Experimental and Computational Tools for Analysis of Signaling Networks in Primary Cells

Erwin M. Schoof1, Rune Linding1

1 Cellular Signal Integration Group (C‐SIG), Center for Biological Sequence Analysis (CBS), Department of Systems Biology, Technical University of Denmark (DTU), Lyngby
Publication Name:  Current Protocols in Immunology
Unit Number:  Unit 11.11
DOI:  10.1002/0471142735.im1111s104
Online Posting Date:  February, 2014
GO TO THE FULL TEXT: PDF or HTML at Wiley Online Library

Abstract

Cellular information processing in signaling networks forms the basis of responses to environmental stimuli. At any given time, cells receive multiple simultaneous input cues, which are processed and integrated to determine cellular responses such as migration, proliferation, apoptosis, or differentiation. Protein phosphorylation events play a major role in this process and are often involved in fundamental biological and cellular processes such as protein‐protein interactions, enzyme activity, and immune responses. Determining which kinases phosphorylate specific phospho sites poses a challenge; this information is critical when trying to elucidate key proteins involved in specific cellular responses. Here, methods to generate high‐quality quantitative phosphorylation data from cell lysates originating from primary cells, and how to analyze the generated data to construct quantitative signaling network models, are presented. These models can subsequently be used to guide follow‐up in vitro/in vivo validation studies. Curr. Protoc. Immunol. 104:11.11.1‐11.11.23. © 2014 by John Wiley & Sons, Inc.

Keywords: phosphorylation; mass spectrometry; network biology; primary cell signaling

     
 
GO TO THE FULL PROTOCOL:
PDF or HTML at Wiley Online Library

Table of Contents

  • Cell Signaling in Primary Cells
  • Generating Quantitative Phospho‐Proteomics Data Using Mass Spectrometry
  • Basic Protocol 1: Cell Lysis, Protein Digestion, and Dimethyl Labeling
  • Basic Protocol 2: SCX Fractionation
  • Basic Protocol 3: Titanium Dioxide Phosphopeptide Enrichment
  • Basic Protocol 4: Mass Spectrometry Sample Preparation
  • Basic Protocol 5: Analyzing Phosphorylation Data and Constructing Quantitative Network Models
  • Reagents and Solutions
  • Commentary
  • Literature Cited
  • Figures
     
 
GO TO THE FULL PROTOCOL:
PDF or HTML at Wiley Online Library

Materials

Basic Protocol 1: Cell Lysis, Protein Digestion, and Dimethyl Labeling

  Materials
  • Cell line(s) of interest
  • Phosphate‐buffered saline (PBS; Sigma, cat. no. P5368), ice cold
  • Modified RIPA buffer (see recipe), ice cold
  • Acetone, HPLC‐grade (Sigma, cat. no. 650501), −20°C
  • Denaturation buffer (see recipe)
  • Bradford reagent (Sigma, cat. no. B6916)
  • Dithiothreitol (DTT; Sigma, cat. no. 43815)
  • Chloroacetamide (CAA; Sigma, cat. no. 22790)
  • Lysyl endopeptidase (Lys‐C; Wako, cat. no. 129‐02541; 0.5 µg/µl stock solution made up in MilliQ water)
  • Triethyl ammonium bicarbonate (TEAB; Sigma, cat. no. T7408)
  • Trypsin (Sigma, cat. no. T6567; 0.5 µg/µl stock solution made up in 50 mM acetic acid)
  • Trifluoroacetic acid (TFA; Sigma, cat. no. T6508)
  • Acetic acid (Fisher Scientific, cat. no. A35‐500)
  • Dimethyl labeling solution (see recipe)
  • 15‐ or 50‐ml tubes
  • Sonicator
  • Refrigerated centrifuge
  • Axial rotator
  • SepPak C18 columns (Waters, cat. no. WAT020515)
  • 10‐ml syringe (polypropylene)
  • Additional reagents and equipment for Bradford assay (Bradford, )

Basic Protocol 2: SCX Fractionation

  Materials
  • Sample
  • Acetonitrile, HPLC‐grade (Sigma, cat. no. 34851N)
  • SCX buffer A (see recipe)
  • SCX buffer B (see recipe)
  • Loading buffer: 1%TFA and 2% acetonitrile in MS H 2O
  • HPLC/FPLC system (e.g., GE Healthcare AktaMicro)
  • 1‐ml SCX column or equivalent (e.g., Resource S 1ml; GE Healthcare Resources)
  • 2‐ml microcentrifuge tubes

Basic Protocol 3: Titanium Dioxide Phosphopeptide Enrichment

  Materials
  • TiO 2 beads (GL Sciences, cat. no. 5020‐75010)
  • TiO 2 loading solution (see recipe)
  • SCX samples (see protocol 2)
  • SCX buffer B (see recipe)
  • TiO 2 washing solution 1 (see recipe)
  • TiO 2 washing solution 2 (see recipe)
  • Acidification buffer (see recipe)
  • TiO 2 elution buffer 1 (see recipe)
  • TiO 2 elution buffer 2 (see recipe)
  • Automated sample shaker (e.g., Eppendorf Thermomixer)
  • End‐over‐end rotator
  • Centrifuge
  • C8 StageTips (Thermo Fisher, cat. no. SP321)
  • 10‐ml luer‐lock syringes and StageTip adaptor (Millian, cat. no. HAM‐31330)
  • 96‐well PCR plates
  • Vacuum centrifuge with microplate rotor (e.g., Thermo Savant SC250)
  • Litmus paper
  • Vortex

Basic Protocol 4: Mass Spectrometry Sample Preparation

  Materials
  • Methanol, HPLC‐grade (Sigma, cat. no. 34860)
  • Buffer B (see recipe)
  • Sample buffer (see recipe)
  • Samples (see Basic Protocol protocol 11, protocol 22, or protocol 33)
  • Buffer A (see recipe)
  • Loading buffer (see recipe)
  • C18 StageTips (Thermo Fisher, cat. no. SP301)
  • 10‐ml luer‐lock syringes and StageTip adaptors (Millian, cat. no. HAM‐31330)
  • Vacuum centrifuge (e.g., Thermo Savant SC250)
  • Mass spectrometer with nanospray source (e.g., Thermo Fisher Q Exactive or Orbitrap Fusion)

Basic Protocol 5: Analyzing Phosphorylation Data and Constructing Quantitative Network Models

  Materials
  • Desktop computer with Internet access
  • Mass spectrometry spectral matching software (e.g., MaxQuant or Proteome Discoverer/SEQUEST)
  • R statistical software
  • Visual network editing software (e.g., Gephi.org or Cytoscape.org)
GO TO THE FULL PROTOCOL:
PDF or HTML at Wiley Online Library

Figures

Videos

Literature Cited

  Alpert, A.J. 2008. Electrostatic repulsion hydrophilic interaction chromatography for isocratic separation of charged solutes and selective isolation of phosphopeptides. Anal. Chem. 80:62‐76.
  Bakal, C., Linding, R., Llense, F., Heffern, E., Martin‐Blanco, E., Pawson, T., and Perrimon, N. 2008. Phosphorylation networks regulating JNK activity in diverse genetic backgrounds. Science 322:453‐456.
  Bastian, M. and Heymann, S. 2009. Gephi: An open source software for exploring and manipulating network. International AAAI Conference on Weblogs and Social Media. https://gephi.org
  Beausoleil, S.A., Villén, J., Gerber, S.A., Rush, J., and Gygi, S.P. 2006. A probability‐based approach for high‐throughput protein phosphorylation analysis and site localization. Nat. Biotechnol. 24:1285‐1292.
  Bodenmiller, B. and Aebersold, R. 2010. Quantitative analysis of protein phosphorylation on a system‐wide scale by mass spectrometry‐based proteomics. Methods Enzymol. 470:317‐334.
  Boersema, P.J., Raijmakers, R., Lemeer, S., Mohammed, S., and Heck, A.J. 2009. Multiplex peptide stable isotope dimethyl labeling for quantitative proteomics. Nat. Protoc. 4:484‐494.
  Bradford, M.M. 1976. A rapid and sensitive method for the quantitation of microgram quantities of protein utilizing the principle of protein‐dye binding. Anal. Biochem. 72:248‐254.
  Brognard, J. and Hunter, T. 2011. Protein kinase signaling networks in cancer. Curr. Opin. Genet. Dev. 21:4‐11.
  Burckstummer, T., Bennett, K.L., Preradovic, A., Schütze, G., Hantschel, O., Superti‐Furga, G., and Bauch, A. 2006. An efficient tandem affinity purification procedure for interaction proteomics in mammalian cells. Nat. Methods 3:1013‐1019.
  Callister, S.J., Barry, R.C., Adkins, J.N., Johnson, E.T., Qian, W.J., Webb‐Robertson, B.J., Smith, R.D., and Lipton, M.S. 2006. Normalization approaches for removing systematic biases associated with mass spectrometry and label‐free proteomics. J. Proteome Res. 5:277‐286.
  Cannons, J.L. and Schwartzberg, P.L. 2004. Fine‐tuning lymphocyte regulation: What's new with tyrosine kinases and phosphatases? Curr. Opin. Immunol. 16:296‐303.
  Cantley, L.C., Auger, K.R., Carpenter, C., Duckworth, B., Graziani, A., Kapeller, R., and Soltoff, S. 1991. Oncogenes and signal transduction. Cell 64:281‐302.
  Cox, J. and Mann, M. 2008. MaxQuant enables high peptide identification rates, individualized p.p.b.‐range mass accuracies and proteome‐wide protein quantification. Nat. Biotechnol. 26:1367‐1372.
  Cox, J., Neuhauser, N., Michalski, A., Scheltema, R.A., Olsen, J.V., and Mann, M. 2011. Andromeda: A peptide search engine integrated into the MaxQuant environment. J. Proteome Res. 10:1794‐1805.
  Creixell, P., Schoof, E.M., Erler, J.T., and Linding, R. 2012. Navigating cancer network attractors for tumor‐specific therapy. Nat. Biotechnol. 30:842‐848.
  Dinkel, H., Chica, C., Via, A., Gould, C.M., Jensen, L.J., Gibson, T.J., and Diella, F. 2011. Phospho.ELM: A database of phosphorylation sites—Update 2011. Nucleic Acids Res. 39:D261‐D267.
  Dustin, M.L. 2009. The cellular context of T cell signaling. Immunity 30:482‐492.
  Dyson, M.R., Zheng, Y., Zhang, C., Colwill, K., Pershad, K., Kay, B.K., Pawson, T., and McCafferty, J. 2011. Mapping protein interactions by combining antibody affinity maturation and mass spectrometry. Anal. Biochem. 417:25‐35.
  Elias, J.E. and Gygi, S.P. 2007. Target‐decoy search strategy for increased confidence in large‐scale protein identifications by mass spectrometry. Nat. Methods 4:207‐214.
  Engholm‐Keller, K., Birck, P., Størling, J., Pociot, F., Mandrup‐Poulsen, T., and Larsen, M.R. 2012. TiSH—A robust and sensitive global phosphoproteomics strategy employing a combination of TiO(2), SIMAC, and HILIC. J. Proteomics 75:5749‐5761.
  Erler, J.T. and Linding, R. 2010. Network‐based drugs and biomarkers. J. Pathol. 220:290‐296.
  Fedorov, O., Muller, S., and Knapp, S. 2010. The (un)targeted cancer kinome. Nat. Chem. Biol. 6:166‐169.
  Gordon, J.A. 1991. Use of vanadate as protein‐phosphotyrosine phosphatase inhibitor. Methods Enzymol. 201:477‐482.
  Hjerrild, M., Stensballe, A., Rasmussen, T.E., Kofoed, C.B., Blom, N., Sicheritz‐Ponten, T., Larsen, M.R., Brunak, S., Jensen, O.N., and Gammeltoft, S. 2004. Identification of phosphorylation sites in protein kinase A substrates using artificial neural networks and mass spectrometry. J. Proteome Res. 3:426‐433.
  Hornbeck, P.V., Kornhauser, J.M., Tkachev, S., Zhang, B., Skrzypek, E., Murray, B., Latham, V., and Sullivan, M. 2012. PhosphoSitePlus: A comprehensive resource for investigating the structure and function of experimentally determined post‐translational modifications in man and mouse. Nucleic Acids Res. 40:D261‐D270.
  Hörth, P., Miller, C.A., Preckel, T., and Wenz, C. 2006. Efficient fractionation and improved protein identification by peptide OFFGEL electrophoresis. Mol. Cell. Proteomics 5:1968‐1974.
  Isakov, N. and Altman, A. 2002. Protein kinase C(theta) in T cell activation. Annu. Rev. Immunol. 20:761‐794.
  Janes, K.A., Albeck, J.G., Gaudet, S., Sorger, P.K., Lauffenburger, D.A., and Yaffe, M.B. 2005. A systems model of signaling identifies a molecular basis set for cytokine‐induced apoptosis. Science 310:1646‐1653.
  Jensen, K.J. and Janes, K.A. 2012. Modeling the latent dimensions of multivariate signaling datasets. Phys. Biol. 9:045004.
  Jiang, Z.T. and Zuo, Y.M. 2001. Synthesis of porous titania microspheres for HPLC packings by polymerization‐induced colloid aggregation (PICA). Anal. Chem. 73:686‐688.
  Jin, L.L., Tong, J., Prakash, A., Peterman, S.M., St‐Germain, J.R., Taylor, P., Trudel, S., and Moran, M.F. 2010. Measurement of protein phosphorylation stoichiometry by selected reaction monitoring mass spectrometry. J. Proteome Res. 9:2752‐2761.
  Jorgensen, C., Sherman, A., Chen, G.I., Pasculescu, A., Poliakov, A., Hsiung, M., Larsen, B., Wilkinson, D.G., Linding, R., and Pawson, T. 2009. Cell‐specific information processing in segregating populations of Eph receptor ephrin‐expressing cells. Science 326:1502‐1509.
  Kawahara, M., Nakamura, H., and Nakajima, T. 1990. Titania and zirconia: Possible new ceramic microparticulates for high‐performance liquid chromatography. J. Chromatogr. A 515:149‐158.
  Kreeger, P.K., Wang, Y., Haigis, K.M., and Lauffenburger, D.A. 2010. Integration of multiple signaling pathway activities resolves K‐RAS/N‐RAS mutation paradox in colon epithelial cell response to inflammatory cytokine stimulation. Integr. Biol. (Camb.) 2:202‐208.
  Larsen, M.R., Thingholm, T.E., Jensen, O.N., Roepstorff, P., and Jørgensen, T.J. 2005. Highly selective enrichment of phosphorylated peptides from peptide mixtures using titanium dioxide microcolumns. Mol. Cell. Proteomics 4:873‐886.
  Lee, M.J., Ye, A.S., Gardino, A.K., Heijink, A.M., Sorger, P.K., MacBeath, G., and Yaffe, M.B. 2012. Sequential application of anticancer drugs enhances cell death by rewiring apoptotic signaling networks. Cell 149:780‐794.
  Lemmon, M.A. and Schlessinger, J. 2010. Cell signaling by receptor tyrosine kinases. Cell 141:1117‐1134.
  Linding, R. 2010. Multivariate signal integration. Nat. Rev. Mol. Cell. Biol. 11:391.
  Linding, R., Jensen, L.J., Ostheimer, G.J., van Vugt, M.A., Jørgensen, C., Miron, I.M., Diella, F., Colwill, K., Taylor, L., Elder, K., Metalnikov, P., Nguyen, V., Pasculescu, A., Jin, J., Park, J.G., Samson, L.D., Woodgett, J.R., Russell, R.B., Bork, P., Yaffe, M.B., and Pawson, T. 2007. Systematic discovery of in vivo phosphorylation networks. Cell 129:1415‐1426.
  Link, A.J., Eng, J., Schieltz, D.M., Carmack, E., Mize, G.J., Morris, D.R., Garvik, B.M., and Yates, J.R. 3rd. 1999. Direct analysis of protein complexes using mass spectrometry. Nat. Biotechnol. 17:676‐682.
  Manning, G., Whyte, D.B., Martinez, R., Hunter, T., and Sudarsanam, S. 2002. The protein kinase complement of the human genome. Science 298:1912‐1934.
  McNulty, D.E. and Annan, R.S. 2008. Hydrophilic interaction chromatography reduces the complexity of the phosphoproteome and improves global phosphopeptide isolation and detection. Mol. Cell. Proteomics 7:971‐980.
  Michel, P.E., Reymond, F., Arnaud, I.L., Josserand, J., Girault, H.H., and Rossier, J.S. 2003. Protein fractionation in a multicompartment device using off‐gel isoelectric focusing. Electrophoresis 24:3‐11.
  Miller, A.T. and Berg, L.J. 2002. New insights into the regulation and functions of Tec family tyrosine kinases in the immune system. Curr. Opin. Immunol. 14:331‐340.
  Miller, M.L. and Blom, N. 2009. Kinase‐specific prediction of protein phosphorylation sites. Methods Mol. Biol. 527:299‐310.
  Miller, M.L., Jensen, L.J., Diella, F., Jørgensen, C., Tinti, M., Li, L., Hsiung, M., Parker, S.A., Bordeaux, J., Sicheritz‐Ponten, T., Olhovsky, M., Pasculescu, A., Alexander, J., Knapp, S., Blom, N., Bork, P., Li, S., Cesareni, G., Pawson, T., Turk, B.E., Yaffe, M.B., Brunak, S., and Linding, R. 2008. Linear motif atlas for phosphorylation‐dependent signaling. Sci. Signal. 1:ra2.
  Miller‐Jensen, K., Janes, K.A., Brugge, J.S., and Lauffenburger, D.A. 2007. Common effector processing mediates cell‐specific responses to stimuli. Nature 448:604‐608.
  Mohammed, S. and Heck, A. Jr. 2011. Strong cation exchange (SCX) based analytical methods for the targeted analysis of protein post‐translational modifications. Curr. Opin. Biotechnol. 22:9‐16.
  Monetti, M., Nagaraj, N., Sharma, K., and Mann, M. 2011. Large‐scale phosphosite quantification in tissues by a spike‐in SILAC method. Nat. Methods 8:655‐658.
  Monks, C.R., Freiberg, B.A., Kupfer, H., Sciaky, N., and Kupfer, A. 1998. Three‐dimensional segregation of supramolecular activation clusters in T cells. Nature 395:82‐86.
  Munoz, J. and Heck, A.J. 2011. Quantitative proteome and phosphoproteome analysis of human pluripotent stem cells. Methods Mol. Biol. 767:297‐312.
  Obenauer, J.C., Cantley, L.C., and Yaffe, M.B. 2003. Scansite 2.0: Proteome‐wide prediction of cell signaling interactions using short sequence motifs. Nucleic Acids Res. 31:3635‐3641.
  Olsen, J.V. and Macek, B. 2009. High accuracy mass spectrometry in large‐scale analysis of protein phosphorylation. Methods Mol. Biol. 492:131‐142.
  Olsen, J.V., Blagoev, B., Gnad, F., Macek, B., Kumar, C., Mortensen, P., and Mann, M. 2006. Global, in vivo, and site‐specific phosphorylation dynamics in signaling networks. Cell 127:635‐648.
  Ong, S.E., Blagoev, B., Kratchmarova, I., Kristensen, D.B., Steen, H., Pandey, A., and Mann, M. 2002. 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.
  Pandey, A., Podtelejnikov, A.V., Blagoev, B., Bustelo, X.R., Mann, M., and Lodish, H.F. 2000. Analysis of receptor signaling pathways by mass spectrometry: Identification of vav‐2 as a substrate of the epidermal and platelet‐derived growth factor receptors. Proc. Natl. Acad. Sci. U.S.A. 97:179‐184.
  Pawson, T. 1995. Protein modules and signaling networks. Nature 373:573‐580.
  Pawson, T. and Hunter, T. 1994. Signal transduction and growth control in normal and cancer cells. Curr. Opin. Genet. Dev. 4:1‐4.
  Pawson, T. and Kofler, M. 2009. Kinome signaling through regulated protein‐protein interactions in normal and cancer cells. Curr. Opin. Cell. Biol. 21:147‐153.
  Pawson, T. and Linding, R. 2008. Network medicine. FEBS Lett. 582:1266‐1270.
  Pedersen, M.W., Jacobsen, H.J., Koefoed, K., Hey, A., Pyke, C., Haurum, J.S., and Kragh, M. 2010. Sym004: A novel synergistic anti‐epidermal growth factor receptor antibody mixture with superior anticancer efficacy. Cancer Res. 70:588‐597.
  Perkins, D.N., Pappin, D.J., Creasy, D.M., and Cottrell, J.S. 1999. Probability‐based protein identification by searching sequence databases using mass spectrometry data. Electrophoresis 20:3551‐3567.
  Posewitz, M.C. and Tempst, P. 1999. Immobilized gallium(III) affinity chromatography of phosphopeptides. Anal. Chem. 71:2883‐2892.
  Prakash, A., Piening, B., Whiteaker, J., Zhang, H., Shaffer, S.A., Martin, D., Hohmann, L., Cooke, K., Olson, J.M., Hansen, S., Flory, M.R., Lee, H., Watts, J., Goodlett, D.R., Aebersold, R., Paulovich, A., and Schwikowski, B. 2007. Assessing bias in experiment design for large scale mass spectrometry‐based quantitative proteomics. Mol. Cell. Proteomics 6:1741‐1748.
  Rappsilber, J., Mann, M., and Ishihama, Y. 2007. Protocol for micro‐purification, enrichment, pre‐fractionation and storage of peptides for proteomics using StageTips. Nat. Protoc. 2:1896‐1906.
  Readinger, J.A., Mueller, K.L., Venegas, A.M., Horai, R., and Schwartzberg, P.L. 2009. Tec kinases regulate T‐lymphocyte development and function: New insights into the roles of Itk and Rlk/Txk. Immunol. Rev. 228:93‐114.
  Rikova, K., Guo, A., Zeng, Q., Possemato, A., Yu, J., Haack, H., Nardone, J., Lee, K., Reeves, C., Li, Y., Hu, Y., Tan, Z., Stokes, M., Sullivan, L., Mitchell, J., Wetzel, R., Macneill, J., Ren, J.M., Yuan, J., Bakalarski, C.E., Villen, J., Kornhauser, J.M., Smith, B., Li, D., Zhou, X., Gygi, S.P., Gu, T.L., Polakiewicz, R.D., Rush, J., and Comb, M.J. 2007. Global survey of phosphotyrosine signaling identifies oncogenic kinases in lung cancer. Cell 131:1190‐1203.
  Ross, P.L., Huang, Y.N., Marchese, J.N., Williamson, B., Parker, K., Hattan, S., Khainovski, N., Pillai, S., Dey, S., Daniels, S., Purkayastha, S., Juhasz, P., Martin, S., Bartlet‐Jones, M., He, F., Jacobson, A., and Pappin, D.J. 2004. Multiplexed protein quantitation in Saccharomyces cerevisiae using amine‐reactive isobaric tagging reagents. Mol. Cell. Proteomics 3:1154‐1169.
  Saez‐Rodriguez, J., Alexopoulos, L.G., Epperlein, J., Samaga, R., Lauffenburger, D.A., Klamt, S., and Sorger, P.K. 2009. Discrete logic modelling as a means to link protein signaling networks with functional analysis of mammalian signal transduction. Mol. Syst. Biol. 5:331.
  Samelson, L.E., Patel, M.D., Weissman, A.M., Harford, J.B., and Klausner, R.D. 1986. Antigen activation of murine T cells induces tyrosine phosphorylation of a polypeptide associated with the T cell antigen receptor. Cell 46:1083‐1090.
  Schirle, M., Heurtier, M.A., and Kuster, B. 2003. Profiling core proteomes of human cell lines by one‐dimensional PAGE and liquid chromatography‐tandem mass spectrometry. Mol. Cell. Proteomics 2:1297‐1305.
  Seet, B.T., Dikic, I., Zhou, M.M., and Pawson, T. 2006. Reading protein modifications with interaction domains. Nat. Rev. Mol. Cell. Biol. 7:473‐483.
  Shah, K. and Shokat, K.M. 2003. A chemical genetic approach for the identification of direct substrates of protein kinases. Methods Mol. Biol. 233:253‐271.
  Shannon, P., Markiel, A., Ozier, O., Baliga, N.S., Wang, J.T., Ramage, D., Amin, N., Schwikowski, B., and Ideker, T. 2003. Cytoscape: A software environment for integrated models of biomolecular interaction networks. Genome Res. 13:2498‐2504.
  Shawver, L.K., Slamon, D., and Ullrich, A. 2002. Smart drugs: Tyrosine kinase inhibitors in cancer therapy. Cancer Cell 1:117‐123.
  Szklarczyk, D., Franceschini, A., Kuhn, M., Simonovic, M., Roth, A., Minguez, P., Doerks, T., Stark, M., Muller, J., Bork, P., Jensen, L.J., and von Mering, C. 2011. The STRING database in 2011: Functional interaction networks of proteins, globally integrated and scored. Nucleic Acids Res. 39:D561‐D568.
  Tan, C.S., Bodenmiller, B., Pasculescu, A., Jovanovic, M., Hengartner, M.O., Jørgensen, C., Bader, G.D., Aebersold, R., Pawson, T., and Linding, R. 2009. Comparative analysis reveals conserved protein phosphorylation networks implicated in multiple diseases. Sci. Signal. 2:ra39.
  Tani, K. and Suzuki, Y. 1994. Syntheses of spherical silica and titania from alkoxides on a laboratory scale. Chromatographia 38:291‐294.
  Tanl, K., Sumizawa, T., Watanabe, M., Tachibana, M. Koizumi, H., and Kiba, T. 2002. Evaluation of titania as an ion‐exchanger and as a ligand‐exchanger in HPLC. Chromatographia 55:33‐37.
  Taus, T., Köcher, T., Pichler, P., Paschke, C., Schmidt, A., Henrich, C., and Mechtler, K. 2011. Universal and confident phosphorylation site localization using phosphoRS. J. Proteome Res. 10:5354‐5362.
  Thingholm, T.E., Jørgensen, T.J., Jensen, O.N., and Larsen, M.R. 2006. Highly selective enrichment of phosphorylated peptides using titanium dioxide. Nat. Protoc. 1:1929‐1935.
  Thompson, A., Schäfer, J., Kuhn, K., Kienle, S., Schwarz, J., Schmidt, G., Neumann, T., Johnstone, R., Mohammed, A.K., and Hamon, C. 2003. Tandem mass tags: A novel quantification strategy for comparative analysis of complex protein mixtures by MS/MS. Anal. Chem. 75:1895‐1904.
  Van Hoof, D., Muñoz, J., Braam, S.R., Pinkse, M.W., Linding, R., Heck, A.J., Mummery, C.L., and Krijgsveld, J. 2009. Phosphorylation dynamics during early differentiation of human embryonic stem cells. Cell Stem Cell 5:214‐226.
  Wong, Y.H., Lee, T.Y., Liang, H.K., Huang, C.M., Wang, T.Y., Yang, Y.H., Chu, C.H., Huang, H.D., Ko, M.T., and Hwang, J.K. 2007. KinasePhos 2.0: A web server for identifying protein kinase‐specific phosphorylation sites based on sequences and coupling patterns. Nucleic Acids Res. 35:W588‐W594.
  Wu, R., Dephoure, N., Haas, W., Huttlin, E.L., Zhai, B., Sowa, M.E., and Gygi, S.P. 2011a. Correct interpretation of comprehensive phosphorylation dynamics requires normalization by protein expression changes. Mol. Cell. Proteomics 10:M111.009654.
  Wu, R., Haas, W., Dephoure, N., Huttlin, E.L., Zhai, B., Sowa, M.E., and Gygi, S.P. 2011b. A large‐scale method to measure absolute protein phosphorylation stoichiometries. Nat. Methods 8:677‐683.
  Xue, Y., Ren, J., Gao, X., Jin, C., Wen, L., and Yao, X. 2008. GPS 2.0, a tool to predict kinase‐specific phosphorylation sites in hierarchy. Mol. Cell. Proteomics 7:1598‐1608.
  Zhou, H., Ye, M., Dong, J., Corradini, E., Cristobal, A., Heck, A.J.R., Zou, H., and Mohammed, S. 2013. Robust phosphoproteome enrichment using monodisperse microsphere‐based immobilized titanium (IV) ion affinity chromatography. Nat. Protoc. 8:461‐480.
Internet Resources
  http://www.kinomexplorer.info
  Internet portal to access the integrated NetPhorest and NetworKIN frameworks.
  http://www.networkin.info
  Internet portal to access the original NetworKIN framework.
  http://www.netphorest.info
  Internet portal to access the original NetPhorest framework.
GO TO THE FULL PROTOCOL:
PDF or HTML at Wiley Online Library