Multiplexed MRM-based assays for the quantitation of proteins in mouse plasma and heart tissue

Proteomics

Volumn 17, Issue 7, 2017, Pages

  Percy, Andrew J ^a,f   Michaud, Sarah A ^b   Jardim, Armando ^c   Sinclair, Nicholas J ^a   Zhang, Suping ^b   Mohammed, Yassene ^a,d   Palmer, Andrea L ^b   Hardie, Darryl B ^a   Yang, Juncong ^a   LeBlanc, Andre M ^a   Borchers, Christoph H ^a,e  

^a UNIVERSITY OF VICTORIA   (Canada)

^b MRM Proteomics Inc   (Canada)

^c MCGILL UNIVERSITY   (Canada)

^d LEIDEN UNIVERSITY MEDICAL CENTER   (Netherlands)

^e UNIVERSITY OF VICTORIA   (Canada)

^f CAMBRIDGE ISOTOPE LABORATORIES INC   (United States)

Author keywords

Animal proteomics; Biomarker; Heart; Molecular phenotyping; Mouse models; MRM; Plasma; Quantitative proteomics; Tissue

Indexed keywords

CARBONATE DEHYDRATASE II; FRUCTOSE BISPHOSPHATE ALDOLASE; N ACETYLMURAMOYLALANINE AMIDASE; PEROXIREDOXIN 2; PROTEOME; STABLE ISOTOPE; BIOLOGICAL MARKER; PLASMA PROTEIN;

ACCURACY; ANIMAL TISSUE; ARTICLE; COMPARATIVE STUDY; CONTROLLED STUDY; HEART TISSUE; LIMIT OF QUANTITATION; LIQUID CHROMATOGRAPHY-MASS SPECTROMETRY; MOLECULAR WEIGHT; MOUSE; NONHUMAN; PHENOTYPE; PHYSICAL CHEMISTRY; PRIORITY JOURNAL; PROTEIN ANALYSIS; PROTEOMICS; QUANTITATIVE ANALYSIS; REPRODUCIBILITY; RETENTION TIME; STEREOSPECIFICITY; ANIMAL; BLOOD; C57BL MOUSE; CARDIAC MUSCLE; CHEMISTRY; ISOTOPE LABELING; LIQUID CHROMATOGRAPHY; MASS SPECTROMETRY; METABOLISM; PROCEDURES;

ANIMALS; BIOMARKERS; BLOOD PROTEINS; CHROMATOGRAPHY, LIQUID; ISOTOPE LABELING; MASS SPECTROMETRY; MICE; MICE, INBRED C57BL; MYOCARDIUM;

EID: 85002662240     PISSN: 16159853     EISSN: 16159861     Source Type: Journal    
DOI: 10.1002/pmic.201600097     Document Type: Article

References (96)

1. Vandamme, T. F., Use of rodents as models of human diseases. J. Pharm. Bioallied Sci. 2014, 6, 2–9.
2. Kislinger, T., Gramolini, A. O., Proteome analysis of mouse model systems: a tool to model human disease and for the investigation of tissue-specific biology. J. Proteomics 2010, 73, 2205–2218.
3. Parisotto, M., Metzger, D., Genetically engineered mouse models of prostate cancer. Mol. Oncol. 2013, 7, 190–205.
4. Klarenbeek, S., van Miltenburg, M. H., Jonkers, J., Genetically engineered mouse models of PI3K signaling in breast cancer. Mol. Oncol. 2013, 7, 146–164.
5. Ijichi, H., Genetically-engineered mouse models for pancreatic cancer: advances and current limitations. World J. Clin. Oncol. 2011, 2, 195–202.
6. Walrath, J. C., Hawes, J. J., Van Dyke, T., Reilly, K. M., Genetically engineered mouse models in cancer research. Adv. Cancer Res. 2010, 106, 113–164.
7. Mou, H., Kennedy, Z., Anderson, D. G., Yin, H., Xue, W., Precision cancer mouse models through genome editing with CRISPR-Cas9. Genome Med. 2015, 7, 53.
8. Yang, H., Wang, H., Jaenisch, R., Generating genetically modified mice using CRISPR/Cas-mediated genome engineering. Nat. Protocols 2014, 9, 1956–1968.
9. Faca, V. M., Song, K. S., Wang, H., Zhang, Q. et al., A mouse to human search for plasma proteome changes associated with pancreatic tumor development. PLoS Med. 2008, 5, e123.
10. Ong, S., Mann, M., Mass spectrometry-based proteomics turns quantitative. Nat. Chem. Biol. 2005, 1, 252–262.
11. Wasinger, V. C., Zeng, M., Yau, Y., Current status and advances in quantitative proteomic mass spectrometry. Int. J. Proteomics 2013, 2013, 12.
12. Liu, Y., Hüttenhain, R., Collins, B. C., Aebersold, R., Mass spectrometric protein maps for biomarker discovery and clinical research. Expert. Rev. Mol. Diagn. 2013, 13, 811–825.
13. Bourmaud, A., Gallien, S., Domon, B., Parallel reaction monitoring using quadrupole-orbitrap mass spectrometer: principle and applications. Proteomics 2016, 16, 2146–2159.
14. Zhu, W., Smith, J. W., Huang, C. M., Mass spectrometry-based label-free quantitative proteomics. J. Biomed. Biotechnol. 2010, 2010, 840518.
15. Neilson, K. A., Ali, N. A., Muralidharan, S., Mirzaei, M. et al., Less label, more free: approaches in label-free quantitative mass spectrometry. Proteomics 2011, 11, 535–553.
16. Tang, H. Y., Beer, L. A., Chang-Wong, T., Hammond, R. et al., A xenograft mouse model coupled with in-depth plasma proteome analysis facilitates identification of novel serum biomarkers for human ovarian cancer. J. Proteome Res. 2012, 11, 678–691.
17. Beer, L. A., Wang, H., Tang, H. Y., Cao, Z. et al., Identification of multiple novel protein biomarkers shed by human serous ovarian tumors into the blood of immunocompromised mice and verified in patient sera. PLoS One 2013, 8, e60129.
18. Huang, Q., Yang, L., Luo, J., Guo, L. et al., SWATH enables precise label-free quantification on proteome scale. Proteomics 2015, 15, 1215–1223.
19. English, J. A., Scaife, C., Harauma, A., Focking, M. et al., Dataset of mouse hippocampus profiled by LC-MS/MS for label-free quantitation. Data Brief 2016, 7, 341–343.
20. 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.
21. Kirkpatrick, D. S., Gerber, S. A., Gygi, S. P., The absolute quantification strategy: a general procedure for the quantification of proteins and post-translational modifications. Methods 2005, 35, 265–273.
22. 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 2003, 100, 6940–6945.
23. 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.
24. Picard, G., Lebert, D., Louwagie, M., Adrait, A. et al., PSAQ™ standards for accurate MS-based quantification of proteins: from the concept to biomedical applications. J. Mass Spectrom. 2012, 47, 1353–1363.
25. Zanivan, S., Krueger, M., Mann, M., In vivo quantitative proteomics: the SILAC mouse. Methods Mol. Biol. 2012, 757, 435–450.
26. Konzer, A., Ruhs, A., Braun, T., Krüger, M., Global protein quantification of mouse heart tissue based on the SILAC mouse. Methods Mol. Biol. 2013, 1005, 39–52.
27. McClatchy, D. B., Yates, J. R., Stable isotope labeling in mammals (SILAM). Methods Mol. Biol. 2014, 1156, 133–146.
28. McClatchy, D. B., Ma, Y., Liu, C., Stein, B. D. et al., Pulsed Azidohomoalanine labeling in mammals (PALM) detects changes in liver-specific LKB1 knockout mice. J. Proteome Res. 2015, 14, 4815–4822.
29. Chandrudu, S., Simerska, P., Toth, I., Chemical methods for peptide and protein production. Molecules 2013, 18, 4373–4388.
30. Ragionieri, L., Vitorino, R., Frommlet, J., Oliveira, J. L. et al., Improving the accuracy of recombinant protein production through integration of bioinformatics, statistical and mass spectrometry methodologies. FEBS J. 2015, 282, 769–787.
31. Whiteaker, J. R., Zhang, H., Zhao, L., Wang, P. et al., Integrated pipeline for mass spectrometry-based discovery and confirmation of biomarkers demonstrated in a mouse model of breast cancer. J. Proteome Res. 2007, 6, 3962–3975.
32. Pitteri, S. J., Faca, V. M., Kelly-Spratt, K. S., Kasarda, A. E. et al., Plasma proteome profiling of a mouse model of breast cancer identifies a set of up-regulated proteins in common with human breast cancer cells. J. Proteome Res. 2008, 7, 1481–1489.
33. Anderson, N. L., Anderson, N. G., Haines, L. R., Hardie, D. B. et al., Mass spectrometric quantitation of peptides and proteins using stable isotope standards and capture by anti-peptide antibodies (SISCAPA). J. Proteome Res. 2004, 3, 235–244.
34. Katafuchi, T., Esterházy, 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 Metab. 2015, 21, 898–904.
35. Xian, F., Zi, J., Wang, Q., Lou, X. et al., Peptide biosynthesis with stable isotope labeling from a cell-free expression system for targeted proteomics with absolute quantification. Mol. Cell. Proteomics 2016, 15, 2819–2828.
36. Chen, J. Q., Wakefield, L. M., Goldstein, D. J., Capillary nano-immunoassays: advancing quantitative proteomics analysis, biomarker assessment, and molecular diagnostics. J. Transl. Med. 2015, 13, 182.
37. Imai, S., Takahashi, T., Naito, S., Yamauchi, A. et al., Development of a novel immunoassay specific for mouse intact proinsulin. Anal. Biochem. 2015, 484, 91–98.
38. He, Y., Zhang, M., Ju, Y., Yu, Z. et al., dbDEPC 2.0: updated database of differentially expressed proteins in human cancers. Nucleic Acids Res. 2012, 40, D964–D971.
39. Kuzyk, M. A., Parker, C. E., Domanski, D., Borchers, C. H., Development of MRM-based assays for the absolute quantitation of plasma proteins. Methods Mol. Biol. 2013, 1023, 53–82.
40. Liebler, D. C., Zimmerman, L. J., Targeted quantitation of proteins by mass spectrometry. Biochemistry 2013, 52, 3797–3806.
41. Schmidt, C., Urlaub, H., Absolute quantification of proteins using standard peptides and multiple reaction monitoring. Methods Mol. Biol. 2012, 893, 249–265.
42. Keil, B., Specificity of Proteolysis, Springer-Verlag, Berlin-Heidelberg-NewYork 1992.
43. Mohammed, Y., Borchers, C. H., An extensive library of surrogate peptides for all human proteins. J. Proteomics 2015, 129, 93–97.
44. Mohammed, Y., Domański, D., Jackson, A. M., Smith, D. S. et al., PeptidePicker: a scientific workflow with web interface for selecting appropriate peptides for targeted proteomics experiments. J. Proteomics 2014, 106, 151–161.
45. Percy, A. J., Chambers, A. G., Yang, J., Hardie, D. B., Borchers, C. H., Advances in multiplexed MRM-based protein biomarker quantitation toward clinical utility. Biochim. Biophys. Acta 2014, 1844, 917–926.
46. Hoofnagle, A. N., Whiteaker, J. R., Carr, S. A., Kuhn, E. et al., Recommendations for the generation, quantification, storage, and handling of peptides used for mass spectrometry-based assays. Clin. Chem. 2016, 62, 48–69.
47. Percy, A. J., Mohammed, Y., Yang, J., Borchers, C. H., A standardized kit for automated quantitative assessment of candidate protein biomarkers in human plasma. Bioanalysis 2015, 7, 2991–3004.
48. Dittrich, J., Becker, S., Hecht, M., Ceglarek, U., Sample preparation strategies for targeted proteomics via proteotypic peptides in human blood using liquid chromatography tandem mass spectrometry. Proteomics 2015, 9, 5–16.
49. Percy, A. J., Yang, J., Hardie, D. B., Chambers, A. G. et al., Precise quantitation of 136 urinary proteins by LC/MRM-MS using stable isotope labeled peptides as internal standards for biomarker discovery and/or verification studies. Methods 2015, 81, 24–33.
50. Percy, A. J., Chambers, A. G., Smith, D. S., Borchers, C. H., Standardized protocols for quality control of MRM-based plasma proteomic workflow. J. Proteome Res. 2013, 12, 222–233.
51. Cooper, B., The problem with peptide presumption and low Mascot scoring. J. Proteome Res. 2011, 10, 1432–1435.
52. 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.
53. Colangelo, C. M., Chung, L., Bruce, C., Cheung, K. H., Review of software tools for design and analysis of large scale MRM proteomic datasets. Methods 2013, 61, 287–298.
54. Brusniak, M. Y., Chu, C. S., Kusebauch, U., Sartain, M. J. et al., An assessment of current bioinformatic solutions for analyzing LC-MS data acquired by selected reaction monitoring technology. Proteomics 2012, 12, 1176–1184.
55. 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.
56. Lai, K. K., Kolippakkam, D., Beretta, L., Comprehensive and quantitative proteome profiling of the mouse liver and plasma. Hepatology 2008, 47, 1043–1051.
57. Babuin, L., Jaffe, A. S., Troponin: the biomarker of choice for the detection of cardiac injury. Cand. Med. Assoc. J. 2005, 173, 1191–1202.
58. Anderson, L., Candidate-based proteomics in the search for biomarkers of cardiovascular disease. J. Physiol. 2005, 563, 23–60.
59. 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.
60. Wang, A. Y., Lai, K. N., Use of cardiac biomarkers in end-stage renal disease. J. Am. Soc. Nephrol. 2008, 19, 1643–1652.
61. Lippi, G., Mattiuzzi, C., Cervellin, G., Critical review and meta-analysis on the combination of heart-type fatty acid binding protein (H-FABP) and troponin for early diagnosis of acute myocardial infarction. Clin. Biochem. 2013, 46, 26–30.
62. Carroll, C., Al Khalaf, M., Stevens, J. W., Leaviss, J. et al., Heart-type fatty acid binding protein as an early marker for myocardial infarction: systematic review and meta-analysis. Emerg. Med. J. 2013, 30, 280–286.
63. 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.
64. Smit, N. P., Romijn, F. P., van den Broek, I., Drijfhout, J. W. et al., Metrological traceability in mass spectrometry-based targeted protein quantitation: a proof-of-principle study for serum apolipoproteins A-I and B100. J. Proteomics 2014, 109, 143–161.
65. 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.
66. 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.
67. Zee, B. M., Garcia, B. A., Discovery of lysine post-translational modifications through mass spectrometric detection. Essays Biochem. 2012, 52, 147–163.
68. Uchida, Y., Tachikawa, M., Obuchi, W., Hoshi, Y. et al., A study protocol for quantitative targeted absolute proteomics (QTAP) by LC-MS/MS: application for inter-strain differences in protein expression levels of transporters, receptors, claudin-5, and marker proteins at the blood-brain barrier in ddY, FVB, and C57BL/6J mice. Fluids Barriers CNS 2013, 10, 21.
69. Scott, K. B., Turko, I. V., Phinney, K. W., Quantitative performance of internal standard platforms for absolute protein quantification using multiple reaction monitoring-mass spectrometry. Anal. Chem. 2015, 87, 4429–4435.
70. Ludwig, C., Aebersold, R., in: Eyers, C. E., Gaskell, S. J. (Eds.), Quantitative Proteomics, Royal Society of Chemistry, Cambridge 2014, pp. 80–109.
71. Gillette, M. A., Carr, S. A., Quantitative analysis of peptides and proteins in biomedicine by targeted mass spectrometry. Nat. Methods 2013, 10, 28–34.
72. Anderson, N. L., The clinical plasma proteome: a survey of clinical assays for proteins in plasma and serum. Clin. Chem. 2010, 56, 177–185.
73. Abbatiello, S. E., Schilling, B., Mani, D. R., Zimmerman, L. J. et al., Large-scale inter-laboratory 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.
74. Percy, A. J., Parker, C. E., Borchers, C. H., Pre-analytical and analytical variability in absolute quantitative MRM-based plasma proteomic studies. Bioanalysis 2013, 5, 2837–2856.
75. Barrios, M., Rodríguez-Acosta, A., Gil, A., Salazar, A. M. et al., Comparative hemostatic parameters in BALB/c, C57BL/6 and C3H/He mice. Thromb. Res. 2009, 124, 338–343.
76. Qian, W. J., Jacobs, J. M., Liu, T., Camp, D. G., Smith, R. D., Advances and challenges in liquid chromatography-mass spectrometry-based proteomics profiling for clinical applications. Mol. Cell. Proteomics 2006, 5, 1727–1744.
77. Gallego, R., Codony-Servat, J., García-Albéniz, X., Carcereny, E. et al., Serum IGF-I, IGFBP-3, and matrix metalloproteinase-7 levels and acquired chemo-resistance in advanced colorectal cancer. Endocr. Relat. Cancer 2009, 16, 311–317.
78. Suzuki, K., Moriguchi, A., Nagayoshi, A., Mutoh, S. et al., Enzyme immunoassay of human protein C by using monoclonal antibodies. Thromb. Res. 1985, 38, 611–621.
79. Stattin, P., Bylund, A., Rinaldi, S., Biessy, C. et al., Plasma insulin-like growth factor-I, insulin-like growth factor-binding proteins, and prostate cancer risk: a prospective study. J. Natl. Cancer Inst. 2000, 92, 1910–1917.
80. Chambers, A. G., Percy, A. J., Yang, J., Borchers, C. H., Multiple reaction monitoring enables precise quantification of 97 proteins in dried blood spots. Mol. Cell. Proteomics 2015, 14, 3094–3104.
81. 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.
82. Mi, H., Muruganujan, A., Casagrande, J. T., Thomas, P. D., Large-scale gene function analysis with the PANTHER classification system. Nat. Protocols 2013, 8, 1551–1566.
83. Mi, H., Poudel, S., Muruganujan, A., Casagrande, J. T., Thomas, P. D., PANTHER version 10: expanded protein families and functions, and analysis tools. Nucleic Acids Res. 2016, 44, D336–D342.
84. Lin, T., Maita, D., Thundivalappil, S. R., Riley, F. E. et al., Hemopexin in severe inflammation and infection: mouse models and human diseases. Crit. Care 2015, 19, 166.
85. Kristensen, L. P., Larsen, M. R., Mickley, H., Saaby, L. et al., Plasma proteome profiling of atherosclerotic disease manifestations reveals elevated levels of the cytoskeletal protein vinculin. J. Proteomics 2014, 101, 141–153.
86. Kim, P. Y., Tan, O., Diakiw, S. M., Carter, D. et al., Identification of plasma complement C3 as a potential biomarker for neuroblastoma using a quantitative proteomic approach. J. Proteomics 2014, 96, 1–12.
87. Velayudhan, L., Killick, R., Hye, A., Kinsey, A. et al., Plasma transthyretin as a candidate marker for Alzheimer's disease. J. Alzheimers Dis. 2012, 28, 369–375.
88. Percy, A. J., Byrns, S., Pennington, S. R., Holmes, D. T. et al., Clinical translation of MS-based, quantitative plasma proteomics: status, challenges, requirements, and potential. Exp. Rev. Proteomics 2016, 13, 673–684.
89. Kim, Y. J., Gallien, S., van Oostrum, J., Domon, B., Targeted proteomics strategy applied to biomarker evaluation. Proteomics Clin. Appl. 2013, 7, 11–12.
90. Zou, M., Zhang, P. J., Wen, X. Y., Chen, L. et al., A novel mixed integer programming for multi-biomarker panel identification by distinguishing malignant from benign colorectal tumors. Methods 2015, 83, 3–17.
91. Li, Q. R., Chen, W. J., Shen, J. W., Wu, Y. et al., Personalized evaluation based on quantitative proteomics for drug-treated patients with chronic kidney disease. J. Mol. Cell Biol. 2016, 8, 184–194.
92. Arora, D., Siddiqui, M. H., Sharma, P. K., Singh, S. P. et al., Evaluation and physiological correlation of plasma proteomic fingerprints for deltamethrin-induced hepatotoxicity in Wistar rats. Life Sci. 2016, 160, 72–83.
93. Parsons, H. T., Heazlewood, J. L., Beyond the Western front: targeted proteomics and organelle abundance profiling. Front. Plant Sci. 2015, 6, 301.
94. Bakalarski, C. E., Kirkpatrick, D. S., A biologist's field guide to multiplexed quantitative proteomics. Mol. Cell. Proteomics 2016, 15, 1489–1497.
95. Ebhardt, H. A., Root, A., Sander, C., Aebersold, R., Applications of targeted proteomics in systems biology and translational medicine. Proteomics 2015, 15, 3193–3208.
96. Sharma, V., Eckels, J., Taylor, G. K., Shulman, N. J. et al., Panorama: a targeted proteomics knowledge base. J. Proteome Res. 2014, 13, 4205–4210.