Mass-spectrometric exploration of proteome structure and function

Abstract

Numerous biological processes are concurrently and coordinately active in every living cell. Each of them encompasses synthetic, catalytic and regulatory functions that are, almost always, carried out by proteins organized further into higher-order structures and networks. For decades, the structures and functions of selected proteins have been studied using biochemical and biophysical methods. However, the properties and behaviour of the proteome as an integrated system have largely remained elusive. Powerful mass-spectrometry-based technologies now provide unprecedented insights into the composition, structure, function and control of the proteome, shedding light on complex biological processes and phenotypes.

Access options

Rent or Buy article

Get time limited or full article access on ReadCube.

from$8.99

All prices are NET prices.

Figure 1: Bottom-up proteomics workflows.
Figure 2: Analysis of post-translational modifications.
Figure 3: Interaction proteomics and structural proteomics.
Figure 4: Proteotype states and phenotypes.

References

  1. 1

    Marguerat, S. et al. Quantitative analysis of fission yeast transcriptomes and proteomes in proliferating and quiescent cells. Cell 151, 671–683 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  2. 2

    Milo, R. What is the total number of protein molecules per cell volume? A call to rethink some published values. BioEssays 35, 1050–1055 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  3. 3

    Edwards, A. M. et al. Too many roads not taken. Nature 470, 163–165 (2011).

    ADS  CAS  PubMed  Google Scholar 

  4. 4

    Aebersold, R. & Mann, M. Mass spectrometry-based proteomics. Nature 422, 198–207 (2003).

    ADS  CAS  PubMed  Google Scholar 

  5. 5

    Cravatt, B. F., Simon, G. M. & Yates, J. R. The biological impact of mass-spectrometry-based proteomics. Nature 450, 991–1000 (2007).

    ADS  CAS  Article  Google Scholar 

  6. 6

    de Godoy, L. M. F. et al. Comprehensive mass-spectrometry-based proteome quantification of haploid versus diploid yeast. Nature 455, 1251–1254 (2008). This paper demonstrates that complete proteomes of a model organism can be obtained and quantified in different biological states.

    ADS  CAS  PubMed  Google Scholar 

  7. 7

    Beck, M. et al. The quantitative proteome of a human cell line. Mol. Syst. Biol. 7, 549 (2011).

    PubMed  PubMed Central  Google Scholar 

  8. 8

    Nagaraj, N. et al. Deep proteome and transcriptome mapping of a human cancer cell line. Mol. Syst. Biol. 7, 548 (2011).

    PubMed  PubMed Central  Google Scholar 

  9. 9

    Hebert, A. S. et al. The one hour yeast proteome. Mol. Cell. Proteomics 13, 339–347 (2014).

    CAS  PubMed  Google Scholar 

  10. 10

    Kulak, N. A., Pichler, G., Paron, I., Nagaraj, N. & Mann, M. Minimal, encapsulated proteomic-sample processing applied to copy-number estimation in eukaryotic cells. Nature Methods 11, 319–324 (2014).

    CAS  PubMed  Google Scholar 

  11. 11

    Mann, M., Kulak, N. A., Nagaraj, N. & Cox, J. The coming age of complete, accurate, and ubiquitous proteomes. Mol. Cell 49, 583–590 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  12. 12

    Azimifar, S. B., Nagaraj, N., Cox, J. & Mann, M. Cell-type-resolved quantitative proteomics of murine liver. Cell Metab. 20, 1076–1087 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  13. 13

    Richards, A. L., Merrill, A. E. & Coon, J. J. Proteome sequencing goes deep. Curr. Opin. Chem. Biol. 24, 11–17 (2015).

    CAS  PubMed  Google Scholar 

  14. 14

    Sharma, K. et al. Cell type- and brain region-resolved mouse brain proteome. Nature Neurosci. 18, 1819–1831 (2015).

    CAS  PubMed  Google Scholar 

  15. 15

    Lundberg, E. et al. Defining the transcriptome and proteome in three functionally different human cell lines. Mol. Syst. Biol. 6, 450 (2010).

    PubMed  PubMed Central  Google Scholar 

  16. 16

    Uhlén, M. et al. Tissue-based map of the human proteome. Science 347, 1260419 (2015). This paper provides an integrative analysis of the human proteome through large-scale antibody localization and transcriptomics; the findings are organized in an accompanying database.

    Google Scholar 

  17. 17

    Kim, M.-S. et al. A draft map of the human proteome. Nature 509, 575–581 (2014).

    ADS  CAS  PubMed  PubMed Central  Google Scholar 

  18. 18

    Wilhelm, M. et al. Mass-spectrometry-based draft of the human proteome. Nature 509, 582–587 (2014). This study aggregates data on diverse human proteomes from the authors and the research community and, like ref. 17 , argues that a large part of the genome is accessible to mass-spectrometric detection.

    ADS  CAS  Google Scholar 

  19. 19

    Ezkurdia, I., Vázquez, J., Valencia, A. & Tress, M. Analyzing the first drafts of the human proteome. J. Proteome Res. 13, 3854–3855 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  20. 20

    Omenn, G. S. et al. Metrics for the Human Proteome Project 2015: progress on the human proteome and guidelines for high-confidence protein identification. J. Proteome Res. 14, 3452–3460 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  21. 21

    Tran, J. C. et al. Mapping intact protein isoforms in discovery mode using top-down proteomics. Nature 480, 254–258 (2011).

    ADS  CAS  PubMed  PubMed Central  Google Scholar 

  22. 22

    Meissner, F., Scheltema, R. A., Mollenkopf, H.-J. & Mann, M. Direct proteomic quantification of the secretome of activated immune cells. Science 340, 475–478 (2013).

    ADS  CAS  PubMed  PubMed Central  Google Scholar 

  23. 23

    Secher, A. et al. Analytic framework for peptidomics applied to large-scale neuropeptide identification. Nature Commun. 7, 11436 (2016).

    ADS  CAS  Google Scholar 

  24. 24

    Caron, E. et al. Analysis of major histocompatibility complex (MHC) immunopeptidomes using mass spectrometry. Mol. Cell. Proteomics 14, 3105–3117 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  25. 25

    Schiller, H. B. et al. Time- and compartment-resolved proteome profiling of the extracellular niche in lung injury and repair. Mol. Syst. Biol. 11, 819 (2015).

    PubMed  PubMed Central  Google Scholar 

  26. 26

    Malmström, J. et al. Proteome-wide cellular protein concentrations of the human pathogen Leptospira interrogans. Nature 460, 762–765 (2009).

    ADS  PubMed  PubMed Central  Google Scholar 

  27. 27

    Schwanhäusser, B. et al. Global quantification of mammalian gene expression control. Nature 473, 337–342 (2011). A pioneering investigation of the degree of correlation between the transcriptome and the proteome — a question that is still unresolved.

    ADS  Google Scholar 

  28. 28

    Wiśniewski, J. R., Hein, M. Y., Cox, J. & Mann, M. A “proteomic ruler” for protein copy number and concentration estimation without spike-in standards. Mol. Cell. Proteomics 13, 3497–3506 (2014).

    PubMed  PubMed Central  Google Scholar 

  29. 29

    Doll, S. & Burlingame, A. L. Mass spectrometry-based detection and assignment of protein posttranslational modifications. ACS Chem. Biol. 10, 63–71 (2015).

    CAS  PubMed  Google Scholar 

  30. 30

    Sharma, K. et al. Ultradeep human phosphoproteome reveals a distinct regulatory nature of Tyr and Ser/Thr-based signaling. Cell Rep. 8, 1583–1594 (2014).

    CAS  PubMed  Google Scholar 

  31. 31

    Hsu, P. P. et al. The mTOR-regulated phosphoproteome reveals a mechanism of mTORC1-mediated inhibition of growth factor signaling. Science 332, 1317–1322 (2011).

    ADS  CAS  PubMed  PubMed Central  Google Scholar 

  32. 32

    Huttlin, E. L. et al. A tissue-specific atlas of mouse protein phosphorylation and expression. Cell 143, 1174–1189 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  33. 33

    Olsen, J. V. et al. Global, in vivo, and site-specific phosphorylation dynamics in signaling networks. Cell 127, 635–648 (2006).

    CAS  PubMed  Google Scholar 

  34. 34

    Francavilla, C. et al. Functional proteomics defines the molecular switch underlying FGF receptor trafficking and cellular outputs. Mol. Cell 51, 707–722 (2013).

    CAS  PubMed  Google Scholar 

  35. 35

    Steger, M. et al. Phosphoproteomics reveals that Parkinson's disease kinase LRRK2 regulates a subset of Rab GTPases. eLife 5, e12813 (2016). This study used a combination of genetics, chemical proteomics and cutting-edge phosphoproteomics to reveal genuine, in vivo substrates of the Parkinson's disease kinase LRRK2, opening the way to clinical trials.

    PubMed  PubMed Central  Google Scholar 

  36. 36

    Humphrey, S. J., Azimifar, S. B. & Mann, M. High-throughput phosphoproteomics reveals in vivo insulin signaling dynamics. Nature Biotechnol. 33, 990–995 (2015).

    CAS  Google Scholar 

  37. 37

    Weinert, B. T. et al. Acetyl-phosphate is a critical determinant of lysine acetylation in E. coli. Mol. Cell 51, 265–272 (2013).

    CAS  PubMed  Google Scholar 

  38. 38

    Choudhary, C., Weinert, B. T., Nishida, Y., Verdin, E. & Mann, M. The growing landscape of lysine acetylation links metabolism and cell signalling. Nature Rev. Mol. Cell Biol. 15, 536–550 (2014).

    CAS  Google Scholar 

  39. 39

    Hendriks, I. A. et al. Uncovering global SUMOylation signaling networks in a site-specific manner. Nature Struct. Mol. Biol. 21, 927–936 (2014).

    CAS  Google Scholar 

  40. 40

    Huang, H., Lin, S., Garcia, B. A. & Zhao, Y. Quantitative proteomic analysis of histone modifications. Chem. Rev. 115, 2376–2418 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  41. 41

    Zheng, Y., Huang, X. & Kelleher, N. L. Epiproteomics: quantitative analysis of histone marks and codes by mass spectrometry. Curr. Opin. Chem. Biol. 33, 142–150 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  42. 42

    Savitski, M. M., Nielsen, M. L. & Zubarev, R. A. ModifiComb, a new proteomic tool for mapping substoichiometric post-translational modifications, finding novel types of modifications, and fingerprinting complex protein mixtures. Mol. Cell. Proteomics 5, 935–948 (2006).

    CAS  PubMed  Google Scholar 

  43. 43

    Jungmichel, S. et al. Proteome-wide identification of poly(ADP-ribosyl)ation targets in different genotoxic stress responses. Mol. Cell 52, 272–285 (2013).

    CAS  Google Scholar 

  44. 44

    Chick, J. M. et al. A mass-tolerant database search identifies a large proportion of unassigned spectra in shotgun proteomics as modified peptides. Nature Biotechnol. 33, 743–749 (2015).

    CAS  Google Scholar 

  45. 45

    Rix, U. & Superti-Furga, G. Target profiling of small molecules by chemical proteomics. Nature Chem. Biol. 5, 616–624 (2009).

    CAS  Google Scholar 

  46. 46

    Gawron, D., Ndah, E., Gevaert, K. & Van Damme, P. Positional proteomics reveals differences in N-terminal proteoform stability. Mol. Syst. Biol. 12, 858 (2016).

    PubMed  PubMed Central  Google Scholar 

  47. 47

    Kleifeld, O. et al. Identifying and quantifying proteolytic events and the natural N terminome by terminal amine isotopic labeling of substrates. Nature Protocols 6, 1578–1611 (2011).

    CAS  PubMed  Google Scholar 

  48. 48

    Hartwell, L. H., Hopfield, J. J., Leibler, S. & Murray, A. W. From molecular to modular cell biology. Nature 402 (suppl.), C47–C52 (1999).

    CAS  PubMed  Google Scholar 

  49. 49

    Pawson, T. Protein modules and signalling networks. Nature 373, 573–580 (1995).

    ADS  CAS  PubMed  PubMed Central  Google Scholar 

  50. 50

    Ward, A. B., Sali, A. & Wilson, I. A. Integrative structural biology. Science 339, 913–915 (2013).

    ADS  CAS  PubMed  PubMed Central  Google Scholar 

  51. 51

    Dunham, W. H., Mullin, M. & Gingras, A.-C. Affinity-purification coupled to mass spectrometry: basic principles and strategies. Proteomics 12, 1576–1590 (2012).

    CAS  PubMed  Google Scholar 

  52. 52

    Choi, H. et al. SAINT: probabilistic scoring of affinity purification-mass spectrometry data. Nature Methods 8, 70–73 (2011).

    CAS  PubMed  Google Scholar 

  53. 53

    Keilhauer, E. C., Hein, M. Y. & Mann, M. Accurate protein complex retrieval by affinity enrichment mass spectrometry (AE-MS) rather than affinity purification mass spectrometry (AP-MS). Mol. Cell. Proteomics 14, 120–135 (2015).

    CAS  PubMed  Google Scholar 

  54. 54

    Huttlin, E. L. et al. The BioPlex network: a systematic exploration of the human interactome. Cell 162, 425–440 (2015). A large-scale investigation of proteins binding to tagged constructs to establish a human interactome.

    CAS  PubMed  PubMed Central  Google Scholar 

  55. 55

    Hein, M. Y. et al. A human interactome in three quantitative dimensions organized by stoichiometries and abundances. Cell 163, 712–723 (2015). This paper describes the characterization of a human interactome using bait proteins that are expressed under endogenous control; its analysis in several quantitative dimensions revealed a preponderance of weak interactions.

    CAS  PubMed  PubMed Central  Google Scholar 

  56. 56

    Ori, A. et al. Spatiotemporal variation of mammalian protein complex stoichiometries. Genome Biol. 17, 47 (2016).

    PubMed  PubMed Central  Google Scholar 

  57. 57

    Räschle, M. et al. Proteomics reveals dynamic assembly of repair complexes during bypass of DNA cross-links. Science 348, 1253671 (2015).

    PubMed  PubMed Central  Google Scholar 

  58. 58

    Roux, K. J., Kim, D. I., Raida, M. & Burke, B. A promiscuous biotin ligase fusion protein identifies proximal and interacting proteins in mammalian cells. J. Cell Biol. 196, 801–810 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  59. 59

    Rhee, H.-W. et al. Proteomic mapping of mitochondria in living cells via spatially restricted enzymatic tagging. Science 339, 1328–1331 (2013).

    ADS  CAS  PubMed  PubMed Central  Google Scholar 

  60. 60

    Havugimana, P. C. et al. A census of human soluble protein complexes. Cell 150, 1068–1081 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  61. 61

    Kristensen, A. R., Gsponer, J. & Foster, L. J. A high-throughput approach for measuring temporal changes in the interactome. Nature Methods 9, 907–909 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  62. 62

    Wan, C. et al. Panorama of ancient metazoan macromolecular complexes. Nature 525, 339–344 (2015).

    ADS  CAS  PubMed  PubMed Central  Google Scholar 

  63. 63

    Christoforou, A. et al. A draft map of the mouse pluripotent stem cell spatial proteome. Nature Commun. 7, 8992 (2016).

    Google Scholar 

  64. 64

    Larance, M. & Lamond, A. I. Multidimensional proteomics for cell biology. Nature Rev. Mol. Cell Biol. 16, 269–280 (2015).

    CAS  Google Scholar 

  65. 65

    Yates, J. R., Gilchrist, A., Howell, K. E. & Bergeron, J. J. M. Proteomics of organelles and large cellular structures. Nature Rev. Mol. Cell Biol. 6, 702–714 (2005).

    CAS  Google Scholar 

  66. 66

    Itzhak, D. N., Tyanova, S., Cox, J. & Borner, G. H. Global, quantitative and dynamic mapping of protein subcellular localization. eLife 5, e16950 (2016).

    PubMed  PubMed Central  Google Scholar 

  67. 67

    Alber, F. et al. The molecular architecture of the nuclear pore complex. Nature 450, 695–701 (2007).

    ADS  CAS  PubMed  Google Scholar 

  68. 68

    Marcoux, J. & Robinson, C. V. Twenty years of gas phase structural biology. Structure 21, 1541–1550 (2013).

    CAS  PubMed  Google Scholar 

  69. 69

    Politis, A. et al. A mass spectrometry-based hybrid method for structural modeling of protein complexes. Nature Methods 11, 403–406 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  70. 70

    Zhou, M. et al. Mass spectrometry of intact V-type ATPases reveals bound lipids and the effects of nucleotide binding. Science 334, 380–385 (2011). An elegant demonstration of native mass spectrometry in structural studies of intact membrane complexes.

    ADS  CAS  PubMed  PubMed Central  Google Scholar 

  71. 71

    Leitner, A., Faini, M., Stengel, F. & Aebersold, R. Crosslinking and mass spectrometry: an integrated technology to understand the structure and function of molecular machines. Trends Biochem. Sci. 41, 20–32 (2016).

    CAS  PubMed  Google Scholar 

  72. 72

    Liu, F. & Heck, A. J. Interrogating the architecture of protein assemblies and protein interaction networks by cross-linking mass spectrometry. Curr. Opin. Struct. Biol. 35, 100–108 (2015).

    PubMed  Google Scholar 

  73. 73

    Joachimiak, L. A., Walzthoeni, T., Liu, C. W., Aebersold, R. & Frydman, J. The structural basis of substrate recognition by the eukaryotic chaperonin TRiC/CCT. Cell 159, 1042–1055 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  74. 74

    Walzthoeni, T. et al. xTract: software for characterizing conformational changes of protein complexes by quantitative cross-linking mass spectrometry. Nature Methods 12, 1185–1190 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  75. 75

    Kramer, K. et al. Photo-cross-linking and high-resolution mass spectrometry for assignment of RNA-binding sites in RNA-binding proteins. Nature Methods 11, 1064–1070 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  76. 76

    Frei, A. P. et al. Direct identification of ligand-receptor interactions on living cells and tissues. Nature Biotechnol. 30, 997–1001 (2012).

    CAS  Google Scholar 

  77. 77

    Herzog, F. et al. Structural probing of a protein phosphatase 2A network by chemical cross-linking and mass spectrometry. Science 337, 1348–1352 (2012). This study pioneered the use of chemical crosslinking to reveal the topology of an important phosphatase complex.

    ADS  CAS  Google Scholar 

  78. 78

    Liu, F., Rijkers, D. T. S., Post, H. & Heck, A. J. R. Proteome-wide profiling of protein assemblies by cross-linking mass spectrometry. Nature Methods 12, 1179–1184 (2015).

    CAS  PubMed  Google Scholar 

  79. 79

    Navare, A. T. et al. Probing the protein interaction network of Pseudomonas aeruginosa cells by chemical cross-linking mass spectrometry. Structure 23, 762–773 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  80. 80

    Makowski, M. M., Willems, E., Jansen, P. W. T. C. & Vermeulen, M. Cross-linking immunoprecipitation-MS (xIP-MS): topological analysis of chromatin-associated protein complexes using single affinity purification. Mol. Cell. Proteomics 15, 854–865 (2016).

    CAS  PubMed  Google Scholar 

  81. 81

    Shi, Y. et al. A strategy for dissecting the architectures of native macromolecular assemblies. Nature Methods 12, 1135–1138 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  82. 82

    Aufderheide, A. et al. Structural characterization of the interaction of Ubp6 with the 26S proteasome. Proc. Natl Acad. Sci. USA 112, 8626–8631 (2015).

    ADS  CAS  PubMed  Google Scholar 

  83. 83

    Mahamid, J. et al. Visualizing the molecular sociology at the HeLa cell nuclear periphery. Science 351, 969–972 (2016).

    ADS  CAS  PubMed  Google Scholar 

  84. 84

    Engen, J. R. Analysis of protein conformation and dynamics by hydrogen/deuterium exchange MS. Anal. Chem. 81, 7870–7875 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  85. 85

    Wang, L. & Chance, M. R. Structural mass spectrometry of proteins using hydroxyl radical based protein footprinting. Anal. Chem. 83, 7234–7241 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  86. 86

    Savitski, M. M. et al. Tracking cancer drugs in living cells by thermal profiling of the proteome. Science 346, 1255784 (2014). In this paper, isobaric chemical labelling was used to measure the proportion of proteins that bound to a drug as a function of temperature, on a proteome-wide scale.

    Google Scholar 

  87. 87

    Feng, Y. et al. Global analysis of protein structural changes in complex proteomes. Nature Biotechnol. 32, 1036–1044 (2014).

    CAS  Google Scholar 

  88. 88

    Pauling, L., Itano, H. A., Singer, S. J. & Wells, I. C. Sickle cell anemia, a molecular disease. Science 110, 543–548 (1949).

    ADS  CAS  Google Scholar 

  89. 89

    Picotti, P. et al. A complete mass-spectrometric map of the yeast proteome applied to quantitative trait analysis. Nature 494, 266–270 (2013).

    ADS  CAS  PubMed  PubMed Central  Google Scholar 

  90. 90

    Andreux, P. A. et al. Systems genetics of metabolism: the use of the BXD murine reference panel for multiscalar integration of traits. Cell 150, 1287–1299 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  91. 91

    Wu, Y. et al. Multilayered genetic and omics dissection of mitochondrial activity in a mouse reference population. Cell 158, 1415–1430 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  92. 92

    Williams, E. G. et al. Systems proteomics of liver mitochondria function. Science 352, aad0189 (2016). A demonstration of the combined use of proteomics and genetics to interrogate mitochondrial function.

    PubMed  Google Scholar 

  93. 93

    Mertins, P. et al. Proteogenomics connects somatic mutations to signalling in breast cancer. Nature 534, 55–62 (2016). This analysis of breast cancer tissues revealed that proteomics is almost on a par with transcriptomics in terms of achievable depth of coverage of gene expression.

    CAS  PubMed  PubMed Central  Google Scholar 

  94. 94

    Carr, S. A. 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 13, 907–917 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  95. 95

    Rifai, N., Gillette, M. A. & Carr, S. A. Protein biomarker discovery and validation: the long and uncertain path to clinical utility. Nature Biotechnol. 24, 971–983 (2006).

    CAS  Google Scholar 

  96. 96

    Geyer, P. E. et al. Plasma proteome profiling to assess human health and disease. Cell Syst. 2, 185–195 (2016).

    CAS  PubMed  Google Scholar 

  97. 97

    Surinova, S. et al. Prediction of colorectal cancer diagnosis based on circulating plasma proteins. EMBO Mol. Med. 7, 1166–1178 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  98. 98

    Liu, Y. et al. Quantitative variability of 342 plasma proteins in a human twin population. Mol. Syst. Biol. 11, 786 (2015).

    PubMed  PubMed Central  Google Scholar 

  99. 99

    Bandura, D. R. et al. Mass cytometry: technique for real time single cell multitarget immunoassay based on inductively coupled plasma time-of-flight mass spectrometry. Anal. Chem. 81, 6813–6822 (2009).

    CAS  PubMed  Google Scholar 

  100. 100

    Cox, J. & Mann, M. MaxQuant enables high peptide identification rates, individualized p.p.b.-range mass accuracies and proteome-wide protein quantification. Nature Biotechnol. 26, 1367–1372 (2008).

    CAS  Google Scholar 

  101. 101

    Tyanova, S. et al. The Perseus computational platform for comprehensive analysis of (prote)omics data. Nature Methods 13, 731–740 (2016).

    CAS  PubMed  Google Scholar 

  102. 102

    MacLean, B. et al. Skyline: an open source document editor for creating and analyzing targeted proteomics experiments. Bioinformatics 26, 966–968 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  103. 103

    Gillet, L. C. et al. Targeted data extraction of the MS/MS spectra generated by data-independent acquisition: a new concept for consistent and accurate proteome analysis. Mol. Cell. Proteomics 11, O111.016717 (2012).

    PubMed  PubMed Central  Google Scholar 

  104. 104

    Röst, H. L. et al. OpenSWATH enables automated, targeted analysis of data-independent acquisition MS data. Nature Biotechnol. 32, 219–223 (2014).

    Google Scholar 

  105. 105

    Tsou, C.-C. et al. DIA-Umpire: comprehensive computational framework for data-independent acquisition proteomics. Nature Methods 12, 258–264 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  106. 106

    Olsen, J. V. et al. Quantitative phosphoproteomics reveals widespread full phosphorylation site occupancy during mitosis. Sci. Signal. 3, ra3 (2010).

    PubMed  Google Scholar 

  107. 107

    Smith, L. M., Kelleher, N. L. & The Consortium for Top Down Proteomics. Proteoform: a single term describing protein complexity. Nature Methods 10, 186–187 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  108. 108

    Olsen, J. V. et al. Higher-energy C-trap dissociation for peptide modification analysis. Nature Methods 4, 709–712 (2007).

    ADS  CAS  PubMed  Google Scholar 

  109. 109

    Syka, J. E. P., Coon, J. J., Schroeder, M. J., Shabanowitz, J. & Hunt, D. F. Peptide and protein sequence analysis by electron transfer dissociation mass spectrometry. Proc. Natl Acad. Sci. USA 101, 9528–9533 (2004).

    ADS  CAS  PubMed  Google Scholar 

  110. 110

    Zubarev, R. A. & Makarov, A. Orbitrap mass spectrometry. Anal. Chem. 85, 5288–5296 (2013).

    CAS  PubMed  Google Scholar 

  111. 111

    Picotti, P. & Aebersold, R. Selected reaction monitoring-based proteomics: workflows, potential, pitfalls and future directions. Nature Methods 9, 555–566 (2012).

    CAS  PubMed  Google Scholar 

  112. 112

    Peterson, A. C., Russell, J. D., Bailey, D. J., Westphall, M. S. & Coon, J. J. Parallel reaction monitoring for high resolution and high mass accuracy quantitative, targeted proteomics. Mol. Cell. Proteomics 11, 1475–1488 (2012).

    PubMed  PubMed Central  Google Scholar 

  113. 113

    Chapman, J. D., Goodlett, D. R. & Masselon, C. D. Multiplexed and data-independent tandem mass spectrometry for global proteome profiling. Mass Spectrom. Rev. 33, 452–470 (2014).

    ADS  CAS  PubMed  Google Scholar 

  114. 114

    Rosenberger, G. et al. A repository of assays to quantify 10,000 human proteins by SWATH-MS. Sci. Data 1, 140031 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  115. 115

    Meier, F. et al. Parallel accumulation–serial fragmentation (PASEF): multiplying sequencing speed and sensitivity by synchronized scans in a trapped ion mobility device. J. Proteome Res. 14, 5378–5387 (2015).

    CAS  PubMed  Google Scholar 

  116. 116

    Ow, S. Y. et al. iTRAQ underestimation in simple and complex mixtures: “the good, the bad and the ugly”. J. Proteome Res. 8, 5347–5355 (2009).

    CAS  PubMed  Google Scholar 

  117. 117

    Ting, L., Rad, R., Gygi, S. P. & Haas, W. MS3 eliminates ratio distortion in isobaric multiplexed quantitative proteomics. Nature Methods 8, 937–940 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  118. 118

    Wühr, M. et al. The nuclear proteome of a vertebrate. Curr. Biol. 25, 2663–2671 (2015).

    PubMed  PubMed Central  Google Scholar 

  119. 119

    Cox, J. et al. Accurate proteome-wide label-free quantification by delayed normalization and maximal peptide ratio extraction, termed MaxLFQ. Mol. Cell. Proteomics 13, 2513–2526 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  120. 120

    Ludwig, C., Claassen, M., Schmidt, A. & Aebersold, R. Estimation of absolute protein quantities of unlabeled samples by selected reaction monitoring mass spectrometry. Mol. Cell. Proteomics 11, M111.013987 (2012).

    PubMed  Google Scholar 

Download references

Acknowledgements

We thank M. Faini and R. Ciuffa for help in preparing the figures and Y. Liu for help in compiling the literature citations. M. Hein provided inspiration for this Review, read the manuscript critically and helped with preparing the figures, as did F. Hosp, P. Geyer and S. Beck. We thank members of our groups for critical discussions.

Author information

Affiliations

Authors

Corresponding authors

Correspondence to Ruedi Aebersold or Matthias Mann.

Ethics declarations

Competing interests

The authors declare no competing financial interests.

Additional information

Reprints and permissions information is available at www.nature.com/reprints.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

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

Download citation

Further reading

Comments

By submitting a comment you agree to abide by our Terms and Community Guidelines. If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.