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In-Depth Blood Proteome Profiling by Extensive Fractionation and Multiplexed Quantitative Mass Spectrometry

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Serum/Plasma Proteomics

Part of the book series: Methods in Molecular Biology ((MIMB,volume 2628))

Abstract

Blood in the circulatory system carries information of physiological and pathological status of the human body, so blood proteins are often used as biomarkers for diagnosis, prognosis, and therapy. Human blood proteome can be explored by the latest technologies in mass spectrometry (MS), creating an opportunity of discovering new disease biomarkers. The extreme dynamic range of protein concentrations in blood, however, poses a challenge to detect proteins of low abundance, namely, tissue leakage proteins. Here, we describe a strategy to directly analyze undepleted blood samples by extensive liquid chromatography (LC) fractionation and 18-plex tandem-mass-tag (TMT) mass spectrometry. The proteins in blood specimens (e.g., plasma or serum) are isolated by acetone precipitation and digested into peptides. The resulting peptides are TMT-labeled, separated by basic pH reverse-phase (RP) LC into at least 40 fractions, and analyzed by acidic pH RPLC and high-resolution MS/MS, leading to the quantification of ~3000 unique proteins. Further increase of basic pH RPLC fractions and adjustment of the fraction concatenation strategy can enhance the proteomic coverage (up to ~5000 proteins). Finally, the combination of multiple batches of TMT experiments allows the profiling of hundreds of blood samples. This TMT-MS-based method provides a powerful platform for deep proteome profiling of human blood samples.

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References

  1. Anderson NL, Anderson NG (2002) The human plasma proteome: history, character, and diagnostic prospects. Mol Cell Proteomics 1(11):845–867

    Article  CAS  Google Scholar 

  2. Rifai N, Gillette MA, Carr SA (2006) Protein biomarker discovery and validation: the long and uncertain path to clinical utility. Nat Biotechnol 24(8):971–983

    Article  CAS  Google Scholar 

  3. Geyer PE, Holdt LM, Teupser D, Mann M (2017) Revisiting biomarker discovery by plasma proteomics. Mol Syst Biol 13(9):942. https://doi.org/10.15252/msb.20156297

    Article  CAS  Google Scholar 

  4. Bai B, Vanderwall D, Li Y, Wang X, Poudel S, Wang H, Dey KK, Chen PC, Yang K, Peng J (2021) Proteomic landscape of Alzheimer’s disease: novel insights into pathogenesis and biomarker discovery. Mol Neurodegener 16(1):55

    Article  CAS  Google Scholar 

  5. Fuzery AK, Levin J, Chan MM, Chan DW (2013) Translation of proteomic biomarkers into FDA approved cancer diagnostics: issues and challenges. Clin Proteomics 10(1):13. https://doi.org/10.1186/1559-0275-10-13. Artn 13

    Article  CAS  Google Scholar 

  6. Geyer PE, Kulak NA, Pichler G, Holdt LM, Teupser D, Mann M (2016) Plasma proteome profiling to assess human health and disease. Cell Syst 2(3):185–195. https://doi.org/10.1016/j.cels.2016.02.015

    Article  CAS  Google Scholar 

  7. Angel TE, Aryal UK, Hengel SM, Baker ES, Kelly RT, Robinson EW, Smith RD (2012) Mass spectrometry-based proteomics: existing capabilities and future directions. Chem Soc Rev 41(10):3912–3928. https://doi.org/10.1039/c2cs15331a

    Article  CAS  Google Scholar 

  8. Aebersold R, Mann M (2016) Mass-spectrometric exploration of proteome structure and function. Nature 537(7620):347–355. https://doi.org/10.1038/nature19949

    Article  CAS  Google Scholar 

  9. Zhang Y, Fonslow BR, Shan B, Baek MC, Yates JR 3rd (2013) Protein analysis by shotgun/bottom-up proteomics. Chem Rev 113(4):2343–2394. https://doi.org/10.1021/cr3003533

    Article  CAS  Google Scholar 

  10. Baker ES, Liu T, Petyuk VA, Burnum-Johnson KE, Ibrahim YM, Anderson GA, Smith RD (2012) Mass spectrometry for translational proteomics: progress and clinical implications. Genome Med 4(8):63. https://doi.org/10.1186/gm364

    Article  CAS  Google Scholar 

  11. Pieper R, Su Q, Gatlin CL, Huang ST, Anderson NL, Steiner S (2003) Multi-component immunoaffinity subtraction chromatography: an innovative step towards a comprehensive survey of the human plasma proteome. Proteomics 3(4):422–432. https://doi.org/10.1002/pmic.200390057

    Article  CAS  Google Scholar 

  12. Qian WJ, Kaleta DT, Petritis BO, Jiang H, Liu T, Zhang X, Mottaz HM, Varnum SM, Camp DG 2nd, Huang L, Fang X, Zhang WW, Smith RD (2008) Enhanced detection of low abundance human plasma proteins using a tandem IgY12-SuperMix immunoaffinity separation strategy. Mol Cell Proteomics 7(10):1963–1973. https://doi.org/10.1074/mcp.M800008-MCP200

    Article  CAS  Google Scholar 

  13. Tu C, Rudnick PA, Martinez MY, Cheek KL, Stein SE, Slebos RJ, Liebler DC (2010) Depletion of abundant plasma proteins and limitations of plasma proteomics. J Proteome Res 9(10):4982–4991. https://doi.org/10.1021/pr100646w

    Article  CAS  Google Scholar 

  14. Dey KK, Wang H, Niu M, Bai B, Wang X, Li Y, Cho JH, Tan H, Mishra A, High AA, Chen PC, Wu Z, Beach TG, Peng J (2019) Deep undepleted human serum proteome profiling toward biomarker discovery for Alzheimer’s disease. Clin Proteomics 16:16. https://doi.org/10.1186/s12014-019-9237-1

    Article  CAS  Google Scholar 

  15. Thompson A, Schafer J, Kuhn K, Kienle S, Schwarz J, Schmidt G, Neumann T, Hamon C (2003) Tandem mass tags: a novel quantification strategy for comparative analysis of complex protein mixtures by MS/MS. Anal Chem 75(8):1895–1904. https://doi.org/10.1021/ac0262560

    Article  CAS  Google Scholar 

  16. Rauniyar N, Yates JR 3rd (2014) Isobaric labeling-based relative quantification in shotgun proteomics. J Proteome Res 13(12):5293–5309. https://doi.org/10.1021/pr500880b

    Article  CAS  Google Scholar 

  17. Tan H, Yang K, Li Y, Shaw TI, Wang Y, Blanco DB, Wang X, Cho JH, Wang H, Rankin S, Guy C, Peng J, Chi H (2017) Integrative proteomics and phosphoproteomics profiling reveals dynamic signaling networks and bioenergetics pathways underlying T cell activation. Immunity 46(3):488–503. https://doi.org/10.1016/j.immuni.2017.02.010

    Article  CAS  Google Scholar 

  18. Niu M, Cho JH, Kodali K, Pagala V, High AA, Wang H, Wu Z, Li Y, Bi W, Zhang H, Wang X, Zou W, Peng J (2017) Extensive peptide fractionation and y1 ion-based interference detection method for enabling accurate quantification by isobaric labeling and mass spectrometry. Anal Chem 89(5):2956–2963. https://doi.org/10.1021/acs.analchem.6b04415

    Article  CAS  Google Scholar 

  19. Wang Z, Ma J, Miyoshi C, Li Y, Sato M, Ogawa Y, Lou T, Ma C, Gao X, Lee C, Fujiyama T, Yang X, Zhou S, Hotta-Hirashima N, Klewe-Nebenius D, Ikkyu A, Kakizaki M, Kanno S, Cao L, Takahashi S, Peng J, Yu Y, Funato H, Yanagisawa M, Liu Q (2018) Quantitative phosphoproteomic analysis of the molecular substrates of sleep need. Nature 558(7710):435–439. https://doi.org/10.1038/s41586-018-0218-8

    Article  CAS  Google Scholar 

  20. Li J, Van Vranken JG, Pontano Vaites L, Schweppe DK, Huttlin EL, Etienne C, Nandhikonda P, Viner R, Robitaille AM, Thompson AH, Kuhn K, Pike I, Bomgarden RD, Rogers JC, Gygi SP, Paulo JA (2020) TMTpro reagents: a set of isobaric labeling mass tags enables simultaneous proteome-wide measurements across 16 samples. Nat Methods 17(4):399–404. https://doi.org/10.1038/s41592-020-0781-4

    Article  CAS  Google Scholar 

  21. Wang Z, Yu KW, Tan H, Wu Z, Cho JH, Han X, Sun H, Beach TG, Peng JM (2020) 27-plex tandem mass tag mass spectrometry for profiling brain proteome in Alzheimer’s disease. Anal Chem 92(10):7162–7170

    Article  CAS  Google Scholar 

  22. Li J, Cai Z, Bomgarden RD, Pike I, Kuhn K, Rogers JC, Roberts TM, Gygi SP, Paulo JA (2021) TMTpro-18plex: the expanded and complete set of TMTpro reagents for sample multiplexing. J Proteome Res 20(5):2964–2972. https://doi.org/10.1021/acs.jproteome.1c00168

    Article  CAS  Google Scholar 

  23. Sun H, Poudel S, Vanderwall D, Lee DG, Li Y, Peng J (2022) 29-plex tandem mass tag mass spectrometry enabling accurate quantification by interference correction. Proteomics 22:e2100243. https://doi.org/10.1002/pmic.202100243

    Article  CAS  Google Scholar 

  24. Stewart E, McEvoy J, Wang H, Chen X, Honnell V, Ocarz M, Gordon B, Dapper J, Blankenship K, Yang Y, Li Y, Shaw TI, Cho JH, Wang X, Xu B, Gupta P, Fan Y, Liu Y, Rusch M, Griffiths L, Jeon J, Freeman BB 3rd, Clay MR, Pappo A, Easton J, Shurtleff S, Shelat A, Zhou X, Boggs K, Mulder H, Yergeau D, Bahrami A, Mardis ER, Wilson RK, Zhang J, Peng J, Downing JR, Dyer MA, St. Jude Children’s Research Hospital – Washington University Pediatric Cancer Genome P (2018) Identification of therapeutic targets in rhabdomyosarcoma through integrated genomic, epigenomic, and proteomic analyses. Cancer Cell 34(3):411–426. https://doi.org/10.1016/j.ccell.2018.07.012

    Article  CAS  Google Scholar 

  25. Wang H, Diaz AK, Shaw TI, Li Y, Niu M, Cho JH, Paugh BS, Zhang Y, Sifford J, Bai B, Wu Z, Tan H, Zhou S, Hover LD, Tillman HS, Shirinifard A, Thiagarajan S, Sablauer A, Pagala V, High AA, Wang X, Li C, Baker SJ, Peng J (2019) Deep multiomics profiling of brain tumors identifies signaling networks downstream of cancer driver genes. Nat Commun 10(1):3718. https://doi.org/10.1038/s41467-019-11661-4

    Article  CAS  Google Scholar 

  26. Bai B, Wang X, Li Y, Chen PC, Yu K, Dey KK, Yarbro JM, Han X, Lutz BM, Rao S, Jiao Y, Sifford JM, Han J, Wang M, Tan H, Shaw TI, Cho JH, Zhou S, Wang H, Niu M, Mancieri A, Messler KA, Sun X, Wu Z, Pagala V, High AA, Bi W, Zhang H, Chi H, Haroutunian V, Zhang B, Beach TG, Yu G, Peng J (2020) Deep multilayer brain proteomics identifies molecular networks in Alzheimer’s disease progression. Neuron 105(6):975–991

    Article  CAS  Google Scholar 

  27. Liu D, Yang S, Kavdia K, Sifford JM, Wu Z, Xie B, Wang Z, Pagala VR, Wang H, Yu K, Dey KK, High AA, Serrano GE, Beach TG, Peng J (2021) Deep profiling of microgram-scale proteome by tandem mass tag mass spectrometry. J Proteome Res 20(1):337–345. https://doi.org/10.1021/acs.jproteome.0c00426

    Article  CAS  Google Scholar 

  28. Wang H, Dey KK, Chen PC, Li Y, Niu M, Cho JH, Wang X, Bai B, Jiao Y, Chepyala SR, Haroutunian V, Zhang B, Beach TG, Peng J (2020) Integrated analysis of ultra-deep proteomes in cortex, cerebrospinal fluid and serum reveals a mitochondrial signature in Alzheimer’s disease. Mol Neurodegener 15(1):43. https://doi.org/10.1186/s13024-020-00384-6

    Article  CAS  Google Scholar 

  29. Dey KK, Sun H, Wang Z, Niu M, Wang H, Jiao Y, Sun X, Li Y, Peng J (2022) Proteomic profiling of cerebrospinal fluid by 16-Plex TMT-based mass spectrometry. Methods Mol Biol 2420:21–37. https://doi.org/10.1007/978-1-0716-1936-0_3

    Article  CAS  Google Scholar 

  30. Tuck MK, Chan DW, Chia D, Godwin AK, Grizzle WE, Krueger KE, Rom W, Sanda M, Sorbara L, Stass S, Wang W, Brenner DE (2009) Standard operating procedures for serum and plasma collection: early detection research network consensus statement standard operating procedure integration working group. J Proteome Res 8(1):113–117. https://doi.org/10.1021/pr800545q

    Article  CAS  Google Scholar 

  31. Xu P, Duong DM, Peng J (2009) Systematical optimization of reverse-phase chromatography for shotgun proteomics. J Proteome Res 8(8):3944–3950. https://doi.org/10.1021/pr900251d

    Article  CAS  Google Scholar 

  32. Bai B, Hales CM, Chen PC, Gozal Y, Dammer EB, Fritz JJ, Wang X, Xia Q, Duong DM, Street C, Cantero G, Cheng D, Jones DR, Wu Z, Li Y, Diner I, Heilman CJ, Rees HD, Wu H, Lin L, Szulwach KE, Gearing M, Mufson EJ, Bennett DA, Montine TJ, Seyfried NT, Wingo TS, Sun YE, Jin P, Hanfelt J, Willcock DM, Levey A, Lah JJ, Peng J (2013) U1 small nuclear ribonucleoprotein complex and RNA splicing alterations in Alzheimer’s disease. Proc Natl Acad Sci U S A 110(41):16562–16567. https://doi.org/10.1073/pnas.1310249110

    Article  Google Scholar 

  33. Bai B, Tan H, Pagala VR, High AA, Ichhaporia VP, Hendershot L, Peng J (2017) Deep profiling of proteome and phosphoproteome by isobaric labeling, extensive liquid chromatography, and mass spectrometry. Methods Enzymol 585:377–395. https://doi.org/10.1016/bs.mie.2016.10.007

    Article  CAS  Google Scholar 

  34. Kelstrup CD, Aizikov K, Batth TS, Kreutzman A, Grinfeld D, Lange O, Mourad D, Makarov AA, Olsen JV (2018) Limits for resolving isobaric tandem mass tag reporter ions using phase-constrained spectrum deconvolution. J Proteome Res 17(11):4008–4016. https://doi.org/10.1021/acs.jproteome.8b00381

    Article  CAS  Google Scholar 

  35. Thompson A, Wolmer N, Koncarevic S, Selzer S, Bohm G, Legner H, Schmid P, Kienle S, Penning P, Hohle C, Berfelde A, Martinez-Pinna R, Farztdinov V, Jung S, Kuhn K, Pike I (2019) TMTpro: design, synthesis, and initial evaluation of a proline-based isobaric 16-Plex tandem mass tag reagent set. Anal Chem 91(24):15941–15950. https://doi.org/10.1021/acs.analchem.9b04474

    Article  CAS  Google Scholar 

  36. Nesvizhskii AI, Vitek O, Aebersold R (2007) Analysis and validation of proteomic data generated by tandem mass spectrometry. Nat Methods 4(10):787–797. https://doi.org/10.1038/nmeth1088

    Article  CAS  Google Scholar 

  37. Kall L, Canterbury JD, Weston J, Noble WS, MacCoss MJ (2007) Semi-supervised learning for peptide identification from shotgun proteomics datasets. Nat Methods 4(11):923–925. https://doi.org/10.1038/nmeth1113

    Article  CAS  Google Scholar 

  38. Cox J, Mann M (2011) Quantitative, high-resolution proteomics for data-driven systems biology. Annu Rev Biochem 80(1):273–299. https://doi.org/10.1146/annurev-biochem-061308-093216

    Article  CAS  Google Scholar 

  39. Eng JK, Jahan TA, Hoopmann MR (2013) Comet: an open-source MS/MS sequence database search tool. Proteomics 13(1):22–24. https://doi.org/10.1002/pmic.201200439

    Article  CAS  Google Scholar 

  40. Kong AT, Leprevost FV, Avtonomov DM, Mellacheruvu D, Nesvizhskii AI (2017) MSFragger: ultrafast and comprehensive peptide identification in mass spectrometry-based proteomics. Nat Methods 14(5):513–520. https://doi.org/10.1038/nmeth.4256

    Article  CAS  Google Scholar 

  41. Wang X, Li Y, Wu Z, Wang H, Tan H, Peng J (2014) JUMP: a tag-based database search tool for peptide identification with high sensitivity and accuracy. Mol Cell Proteomics 13(12):3663–3673. https://doi.org/10.1074/mcp.O114.039586

    Article  CAS  Google Scholar 

  42. Wang X, Jones DR, Shaw TI, Cho JH, Wang Y, Tan H, Xie B, Zhou S, Li Y, Peng J (2018) Target-decoy-based false discovery rate estimation for large-scale metabolite identification. J Proteome Res 17(7):2328–2334. https://doi.org/10.1021/acs.jproteome.8b00019

    Article  CAS  Google Scholar 

  43. Wang X, Cho JH, Poudel S, Li Y, Jones DR, Shaw TI, Tan H, Xie B, Peng J (2020) JUMPm: a tool for large-scale identification of metabolites in untargeted metabolomics. Meta 10(5):190. https://doi.org/10.3390/metabo10050190

    Article  CAS  Google Scholar 

  44. Li Y, Wang X, Cho JH, Shaw TI, Wu Z, Bai B, Wang H, Zhou S, Beach TG, Wu G, Zhang J, Peng J (2016) JUMPg: an integrative proteogenomics pipeline identifying unannotated proteins in human brain and cancer cells. J Proteome Res 15(7):2309–2320. https://doi.org/10.1021/acs.jproteome.6b00344

    Article  CAS  Google Scholar 

  45. Vanderwall D, Suresh P, Fu Y, Cho JH, Shaw TI, Mishra A, High AA, Peng J, Li Y (2021) JUMPn: a streamlined application for protein co-expression clustering and network analysis in proteomics. J Vis Exp 176. https://doi.org/10.3791/62796

  46. Peng J, Elias JE, Thoreen CC, Licklider LJ, Gygi SP (2003) Evaluation of multidimensional chromatography coupled with tandem mass spectrometry (LC/LC-MS/MS) for large-scale protein analysis: the yeast proteome. J Proteome Res 2(1):43–50. https://doi.org/10.1021/pr025556v

    Article  CAS  Google Scholar 

  47. Elias JE, Gygi SP (2007) Target-decoy search strategy for increased confidence in large-scale protein identifications by mass spectrometry. Nat Methods 4(3):207–214

    Article  CAS  Google Scholar 

  48. Link AJ, LaBaer J (2011) Solution protein digest. Cold Spring Harb Protoc 2011(2):pdb.prot5569. https://doi.org/10.1101/pdb.prot5569

    Article  Google Scholar 

  49. Xu P, Cheng D, Duong DM, Rush J, Roelofs J, Finley D, Peng J (2006) A proteomic strategy for quantifying polyubiquitin chain topologies. Isr J Chem 46(2):171–182

    Article  CAS  Google Scholar 

  50. Betancourt LH, Sanchez A, Pla I, Kuras M, Zhou Q, Andersson R, Marko-Varga G (2018) Quantitative assessment of urea in-solution Lys-C/trypsin digestions reveals superior performance at room temperature over traditional proteolysis at 37 °C. J Proteome Res 17(7):2556–2561. https://doi.org/10.1021/acs.jproteome.8b00228

    Article  CAS  Google Scholar 

  51. Shen J, Pagala VR, Breuer AM, Peng J, Bin M, Wang X (2018) Spectral library search improves assignment of TMT labeled MS/MS spectra. J Proteome Res 17(9):3325–3331. https://doi.org/10.1021/acs.jproteome.8b00594

    Article  CAS  Google Scholar 

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Zhang, X. et al. (2023). In-Depth Blood Proteome Profiling by Extensive Fractionation and Multiplexed Quantitative Mass Spectrometry. In: Greening, D.W., Simpson, R.J. (eds) Serum/Plasma Proteomics. Methods in Molecular Biology, vol 2628. Humana, New York, NY. https://doi.org/10.1007/978-1-0716-2978-9_8

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  • DOI: https://doi.org/10.1007/978-1-0716-2978-9_8

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