Jun ^a   Smith, Richard D ^a  

^a PACIFIC NORTHWEST NATIONAL LABORATORY   (United States)

Author keywords

Biomarker; DIA; PRISM; PRM; Signaling pathway; SRM; Technology

Indexed keywords

ANTIBODY; APTAMER; PROTEOME;

ANTIBODY AFFINITY; BIOINFORMATICS; DATA ANALYSIS; DATA INDEPENDENT ACQUISITION; HIGH PRESSURE HIGH RESOLUTION SEPARATIONS WITH INTELLIGENT SELECTION AND MULTIPLEXING; HIGH RESOLUTION LIQUID CHROMATOGRAPHY; HUMAN; LIQUID CHROMATOGRAPHY; LONG GRADIENT SEPARATION; MASS SPECTROMETRY; MEDICAL RESEARCH; MULTIPLEXING; NONHUMAN; PARALLEL REACTION MONITORING; PRIORITY JOURNAL; PROTEOMICS; REVIEW; SELECTED REACTION MONITORING; SEPARATION TECHNIQUE; SYSTEMS BIOLOGY;

EID: 84983285025     PISSN: 16159853     EISSN: 16159861     Source Type: Journal    
DOI: 10.1002/pmic.201500449     Document Type: Review

References (201)

1. Zhou, F., Lu, Y., Ficarro, S. B., Adelmant, G. et al., Genome-scale proteome quantification by DEEP SEQ mass spectrometry. Nat. Commun. 2013, 4, 2171.
2. Kim, M. S., Pinto, S. M., Getnet, D., Nirujogi, R. S. et al., A draft map of the human proteome. Nature 2014, 509, 575–581.
3. Wilhelm, M., Schlegl, J., Hahne, H., Moghaddas Gholami, A. et al., Mass-spectrometry-based draft of the human proteome. Nature 2014, 509, 582–587.
4. Olsen, J. V., Mann, M., Status of large-scale analysis of post-translational modifications by mass spectrometry. Mol. Cell. Proteomics 2013, 12, 3444–3452.
5. Humphrey, S. J., Azimifar, S. B., Mann, M., High-throughput phosphoproteomics reveals in vivo insulin signaling dynamics. Nat. Biotechnol. 2015, 33, 990–995.
6. Gstaiger, M., Aebersold, R., Applying mass spectrometry-based proteomics to genetics, genomics and network biology. Nature Rev. Genetics 2009, 10, 617–627.
7. Zheng, Y., Zhang, C., Croucher, D. R., Soliman, M. A. et al., Temporal regulation of EGF signalling networks by the scaffold protein Shc1. Nature 2013, 499, 166–171.
8. Zhang, Q., Fillmore, T. L., Schepmoes, A. A., Clauss, T. R. et al., Serum proteomics reveals systemic dysregulation of innate immunity in type 1 diabetes. J. Exp. Med. 2013, 210, 191–203.
9. Keshishian, H., Burgess, M. W., Gillette, M. A., Mertins, P. et al., Multiplexed, quantitative workflow for sensitive biomarker discovery in plasma yields novel candidates for early myocardial injury. Mol. Cell. Proteomics 2015, 14, 2375–2393.
10. Bensimon, A., Heck, A. J., Aebersold, R., Mass spectrometry-based proteomics and network biology. Annu. Rev. Biochem. 2012, 81, 379–405.
11. Gillet, L. C., Navarro, P., Tate, S., Rost, H. 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 2012, 11, O111 016717.
12. Anderson, N. L., Anderson, N. G., The human plasma proteome: history, character, and diagnostic prospects. Mol. Cell. Proteomics 2002, 1, 845–867.
13. Polanski, M., Anderson, N. L., A list of candidate cancer biomarkers for targeted proteomics. Biomark Insights 2007, 1, 1–48.
14. Lee, B. T., Liew, L., Lim, J., Tan, J. K. et al., Candidate List of yoUr Biomarker (CLUB): A Web-based Platform to Aid Cancer Biomarker Research. Biomark Insights 2008, 3, 65–71.
15. Anderson, N. L., The clinical plasma proteome: a survey of clinical assays for proteins in plasma and serum. Clin. Chem. 2010, 56, 177–185.
16. Anderson, L., Six decades searching for meaning in the proteome. J. Proteomics 2014, 107, 24–30.
17. Shi, T., Su, D., Liu, T., Tang, K. et al., Advancing the sensitivity of selected reaction monitoring-based targeted quantitative proteomics. Proteomics 2012, 12, 1074–1092.
18. Shi, T., Fillmore, T. L., Sun, X., Zhao, R. et al., Antibody-free, targeted mass-spectrometric approach for quantification of proteins at low picogram per milliliter levels in human plasma/serum. Proc. Natl. Acad. Sci. U. S. A. 2012, 109, 15395–15400.
19. Picotti, P., Aebersold, R., Selected reaction monitoring-based proteomics: workflows, potential, pitfalls and future directions. Nat. Methods 2012, 9, 555–566.
20. Gillette, M. A., Carr, S. A., Quantitative analysis of peptides and proteins in biomedicine by targeted mass spectrometry. Nat. Methods 2013, 10, 28–34.
21. Lange, V., Picotti, P., Domon, B. and Aebersold, R., Selected reaction monitoring for quantitative proteomics: a tutorial. Mol Syst Biol 2008, 4, 222.
22. Peterson, A. C., Russell, J. D., Bailey, D. J., Westphall, M. S. et al., Parallel reaction monitoring for high resolution and high mass accuracy quantitative, targeted proteomics. Mol. Cell. Proteomics 2012, 11, 1475–1488.
23. Gallien, S., Duriez, E., Crone, C., Kellmann, M. et al., Targeted proteomic quantification on quadrupole-orbitrap mass spectrometer. Mol. Cell. Proteomics 2012, 11, 1709–1723.
24. Aebersold, R., Burlingame, A. L., Bradshaw, R. A., Western blots versus selected reaction monitoring assays: time to turn the tables? Mol. Cell. Proteomics 2013, 12, 2381–2382.
25. Schilling, B., MacLean, B., Held, J. M., Sahu, A. K. et al., Multiplexed, scheduled, high-resolution parallel reaction monitoring on a full scan qqtof instrument with integrated data-dependent and targeted mass spectrometric workflows. Anal. Chem. 2015, 87, 10222–10229.
26. Domon, B., Gallien, S., Recent advances in targeted proteomics for clinical applications. Proteomics Clin. Appl. 2015, 9, 423–431.
27. Gallien, S., Kim, S. Y., Domon, B., Large-scale targeted proteomics using internal standard triggered-parallel reaction monitoring (IS-PRM). Mol. Cell. Proteomics 2015, 14, 1630–1644.
28. Gallien, S., Bourmaud, A., Kim, S. Y., Domon, B., Technical considerations for large-scale parallel reaction monitoring analysis. J. Proteomics 2014, 100, 147–159.
29. Rost, H. L., Rosenberger, G., Navarro, P., Gillet, L. et al., OpenSWATH enables automated, targeted analysis of data-independent acquisition MS data. Nat. Biotechnol. 2014, 32, 219–223.
30. MacLean, B., Tomazela, D. M., Shulman, N., Chambers, M. et al., Skyline: an open source document editor for creating and analyzing targeted proteomics experiments. Bioinformatics. 2010, 26, 966–968.
31. Addona, T. A., Abbatiello, S. E., Schilling, B., Skates, S. J. et al., Multi-site assessment of the precision and reproducibility of multiple reaction monitoring-based measurements of proteins in plasma. Nat. Biotechnol. 2009, 27, 633–641.
32. Kuhn, E., Whiteaker, J. R., Mani, D. R., Jackson, A. M. et al., Interlaboratory evaluation of automated, multiplexed peptide immunoaffinity enrichment coupled to multiple reaction monitoring mass spectrometry for quantifying proteins in plasma. Mol. Cell. Proteomics 2012, 11, M111 013854.
33. Kennedy, J. J., Abbatiello, S. E., Kim, K., Yan, P. et al., Demonstrating the feasibility of large-scale development of standardized assays to quantify human proteins. Nat. Methods 2014, 11, 149–155.
34. Abbatiello, S. E., Mani, D. R., Schilling, B., Maclean, B. et al., Design, implementation and multisite evaluation of a system suitability protocol for the quantitative assessment of instrument performance in liquid chromatography-multiple reaction monitoring-MS (LC-MRM-MS). Mol. Cell. Proteomics 2013, 12, 2623–2639.
35. Ruppen-Canas, I., Lopez-Casas, P. P., Garcia, F., Ximenez-Embun, P. et al., An improved quantitative mass spectrometry analysis of tumor specific mutant proteins at high sensitivity. Proteomics 2012, 12, 1319–1327.
36. Katafuchi, T., Esterhazy, D., Lemoff, A., Ding, X. et al., Detection of FGF15 in plasma by stable isotope standards and capture by anti-peptide antibodies and targeted mass spectrometry. Cell metabolism 2015, 21, 898–904.
37. Whiteaker, J. R., Zhao, L., Lin, C., Yan, P. et al., Sequential multiplexed analyte quantification using peptide immunoaffinity enrichment coupled to mass spectrometry. Mol. Cell. Proteomics 2012, 11, M111 015347.
38. Krastins, B., Prakash, A., Sarracino, D. A., Nedelkov, D. et al., Rapid development of sensitive, high-throughput, quantitative and highly selective mass spectrometric targeted immunoassays for clinically important proteins in human plasma and serum. Clin. Biochem. 2013, 46, 399–410.
39. Whiteaker, J. R., Zhao, L., Frisch, C., Ylera, F. et al., High-affinity recombinant antibody fragments (Fabs) can be applied in peptide enrichment immuno-MRM assays. J. Proteome Res. 2014, 13, 2187–2196.
40. Whiteaker, J. R., Zhao, L., Yan, P., Ivey, R. G. et al., Peptide immunoaffinity enrichment and targeted mass spectrometry enables multiplex, quantitative pharmacodynamic studies of phospho-signaling. Mol. Cell. Proteomics 2015, 14, 2261–2273.
41. Neubert, H., Muirhead, D., Kabir, M., Grace, C. et al., Sequential protein and peptide immunoaffinity capture for mass spectrometry-based quantification of total human beta-nerve growth factor. Anal. Chem. 2013, 85, 1719–1726.
42. Zhao, C., Trudeau, B., Xie, H., Prostko, J. et al., Epitope mapping and targeted quantitation of the cardiac biomarker troponin by SID-MRM mass spectrometry. Proteomics 2014, 14, 1311–1321.
43. Xu, Q., Zhu, M., Yang, T., Xu, F. et al., Quantitative assessment of human serum transferrin receptor in breast cancer patients pre- and post-chemotherapy using peptide immunoaffinity enrichment coupled with targeted proteomics. Clin. Chim. Acta 2015, 448, 118–123.
44. Gauthier, M. S., Perusse, J. R., Awan, Z., Bouchard, A. et al., A semi-automated mass spectrometric immunoassay coupled to selected reaction monitoring (MSIA-SRM) reveals novel relationships between circulating PCSK9 and metabolic phenotypes in patient cohorts. Methods 2015, 81, 66–73.
45. Gold, L., Ayers, D., Bertino, J., Bock, C. et al., Aptamer-based multiplexed proteomic technology for biomarker discovery. PLoS One 2010, 5, e15004.
46. Ostroff, R. M., Bigbee, W. L., Franklin, W., Gold, L. et al., Unlocking biomarker discovery: large scale application of aptamer proteomic technology for early detection of lung cancer. PLoS One 2010, 5, e15003.
47. Kraemer, S., Vaught, J. D., Bock, C., Gold, L. et al., From SOMAmer-based biomarker discovery to diagnostic and clinical applications: a SOMAmer-based, streamlined multiplex proteomic assay. PLoS One 2011, 6, e26332.
48. Ostroff, R. M., Mehan, M. R., Stewart, A., Ayers, D. et al., Early detection of malignant pleural mesothelioma in asbestos-exposed individuals with a noninvasive proteomics-based surveillance tool. PLoS One 2012, 7, e46091.
49. De Groote, M. A., Nahid, P., Jarlsberg, L., Johnson, J. L. et al., Elucidating novel serum biomarkers associated with pulmonary tuberculosis treatment. PLoS One 2013, 8, e61002.
50. Kiddle, S. J., Sattlecker, M., Proitsi, P., Simmons, A. et al., Candidate blood proteome markers of Alzheimer's disease onset and progression: a systematic review and replication study. J. Alzheimers Dis. 2014, 38, 515–531.
51. Hathout, Y., Brody, E., Clemens, P. R., Cripe, L. et al., Large-scale serum protein biomarker discovery in Duchenne muscular dystrophy. Proc. Natl. Acad. Sci. U. S. A. 2015, 112, 7153–7158.
52. Mehan, M. R., Williams, S. A., Siegfried, J. M., Bigbee, W. L. et al., Validation of a blood protein signature for non-small cell lung cancer. Clinical proteomics 2014, 11, 32.
53. Zhao, Y., Widen, S. G., Jamaluddin, M., Tian, B. et al., Quantification of activated NF-kappaB/RelA complexes using ssDNA aptamer affinity-stable isotope dilution-selected reaction monitoring-mass spectrometry. Mol. Cell. Proteomics 2011, 10, M111 008771.
54. Simicevic, J., Schmid, A. W., Gilardoni, P. A., Zoller, B. et al., Absolute quantification of transcription factors during cellular differentiation using multiplexed targeted proteomics. Nature Methods 2013, 10, 570–576.
55. Such-Sanmartin, G., Bache, N., Callesen, A. K., Rogowska-Wrzesinska, A. et al., Targeted mass spectrometry analysis of the proteins IGF1, IGF2, IBP2, IBP3 and A2GL by blood protein precipitation. J. Proteomics 2015, 113, 29–37.
56. Halvey, P. J., Ferrone, C. R., Liebler, D. C., GeLC-MRM quantitation of mutant KRAS oncoprotein in complex biological samples. J. Proteome Res. 2012, 11, 3908–3913.
57. Sano, S., Tagami, S., Hashimoto, Y., Yoshizawa-Kumagaye, K. et al., Absolute quantitation of low abundance plasma apl1 beta peptides at sub-fmol/ml level by srm/mrm without immunoaffinity enrichment. J. Proteome Res. 2014, 13, 1012–1020.
58. Shi, T., Sun, X., Gao, Y., Fillmore, T. L. et al., Targeted quantification of low ng/mL level proteins in human serum without immunoaffinity depletion. J. Proteome Res. 2013, 12, 3353–3361.
59. Shi, T., Gao, Y., Quek, S. I., Fillmore, T. L. et al., A Highly sensitive targeted mass spectrometric assay for quantification of agr2 protein in human urine and serum. J. Proteome Res. 2014, 13, 875–882.
60. He, J., Sun, X., Shi, T., Schepmoes, A. A. et al., Antibody-independent targeted quantification of TMPRSS2-ERG fusion protein products in prostate cancer. Molecular Oncology 2014, 8, 1169–1180.
61. He, J., Schepmoes, A. A., Shi, T., Wu, C. et al., Analytical platform evaluation for quantification of ERG in prostate cancer using protein and mRNA detection methods. J. Transl. Med. 2015, 13, 54.
62. Shi, T. J., Gao, Y. Q., Gaffrey, M. J., Nicora, C. D. et al., Sensitive targeted quantification of ERK phosphorylation dynamics and stoichiometry in human cells without affinity enrichment. Anal Chem 2015, 87, 1103–1110.
63. Kim, J. S., Ahn, H. S., Cho, S. M., Lee, J. E. et al., Detection and quantification of plasma amyloid-beta by selected reaction monitoring mass spectrometry. Anal. Chim. Acta. 2014, 840, 1–9.
64. Percy, A. J., Simon, R., Chambers, A. G., Borchers, C. H., Enhanced sensitivity and multiplexing with 2D LC/MRM-MS and labeled standards for deeper and more comprehensive protein quantitation. J. Proteomics 2014, 106, 113–124.
65. Keshishian, H., Addona, T., Burgess, M., Kuhn, E. et al., multiplexed assays for low abundance proteins in plasma by targeted mass spectrometry and stable isotope dilution. Mol. Cell. Proteomics 2007, 6, 2212–2229.
66. Gilar, M., Olivova, P., Daly, A. E., Gebler, J. C., Two-dimensional separation of peptides using RP-RP-HPLC system with different pH in first and second separation dimensions. J. Sep. Sci. 2005, 28, 1694–1703.
67. Wang, Y., Yang, F., Gritsenko, M. A., Wang, Y. et al., Reversed-phase chromatography with multiple fraction concatenation strategy for proteome profiling of human MCF10A cells. Proteomics 2011, 11, 2019–2026.
68. Simon, R., Passeron, S., Lemoine, J., Salvador, A., Hydrophilic interaction liquid chromatography as second dimension in multidimensional chromatography with an anionic trapping strategy: application to prostate-specific antigen quantification. J. Chromatogr A. 2014, 1354, 75–84.
69. Shi, T., Fillmore, T. L., Gao, Y., Zhao, R. et al., Long-gradient separations coupled with selected reaction monitoring for highly sensitive, large scale targeted protein quantification in a single analysis. Anal. Chem. 2013, 85, 9196–9203.
70. Burgess, M. W., Keshishian, H., Mani, D. R., Gillette, M. A. et al., Simplified and efficient quantification of low-abundance proteins at very high multiplex via targeted mass spectrometry. Mol. Cell. Proteomics 2014, 13, 1137–1149.
71. Shi, T., Zhou, J. Y., Gritsenko, M. A., Hossain, M. et al., IgY14 and SuperMix immunoaffinity separations coupled with liquid chromatography-mass spectrometry for human plasma proteomics biomarker discovery. Methods 2012, 56, 246–253.
72. Mani, D. R., Abbatiello, S. E., Carr, S. A., Statistical characterization of multiple-reaction monitoring mass spectrometry (MRM-MS) assays for quantitative proteomics. BMC Bioinformatics 2012, 13 Suppl 16, S9.
73. Mohammed, Y., Percy, A. J., Chambers, A. G., Borchers, C. H., Qualis-SIS: automated standard curve generation and quality assessment for multiplexed targeted quantitative proteomic experiments with labeled standards. J. Proteome Res. 2015, 14, 1137–1146.
74. Percy, A. J., Tamura-Wells, J., Albar, J. P., Aloria, K. et al., Inter-laboratory evaluation of instrument platforms and experimental workflows for quantitative accuracy and reproducibility assessment. EuPA Open Proteomics 2015, 8, 6–15.
75. Abbatiello, S. E., Schilling, B., Mani, D. R., Zimmerman, L. J. et al., Large-scale interlaboratory study to develop, analytically validate and apply highly multiplexed, quantitative peptide assays to measure cancer-relevant proteins in plasma. Mol. Cell. Proteomics 2015, 14, 2357–2374.
76. Carr, S. A., Abbatiello, S. E., Ackermann, B. L., Borchers, C. et al., Targeted peptide measurements in biology and medicine: best practices for mass spectrometry-based assay development using a fit-for-purpose approach. Mol. Cell. Proteomics 2014, 13, 907–917.
77. Method of the Year 2012. Nat. Methods 2013, 10, 1.
78. Ebhardt, H. A., Root, A., Sander, C., Aebersold, R., Applications of targeted proteomics in systems biology and translational medicine. Proteomics 2015, 15, 3193–3208.
79. Picotti, P., Bodenmiller, B., Aebersold, R., Proteomics meets the scientific method. Nat. Methods 2013, 10, 24–27.
80. Shi, T., Qian, W. J., Antibody-free PRISM-SRM for multiplexed protein quantification: is this the new competition for immunoassays in bioanalysis? Bioanalysis 2013, 5, 267–269.
81. Diamandis, E. P., Towards identification of true cancer biomarkers. BMC medicine 2014, 12, 156.
82. Rodland, K. D., As if biomarker discovery isn't hard enough: the consequences of poorly characterized reagents. Clin. Chem. 2014, 60, 290–291.
83. Liu, T., Hossain, M., Schepmoes, A. A., Fillmore, T. L. et al., Analysis of serum total and free PSA using immunoaffinity depletion coupled to SRM: correlation with clinical immunoassay tests. J. Proteomics 2012, 75, 4747–4757.
84. Huillet, C., Adrait, A., Lebert, D., Picard, G. et al., Accurate quantification of cardiovascular biomarkers in serum using Protein Standard Absolute Quantification (PSAQ) and selected reaction monitoring. Mol. Cell. Proteomics 2012, 11, M111 008235.
85. Lin, D., Alborn, W. E., Slebos, R. J., Liebler, D. C., Comparison of protein immunoprecipitation-multiple reaction monitoring with ELISA for assay of biomarker candidates in plasma. J. Proteome Res. 2013, 12, 5996–6003.
86. Thomas, S. N., Harlan, R., Chen, J., Aiyetan, P. et al., Multiplexed targeted mass spectrometry-based assays for the quantification of n-linked glycosite-containing peptides in serum. Anal. Chem. 2015, 87, 10830–10838.
87. Kim, Y. J., Sertamo, K., Pierrard, M. A., Mesmin, C. et al., Verification of the biomarker candidates for non-small-cell lung cancer using a targeted proteomics approach. J. Proteome Res 2015, 14, 1412–1419.
88. Kim, Y. J., Gallien, S., El-Khoury, V., Goswami, P. et al., Quantification of SAA1 and SAA2 in lung cancer plasma using the isotype-specific PRM assays. Proteomics 2015, 15, 3116–3125.
89. Sung, H. J., Jeon, S. A., Ahn, J. M., Seul, K. J. et al., Large-scale isotype-specific quantification of Serum amyloid A 1/2 by multiple reaction monitoring in crude sera. J. Proteomics 2012, 75, 2170–2180.
90. Birse, C. E., Lagier, R. J., FitzHugh, W., Pass, H. I. et al., Blood-based lung cancer biomarkers identified through proteomic discovery in cancer tissues, cell lines and conditioned medium. Clinical Proteomics 2015, 12, 18.
91. Li, X. J., Hayward, C., Fong, P. Y., Dominguez, M. et al., A blood-based proteomic classifier for the molecular characterization of pulmonary nodules. Sci. Transl. Med. 2013, 5, 207ra142.
92. Chen, C. D., Wang, C. L., Yu, C. J., Chien, K. Y. et al., Targeted proteomics pipeline reveals potential biomarkers for the diagnosis of metastatic lung cancer in pleural effusion. J. Proteome Res. 2014, 13, 2818–2829.
93. Kume, H., Muraoka, S., Kuga, T., Adachi, J. et al., Discovery of colorectal cancer biomarker candidates by membrane proteomic analysis and subsequent verification using selected reaction monitoring (SRM) and tissue microarray (TMA) analysis. Mol. Cell. Proteomics 2014, 13, 1471–1484.
94. Surinova, S., Choi, M., Tao, S., Schuffler, P. J. et al., Prediction of colorectal cancer diagnosis based on circulating plasma proteins. Embo Mol. Med. 2015, 7, 1166–1178.
95. Surinova, S., Radova, L., Choi, M., Srovnal, J. et al., Non-invasive prognostic protein biomarker signatures associated with colorectal cancer. Embo Mol. Med. 2015, 7, 1153–1165.
96. Yoneyama, T., Ohtsuki, S., Ono, M., Ohmine, K. et al., Quantitative targeted absolute proteomics-based large-scale quantification of proline-hydroxylated alpha-fibrinogen in plasma for pancreatic cancer diagnosis. J. Proteome Res. 2013, 12, 753–762.
97. Pan, S., Chen, R., Brand, R. E., Hawley, S. et al., Multiplex targeted proteomic assay for biomarker detection in plasma: a pancreatic cancer biomarker case study. J. Proteome Res. 2012, 11, 1937–1948.
98. Pan, C., Zhou, Y., Dator, R., Ginghina, C. et al., Targeted discovery and validation of plasma biomarkers of Parkinson's disease. J. Proteome Res. 2014, 13, 4535–4545.
99. Shi, M., Movius, J., Dator, R., Aro, P. et al., Cerebrospinal fluid peptides as potential parkinson disease biomarkers: a staged pipeline for discovery and validation. Mol. Cell. Proteomics 2015, 14, 544–555.
100. Alberio, T., McMahon, K., Cuccurullo, M., Gethings, L. A. et al., Verification of a Parkinson's disease protein signature in T-lymphocytes by multiple reaction monitoring. J. Proteome Res. 2014, 13, 3554–3561.
101. Oeckl, P., Steinacker, P., von Arnim, C. A. F., Straub, S. et al., Intact protein analysis of ubiquitin in cerebrospinal fluid by multiple reaction monitoring reveals differences in alzheimer's disease and frontotemporal lobar degeneration. J. Proteome Res. 2014, 13, 4518–4525.
102. Martinez-Morillo, E., Cho, C. K. J., Drabovich, A. P., Shaw, J. L. V. et al., Development of a multiplex selected reaction monitoring assay for quantification of biochemical markers of down syndrome in amniotic fluid samples. J. Proteome Res. 2012, 11, 3880–3887.
103. Cho, C. K., Drabovich, A. P., Batruch, I., Diamandis, E. P., Verification of a biomarker discovery approach for detection of Down syndrome in amniotic fluid via multiplex selected reaction monitoring (SRM) assay. J. Proteomics 2011, 74, 2052–2059.
104. Heywood, W., Wang, D., Madgett, T. E., Avent, N. D. et al., The development of a peptide SRM-based tandem mass spectrometry assay for prenatal screening of Down syndrome. J. Proteomics 2012, 75, 3248–3257.
105. Qin, S., Zhou, Y., Lok, A. S., Tsodikov, A. et al., SRM targeted proteomics in search for biomarkers of HCV-induced progression of fibrosis to cirrhosis in HALT-C patients. Proteomics 2012, 12, 1244–1252.
106. Cretu, D., Prassas, I., Saraon, P., Batruch, I. et al., Identification of psoriatic arthritis mediators in synovial fluid by quantitative mass spectrometry. Clinical proteomics 2014, 11, 27.
107. Jia, Y., Wu, T., Jelinek, C. A., Bielekova, B. et al., Development of protein biomarkers in cerebrospinal fluid for secondary progressive multiple sclerosis using selected reaction monitoring mass spectrometry (SRM-MS). Clinical Proteomics 2012, 9, 9.
108. Cerciello, F., Choi, M., Nicastri, A., Bausch-Fluck, D. et al., Identification of a seven glycopeptide signature for malignant pleural mesothelioma in human serum by selected reaction monitoring. Clin. Proteomics 2013, 10, 16.
109. Ahn, Y. H., Shin, P. M., Oh, N. R., Park, G. W. et al., A lectin-coupled, targeted proteomic mass spectrometry (MRM MS) platform for identification of multiple liver cancer biomarkers in human plasma. J. Proteomics 2012, 75, 5507–5515.
110. Huttenhain, R., Soste, M., Selevsek, N., Rost, H. et al., Reproducible quantification of cancer-associated proteins in body fluids using targeted proteomics. Sci. Transl. Med. 2012, 4, 142ra194.
111. Percy, A. J., Chambers, A. G., Yang, J., Borchers, C. H., Multiplexed MRM-based quantitation of candidate cancer biomarker proteins in undepleted and non-enriched human plasma. Proteomics 2013, 13, 2202–2215.
112. Vegvari, A., Sjodin, K., Rezeli, M., Marko-Varga, G., Quantification of human kallikrein-2 in clinical samples by selected reaction monitoring. J. Proteome Res. 2013, 12, 4612–4616.
113. Percy, A. J., Yang, J., Chambers, A. G., Simon, R. et al., Multiplexed MRM with internal standards for cerebrospinal fluid candidate protein biomarker quantitation. J. Proteome Res. 2014, 13, 3733–3747.
114. Fallon, J. K., Neubert, H., Hyland, R., Goosen, T. C. et al., Targeted quantitative proteomics for the analysis of 14 UGT1As and -2Bs in human liver using NanoUPLC-MS/MS with selected reaction monitoring. J. Proteome Res. 2013, 12, 4402–4413.
115. Martinez-Marquez, A., Morante-Carriel, J., Selles-Marchart, S., Martinez-Esteso, M. J. et al., Development and validation of MRM methods to quantify protein isoforms of polyphenol oxidase in loquat fruits. J. Proteome Res. 2013, 12, 5709–5722.
116. Martinez-Morillo, E., Nielsen, H. M., Batruch, I., Drabovich, A. P. et al., Assessment of peptide chemical modifications on the development of an accurate and precise multiplex selected reaction monitoring assay for apolipoprotein e isoforms. J. Proteome Res. 2014, 13, 1077–1087.
117. Vegvari, A., Sjodin, K., Rezeli, M., Malm, J. et al., Identification of a novel proteoform of prostate specific antigen (SNP-L132I) in clinical samples by multiple reaction monitoring. Mol. Cell. Proteomics 2013, 12, 2761–2773.
118. Plumel, M. I., Wasselin, T., Plot, V., Strub, J. M. et al., Mass spectrometry-based sequencing and srm-based quantitation of two novel vitellogenin isoforms in the leatherback sea turtle (Dermochelys coriacea). J. Proteome Res. 2013, 12, 4122–4135.
119. Sabido, E., Wu, Y. B., Bautista, L., Porstmann, T. et al., Targeted proteomics reveals strain-specific changes in the mouse insulin and central metabolic pathways after a sustained high-fat diet. Mol. Syst. Biol. 2013, 9, 681.
120. de Graaf, E. L., Kaplon, J., Mohammed, S., Vereijken, L. A. M. et al., Signal transduction reaction monitoring deciphers site-specific pi3k-mtor/mapk pathway dynamics in onaigene-induced senescence. J. Proteome Res. 2015, 14, 2906–2914.
121. Drabovich, A. P., Pavlou, M. P., Dimitromanolakis, A., Diamandis, E. P., Quantitative analysis of energy metabolic pathways in mcf-7 breast cancer cells by selected reaction monitoring assay. Mol. Cell. Proteomics 2012, 11, 422–434.
122. Krieger, J. R., Taylor, P., Gajadhar, A. S., Guha, A. et al., Identification and selected reaction monitoring (srm) quantification of endocytosis factors associated with numb. Mol. Cell. Proteomics 2013, 12, 499–514.
123. Zhao, Y. X., Tian, B., Edeh, C. B., Brasier, A. R., Quantitation of the dynamic profiles of the innate immune response using multiplex selected reaction monitoring-mass spectrometry. Mol. Cell. Proteomics 2013, 12, 1513–1529.
124. Tong, J. F., Taylor, P., Moran, M. F., Proteomic analysis of the epidermal growth factor receptor (EGFR) interactome and post-translational modifications associated with receptor endocytosis in response to EGF and stress. Mol. Cell. Proteomics 2014, 13, 1644–1658.
125. Rebecca, V. W., Wood, E., Fedorenko, I. V., Paraiso, K. H. et al., Evaluating melanoma drug response and therapeutic escape with quantitative proteomics. Mol. Cell. Proteomics 2014, 13, 1844–1854.
126. Manes, N. P., Angermann, B. R., Koppenol-Raab, M., An, E. et al., Targeted proteomics-driven computational modeling of macrophage S1P chemosensing. Mol. Cell. Proteomics 2015, 14, 2661–2681.
127. Kiel, C., Ebhardt, H. A., Burnier, J., Portugal, C. et al., Quantification of ErbB network proteins in three cell types using complementary approaches identifies cell-general and cell-type-specific signaling proteins. J. Proteome Res. 2014, 13, 300–313.
128. Sangar, V., Funk, C. C., Kusebauch, U., Campbell, D. S. et al., Quantitative proteomic analysis reveals effects of epidermal growth factor receptor (EGFR) on invasion-promoting proteins secreted by glioblastoma cells. Mol. Cell. Proteomics 2014, 13, 2618–2631.
129. Tian, R., Jin, J., Taylor, L., Larsen, B. et al., Rapid and sensitive MRM-based mass spectrometry approach for systematically exploring ganglioside-protein interactions. Proteomics 2013, 13, 1334–1338.
130. Altvater, M., Chang, Y. M., Melnik, A., Occhipinti, L. et al., Targeted proteomics reveals compositional dynamics of 60S pre-ribosomes after nuclear export. Molecular Systems Biology 2012, 8, 628.
131. Mirzaei, H., Knijnenburg, T. A., Kim, B., Robinson, M. et al., Systematic measurement of transcription factor-DNA interactions by targeted mass spectrometry identifies candidate gene regulatory proteins. Proc. Natl. Acad. Sci. U. S. A. 2013, 110, 3645–3650.
132. Agard, N. J., Mahrus, S., Trinidad, J. C., Lynn, A. et al., Global kinetic analysis of proteolysis via quantitative targeted proteomics. Proc. Natl. Acad. Sci. U. S. A. 2012, 109, 1913–1918.
133. Soste, M., Hrabakova, R., Wanka, S., Melnik, A. et al., A sentinel protein assay for simultaneously quantifying cellular processes. Nat. Methods 2014, 11, 1045–1048.
134. Feng, Y., De Franceschi, G., Kahraman, A., Soste, M. et al., Global analysis of protein structural changes in complex proteomes. Nat. Biotechnol. 2014, 32, 1036–1044.
135. Gallien, S., Duriez, E., Demeure, K., Domon, B., Selectivity of LC-MS/MS analysis: implication for proteomics experiments. J. Proteomics 2013, 81, 148–158.
136. Schiffmann, C., Hansen, R., Baumann, S., Kublik, A. et al., Comparison of targeted peptide quantification assays for reductive dehalogenases by selective reaction monitoring (SRM) and precursor reaction monitoring (PRM). Anal. Bioanal. Chem. 2014, 406, 283–291.
137. Ronsein, G. E., Pamir, N., von Haller, P. D., Kim, D. S. et al., Parallel reaction monitoring (PRM) and selected reaction monitoring (SRM) exhibit comparable linearity, dynamic range and precision for targeted quantitative HDL proteomics. J. Proteomics 2015, 113, 388–399.
138. Majovsky, P., Naumann, C., Lee, C. W., Lassowskat, I. et al., Targeted proteomics analysis of protein degradation in plant signaling on an LTQ-Orbitrap mass spectrometer. J. Proteome Res. 2014, 13, 4246–4258.
139. Morin, L. P., Mess, J. N., Garofolo, F., Large-molecule quantification: sensitivity and selectivity head-to-head comparison of triple quadrupole with Q-TOF. Bioanalysis 2013, 5, 1181–1193.
140. Gallien, S., Domon, B., Detection and quantification of proteins in clinical samples using high resolution mass spectrometry. Methods 2015, 81, 15–23.
141. Scheltema, R. A., Hauschild, J. P., Lange, O., Hornburg, D. et al., The Q Exactive HF, a benchtop mass spectrometer with a pre-filter, high-performance quadrupole and an ultra-high-field orbitrap analyzer. Mol. Cell. Proteomics 2014, 13, 3698–3708.
142. Gallien, S., Domon, B., Quantitative proteomics using the high resolution accurate mass capabilities of the quadrupole-orbitrap mass spectrometer. Bioanalysis 2014, 6, 2159–2170.
143. Lesur, A., Domon, B., Advances in high-resolution accurate mass spectrometry application to targeted proteomics. Proteomics 2015, 15, 880–890.
144. Lesur, A., Ancheva, L., Kim, Y. J., Berchem, G. et al., Screening protein isoforms predictive for cancer using immunoaffinity capture and fast LC-MS in PRM mode. Proteomics Clin. Appl. 2015, 9, 695–705.
145. Kim, H. J., Lin, D., Li, M., Liebler, D. C., Quantitative profiling of protein tyrosine kinases in human cancer cell lines by multiplexed parallel reaction monitoring assays. Mol. Cell. Proteomics 2016, 15, 682–691.
146. Tang, H., Fang, H., Yin, E., Brasier, A. R. et al., Multiplexed parallel reaction monitoring targeting histone modifications on the QExactive mass spectrometer. Anal. Chem. 2014, 86, 5526–5534.
147. Sowers, J. L., Mirfattah, B., Xu, P., Tang, H. et al., Quantification of Histone Modifications by Parallel-Reaction Monitoring: A Method Validation. Anal. Chem. 2015, 87, 10006–10014.
148. Tsuchiya, H., Tanaka, K., Saeki, Y., The parallel reaction monitoring method contributes to a highly sensitive polyubiquitin chain quantification. Biochem. Biophys. Res. Commun. 2013, 436, 223–229.
149. Liu, Y., Huttenhain, R., Collins, B., Aebersold, R., Mass spectrometric protein maps for biomarker discovery and clinical research. Exp. Rev. Mol. Diagn. 2013, 13, 811–825.
150. Sajic, T., Liu, Y. S., Aebersold, R., Using data-independent, high-resolution mass spectrometry in protein biomarker research: Perspectives and clinical applications. Proteom Clin. Appl. 2015, 9, 307–321.
151. Venable, J. D., Dong, M. Q., Wohlschlegel, J., Dillin, A., Yates, J. R., Automated approach for quantitative analysis of complex peptide mixtures from tandem mass spectra. Nat. Methods 2004, 1, 39–45.
152. Silva, J. C., Denny, R., Dorschel, C. A., Gorenstein, M. et al., Quantitative proteomic analysis by accurate mass retention time pairs. Anal. Chem. 2005, 77, 2187–2200.
153. Panchaud, A., Scherl, A., Shaffer, S. A., von Haller, P. D. et al., Precursor acquisition independent from ion count: how to dive deeper into the proteomics Ocean. Anal. Chem. 2009, 81, 6481–6488.
154. Panchaud, A., Jung, S., Shaffer, S. A., Aitchison, J. D., Goodlett, D. R., Faster, quantitative, and accurate precursor acquisition independent from ion count. Anal. Chem. 2011, 83, 2250–2257.
155. Geiger, T., Cox, J., Mann, M., Proteomics on an orbitrap benchtop mass spectrometer using all-ion fragmentation. Mol. Cell. Proteomics 2010, 9, 2252–2261.
156. Carvalho, P. C., Han, X. M., Xu, T., Cociorva, D. et al., XDIA: improving on the label-free data-independent analysis. Bioinformatics 2010, 26, 847–848.
157. Weisbrod, C. R., Eng, J. K., Hoopmann, M. R., Baker, T. et al., Accurate peptide fragment mass analysis: multiplexed peptide identification and quantification. J. Proteome Res. 2012, 11, 1621–1632.
158. Egertson, J. D., Kuehn, A., Merrihew, G. E., Bateman, N. W. et al., Multiplexed MS/MS for improved data-independent acquisition. Nature Methods 2013, 10, 744–746.
159. Prakash, A., Peterman, S., Ahmad, S., Sarracino, D. et al., Hybrid data acquisition and processing strategies with increased throughput and selectivity: pSMART analysis for global qualitative and quantitative analysis. J. Proteome Res. 2014, 13, 5415–5430.
160. Reiko Kiyonami, Bhavin Patel, Michael Senko, Vlad Zabrouskov et al., Large scale targeted protein quantification using WiSIM-DIA workflow on a orbitrap fusion tribrid mass spectrometer. Thermo Scientific Application Note 600 2014.
161. Bruderer, R., Bernhardt, O. M., Gandhi, T., Miladinovic, S. M. et al., Extending the limits of quantitative proteome profiling with data-independent acquisition and application to acetaminophen-treated three-dimensional liver microtissues. Mol. Cell. Proteomics 2015, 14, 1400–1410.
162. Bilbao, A., Varesio, E., Luban, J., Strambio-De-Castillia, C. et al., Processing strategies and software solutions for data-independent acquisition in mass spectrometry. Proteomics 2015, 15, 964–980.
163. Bern, M., Finney, G., Hoopmann, M. R., Merrihew, G. et al., Deconvolution of mixture spectra from ion-trap data-independent-acquisition tandem mass spectrometry. Anal. Chem. 2010, 82, 833–841.
164. Ledvina, A. R., Savitski, M. M., Zubarev, A. R., Good, D. M. et al., Increased throughput of proteomics analysis by multiplexing high-resolution tandem mass spectra. Anal. Chem. 2011, 83, 7651–7656.
165. Pak, H., Nikitin, F., Gluck, F., Lisacek, F. et al., Clustering and filtering tandem mass spectra acquired in data-independent mode. J. Am. Soc. Mass Spectrom. 2013, 24, 1862–1871.
166. Kryuchkov, F., Verano-Braga, T., Hansen, T. A., Sprenger, R. R. et al., Deconvolution of mixture spectra and increased throughput of peptide identification by utilization of intensified complementary ions formed in tandem mass spectrometry. J. Proteome Res. 2013, 12, 3362–3371.
167. Kim, S., Mischerikow, N., Bandeira, N., Navarro, J. D. et al., The generating function of CID, ETD, and CID/ETD pairs of tandem mass spectra: applications to database search. Mol. Cell. Proteomics 2010, 9, 2840–2852.
168. Cox, J., Mann, M., MaxQuant enables high peptide identification rates, individualized p.p.b.-range mass accuracies and proteome-wide protein quantification. Nat. Biotechnol. 2008, 26, 1367–1372.
169. Perkins, D. N., Pappin, D. J. C., Creasy, D. M., Cottrell, J. S., Probability-based protein identification by searching sequence databases using mass spectrometry data. Electrophoresis 1999, 20, 3551–3567.
170. Craig, R., Cortens, J. P., Beavis, R. C., Open source system for analyzing, validating, and storing protein identification data. J. Proteome Res. 2004, 3, 1234–1242.
171. Eng, J. K., Jahan, T. A., Hoopmann, M. R., Comet: An open-source MS/MS sequence database search tool. Proteomics 2013, 13, 22–24.
172. Tsou, C. C., Avtonomov, D., Larsen, B., Tucholska, M. et al., DIA-Umpire: comprehensive computational framework for data-independent acquisition proteomics. Nature Methods 2015, 12, 258–264.
173. Zhang, N., Li, X. J., Ye, M., Pan, S. et al., ProbIDtree: an automated software program capable of identifying multiple peptides from a single collision-induced dissociation spectrum collected by a tandem mass spectrometer. Proteomics 2005, 5, 4096–4106.
174. Li, G. Z., Vissers, J. P., Silva, J. C., Golick, D. et al., Database searching and accounting of multiplexed precursor and product ion spectra from the data independent analysis of simple and complex peptide mixtures. Proteomics 2009, 9, 1696–1719.
175. Wang, J., Perez-Santiago, J., Katz, J. E., Mallick, P., Bandeira, N., Peptide identification from mixture tandem mass spectra. Mol. Cell. Proteomics 2010, 9, 1476–1485.
176. Wang, J., Bourne, P. E., Bandeira, N., Peptide identification by database search of mixture tandem mass spectra. Mol. Cell. Proteomics 2011, 10, M111 010017.
177. Egertson, J. D., MacLean, B., Johnson, R., Xuan, Y. et al., Multiplexed peptide analysis using data-independent acquisition and Skyline. Nat. Protoc. 2015, 10, 887–903.
178. Reiter, L., Rinner, O., Picotti, P., Huttenhain, R. et al., mProphet: automated data processing and statistical validation for large-scale SRM experiments. Nat. Methods 2011, 8, 430–435.
179. Teleman, J., Rost, H. L., Rosenberger, G., Schmitt, U. et al., DIANA-algorithmic improvements for analysis of data-independent acquisition MS data. Bioinformatics. 2015, 31, 555–562.
180. Liu, Y., Huttenhain, R., Surinova, S., Gillet, L. C. et al., Quantitative measurements of N-linked glycoproteins in human plasma by SWATH-MS. Proteomics 2013, 13, 1247–1256.
181. Guo, T., Kouvonen, P., Koh, C. C., Gillet, L. C. et al., Rapid mass spectrometric conversion of tissue biopsy samples into permanent quantitative digital proteome maps. Nature Medicine 2015, 21, 407–413.
182. Selevsek, N., Chang, C. Y., Gillet, L. C., Navarro, P. et al., Reproducible and consistent quantification of the Saccharomyces cerevisiae Proteome by SWATH-mass spectrometry. Mol. Cell. Proteomics 2015, 14, 739–749.
183. Collins, B. C., Gillet, L. C., Rosenberger, G., Rost, H. L. et al., Quantifying protein interaction dynamics by SWATH mass spectrometry: application to the 14-3-3 system. Nat. Methods 2013, 10, 1246–1253.
184. Lambert, J. P., Ivosev, G., Couzens, A. L., Larsen, B. et al., Mapping differential interactomes by affinity purification coupled with data-independent mass spectrometry acquisition. Nature Methods 2013, 10, 1239–1245.
185. Liu, Y. S., Chen, J., Sethi, A., Li, Q. K. et al., Glycoproteomic analysis of prostate cancer tissues by SWATH mass spectrometry discovers n-acylethanolamine acid amidase and protein tyrosine kinase 7 as signatures for tumor aggressiveness. Mol. Cell. Proteomics 2014, 13, 1753–1768.
186. Krautkramer, K. A., Reiter, L., Denu, J. M., Dowell, J. A., Quantification of SAHA-dependent changes in histone modifications using data-independent acquisition mass spectrometry. J. Proteome Res. 2015, 14, 3252–3262.
187. Sidoli, S., Lin, S., Xiong, L., Bhanu, N. V. et al., Sequential window acquisition of all theoretical mass spectra (SWATH) analysis for characterization and quantification of histone post-translational modifications. Mol. Cell. Proteomics 2015, 14, 2420–2428.
188. Gallien, S., Domon, B., Advances in high-resolution quantitative proteomics: implications for clinical applications. Exp. Rev. Proteomics 2015, 12, 489–498.
189. Garcia-Bailo, B., Brenner, D. R., Nielsen, D., Lee, H. J. et al., Dietary patterns and ethnicity are associated with distinct plasma proteomic groups. Am. J. Clin. Nutr. 2012, 95, 352–361.
190. Fernandez Ocana, M., James, I. T., Kabir, M., Grace, C. et al., Clinical pharmacokinetic assessment of an anti-MAdCAM monoclonal antibody therapeutic by LC-MS/MS. Anal. Chem. 2012, 84, 5959–5967.
191. Razavi, M., Frick, L. E., LaMarr, W. A., Pope, M. E. et al., High-throughput SISCAPA quantitation of peptides from human plasma digests by ultrafast, liquid chromatography-free mass spectrometry. J. Proteome Res. 2012, 11, 5642–5649.
192. Anderson, N. L., Razavi, M., Pearson, T. W., Kruppa, G. et al., Precision of heavy-light peptide ratios measured by maldi-tof mass spectrometry. J. Proteome Res. 2012, 11, 1868–1878.
193. Razavi, M., Johnson, L. D., Lum, J. J., Kruppa, G. et al., Quantification of a proteotypic peptide from protein C inhibitor by liquid chromatography-free SISCAPA-MALDI mass spectrometry: application to identification of recurrence of prostate cancer. Clin. Chem. 2013, 59, 1514–1522.
194. Webb, I. K., Garimella, S. V., Tolmachev, A. V., Chen, T. C. et al., Experimental evaluation and optimization of structures for lossless ion manipulations for ion mobility spectrometry with time-of-flight mass spectrometry. Anal. Chem. 2014, 86, 9169–9176.
195. Webb, I. K., Garimella, S. V., Tolmachev, A. V., Chen, T. C. et al., Mobility-resolved ion selection in uniform drift field ion mobility spectrometry/mass spectrometry: dynamic switching in structures for lossless ion manipulations. Anal. Chem. 2014, 86, 9632–9637.
196. Zhang, X., Garimella, S. V., Prost, S. A., Webb, I. K. et al., Ion trapping, storage, and ejection in structures for lossless ion manipulations. Anal. Chem. 2015, 87, 6010–6016.
197. Page, J. S., Tang, K., Kelly, R. T., Smith, R. D., Subambient pressure ionization with nanoelectrospray source and interface for improved sensitivity in mass spectrometry. Anal. Chem. 2008, 80, 1800–1805.
198. Shen, Y., Smith, R. D., Unger, K. K., Kumar, D. et al., Ultrahigh-throughput proteomics using fast RPLC separations with ESI-MS/MS. Anal. Chem. 2005, 77, 6692–6701.
199. Shi, T., Niepel, M., McDermott, J. E., Gao, Y. et al., Conserved protein abundance pattern of the EGFR-MAPK pathway reveals its regulatory architecture. Science Signaling 2016, in press.
200. Mortstedt, H., Karedal, M. H., Jonsson, B. A. G., Lindh, C. H., Screening method using selected reaction monitoring for targeted proteomics studies of nasal lavage fluid. J. Proteome Res. 2013, 12, 234–247.
201. Welinder, C., Jonsson, G., Ingvar, C., Lundgren, L. et al., Feasibility study on measuring selected proteins in malignant melanoma tissue by SRM quantification. J. Proteome Res. 2014, 13, 1315–1326.