1932
0
  1. Home
  2. A-Z Publications
  3. Annual Review of Analytical Chemistry
  4. Volume 16, 2023
  5. Article

Annual Review of Analytical Chemistry

Volume 16, 2023

Enhanced Multiplexing Technology for Proteomics

Abstract

The identification of thousands of proteins and their relative levels of expression has furthered understanding of biological processes and disease and stimulated new systems biology hypotheses. Quantitative proteomics workflows that rely on analytical assays such as mass spectrometry have facilitated high-throughput measurements of proteins partially due to multiplexing. Multiplexing allows proteome differences across multiple samples to be measured simultaneously, resulting in more accurate quantitation, increased statistical robustness, reduced analysis times, and lower experimental costs. The number of samples that can be multiplexed has evolved from as few as two to more than 50, with studies involving more than 10 samples being denoted as enhanced multiplexing or hyperplexing. In this review, we give an update on emerging multiplexing proteomics techniques and highlight advantages and limitations for enhanced multiplexing strategies.

Keyword(s): automation, high-throughput, hyperplexing, multiplexing, proteomics
Loading

Article metrics loading...

/content/journals/10.1146/annurev-anchem-091622-092353
2023-06-14
2024-04-29
Loading full text...

Full text loading...

/deliver/fulltext/anchem/16/1/annurev-anchem-091622-092353.html?itemId=/content/journals/10.1146/annurev-anchem-091622-092353&mimeType=html&fmt=ahah

Literature Cited

  1. 1.
    Cha HK, Cheon S, Kim H, Lee K-M, Ryu HS, Han D. 2022. Discovery of proteins responsible for resistance to three chemotherapy drugs in breast cancer cells using proteomics and bioinformatics analysis. Molecules 27:1762
     [Google Scholar]
  2. 2.
    Desaire H, Stepler KE, Robinson RAS. 2022. Exposing the brain proteomic signatures of Alzheimer's disease in diverse racial groups: leveraging multiple data sets and machine learning. J. Proteome Res. 21:1095–104
     [Google Scholar]
  3. 3.
    Bai B, Wang X, Li Y, Chen P-C, Yu K et al. 2020. Deep multilayer brain proteomics identifies molecular networks in Alzheimer's disease progression. Neuron 105:975–91.e7
     [Google Scholar]
  4. 4.
    Yu Q, Xiao H, Jedrychowski MP, Schweppe DK, Navarrete-Perea J et al. 2020. Sample multiplexing for targeted pathway proteomics in aging mice. PNAS 117:9723–32
     [Google Scholar]
  5. 5.
    Amin B, Bowser BL, Robinson RAS. 2022. Quantitative proteomics to study aging in rabbit spleen tissues. Exp. Gerontol. 167:111908
     [Google Scholar]
  6. 6.
    O'Bryant SE, Petersen M, Zhang F, Johnson L, Mason D, Hall J 2022. Analysis of a precision medicine approach to treating Parkinson's disease: analysis of the DATATOP study. Parkinsonism Relat. Disord. 94:15–21
     [Google Scholar]
  7. 7.
    Nie X, Qian L, Sun R, Huang B, Dong X et al. 2021. Multi-organ proteomic landscape of COVID-19 autopsies. Cell 184:775–91.e14
     [Google Scholar]
  8. 8.
    Dou Y, Kawaler EA, Zhou DC, Gritsenko MA, Huang C et al. 2020. Proteogenomic characterization of endometrial carcinoma. Cell 180:729–48.e26
     [Google Scholar]
  9. 9.
    Jiang L, Wang M, Lin S, Jian R, Li X et al. 2020. A quantitative proteome map of the human body. Cell 183:269–83.e19
     [Google Scholar]
  10. 10.
    Suhre K, McCarthy MI, Schwenk JM. 2021. Genetics meets proteomics: perspectives for large population-based studies. Nat. Rev. Genet. 22:19–37
     [Google Scholar]
  11. 11.
    Eastel JM, Lam KW, Lee NL, Lok WY, Tsang AHF et al. 2019. Application of NanoString technologies in companion diagnostic development. Expert Rev. Mol. Diagnost. 19:591–98
     [Google Scholar]
  12. 12.
    O'Connell JD, Paulo JA, O'Brien JJ, Gygi SP 2018. Proteome-wide evaluation of two common protein quantification methods. J. Proteome Res. 17:1934–42
     [Google Scholar]
  13. 13.
    Everley RA, Kunz RC, McAllister FE, Gygi SP. 2013. Increasing throughput in targeted proteomics assays: 54-plex quantitation in a single mass spectrometry run. Anal. Chem. 85:5340–46
     [Google Scholar]
  14. 14.
    Wu Z, Shen Y, Zhang X. 2022. TAG-TMTpro, a hyperplexing quantitative approach for high-throughput proteomic studies. Anal. Chem. 94:12565–69
     [Google Scholar]
  15. 15.
    Schubert OT, Röst HL, Collins BC, Rosenberger G, Aebersold R. 2017. Quantitative proteomics: challenges and opportunities in basic and applied research. Nat. Protoc. 12:1289–94
     [Google Scholar]
  16. 16.
    Lim SY, Lee JH, Welsh SJ, Ahn SB, Breen E et al. 2017. Evaluation of two high-throughput proteomic technologies for plasma biomarker discovery in immunotherapy-treated melanoma patients. Biomarker Res 5:32
     [Google Scholar]
  17. 17.
    Chowdhury F, Williams A, Johnson P. 2009. Validation and comparison of two multiplex technologies, Luminex® and Mesoscale Discovery, for human cytokine profiling. J. Immunol. Methods 340:55–64
     [Google Scholar]
  18. 18.
    Raafs A, Verdonschot J, Ferreira JP, Wang P, Collier T et al. 2021. Identification of sex-specific biomarkers predicting new-onset heart failure. ESC Heart Failure 8:3512–20
     [Google Scholar]
  19. 19.
    Ling MM, Ricks C, Lea P. 2007. Multiplexing molecular diagnostics and immunoassays using emerging microarray technologies. Expert Rev. Mol. Diagnost. 7:87–98
     [Google Scholar]
  20. 20.
    Seidel M, Niessner R. 2008. Automated analytical microarrays: a critical review. Anal. Bioanal. Chem. 391:1521–44
     [Google Scholar]
  21. 21.
    Kersten B, Wanker EE, Hoheisel JD, Angenendt P. 2005. Multiplex approaches in protein microarray technology. Expert Rev. Proteom. 2:499–510
     [Google Scholar]
  22. 22.
    Petrera A, von Toerne C, Behler J, Huth C, Thorand B et al. 2021. Multiplatform approach for plasma proteomics: complementarity of Olink proximity extension assay technology to mass spectrometry-based protein profiling. J. Proteome Res. 20:751–62
     [Google Scholar]
  23. 23.
    Taniuchi M, Verweij JJ, Noor Z, Sobuz SU, Lieshout Lv et al. 2011. High throughput multiplex PCR and probe-based detection with Luminex beads for seven intestinal parasites. Am. Soc. Trop. Med. Hyg. 84:332–37
     [Google Scholar]
  24. 24.
    Lollo B, Steele F, Gold L. 2014. Beyond antibodies: new affinity reagents to unlock the proteome. Proteomics 14:638–44
     [Google Scholar]
  25. 25.
    Gold L, Ayers D, Bertino J, Bock C, Bock A et al. 2010. Aptamer-based multiplexed proteomic technology for biomarker discovery. Nat. Preced. 2010. https://doi.org/10.1038/npre.2010.4538.1
    [Google Scholar]
  26. 26.
    Vafajoo A, Rostami A, Foroutan Parsa S, Salarian R, Rabiee N et al. 2018. Multiplexed microarrays based on optically encoded microbeads. Biomed. Microdevices 20:66
     [Google Scholar]
  27. 27.
    Cui L, Shu C, Liu Z, Tong W, Cui M et al. 2018. The expression of serum sEGFR, sFlt-1, sEndoglin and PLGF in preeclampsia. Pregnancy Hypertens 13:127–32
     [Google Scholar]
  28. 28.
    Pan J, Zheng Q-Z, Li Y, Yu L-L, Wu Q-W et al. 2019. Discovery and validation of a serologic autoantibody panel for early diagnosis of esophageal squamous cell carcinoma. Cancer Epidemiol. Biomarkers Prev. 28:1454–60
     [Google Scholar]
  29. 29.
    Sattlecker M, Kiddle SJ, Newhouse S, Proitsi P, Nelson S et al. 2014. Alzheimer's disease biomarker discovery using SOMAscan multiplexed protein technology. Alzheimer's Dementia 10:724–34
     [Google Scholar]
  30. 30.
    Luo Y, Wadhawan S, Greenfield A, Decato BE, Oseini AM et al. 2021. SOMAscan proteomics identifies serum biomarkers associated with liver fibrosis in patients with NASH. Hepatol. Commun. 5:760–73
     [Google Scholar]
  31. 31.
    Kobayashi H, Looker HC, Satake E, Saulnier PJ, Md Dom ZI et al. 2022. Results of untargeted analysis using the SOMAscan proteomics platform indicates novel associations of circulating proteins with risk of progression to kidney failure in diabetes. Kidney Int 102:370–81
     [Google Scholar]
  32. 32.
    Sun BB, Maranville JC, Peters JE, Stacey D, Staley JR et al. 2018. Genomic atlas of the human plasma proteome. Nature 558:73–79
     [Google Scholar]
  33. 33.
    Candia J, Cheung F, Kotliarov Y, Fantoni G, Sellers B et al. 2017. Assessment of variability in the SOMAscan assay. Sci. Rep. 7:14248
     [Google Scholar]
  34. 34.
    Haslam DE, Li J, Dillon ST, Gu X, Cao Y et al. 2022. Stability and reproducibility of proteomic profiles in epidemiological studies: comparing the Olink and SOMAscan platforms. Proteomics 22:2100170
     [Google Scholar]
  35. 35.
    Ellington AA, Kullo IJ, Bailey KR, Klee GG. 2010. Antibody-based protein multiplex platforms: technical and operational challenges. Clin. Chem. 56:186–93
     [Google Scholar]
  36. 36.
    Boersema PJ, Raijmakers R, Lemeer S, Mohammed S, Heck AJR 2009. Multiplex peptide stable isotope dimethyl labeling for quantitative proteomics. Nat. Protoc. 4:484–94
     [Google Scholar]
  37. 37.
    Hsu J-L, Huang S-Y, Chow N-H, Chen S-H. 2003. Stable-isotope dimethyl labeling for quantitative proteomics. Anal. Chem. 75:6843–52
     [Google Scholar]
  38. 38.
    Buchberger AR, Sauer CS, Vu NQ, DeLaney K, Li L 2020. Temporal study of the perturbation of crustacean neuropeptides due to severe hypoxia using 4-plex reductive dimethylation. J. Proteome Res. 19:1548–55
     [Google Scholar]
  39. 39.
    Liu J, Zhou Y, Hou X, Liu C, Zhao B et al. 2022. A1 ions: peptide-specific and intensity-enhanced fragment ions for accurate and multiplexed proteome quantitation. Anal. Chem. 94:7637–46
     [Google Scholar]
  40. 40.
    Chakraborty A, Regnier FE. 2002. Global internal standard technology for comparative proteomics. J. Chromatogr. A 949:173–84
     [Google Scholar]
  41. 41.
    Jung J, Jeong K, Choi Y, Kim SA, Kim H et al. 2019. Deuterium-free, three-plexed peptide diethylation for highly accurate quantitative proteomics. J. Proteome Res. 18:1078–87
     [Google Scholar]
  42. 42.
    Lundgren DH, Hwang S-I, Wu L, Han DK. 2010. Role of spectral counting in quantitative proteomics. Expert Rev. Proteom. 7:39–53
     [Google Scholar]
  43. 43.
    Bondarenko PV, Chelius D, Shaler TA. 2002. Identification and relative quantitation of protein mixtures by enzymatic digestion followed by capillary reversed-phase liquid chromatography–tandem mass spectrometry. Anal. Chem. 74:4741–49
     [Google Scholar]
  44. 44.
    Chelius D, Bondarenko PV. 2002. Quantitative profiling of proteins in complex mixtures using liquid chromatography and mass spectrometry. J. Proteome Res. 1:317–23
     [Google Scholar]
  45. 45.
    Wu CC, MacCoss MJ, Howell KE, Matthews DE, Yates JR. 2004. Metabolic labeling of mammalian organisms with stable isotopes for quantitative proteomic analysis. Anal. Chem. 76:4951–59
     [Google Scholar]
  46. 46.
    Ong S-E, Blagoev B, Kratchmarova I, Kristensen DB, Steen H et al. 2002. Stable isotope labeling by amino acids in cell culture, SILAC, as a simple and accurate approach to expression proteomics. Mol. Cell. Proteom. 1:376–86
     [Google Scholar]
  47. 47.
    Molina H, Yang Y, Ruch T, Kim J-W, Mortensen P et al. 2009. Temporal profiling of the adipocyte proteome during differentiation using a five-plex SILAC based strategy. J. Proteome Res. 8:48–58
     [Google Scholar]
  48. 48.
    Chen X, Wei S, Ji Y, Guo X, Yang F 2015. Quantitative proteomics using SILAC: principles, applications, and developments. Proteomics 15:3175–92
     [Google Scholar]
  49. 49.
    Merrill AE, Hebert AS, MacGilvray ME, Rose CM, Bailey DJ et al. 2014. NeuCode labels for relative protein quantification. Mol. Cell. Proteom. 13:2503–12
     [Google Scholar]
  50. 50.
    Potts GK, Voigt EA, Bailey DJ, Rose CM, Westphall MS et al. 2016. Neucode labels for multiplexed, absolute protein quantification. Anal. Chem. 88:3295–303
     [Google Scholar]
  51. 51.
    Overmyer KA, Tyanova S, Hebert AS, Westphall MS, Cox J, Coon JJ. 2018. Multiplexed proteome analysis with neutron-encoded stable isotope labeling in cells and mice. Nat. Protoc. 13:293–306
     [Google Scholar]
  52. 52.
    Ye X, Luke B, Andresson T, Blonder J. 2009. 18O stable isotope labeling in MS-based proteomics. Briefings Funct. Genom. 8:136–44
     [Google Scholar]
  53. 53.
    Ji J, Chakraborty A, Geng M, Zhang X, Amini A et al. 2000. Strategy for qualitative and quantitative analysis in proteomics based on signature peptides. J. Chromatogr. B Biomed. Sci. Appl. 745:197–210
     [Google Scholar]
  54. 54.
    Cahill MA, Wozny W, Schwall G, Schroer K, Hölzer K et al. 2003. Analysis of relative isotopologue abundances for quantitative profiling of complex protein mixtures labelled with the acrylamide/D3-acrylamide alkylation tag system. Rapid Commun. Mass Spectrom. 17:1283–90
     [Google Scholar]
  55. 55.
    Wu Z, Cheng Z, Sun M, Wan X, Liu P et al. 2015. A chemical proteomics approach for global analysis of lysine monomethylome profiling. Mol. Cell. Proteom. 14:329–39
     [Google Scholar]
  56. 56.
    Gygi SP, Rist B, Gerber SA, Turecek F, Gelb MH, Aebersold R. 1999. Quantitative analysis of complex protein mixtures using isotope-coded affinity tags. Nat. Biotechnol. 17:994–99
     [Google Scholar]
  57. 57.
    Li J, Steen H, Gygi SP. 2003. Protein profiling with cleavable isotope-coded affinity tag (cICAT) reagents: the yeast salinity stress response. Mol. Cell. Proteom. 2:1198–204
     [Google Scholar]
  58. 58.
    Schmidt A, Kellermann J, Lottspeich F. 2005. A novel strategy for quantitative proteomics using isotope-coded protein labels. Proteomics 5:4–15
     [Google Scholar]
  59. 59.
    Leroy B, Rosier C, Erculisse V, Leys N, Mergeay M, Wattiez R. 2010. Differential proteomic analysis using isotope-coded protein-labeling strategies: comparison, improvements and application to simulated microgravity effect on Cupriavidus metallidurans CH34. Proteomics 10:2281–91
     [Google Scholar]
  60. 60.
    Stadlmeier M, Bogena J, Wallner M, Wühr M, Carell T. 2018. A sulfoxide-based isobaric labelling reagent for accurate quantitative mass spectrometry. Angew. Chem. Int. Ed. 57:2958–62
     [Google Scholar]
  61. 61.
    Tian X, de Vries MP, Permentier HP, Bischoff R. 2020. A versatile isobaric tag enables proteome quantification in data-dependent and data-independent acquisition modes. Anal. Chem. 92:16149–57
     [Google Scholar]
  62. 62.
    Tian X, de Vries MP, Permentier HP, Bischoff R. 2020. A collision-induced dissociation cleavable isobaric tag for peptide fragment ion-based quantification in proteomics. J. Proteome Res. 19:3817–24
     [Google Scholar]
  63. 63.
    Koehler CJ, Strozynski M, Kozielski F, Treumann A, Thiede B. 2009. Isobaric peptide termini labeling for MS/MS-based quantitative proteomics. J. Proteome Res. 8:4333–41
     [Google Scholar]
  64. 64.
    Braun CR, Bird GH, Wühr M, Erickson BK, Rad R et al. 2015. Generation of multiple reporter ions from a single isobaric reagent increases multiplexing capacity for quantitative proteomics. Anal. Chem. 87:9855–63
     [Google Scholar]
  65. 65.
    Virreira Winter S, Meier F, Wichmann C, Cox J, Mann M, Meissner F 2018. EASI-tag enables accurate multiplexed and interference-free MS2-based proteome quantification. Nat. Methods 15:527–30
     [Google Scholar]
  66. 66.
    Zhang J, Wang Y, Li S 2010. Deuterium isobaric amine-reactive tags for quantitative proteomics. Anal. Chem. 82:7588–95
     [Google Scholar]
  67. 67.
    Tian X, de Vries MP, Visscher SWJ, Permentier HP, Bischoff R. 2020. Selective maleylation-directed isobaric peptide termini labeling for accurate proteome quantification. Anal. Chem. 92:7836–44
     [Google Scholar]
  68. 68.
    Ross PL, Huang YN, Marchese JN, Williamson B, Parker K et al. 2004. Multiplexed protein quantitation in Saccharomyces cerevisiae using amine-reactive isobaric tagging reagents. Mol. Cell. Proteom. 3:1154–69
     [Google Scholar]
  69. 69.
    Ren Y, He Y, Lin Z, Zi J, Yang H et al. 2018. Reagents for isobaric labeling peptides in quantitative proteomics. Anal. Chem. 90:12366–71
     [Google Scholar]
  70. 70.
    Thompson A, Schäfer J, Kuhn K, Kienle S, Schwarz J et al. 2003. Tandem mass tags: a novel quantification strategy for comparative analysis of complex protein mixtures by MS/MS. Anal. Chem. 75:1895–904
     [Google Scholar]
  71. 71.
    Thompson A, Wölmer N, Koncarevic S, Selzer S, Böhm G et al. 2019. TMTpro: design, synthesis, and initial evaluation of a proline-based isobaric 16-plex tandem mass tag reagent set. Anal. Chem. 91:15941–50
     [Google Scholar]
  72. 72.
    Frost DC, Feng Y, Li L. 2020. 21-Plex DiLeu isobaric tags for high-throughput quantitative proteomics. Anal. Chem. 92:8228–34
     [Google Scholar]
  73. 73.
    Kleifeld O, Doucet A, auf dem Keller U, Prudova A, Schilling O et al. 2010. Isotopic labeling of terminal amines in complex samples identifies protein N-termini and protease cleavage products. Nat. Biotechnol. 28:281–88
     [Google Scholar]
  74. 74.
    Schlage P, Kockmann T, Kizhakkedathu JN, auf dem Keller U. 2015. Monitoring matrix metalloproteinase activity at the epidermal–dermal interface by SILAC-iTRAQ-TAILS. Proteomics 15:2491–502
     [Google Scholar]
  75. 75.
    Parker J, Balmant K, Zhu F, Zhu N, Chen S 2015. cysTMTRAQ—an integrative method for unbiased thiol-based redox proteomics. Mol. Cell. Proteom. 14:237–42
     [Google Scholar]
  76. 76.
    Jayapal KP, Sui S, Philp RJ, Kok Y-J, Yap MGS et al. 2010. Multitagging proteomic strategy to estimate protein turnover rates in dynamic systems. J. Proteome Res. 9:2087–97
     [Google Scholar]
  77. 77.
    Rothenberg DA, Taliaferro JM, Huber SM, Begley TJ, Dedon PC, White FM 2018. A proteomics approach to profiling the temporal translational response to stress and growth. iScience 9:367–81
     [Google Scholar]
  78. 78.
    Dephoure N, Gygi SP. 2012. Hyperplexing: a method for higher-order multiplexed quantitative proteomics provides a map of the dynamic response to rapamycin in yeast. Sci. Signal. 5:rs2
     [Google Scholar]
  79. 79.
    Kumar A, Jamwal S, Midha MK, Hamza B, Aggarwal S et al. 2016. Dataset generated using hyperplexing and click chemistry to monitor temporal dynamics of newly synthesized macrophage secretome post infection by mycobacterial strains. Data Brief 9:349–54
     [Google Scholar]
  80. 80.
    Welle KA, Zhang T, Hryhorenko JR, Shen S, Qu J, Ghaemmaghami S. 2016. Time-resolved analysis of proteome dynamics by tandem mass tags and stable isotope labeling in cell culture (TMT-SILAC) hyperplexing. Mol. Cell. Proteom. 15:3551–63
     [Google Scholar]
  81. 81.
    Savitski MM, Zinn N, Faelth-Savitski M, Poeckel D, Gade S et al. 2018. Multiplexed proteome dynamics profiling reveals mechanisms controlling protein homeostasis. Cell 173:260–74.e25
     [Google Scholar]
  82. 82.
    Robinson RAS, Evans AR. 2012. Enhanced sample multiplexing for nitrotyrosine-modified proteins using combined precursor isotopic labeling and isobaric tagging. Anal. Chem. 84:4677–86
     [Google Scholar]
  83. 83.
    Wang Z, Yu K, Tan H, Wu Z, Cho J-H et al. 2020. 27-Plex tandem mass tag mass spectrometry for profiling brain proteome in Alzheimer's disease. Anal. Chem. 92:7162–70
     [Google Scholar]
  84. 84.
    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 22:2100243
     [Google Scholar]
  85. 85.
    Xing S, Pai A, Wu R, Lu Y. 2021. NHS-ester tandem labeling in one pot enables 48-plex quantitative proteomics. Anal. Chem. 93:12827–32
     [Google Scholar]
  86. 86.
    Hansen KC, Schmitt-Ulms G, Chalkley RJ, Hirsch J, Baldwin MA, Burlingame AL. 2003. Mass spectrometric analysis of protein mixtures at low levels using cleavable 13C-isotope-coded affinity tag and multidimensional chromatography. Mol. Cell. Proteom. 2:299–314
     [Google Scholar]
  87. 87.
    Dayon L, Núñez Galindo A, Corthésy J, Cominetti O, Kussmann M 2014. Comprehensive and scalable highly automated MS-based proteomic workflow for clinical biomarker discovery in human plasma. J. Proteome Res. 13:3837–45
     [Google Scholar]
  88. 88.
    Li J, Cai Z, Bomgarden RD, Pike I, Kuhn K et al. 2021. TMTpro-18plex: the expanded and complete set of TMTpro reagents for sample multiplexing. J. Proteome Res. 20:2964–72
     [Google Scholar]
  89. 89.
    Choe L, D'Ascenzo M, Relkin NR, Pappin D, Ross P et al. 2007. 8-Plex quantitation of changes in cerebrospinal fluid protein expression in subjects undergoing intravenous immunoglobulin treatment for Alzheimer's disease. Proteomics 7:3651–60
     [Google Scholar]
  90. 90.
    McAlister GC, Huttlin EL, Haas W, Ting L, Jedrychowski MP et al. 2012. Increasing the multiplexing capacity of TMTs using reporter ion isotopologues with isobaric masses. Anal. Chem. 84:7469–78
     [Google Scholar]
  91. 91.
    Werner T, Becher I, Sweetman G, Doce C, Savitski MM, Bantscheff M. 2012. High-resolution enabled TMT 8-plexing. Anal. Chem. 84:7188–94
     [Google Scholar]
  92. 92.
    Dayon L, Hainard A, Licker V, Turck N, Kuhn K et al. 2008. Relative quantification of proteins in human cerebrospinal fluids by MS/MS using 6-plex isobaric tags. Anal. Chem. 80:2921–31
     [Google Scholar]
  93. 93.
    Paulo JA, Gygi SP. 2019. mTMT: an alternative, nonisobaric, tandem mass tag allowing for precursor-based quantification. Anal. Chem. 91:12167–72
     [Google Scholar]
  94. 94.
    Kang U-B, Yeom J, Kim H, Lee C. 2010. Quantitative analysis of mTRAQ-labeled proteome using full MS scans. J. Proteome Res. 9:3750–58
     [Google Scholar]
  95. 95.
    Yu Q, Shi X, Greer T, Lietz CB, Kent KC, Li L. 2016. Evaluation and application of dimethylated amino acids as isobaric tags for quantitative proteomics of the TGF-β/Smad3 signaling pathway. J. Proteome Res. 15:3420–31
     [Google Scholar]
  96. 96.
    Ficarro SB, Biagi JM, Wang J, Scotcher J, Koleva RI et al. 2014. Protected amine labels: a versatile molecular scaffold for multiplexed nominal mass and sub-Da isotopologue quantitative proteomic reagents. J. Am. Soc. Mass Spectrom. 25:636–50
     [Google Scholar]
  97. 97.
    Sauer CS, Li L. 2022. Multiplexed quantitative neuropeptidomics via DiLeu isobaric tagging. Methods Enzymol. 663:235–57
     [Google Scholar]
  98. 98.
    Liu Y, Zhang H, Zhong X, Li Z, Zetterberg H, Li L. 2022. Isotopic N,N-dimethyl leucine tags for absolute quantification of clusterin and apolipoprotein E in Alzheimer's disease. J. Proteom. 257:104507
     [Google Scholar]
  99. 99.
    Xiang F, Ye H, Chen R, Fu Q, Li L. 2010. N,N-Dimethyl leucines as novel isobaric tandem mass tags for quantitative proteomics and peptidomics. Anal. Chem. 82:2817–25
     [Google Scholar]
  100. 100.
    Zhong X, Frost DC, Li L. 2019. High-resolution enabled 5-plex mass defect-based N,N-dimethyl leucine tags for quantitative proteomics. Anal. Chem. 91:7991–95
     [Google Scholar]
  101. 101.
    Zhong X, Frost DC, Yu Q, Li M, Gu T-J, Li L 2020. Mass defect-based DiLeu tagging for multiplexed data-independent acquisition. Anal. Chem. 92:11119–26
     [Google Scholar]
  102. 102.
    Gu L, Evans AR, Robinson RAS. 2015. Sample multiplexing with cysteine-selective approaches: cysDML and cPILOT. J. Am. Soc. Mass Spectrom. 26:615–30
     [Google Scholar]
  103. 103.
    Evans AR, Robinson RAS. 2013. Global combined precursor isotopic labeling and isobaric tagging (cPILOT) approach with selective MS3 acquisition. Proteomics 13:3267–72
     [Google Scholar]
  104. 104.
    Evans AR, Gu L, Guerrero R Jr., Robinson RAS. 2015. Global cPILOT analysis of the APP/PS-1 mouse liver proteome. Proteom. Clin. Appl 9:872–84
     [Google Scholar]
  105. 105.
    King CD, Dudenhoeffer JD, Gu L, Evans AR, Robinson RAS. 2017. Enhanced sample multiplexing of tissues using combined precursor isotopic labeling and isobaric tagging (cPILOT). JoVE 123:e55406
     [Google Scholar]
  106. 106.
    Frost DC, Rust CJ, Robinson RAS, Li L. 2018. Increased N,N-dimethyl leucine isobaric tag multiplexing by a combined precursor isotopic labeling and isobaric tagging approach. Anal. Chem. 90:10664–69
     [Google Scholar]
  107. 107.
    Amin B, Ford KI, Robinson RAS. 2020. Quantitative proteomics to study aging in rabbit liver. Mech. Ageing Dev. 187:111227
     [Google Scholar]
  108. 108.
    Dyer RR, Gu L, Robinson RAS. 2017. S-Nitrosylation in Alzheimer's disease using oxidized cysteine-selective cPILOT. Current Proteomic Approaches Applied to Brain Function E Santamaría, J Fernández-Irigoyen 225–41. New York: Springer
     [Google Scholar]
  109. 109.
    King CD, Robinson RAS. 2020. Evaluating combined precursor isotopic labeling and isobaric tagging performance on Orbitraps to study the peripheral proteome of Alzheimer's disease. Anal. Chem. 92:2911–16
     [Google Scholar]
  110. 110.
    Espino JA, King CD, Jones LM, Robinson RAS. 2020. In vivo fast photochemical oxidation of proteins using enhanced multiplexing proteomics. Anal. Chem. 92:7596–603
     [Google Scholar]
  111. 111.
    Cao Z, Evans AR, Robinson RAS. 2015. MS3-based quantitative proteomics using pulsed-Q dissociation. Rapid Commun. Mass Spectrom. 29:1025–30
     [Google Scholar]
  112. 112.
    Arul AB, Robinson RAS. 2020. Automated sample multiplexing by using combined precursor isotopic labeling and isobaric tagging (cPILOT). JoVE 166:e61342
     [Google Scholar]
  113. 113.
    Bowser BL, Love-Rutledge S, Robinson RAS. 2022. Evaluation and application of an automated 32-plex cPILOT workflow in diabetes. Presented at the 69th ASMS Conference on Mass Spectrometry and Allied Topics Philadelphia, PA: Oct. 31–Nov 4
    [Google Scholar]
  114. 114.
    Aggarwal S, Talukdar NC, Yadav AK. 2019. Advances in higher order multiplexing techniques in proteomics. J. Proteome Res. 18:2360–69
     [Google Scholar]
  115. 115.
    Arul AB, Robinson RAS. 2019. Sample multiplexing strategies in quantitative proteomics. Anal. Chem. 91:178–89
     [Google Scholar]
  116. 116.
    Pichler P, Köcher T, Holzmann J, Mazanek M, Taus T et al. 2010. Peptide labeling with isobaric tags yields higher identification rates using iTRAQ 4-plex compared to TMT 6-plex and iTRAQ 8-plex on LTQ Orbitrap. Anal. Chem. 82:6549–58
     [Google Scholar]
  117. 117.
    Thingholm TE, Palmisano G, Kjeldsen F, Larsen MR. 2010. Undesirable charge-enhancement of isobaric tagged phosphopeptides leads to reduced identification efficiency. J. Proteome Res. 9:4045–52
     [Google Scholar]
  118. 118.
    Aggarwal S, Kumar A, Jamwal S, Midha MK, Talukdar NC, Yadav AK. 2020. HyperQuant—a computational pipeline for higher order multiplexed quantitative proteomics. ACS Omega 5:10857–67
     [Google Scholar]
  119. 119.
    Klann K, Krause D, Münch C. 2021. DynaTMT: a user-friendly tool to process combined SILAC/TMT data. bioRxiv 451268. https://doi.org/10.1101/2021.07.06.451268
  120. 120.
    Bąchor R, Waliczek M, Stefanowicz P, Szewczuk Z. 2019. Trends in the design of new isobaric labeling reagents for quantitative proteomics. Molecules 24:701
     [Google Scholar]
  121. 121.
    Taverna D, Gaspari M. 2021. A critical comparison of three MS-based approaches for quantitative proteomics analysis. J. Mass Spectrom. 56:e4669
     [Google Scholar]
  122. 122.
    Specht H, Slavov N. 2021. Optimizing accuracy and depth of protein quantification in experiments using isobaric carriers. J. Proteome Res. 20:880–87
     [Google Scholar]
  123. 123.
    Yi L, Tsai C-F, Dirice E, Swensen AC, Chen J et al. 2019. Boosting to amplify signal with isobaric labeling (BASIL) strategy for comprehensive quantitative phosphoproteomic characterization of small populations of cells. Anal. Chem. 91:5794–801
     [Google Scholar]
  124. 124.
    Dou M, Clair G, Tsai C-F, Xu K, Chrisler WB et al. 2019. High-throughput single cell proteomics enabled by multiplex isobaric labeling in a nanodroplet sample preparation platform. Anal. Chem. 91:13119–27
     [Google Scholar]
  125. 125.
    Wang D, Ma M, Huang J, Gu T-J, Cui Y et al. 2022. Boost-DiLeu: enhanced isobaric N,N-dimethyl leucine tagging strategy for a comprehensive quantitative glycoproteomic analysis. Anal. Chem. 94:11773–82
     [Google Scholar]
  126. 126.
    Dayon L, Affolter M. 2020. Progress and pitfalls of using isobaric mass tags for proteome profiling. Expert Rev. Proteom. 17:149–61
     [Google Scholar]
  127. 127.
    Varnavides G, Madern M, Anrather D, Hartl N, Reiter W, Hartl M. 2022. In search of a universal method: a comparative survey of bottom-up proteomics sample preparation methods. J. Proteome Res. 21:2397–411
     [Google Scholar]
  128. 128.
    Hutchinson-Bunch C, Sanford JA, Hansen JR, Gritsenko MA, Rodland KD et al. 2021. Assessment of TMT labeling efficiency in large-scale quantitative proteomics: the critical effect of sample pH. ACS Omega 6:12660–66
     [Google Scholar]
  129. 129.
    Zecha J, Satpathy S, Kanashova T, Avanessian SC, Kane MH et al. 2019. TMT labeling for the masses: a robust and cost-efficient, in-solution labeling approach. Mol. Cell. Proteom. 18:1468–78
     [Google Scholar]
  130. 130.
    Ting L, Rad R, Gygi SP, Haas W. 2011. MS3 eliminates ratio distortion in isobaric multiplexed quantitative proteomics. Nat. Methods 8:937–40
     [Google Scholar]
  131. 131.
    Gaun A, Lewis Hardell KN, Olsson N, O'Brien JJ, Gollapudi S et al. 2021. Automated 16-plex plasma proteomics with real-time search and ion mobility mass spectrometry enables large-scale profiling in naked mole-rats and mice. J. Proteome Res. 20:1280–95
     [Google Scholar]
  132. 132.
    Burns AP, Zhang Y-Q, Xu T, Wei Z, Yao Q et al. 2021. A universal and high-throughput proteomics sample preparation platform. Anal. Chem. 93:8423–31
     [Google Scholar]
  133. 133.
    Krieger JR, Wybenga-Groot LE, Tong J, Bache N, Tsao MS, Moran MF. 2019. Evosep One enables robust deep proteome coverage using tandem mass tags while significantly reducing instrument time. J. Proteome Res. 18:2346–53
     [Google Scholar]
  134. 134.
    King CD, Kapp KL, Arul AB, Choi MJ, Robinson RAS. 2022. Advancements in automation for plasma proteomics sample preparation. Mol. Omics 18:828–39
     [Google Scholar]
  135. 135.
    Sialana FJ, Roumeliotis TI, Bouguenina H, Chan Wah Hak L, Wang H et al. 2022. SimPLIT: simplified sample preparation for large-scale isobaric tagging proteomics. J. Proteome Res. 21:1842–56
     [Google Scholar]
  136. 136.
    Lee J, Kim H, Sohn A, Yeo I, Kim Y. 2019. Cost-effective automated preparation of serum samples for reproducible quantitative clinical proteomics. J. Proteome Res. 18:2337–45
     [Google Scholar]
  137. 137.
    Wu R, Pai A, Liu L, Xing S, Lu Y. 2020. NanoTPOT: enhanced sample preparation for quantitative nanoproteomic analysis. Anal. Chem. 92:6235–40
     [Google Scholar]
  138. 138.
    Loroch S, Kopczynski D, Schneider AC, Schumbrutzki C, Feldmann I et al. 2022. Toward zero variance in proteomics sample preparation: positive-pressure FASP in 96-well format (PF96) enables highly reproducible, time- and cost-efficient analysis of sample cohorts. J. Proteome Res. 21:1181–88
     [Google Scholar]
  139. 139.
    Liu D, Yang S, Kavdia K, Sifford JM, Wu Z et al. 2021. Deep profiling of microgram-scale proteome by tandem mass tag mass spectrometry. J. Proteome Res. 20:337–45
     [Google Scholar]
  140. 140.
    Martinez-Val A, Garcia F, Ximénez-Embún P, Ibarz N, Zarzuela E et al. 2016. On the statistical significance of compressed ratios in isobaric labeling: a cross-platform comparison. J. Proteome Res. 15:3029–38
     [Google Scholar]
  141. 141.
    Wenger CD, Lee MV, Hebert AS, McAlister GC, Phanstiel DH et al. 2011. Gas-phase purification enables accurate, multiplexed proteome quantification with isobaric tagging. Nat. Methods 8:933–35
     [Google Scholar]
  142. 142.
    Dayon L, Sonderegger B, Kussmann M. 2012. Combination of gas-phase fractionation and MS3 acquisition modes for relative protein quantification with isobaric tagging. J. Proteome Res. 11:5081–89
     [Google Scholar]
  143. 143.
    Shliaha PV, Jukes-Jones R, Christoforou A, Fox J, Hughes C et al. 2014. Additional precursor purification in isobaric mass tagging experiments by traveling wave ion mobility separation (TWIMS). J. Proteome Res. 13:3360–69
     [Google Scholar]
  144. 144.
    Schweppe DK, Rusin SF, Gygi SP, Paulo JA. 2020. Optimized workflow for multiplexed phosphorylation analysis of TMT-labeled peptides using high-field asymmetric waveform ion mobility spectrometry. J. Proteome Res. 19:554–60
     [Google Scholar]
  145. 145.
    Pfammatter S, Bonneil E, Thibault P. 2016. Improvement of quantitative measurements in multiplex proteomics using high-field asymmetric waveform spectrometry. J. Proteome Res. 15:4653–65
     [Google Scholar]
  146. 146.
    Schweppe DK, Prasad S, Belford MW, Navarrete-Perea J, Bailey DJ et al. 2019. Characterization and optimization of multiplexed quantitative analyses using high-field asymmetric-waveform ion mobility mass spectrometry. Anal. Chem. 91:4010–16
     [Google Scholar]
  147. 147.
    Savitski MM, Sweetman G, Askenazi M, Marto JA, Lang M et al. 2011. Delayed fragmentation and optimized isolation width settings for improvement of protein identification and accuracy of isobaric mass tag quantification on Orbitrap-type mass spectrometers. Anal. Chem. 83:8959–67
     [Google Scholar]
  148. 148.
    Roumeliotis TI, Weisser H, Choudhary JS. 2019. Evaluation of a dual isolation width acquisition method for isobaric labeling ratio decompression. J. Proteome Res. 18:1433–40
     [Google Scholar]
  149. 149.
    McAlister GC, Nusinow DP, Jedrychowski MP, Wühr M, Huttlin EL et al. 2014. MultiNotch MS3 enables accurate, sensitive, and multiplexed detection of differential expression across cancer cell line proteomes. Anal. Chem. 86:7150–58
     [Google Scholar]
  150. 150.
    Erickson BK, Mintseris J, Schweppe DK, Navarrete-Perea J, Erickson AR et al. 2019. Active instrument engagement combined with a real-time database search for improved performance of sample multiplexing workflows. J. Proteome Res. 18:1299–306
     [Google Scholar]
  151. 151.
    Johnson A, Stadlmeier M, Wühr M. 2021. TMTpro complementary ion quantification increases plexing and sensitivity for accurate multiplexed proteomics at the MS2 level. J. Proteome Res. 20:3043–52
     [Google Scholar]
  152. 152.
    Sonnett M, Yeung E, Wühr M. 2018. Accurate, sensitive, and precise multiplexed proteomics using the complement reporter ion cluster. Anal. Chem. 90:5032–39
     [Google Scholar]
  153. 153.
    Wühr M, Haas W, McAlister GC, Peshkin L, Rad R et al. 2012. Accurate multiplexed proteomics at the MS2 level using the complement reporter ion cluster. Anal. Chem. 84:9214–21
     [Google Scholar]
  154. 154.
    Brenes A, Hukelmann J, Bensaddek D, Lamond AI. 2019. Multibatch TMT reveals false positives, batch effects and missing values. Mol. Cell. Proteom. 18:1967–80
     [Google Scholar]
  155. 155.
    Hogrebe A, von Stechow L, Bekker-Jensen DB, Weinert BT, Kelstrup CD, Olsen JV. 2018. Benchmarking common quantification strategies for large-scale phosphoproteomics. Nat. Commun. 9:1045
     [Google Scholar]
  156. 156.
    Tannous A, Boonen M, Zheng H, Zhao C, Germain CJ et al. 2020. Comparative analysis of quantitative mass spectrometric methods for subcellular proteomics. J. Proteome Res. 19:1718–30
     [Google Scholar]
  157. 157.
    Oliver N, Arul A, Robinson RAS. 2022. Establishing quality control procedures for sample preparation of high-throughput plasma proteomics. Presented at the US HUPO 2022 One World Conference Charleston, SC: Febr. 26–March 2
    [Google Scholar]
  158. 158.
    Patterson KL, Choi M, Oliver NC, Hansen S, Jefferson AL et al. 2022. Establishing quality control procedures for large-scale discovery based proteomics analysis of human plasma. Presented at the US HUPO 2022 One World Conference Charleston, SC: Febr. 26–March 2
    [Google Scholar]
  159. 159.
    van den Broek I, Mastali M, Mouapi K, Bystrom C, Bairey Merz CN, Van Eyk JE 2020. Quality control and outlier detection of targeted mass spectrometry data from multiplex protein panels. J. Proteome Res. 19:2278–93
     [Google Scholar]
  160. 160.
    Zhou J-Y, Chen L, Zhang B, Tian Y, Liu T et al. 2017. Quality assessments of long-term quantitative proteomic analysis of breast cancer xenograft tissues. J. Proteome Res. 16:4523–30
     [Google Scholar]
  161. 161.
    Olivella R, Chiva C, Serret M, Mancera D, Cozzuto L et al. 2021. QCloud2: an improved cloud-based quality-control system for mass-spectrometry-based proteomics laboratories. J. Proteome Res. 20:2010–13
     [Google Scholar]
  162. 162.
    Morgenstern D, Barzilay R, Levin Y. 2021. RawBeans: a simple, vendor-independent, raw-data quality-control tool. J. Proteome Res. 20:2098–104
     [Google Scholar]
  163. 163.
    Van Houtven J, Agten A, Boonen K, Baggerman G, Hooyberghs J et al. 2019. QCQuan: a web tool for the automated assessment of protein expression and data quality of labeled mass spectrometry experiments. J. Proteome Res. 18:2221–27
     [Google Scholar]
/content/journals/10.1146/annurev-anchem-091622-092353
Loading
Enhanced Multiplexing Technology for Proteomics
Annual Review of Analytical Chemistry 16, 379 (2023); https://doi.org/10.1146/annurev-anchem-091622-092353
/content/journals/10.1146/annurev-anchem-091622-092353
/content/journals/10.1146/annurev-anchem-091622-092353
Loading

Data & Media loading...

  • Article Type: Review Article

Most Cited Most Cited RSS feed

This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error