Mass-spectrometric exploration of proteome structure and function

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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.

At a glance


  1. Bottom-up proteomics workflows.
    Figure 1: Bottom-up proteomics workflows.

    a, All bottom-up proteomics workflows begin with a sample-preparation stage in which proteins are extracted and digested by a sequence-specific enzyme such as trypsin. Present methods of protein preparation are highly efficient and can be performed in 96-well plates with robotic assistance. Peptides are then separated by means of chromatography and electrosprayed, after which they are introduced into the vacuum of a mass spectrometer. Three classes of methods are shown. In DDA methods, a full spectrum of the peptides (at the MS1 level) is acquired, followed by the collection of as many fragmentation spectra (at the MS2 level) as possible, within a cycle time of about 1 second. A quadrupole–orbitrap mass analyser is depicted, although other types of analyser are also used in DDA. Results are interpreted using software packages such as MaxQuant100 and the downstream Perseus environment101. In targeted analysis, a peptide of known mass-to-charge ratio (m/z) is selected in the first quadrupole, then the peptide is fragmented and several fragments are monitored over time. These transitions are multiplexed and their specificity is checked using software packages such as SkyLine102. In DIA methods, which are exemplified by sequential window acquisition of all theoretical fragment-ion spectra (SWATH)–MS103, ranges of m/z values (that typically span 25 m/z units) are selected and peptides are fragmented, followed by the acquisition of the fragments in a time-of-flight mass spectrometer. The instrument rapidly and seamlessly cycles through the entire mass range within a few seconds. The multiplexed fragment spectra are interpreted — often with the help of known fragment spectra from large spectral libraries — by software such as OpenSWATH104. b, Peptide quantities can be determined at the MS1 level by integrating the signal from peaks of the precursor ions that elute from the high-performance liquid chromatography column. An arbitrary number of runs (stacked mass spectra, left) can be compared using sophisticated alignment and normalization procedures. Quantitative comparison of the isotopic cluster of the same peptide over two runs can be performed. Peptide identities can also be transferred when the peptide is fragmented in only one of the runs but matches precisely the mass and elution time of an aligned peak (known as the 'match between runs' feature in MaxQuant100). Absolute quantities can be estimated by adding up the peak volumes of all peptides that identify a particular protein then determining the proportion of the (known) total proteome mass that has been analysed. Peptides can also be subjected to label-free quantification at the MS2 level (right). In this case, the fragment-ion intensities that are unique to a specific peptide are used for quantification, in a way that is analogous to the use of precursor-ion signal intensities for quantification using MS1-level data. In multiplexed shotgun proteomics, up to ten samples are labelled differentially so that they release reporter ions that can be distinguished in the MS2 spectra. In DIA-based methods, the intensities of fragments that belong to the same precursor ion are extracted to yield a measure of peptide abundance104, 105. Q, quadrupole.

  2. Analysis of post-translational modifications.
    Figure 2: Analysis of post-translational modifications.

    a, In post-translational modification, proteins are modified through the attachment of a chemical moiety such as a phosphate group, usually by a dedicated and highly specific system of enzymes. The most commonly studied post-translational modifications are listed (centre) and these are accompanied by hundreds of other less-well-studied or unknown types of modifications. Such modifications can lead to: alterations in protein conformation (through phosphorylation) and subsequent allosteric regulation; changes in enzyme activity; crosstalk that results from the same amino-acid residue being targeted by more than one type of modification; alterations in the subcellular localization of proteins; changes in protein binding; and alterations in protein lifetimes (for example, through the attachment of the small protein ubiquitin). Ac, acetyl; ERK, extracellular signal-related kinase; Me, methyl; MEK, mitogen-activated protein kinase kinase; MYC, transcription factor cMYC; P, phosphate; RAF, RAF kinase; RAS, RAS GTPase; Ub, ubiquitin. b, After a modified peptide has been identified from the fragment spectra, the amino acid in the peptide chain to which the post-translational modification is attached must be determined. The location of the modification within the three-dimensional structure of the protein can often also be determined, which provides clues about function. c, Global interrogation of the changes in a signalling pathway can be achieved readily by quantitative phosphoproteomics. For example, the suppression of aberrant signalling in cancer cells by drugs known as kinase inhibitors can be followed. d, Detailed time-course experiments yield information on the temporal ordering of events such as the activation of a kinase upstream of one of its substrates. The proportion of proteins that are modified by a particular post-translational modification (also termed the occupancy or stoichiometry) can change drastically depending on the biological conditions (not shown). It can be derived from the changes in protein level and the levels of the modified and unmodified peptide in two cellular states106. e, The modification of a protein often determines its subcellular localization — that is, whether it is found in the nucleus or the cytosol, for instance. Many types of stimuli can be applied to biological systems, after which the level of a particular post-translational modification can be determined. f, The structure of the perturbation matrix that results reveals the regulated sites and how they correlate between stimuli, as indicated by hot spots in the heat map. m, number of modification sites quantified; n, number of stimuli applied.

  3. Interaction proteomics and structural proteomics.
    Figure 3: Interaction proteomics and structural proteomics.

    a, Schematic representations of a protein interaction network with bait proteins (teal), core complex members (dark green) and weak interactors (light green). A bait protein is precipitated with its interaction partners and is measured in replicates by one of the workflows described in Fig. 1. By considering the interaction stoichiometry (the molar ratio of prey proteins and the bait protein expressed under endogenous control) and the relative cellular abundances of the proteins, stable core complexes can be distinguished from weak interactions and unspecific interactions, as well as from asymmetric interactions between proteins of different abundances55. b, A wild-type protein complex and the same complex with mutations (*) are investigated using complementary structural techniques, collectively termed integrative or hybrid structural analysis. For example, XL–MS can reveal information about subunit topology and direct domain–domain interactions. Hydrogen–deuterium exchange mass spectrometry (HDX–MS) is able to determine the interaction surfaces and solvent-exposed regions. Native mass spectrometry (native MS), in which entire protein complexes are electrosprayed into the mass spectrometer, can infer the stoichiometry and the assembly pathway of such complexes, and cryo-EM can obtain their overall shape and their density maps. The heterogeneous structural restraints are integrated in a common computational framework that evaluates subunit configurations (known as conformational sampling). Consensus models that represent the structures of the wild-type and mutated complexes can then be derived.

  4. Proteotype states and phenotypes.
    Figure 4: Proteotype states and phenotypes.

    The proteotype, which is the acute state of the proteome, is shown as a modular network of interacting protein entities (coloured shapes). The composition of the proteotype and the organization of individual proteins into functional modules and interaction networks are determined by the combined effects of genotype and external perturbations, which include physical or chemical stimuli, cell–cell interactions or the microbiota. Genotypic differences such as allele differences or somatic mutations might perturb the proteotype. The relationship between genetic loci and the abundance of a protein can be described by a pQTL. These are identified by associating the abundance of a specific protein with particular alleles in genetically characterized sample populations such as genetic reference panels. In turn, the proteotype determines phenotypes, including clinical phenotypes. Association studies can identify relationships between proteotypes and phenotypes. Establishing such associations requires the generation of quantitatively accurate and highly reproducible datasets in which the same proteins are quantified across a large number of samples (for example, genetic reference panels or cohorts of patients). Datasets that support such association studies can now be generated using various mass spectrometry techniques.


  1. Marguerat, S. et al. Quantitative analysis of fission yeast transcriptomes and proteomes in proliferating and quiescent cells. Cell 151, 671683 (2012).
  2. Milo, R. What is the total number of protein molecules per cell volume? A call to rethink some published values. BioEssays 35, 10501055 (2013).
  3. Edwards, A. M. et al. Too many roads not taken. Nature 470, 163165 (2011).
  4. Aebersold, R. & Mann, M. Mass spectrometry-based proteomics. Nature 422, 198207 (2003).
  5. Cravatt, B. F., Simon, G. M. & Yates, J. R. The biological impact of mass-spectrometry-based proteomics. Nature 450, 9911000 (2007).
  6. de Godoy, L. M. F. et al. Comprehensive mass-spectrometry-based proteome quantification of haploid versus diploid yeast. Nature 455, 12511254 (2008).
    This paper demonstrates that complete proteomes of a model organism can be obtained and quantified in different biological states.
  7. Beck, M. et al. The quantitative proteome of a human cell line. Mol. Syst. Biol. 7, 549 (2011).
  8. Nagaraj, N. et al. Deep proteome and transcriptome mapping of a human cancer cell line. Mol. Syst. Biol. 7, 548 (2011).
  9. Hebert, A. S. et al. The one hour yeast proteome. Mol. Cell. Proteomics 13, 339347 (2014).
  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, 319324 (2014).
  11. Mann, M., Kulak, N. A., Nagaraj, N. & Cox, J. The coming age of complete, accurate, and ubiquitous proteomes. Mol. Cell 49, 583590 (2013).
  12. Azimifar, S. B., Nagaraj, N., Cox, J. & Mann, M. Cell-type-resolved quantitative proteomics of murine liver. Cell Metab. 20, 10761087 (2014).
  13. Richards, A. L., Merrill, A. E. & Coon, J. J. Proteome sequencing goes deep. Curr. Opin. Chem. Biol. 24, 1117 (2015).
  14. Sharma, K. et al. Cell type- and brain region-resolved mouse brain proteome. Nature Neurosci. 18, 18191831 (2015).
  15. Lundberg, E. et al. Defining the transcriptome and proteome in three functionally different human cell lines. Mol. Syst. Biol. 6, 450 (2010).
  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.
  17. Kim, M.-S. et al. A draft map of the human proteome. Nature 509, 575581 (2014).
  18. Wilhelm, M. et al. Mass-spectrometry-based draft of the human proteome. Nature 509, 582587 (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.
  19. Ezkurdia, I., Vázquez, J., Valencia, A. & Tress, M. Analyzing the first drafts of the human proteome. J. Proteome Res. 13, 38543855 (2014).
  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, 34523460 (2015).
  21. Tran, J. C. et al. Mapping intact protein isoforms in discovery mode using top-down proteomics. Nature 480, 254258 (2011).
  22. Meissner, F., Scheltema, R. A., Mollenkopf, H.-J. & Mann, M. Direct proteomic quantification of the secretome of activated immune cells. Science 340, 475478 (2013).
  23. Secher, A. et al. Analytic framework for peptidomics applied to large-scale neuropeptide identification. Nature Commun. 7, 11436 (2016).
  24. Caron, E. et al. Analysis of major histocompatibility complex (MHC) immunopeptidomes using mass spectrometry. Mol. Cell. Proteomics 14, 31053117 (2015).
  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).
  26. Malmström, J. et al. Proteome-wide cellular protein concentrations of the human pathogen Leptospira interrogans. Nature 460, 762765 (2009).
  27. Schwanhäusser, B. et al. Global quantification of mammalian gene expression control. Nature 473, 337342 (2011).
    A pioneering investigation of the degree of correlation between the transcriptome and the proteome — a question that is still unresolved.
  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, 34973506 (2014).
  29. Doll, S. & Burlingame, A. L. Mass spectrometry-based detection and assignment of protein posttranslational modifications. ACS Chem. Biol. 10, 6371 (2015).
  30. Sharma, K. et al. Ultradeep human phosphoproteome reveals a distinct regulatory nature of Tyr and Ser/Thr-based signaling. Cell Rep. 8, 15831594 (2014).
  31. Hsu, P. P. et al. The mTOR-regulated phosphoproteome reveals a mechanism of mTORC1-mediated inhibition of growth factor signaling. Science 332, 13171322 (2011).
  32. Huttlin, E. L. et al. A tissue-specific atlas of mouse protein phosphorylation and expression. Cell 143, 11741189 (2010).
  33. Olsen, J. V. et al. Global, in vivo, and site-specific phosphorylation dynamics in signaling networks. Cell 127, 635648 (2006).
  34. Francavilla, C. et al. Functional proteomics defines the molecular switch underlying FGF receptor trafficking and cellular outputs. Mol. Cell 51, 707722 (2013).
  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.
  36. Humphrey, S. J., Azimifar, S. B. & Mann, M. High-throughput phosphoproteomics reveals in vivo insulin signaling dynamics. Nature Biotechnol. 33, 990995 (2015).
  37. Weinert, B. T. et al. Acetyl-phosphate is a critical determinant of lysine acetylation in E. coli. Mol. Cell 51, 265272 (2013).
  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, 536550 (2014).
  39. Hendriks, I. A. et al. Uncovering global SUMOylation signaling networks in a site-specific manner. Nature Struct. Mol. Biol. 21, 927936 (2014).
  40. Huang, H., Lin, S., Garcia, B. A. & Zhao, Y. Quantitative proteomic analysis of histone modifications. Chem. Rev. 115, 23762418 (2015).
  41. Zheng, Y., Huang, X. & Kelleher, N. L. Epiproteomics: quantitative analysis of histone marks and codes by mass spectrometry. Curr. Opin. Chem. Biol. 33, 142150 (2016).
  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, 935948 (2006).
  43. Jungmichel, S. et al. Proteome-wide identification of poly(ADP-ribosyl)ation targets in different genotoxic stress responses. Mol. Cell 52, 272285 (2013).
  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, 743749 (2015).
  45. Rix, U. & Superti-Furga, G. Target profiling of small molecules by chemical proteomics. Nature Chem. Biol. 5, 616624 (2009).
  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).
  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, 15781611 (2011).
  48. Hartwell, L. H., Hopfield, J. J., Leibler, S. & Murray, A. W. From molecular to modular cell biology. Nature 402 (suppl.), C47C52 (1999).
  49. Pawson, T. Protein modules and signalling networks. Nature 373, 573580 (1995).
  50. Ward, A. B., Sali, A. & Wilson, I. A. Integrative structural biology. Science 339, 913915 (2013).
  51. Dunham, W. H., Mullin, M. & Gingras, A.-C. Affinity-purification coupled to mass spectrometry: basic principles and strategies. Proteomics 12, 15761590 (2012).
  52. Choi, H. et al. SAINT: probabilistic scoring of affinity purification-mass spectrometry data. Nature Methods 8, 7073 (2011).
  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, 120135 (2015).
  54. Huttlin, E. L. et al. The BioPlex network: a systematic exploration of the human interactome. Cell 162, 425440 (2015).
    A large-scale investigation of proteins binding to tagged constructs to establish a human interactome.
  55. Hein, M. Y. et al. A human interactome in three quantitative dimensions organized by stoichiometries and abundances. Cell 163, 712723 (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.
  56. Ori, A. et al. Spatiotemporal variation of mammalian protein complex stoichiometries. Genome Biol. 17, 47 (2016).
  57. Räschle, M. et al. Proteomics reveals dynamic assembly of repair complexes during bypass of DNA cross-links. Science 348, 1253671 (2015).
  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, 801810 (2012).
  59. Rhee, H.-W. et al. Proteomic mapping of mitochondria in living cells via spatially restricted enzymatic tagging. Science 339, 13281331 (2013).
  60. Havugimana, P. C. et al. A census of human soluble protein complexes. Cell 150, 10681081 (2012).
  61. Kristensen, A. R., Gsponer, J. & Foster, L. J. A high-throughput approach for measuring temporal changes in the interactome. Nature Methods 9, 907909 (2012).
  62. Wan, C. et al. Panorama of ancient metazoan macromolecular complexes. Nature 525, 339344 (2015).
  63. Christoforou, A. et al. A draft map of the mouse pluripotent stem cell spatial proteome. Nature Commun. 7, 8992 (2016).
  64. Larance, M. & Lamond, A. I. Multidimensional proteomics for cell biology. Nature Rev. Mol. Cell Biol. 16, 269280 (2015).
  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, 702714 (2005).
  66. Itzhak, D. N., Tyanova, S., Cox, J. & Borner, G. H. Global, quantitative and dynamic mapping of protein subcellular localization. eLife 5, e16950 (2016).
  67. Alber, F. et al. The molecular architecture of the nuclear pore complex. Nature 450, 695701 (2007).
  68. Marcoux, J. & Robinson, C. V. Twenty years of gas phase structural biology. Structure 21, 15411550 (2013).
  69. Politis, A. et al. A mass spectrometry-based hybrid method for structural modeling of protein complexes. Nature Methods 11, 403406 (2014).
  70. Zhou, M. et al. Mass spectrometry of intact V-type ATPases reveals bound lipids and the effects of nucleotide binding. Science 334, 380385 (2011).
    An elegant demonstration of native mass spectrometry in structural studies of intact membrane complexes.
  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, 2032 (2016).
  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, 100108 (2015).
  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, 10421055 (2014).
  74. Walzthoeni, T. et al. xTract: software for characterizing conformational changes of protein complexes by quantitative cross-linking mass spectrometry. Nature Methods 12, 11851190 (2015).
  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, 10641070 (2014).
  76. Frei, A. P. et al. Direct identification of ligand-receptor interactions on living cells and tissues. Nature Biotechnol. 30, 9971001 (2012).
  77. Herzog, F. et al. Structural probing of a protein phosphatase 2A network by chemical cross-linking and mass spectrometry. Science 337, 13481352 (2012).
    This study pioneered the use of chemical crosslinking to reveal the topology of an important phosphatase complex.
  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, 11791184 (2015).
  79. Navare, A. T. et al. Probing the protein interaction network of Pseudomonas aeruginosa cells by chemical cross-linking mass spectrometry. Structure 23, 762773 (2015).
  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, 854865 (2016).
  81. Shi, Y. et al. A strategy for dissecting the architectures of native macromolecular assemblies. Nature Methods 12, 11351138 (2015).
  82. Aufderheide, A. et al. Structural characterization of the interaction of Ubp6 with the 26S proteasome. Proc. Natl Acad. Sci. USA 112, 86268631 (2015).
  83. Mahamid, J. et al. Visualizing the molecular sociology at the HeLa cell nuclear periphery. Science 351, 969972 (2016).
  84. Engen, J. R. Analysis of protein conformation and dynamics by hydrogen/deuterium exchange MS. Anal. Chem. 81, 78707875 (2009).
  85. Wang, L. & Chance, M. R. Structural mass spectrometry of proteins using hydroxyl radical based protein footprinting. Anal. Chem. 83, 72347241 (2011).
  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.
  87. Feng, Y. et al. Global analysis of protein structural changes in complex proteomes. Nature Biotechnol. 32, 10361044 (2014).
  88. Pauling, L., Itano, H. A., Singer, S. J. & Wells, I. C. Sickle cell anemia, a molecular disease. Science 110, 543548 (1949).
  89. Picotti, P. et al. A complete mass-spectrometric map of the yeast proteome applied to quantitative trait analysis. Nature 494, 266270 (2013).
  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, 12871299 (2012).
  91. Wu, Y. et al. Multilayered genetic and omics dissection of mitochondrial activity in a mouse reference population. Cell 158, 14151430 (2014).
  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.
  93. Mertins, P. et al. Proteogenomics connects somatic mutations to signalling in breast cancer. Nature 534, 5562 (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.
  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, 907917 (2014).
  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, 971983 (2006).
  96. Geyer, P. E. et al. Plasma proteome profiling to assess human health and disease. Cell Syst. 2, 185195 (2016).
  97. Surinova, S. et al. Prediction of colorectal cancer diagnosis based on circulating plasma proteins. EMBO Mol. Med. 7, 11661178 (2015).
  98. Liu, Y. et al. Quantitative variability of 342 plasma proteins in a human twin population. Mol. Syst. Biol. 11, 786 (2015).
  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, 68136822 (2009).
  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, 13671372 (2008).
  101. Tyanova, S. et al. The Perseus computational platform for comprehensive analysis of (prote)omics data. Nature Methods 13, 731740 (2016).
  102. MacLean, B. et al. Skyline: an open source document editor for creating and analyzing targeted proteomics experiments. Bioinformatics 26, 966968 (2010).
  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).
  104. Röst, H. L. et al. OpenSWATH enables automated, targeted analysis of data-independent acquisition MS data. Nature Biotechnol. 32, 219223 (2014).
  105. Tsou, C.-C. et al. DIA-Umpire: comprehensive computational framework for data-independent acquisition proteomics. Nature Methods 12, 258264 (2015).
  106. Olsen, J. V. et al. Quantitative phosphoproteomics reveals widespread full phosphorylation site occupancy during mitosis. Sci. Signal. 3, ra3 (2010).
  107. Smith, L. M., Kelleher, N. L. & The Consortium for Top Down Proteomics. Proteoform: a single term describing protein complexity. Nature Methods 10, 186187 (2013).
  108. Olsen, J. V. et al. Higher-energy C-trap dissociation for peptide modification analysis. Nature Methods 4, 709712 (2007).
  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, 95289533 (2004).
  110. Zubarev, R. A. & Makarov, A. Orbitrap mass spectrometry. Anal. Chem. 85, 52885296 (2013).
  111. Picotti, P. & Aebersold, R. Selected reaction monitoring-based proteomics: workflows, potential, pitfalls and future directions. Nature Methods 9, 555566 (2012).
  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, 14751488 (2012).
  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, 452470 (2014).
  114. Rosenberger, G. et al. A repository of assays to quantify 10,000 human proteins by SWATH-MS. Sci. Data 1, 140031 (2014).
  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, 53785387 (2015).
  116. Ow, S. Y. et al. iTRAQ underestimation in simple and complex mixtures: “the good, the bad and the ugly”. J. Proteome Res. 8, 53475355 (2009).
  117. Ting, L., Rad, R., Gygi, S. P. & Haas, W. MS3 eliminates ratio distortion in isobaric multiplexed quantitative proteomics. Nature Methods 8, 937940 (2011).
  118. Wühr, M. et al. The nuclear proteome of a vertebrate. Curr. Biol. 25, 26632671 (2015).
  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, 25132526 (2014).
  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).

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  1. Institute of Molecular Systems Biology, Department of Biology, ETH Zürich, 8093 Zürich, Switzerland.

    • Ruedi Aebersold
  2. Faculty of Science, University of Zürich, 8093 Zürich, Switzerland.

    • Ruedi Aebersold
  3. Department of Proteomics and Signal Transduction, Max Planck Institute of Biochemistry, 82152 Martinsried, Germany.

    • Matthias Mann
  4. Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, 2200 Copenhagen, Denmark.

    • Matthias Mann

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