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Applying mass spectrometry-based proteomics to genetics, genomics and network biology

Key Points

  • Mass spectrometry-based proteomics is the only method currently available to comprehensively analyse changes in mutant proteomes.

  • Several quantitative methods have been introduced to profile mutant proteomes with high sensitivity. However, although full proteome coverage has been achieved by recent studies, proteome profiling still requires substantial experimental efforts.

  • Targeted proteome analysis by selected reaction monitoring is a promising analytical concept to overcome the existing limitations in the analysis of low abundance proteins from complex biological samples.

  • Several workflows have been introduced for the systematic analysis of posttranslational modifications. The first examples in the field of phosphoproteome analysis have shown how global posttranslational modification profiling can be applied to identify the molecular pathways that are affected in mutant cells.

  • Information on protein complexes obtained by affinity purification coupled with mass spectrometry provides important insights into the molecular context of proteins that are encoded by mutated genes.

  • A higher-level understanding of complex genotype–phenotype relationships will depend on the proper annotation and accessibility of mass spectrometry-based proteomics data to integrate these data with functional genomics and phenomics data in a biological cyberinfrastructure.

Abstract

The systematic and quantitative molecular analysis of mutant organisms that has been pioneered by studies on mutant metabolomes and transcriptomes holds great promise for improving our understanding of how phenotypes emerge. Unfortunately, owing to the limitations of classical biochemical analysis, proteins have previously been excluded from such studies. Here we review how technical advances in mass spectrometry-based proteomics can be applied to measure changes in protein abundance, posttranslational modifications and protein–protein interactions in mutants at the scale of the proteome. We finally discuss examples that integrate proteomics data with genomic and phenomic information to build network-centred models, which provide a promising route for understanding how phenotypes emerge.

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Figure 1: Mass spectrometry-based protein identification.
Figure 2: Targeted proteomics.
Figure 3: Strategies for global phosphoproteome profiling of mutant proteomes.
Figure 4: Mass spectrometry-based interaction proteomics for the analysis of mutant phenotypes.
Figure 5: Using proteomic data in network biology.

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References

  1. de Godoy, L. M. et al. Comprehensive mass-spectrometry-based proteome quantification of haploid versus diploid yeast. Nature 455, 1251–1254 (2008).

    Article  CAS  PubMed  Google Scholar 

  2. Ideker, T. et al. Integrated genomic and proteomic analyses of a systematically perturbed metabolic network. Science 292, 929–934 (2001).

    Article  CAS  PubMed  Google Scholar 

  3. Wolters, D. A., Washburn, M. P. & Yates, J. R. 3rd. An automated multidimensional protein identification technology for shotgun proteomics. Anal. Chem. 73, 5683–5690 (2001).

    Article  CAS  PubMed  Google Scholar 

  4. Ghaemmaghami, S. et al. Global analysis of protein expression in yeast. Nature 425, 737–741 (2003).

    Article  CAS  PubMed  Google Scholar 

  5. Anderson, N. L. et al. The human plasma proteome: a nonredundant list developed by combination of four separate sources. Mol. Cell. Proteomics 3, 311–326 (2004).

    Article  CAS  PubMed  Google Scholar 

  6. Schrimpf, S. P. et al. Comparative functional analysis of the Caenorhabditis elegans and Drosophila melanogaster proteomes. PLoS Biol. 7, e48 (2009).

    Article  PubMed  CAS  Google Scholar 

  7. Baerenfaller, K. et al. Genome-scale proteomics reveals Arabidopsis thaliana gene models and proteome dynamics. Science 320, 938–941 (2008).

    Article  CAS  PubMed  Google Scholar 

  8. Brunner, E. et al. A high-quality catalog of the Drosophila melanogaster proteome. Nature Biotechnol. 25, 576–583 (2007).

    Article  CAS  Google Scholar 

  9. Bantscheff, M., Schirle, M., Sweetman, G., Rick, J. & Kuster, B. Quantitative mass spectrometry in proteomics: a critical review. Anal. Bioanal. Chem. 389, 1017–1031 (2007). This review gives a good overview of the various quantitative MS approaches that are currently applied for quantitative proteomics.

    Article  CAS  PubMed  Google Scholar 

  10. MacCoss, M. J., Wu, C. C., Liu, H., Sadygov, R. & Yates, J. R. 3rd. A correlation algorithm for the automated quantitative analysis of shotgun proteomics data. Anal. Chem. 75, 6912–6921 (2003).

    Article  CAS  PubMed  Google Scholar 

  11. Liu, H., Sadygov, R. G. & Yates, J. R. 3rd. A model for random sampling and estimation of relative protein abundance in shotgun proteomics. Anal. Chem. 76, 4193–4201 (2004).

    Article  CAS  PubMed  Google Scholar 

  12. Colinge, J., Chiappe, D., Lagache, S., Moniatte, M. & Bougueleret, L. Differential proteomics via probabilistic peptide identification scores. Anal. Chem. 77, 596–606 (2005).

    Article  CAS  PubMed  Google Scholar 

  13. Strittmatter, E. F., Ferguson, P. L., Tang, K. & Smith, R. D. Proteome analyses using accurate mass and elution time peptide tags with capillary LC time-of-flight mass spectrometry. J. Am. Soc. Mass Spectrom. 14, 980–991 (2003).

    Article  CAS  PubMed  Google Scholar 

  14. Foss, E. J. et al. Genetic basis of proteome variation in yeast. Nature Genet. 39, 1369–1375 (2007).

    Article  CAS  PubMed  Google Scholar 

  15. Dong, M. Q. et al. Quantitative mass spectrometry identifies insulin signaling targets in C. elegans. Science 317, 660–663 (2007).

    Article  CAS  PubMed  Google Scholar 

  16. Jenkins, L. M. et al. Quantitative proteomics analysis of the effects of ionizing radiation in wild type and p53K317R knock-in mouse thymocytes. Mol. Cell. Proteomics 7, 716–727 (2008).

    Article  CAS  PubMed  Google Scholar 

  17. Martin, B. et al. iTRAQ analysis of complex proteome alterations in 3xTgAD Alzheimer's mice: understanding the interface between physiology and disease. PLoS ONE 3, e2750 (2008).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  18. Chiang, M. C. et al. Systematic uncovering of multiple pathways underlying the pathology of Huntington disease by an acid-cleavable isotope-coded affinity tag approach. Mol. Cell. Proteomics 6, 781–797 (2007).

    Article  CAS  PubMed  Google Scholar 

  19. Liao, L., Park, S. K., Xu, T., Vanderklish, P. & Yates, J. R. 3rd. Quantitative proteomic analysis of primary neurons reveals diverse changes in synaptic protein content in fmr1 knockout mice. Proc. Natl Acad. Sci. USA 105, 15281–15286 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Kruger, M. et al. SILAC mouse for quantitative proteomics uncovers kindlin-3 as an essential factor for red blood cell function. Cell 134, 353–364 (2008). In this paper an approach based on the metabolic labelling of entire mice with isotope-labelled amino acids is presented for MS-based profiling of mouse mutant proteomes.

    Article  PubMed  CAS  Google Scholar 

  21. Wu, C. C., MacCoss, M. J., Howell, K. E., Matthews, D. E. & Yates, J. R. 3rd. Metabolic labeling of mammalian organisms with stable isotopes for quantitative proteomic analysis. Anal. Chem. 76, 4951–4959 (2004).

    Article  CAS  PubMed  Google Scholar 

  22. McClatchy, D. B., Liao, L., Park, S. K., Venable, J. D. & Yates, J. R. Quantification of the synaptosomal proteome of the rat cerebellum during post-natal development. Genome Res. 17, 1378–1388 (2007).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Alagaratnam, S. et al. Serum protein profiling in mice: identification of factor XIIIa as a potential biomarker for muscular dystrophy. Proteomics 8, 1552–1563 (2008).

    Article  CAS  PubMed  Google Scholar 

  24. Faca, V. M. et al. A mouse to human search for plasma proteome changes associated with pancreatic tumor development. PLoS Med. 5, e123 (2008). A quantitative MS approach in combination with mouse genetics identified candidate biomarkers for early pancreatic tumour development.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  25. Hung, K. E. et al. Comprehensive proteome analysis of an Apc mouse model uncovers proteins associated with intestinal tumorigenesis. Cancer Prev. Res. 2, 224–233 (2009).

    Article  CAS  Google Scholar 

  26. Kuster, B., Schirle, M., Mallick, P. & Aebersold, R. Scoring proteomes with proteotypic peptide probes. Nature Rev. Mol. Cell Biol. 6, 577–583 (2005).

    Article  CAS  Google Scholar 

  27. Malmstrom, J., Lee, H. & Aebersold, R. Advances in proteomic workflows for systems biology. Curr. Opin. Biotechnol. 18, 378–384 (2007).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  28. Lange, V., Picotti, P., Domon, B. & Aebersold, R. Selected reaction monitoring for quantitative proteomics: a tutorial. Mol. Syst. Biol. 4, 222 (2008). This tutorial gives a good introduction into the emerging field of targeted proteomics using SRM.

    Article  PubMed  PubMed Central  Google Scholar 

  29. Mallick, P. et al. Computational prediction of proteotypic peptides for quantitative proteomics. Nature Biotechnol. 25, 125–131 (2007).

    Article  CAS  Google Scholar 

  30. Deutsch, E. W., Lam, H. & Aebersold, R. PeptideAtlas: a resource for target selection for emerging targeted proteomics workflows. EMBO Rep. 9, 429–434 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Picotti, P. et al. A database of mass spectrometric assays for the yeast proteome. Nature Methods 5, 913–914 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Stahl-Zeng, J. et al. High sensitivity detection of plasma proteins by multiple reaction monitoring of N-glycosites. Mol. Cell. Proteomics 6, 1809–1817 (2007).

    Article  CAS  PubMed  Google Scholar 

  33. Picotti, P., Bodenmiller, B., Mueller, L. N., Domon, B. & Aebersold, R. Full dynamic range proteome analysis of S. cerevisiae by targeted proteomics. Cell 6 Aug 2009 [epub ahead of print]. This paper illustrates how SRM targeted proteomics can be used to resolve the changes in the abundance of yeast metabolic enzymes during metabolic transitions.

  34. Wolf-Yadlin, A., Hautaniemi, S., Lauffenburger, D. A. & White, F. M. Multiple reaction monitoring for robust quantitative proteomic analysis of cellular signaling networks. Proc. Natl Acad. Sci. USA 104, 5860–5865 (2007).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Lange, V. et al. Targeted quantitative analysis of Streptococcus pyogenes virulence factors by multiple reaction monitoring. Mol. Cell. Proteomics 7, 1489–1500 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Addona, T. A. et al. Multi-site assessment of the precision and reproducibility of multiple reaction monitoring-based measurements of proteins in plasma. Nature Biotechnol. 27, 633–641 (2009). A multilaboratory study that shows the high reproducibility and sensitivity of SRM-based assays for proteome profiling.

    Article  CAS  Google Scholar 

  37. Krishna, R. G. & Wold, F. Post-translational modification of proteins. Adv. Enzymol. Relat. Areas Mol. Biol. 67, 265–298 (1993).

    CAS  PubMed  Google Scholar 

  38. Jensen, O. N. Interpreting the protein language using proteomics. Nature Rev. Mol. Cell Biol. 7, 391–403 (2006). This review provides a good overview on the MS-based analysis of PTMs.

    Article  CAS  Google Scholar 

  39. Witze, E. S., Old, W. M., Resing, K. A. & Ahn, N. G. Mapping protein post-translational modifications with mass spectrometry. Nature Methods 4, 798–806 (2007).

    Article  CAS  PubMed  Google Scholar 

  40. Bodenmiller, B., Mueller, L. N., Mueller, M., Domon, B. & Aebersold, R. Reproducible isolation of distinct, overlapping segments of the phosphoproteome. Nature Methods 4, 231–237 (2007).

    Article  CAS  PubMed  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  42. Bodenmiller, B. et al. PhosphoPep — a phosphoproteome resource for systems biology research in Drosophila Kc167 cells. Mol. Syst. Biol. 3, 139 (2007).

    Article  PubMed  PubMed Central  Google Scholar 

  43. Dephoure, N. et al. A quantitative atlas of mitotic phosphorylation. Proc. Natl Acad. Sci. USA 105, 10762–10767 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Smolka, M. B., Albuquerque, C. P., Chen, S. H. & Zhou, H. Proteome-wide identification of in vivo targets of DNA damage checkpoint kinases. Proc. Natl Acad. Sci. USA 104, 10364–10369 (2007). In this paper, a quantitative phosphoproteomics approach is described for the analysis of yeast cells that lack DNA checkpoint kinases, which identified novel in vivo substrates for these kinases.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Glatter, T., Wepf, A., Aebersold, R. & Gstaiger, M. An integrated workflow for charting the human interaction proteome: insights into the PP2A system. Mol. Syst. Biol. 5, 237 (2009).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  46. Rigaut, G. et al. A generic protein purification method for protein complex characterization and proteome exploration. Nature Biotechnol. 17, 1030–1032 (1999).

    Article  CAS  Google Scholar 

  47. Charbonnier, S., Gallego, O. & Gavin, A. C. The social network of a cell: recent advances in interactome mapping. Biotechnol. Annu. Rev. 14, 1–28 (2008).

    Article  CAS  PubMed  Google Scholar 

  48. Pflieger, D. et al. Quantitative proteomic analysis of protein complexes: concurrent identification of interactors and their state of phosphorylation. Mol. Cell. Proteomics 7, 326–346 (2008).

    Article  CAS  PubMed  Google Scholar 

  49. Wepf, A., Glatter, T., Schmidt, A., Aebersold, R. & Gstaiger, M. Quantitative interaction proteomics using mass spectrometry. Nature Methods 6, 203–205 (2009).

    Article  CAS  PubMed  Google Scholar 

  50. Major, M. B. et al. Wilms tumor suppressor WTX negatively regulates WNT/β-catenin signaling. Science 316, 1043–1046 (2007). In this paper, an AP–MS approach was used to identify a link between the Wilms' tumour suppressor protein WTX and degradation of β-catenin.

    Article  CAS  PubMed  Google Scholar 

  51. Jeffery, C. J. Moonlighting proteins. Trends Biochem. Sci. 24, 8–11 (1999).

    Article  CAS  PubMed  Google Scholar 

  52. Gingras, A. C. et al. A novel, evolutionarily conserved protein phosphatase complex involved in cisplatin sensitivity. Mol. Cell. Proteomics 4, 1725–1740 (2005).

    Article  CAS  PubMed  Google Scholar 

  53. Goudreault, M. et al. A PP2A phosphatase high density interaction network identifies a novel striatin-interacting phosphatase and kinase complex linked to the cerebral cavernous malformation 3 (CCM3) protein. Mol. Cell. Proteomics 8, 157–171 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  54. Gavin, A. C. et al. Proteome survey reveals modularity of the yeast cell machinery. Nature 440, 631–636 (2006).

    Article  CAS  PubMed  Google Scholar 

  55. Gavin, A. C. et al. Functional organization of the yeast proteome by systematic analysis of protein complexes. Nature 415, 141–147 (2002).

    Article  CAS  PubMed  Google Scholar 

  56. Ho, Y. et al. Systematic identification of protein complexes in Saccharomyces cerevisiae by mass spectrometry. Nature 415, 180–183 (2002).

    Article  CAS  PubMed  Google Scholar 

  57. Krogan, N. J. et al. Global landscape of protein complexes in the yeast Saccharomyces cerevisiae. Nature 440, 637–643 (2006).

    CAS  PubMed  Google Scholar 

  58. Le Meur, N. & Gentleman, R. Modeling synthetic lethality. Genome Biol. 9, R135 (2008).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  59. Zhu, X., Gerstein, M. & Snyder, M. Getting connected: analysis and principles of biological networks. Genes Dev. 21, 1010–1024 (2007).

    Article  CAS  PubMed  Google Scholar 

  60. Joyce, A. R. & Palsson, B. O. The model organism as a system: integrating 'omics' data sets. Nature Rev. Mol. Cell Biol. 7, 198–210 (2006).

    Article  CAS  Google Scholar 

  61. Makhnevych, T. et al. Global map of SUMO function revealed by protein–protein interaction and genetic networks. Mol. Cell 33, 124–135 (2009).

    Article  CAS  PubMed  Google Scholar 

  62. Collins, S. R. et al. Functional dissection of protein complexes involved in yeast chromosome biology using a genetic interaction map. Nature 446, 806–810 (2007).

    Article  CAS  PubMed  Google Scholar 

  63. Chuang, H. Y., Lee, E., Liu, Y. T., Lee, D. & Ideker, T. Network-based classification of breast cancer metastasis. Mol. Syst. Biol. 3, 140 (2007).

    Article  PubMed  PubMed Central  Google Scholar 

  64. Taylor, I. W. et al. Dynamic modularity in protein interaction networks predicts breast cancer outcome. Nature Biotechnol. 27, 199–204 (2009). Network inference using protein–protein interaction data and gene expression data identified a change in the modularity of oncogenic pathways between patients with breast cancer who had either a good or poor prognosis.

    Article  CAS  Google Scholar 

  65. Pedrioli, P. G. et al. A common open representation of mass spectrometry data and its application to proteomics research. Nature Biotechnol. 22, 1459–1466 (2004).

    Article  CAS  Google Scholar 

  66. Taylor, C. F. Minimum reporting requirements for proteomics: a MIAPE primer. Proteomics 6 (Suppl. 2), 39–44 (2006).

    Article  PubMed  CAS  Google Scholar 

  67. Thorisson, G. A., Muilu, J. & Brookes, A. J. Genotype–phenotype databases: challenges and solutions for the post-genomic era. Nature Rev. Genet. 10, 9–18 (2009).

    Article  CAS  PubMed  Google Scholar 

  68. Stein, L. D. Towards a cyberinfrastructure for the biological sciences: progress, visions and challenges. Nature Rev. Genet. 9, 678–688 (2008).

    Article  CAS  PubMed  Google Scholar 

  69. Bonaldi, T. et al. Combined use of RNAi and quantitative proteomics to study gene function in Drosophila. Mol. Cell 31, 762–772 (2008).

    Article  CAS  PubMed  Google Scholar 

  70. Li, K. W. et al. Quantitative proteomics and protein network analysis of hippocampal synapses of CaMKIIα mutant mice. J. Proteome Res. 6, 3127–3133 (2007).

    Article  CAS  PubMed  Google Scholar 

  71. Shiio, Y. et al. Quantitative proteomic analysis of Myc-induced apoptosis: a direct role for Myc induction of the mitochondrial chloride ion channel, mtCLIC/CLIC4. J. Biol. Chem. 281, 2750–2756 (2006).

    Article  CAS  PubMed  Google Scholar 

  72. Gygi, S. P. et al. Quantitative analysis of complex protein mixtures using isotope-coded affinity tags. Nature Biotechnol. 17, 994–999 (1999).

    Article  CAS  Google Scholar 

  73. Schmidt, A., Kellermann, J. & Lottspeich, F. A novel strategy for quantitative proteomics using isotope-coded protein labels. Proteomics 5, 4–15 (2005).

    Article  CAS  PubMed  Google Scholar 

  74. Choe, L. et al. 8-plex quantitation of changes in cerebrospinal fluid protein expression in subjects undergoing intravenous immunoglobulin treatment for Alzheimer's disease. Proteomics 7, 3651–3660 (2007).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  75. Chong, P. K., Gan, C. S., Pham, T. K. & Wright, P. C. Isobaric tags for relative and absolute quantitation (iTRAQ) reproducibility: implication of multiple injections. J. Proteome Res. 5, 1232–1240 (2006).

    Article  CAS  PubMed  Google Scholar 

  76. Desiderio, D. M. & Zhu, X. Quantitative analysis of methionine enkephalin and β-endorphin in the pituitary by liquid secondary ion mass spectrometry and tandem mass spectrometry. J. Chromatogr. A 794, 85–96 (1998).

    Article  CAS  PubMed  Google Scholar 

  77. Gerber, S. A., Rush, J., Stemman, O., Kirschner, M. W. & Gygi, S. P. Absolute quantification of proteins and phosphoproteins from cell lysates by tandem MS. Proc. Natl Acad. Sci. USA 100, 6940–6945 (2003).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  78. Ong, S. E. et al. Stable isotope labeling by amino acids in cell culture, SILAC, as a simple and accurate approach to expression proteomics. Mol. Cell. Proteomics 1, 376–386 (2002).

    Article  CAS  PubMed  Google Scholar 

  79. Callister, S. J. et al. Normalization approaches for removing systematic biases associated with mass spectrometry and label-free proteomics. J. Proteome Res. 5, 277–286 (2006).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  80. Andreev, V. P. et al. A new algorithm using cross-assignment for label-free quantitation with LC–LTQ-FT MS. J. Proteome Res. 6, 2186–2194 (2007).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  81. Mueller, L. N. et al. SuperHirn — a novel tool for high resolution LC–MS-based peptide/protein profiling. Proteomics 7, 3470–3480 (2007).

    Article  CAS  PubMed  Google Scholar 

  82. Rinner, O. et al. An integrated mass spectrometric and computational framework for the analysis of protein interaction networks. Nature Biotechnol. 25, 345–352 (2007).

    Article  CAS  Google Scholar 

  83. Mueller, L. N., Brusniak, M. Y., Mani, D. R. & Aebersold, R. An assessment of software solutions for the analysis of mass spectrometry based quantitative proteomics data. J. Proteome Res. 7, 51–61 (2008).

    Article  CAS  PubMed  Google Scholar 

  84. Wepf, A., Glatter, T., Schmidt, A., Aebersold, R. & Gstaiger, M. Quantitative interaction proteomics using mass spectrometry. Nature Methods 6, 203–205 (2009).

    Article  CAS  PubMed  Google Scholar 

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Acknowledgements

This work was supported by the by the Swiss National Science Foundation grant 31000-10767, by federal funds from the National Heart, Lung and Blood Institute, the National Institutes of Health grant N01-HV-28179 and by SystemsX.ch, the Swiss initiative for systems biology.

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Correspondence to Matthias Gstaiger or Ruedi Aebersold.

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FURTHER INFORMATION

Matthias Gstaiger's homepage

Ruedi Aebersold's homepage

BioGrid protein interaction database

IntAct protein interaction database

MRMAtlas compendium of targeted proteomics assays

PeptideAtlas MS-based peptide data

PeptideSieve tool for prediction of proteotypic peptides

PhosphoPep MS-based data on phosphopeptides

PhosphoSitePlus

Pride repository for proteomics data

Protein Interaction Network Analysis database (PINA)

Trans Proteomic Pipeline (TPP) tools for MS/MS proteomics

X!Tandem protein identification software

Glossary

Mass spectrometry

An analytical technique for the identification of the chemical composition of compounds on the basis of the mass to charge ratios of charged particles.

Affinity purification–mass spectrometry

A method for the analysis of protein complexes that combines purification of protein complexes using affinity reagents and mass spectrometry.

Tandem mass spectrometry

This combines two mass spectrometers: one (MS1) for the detection and selection of precursor ions, which is followed by a second (MS2) for the analysis of fragment ion spectra generated from selected precursor ions after collision-induced fragmentation. The information from the fragment ion spectra is used for peptide identification.

Dynamic range

The ratios between the highest and lowest possible ion intensities in a mass spectrum for which accurate masses can be determined by a mass spectrometer.

Liquid chromatography–tandem mass spectrometry

Liquid chromatography is used in MS-based proteomics to separate peptides in complex mixtures primarily on the basis of their charge or hydrophobicity using strong cation exchange or reversed-phase chromatography columns.

Selected reaction monitoring

This is a sensitive mass spectrometry-based method for targeted proteomics that is based on the measurement of precursor–fragment ion pairs (transitions) of proteotypic peptides.

Proteotypic peptide

Proteotypic peptides can be observed by mass spectrometry and uniquely identify a specific protein or a specific isoform of a protein.

Synthetic genetic array

This has been primarily applied to yeast and is a technology for the high-throughput analysis of genetic interactions. Yeast deletion strains are crossed with each other to systematically generate double mutant strains. The resulting growth phenotypes are determined based on the size of the resulting double mutant colonies.

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Gstaiger, M., Aebersold, R. Applying mass spectrometry-based proteomics to genetics, genomics and network biology. Nat Rev Genet 10, 617–627 (2009). https://doi.org/10.1038/nrg2633

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