Use of In Situ Proximity Ligation Assays for Systems Analysis of Signaling Pathways

Tzu‐Chi Chen1, Chi‐Ying F. Huang1

1 Institute of Biopharmaceutical Sciences, National Yang‐Ming University, Taipei, Taiwan
Publication Name:  Current Protocols in Cell Biology
Unit Number:  Unit 17.18
DOI:  10.1002/cpcb.1
Online Posting Date:  June, 2016
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Understanding signaling pathway networks via protein‐protein interactions (PPIs) at the cellular level is a significant task that has not yet been completed. Here, a systems approach that computationally infers interlinked pathways from numerous PPIs is described. The endogenous PPIs can be empirically detected using an in situ proximity ligation assay (PLA), which detects and visualizes endogenous PPIs and post‐translational modifications of proteins with a high sensitivity and specificity. This unit includes two parts: (1) conversion of gene lists into PPIs for investigation and (2) large‐scale detection and analysis of endogenous PPIs for elucidating pathway networks. © 2016 by John Wiley & Sons, Inc.

Keywords: in situ proximity ligation assay (PLA); protein‐protein interaction (PPI); signaling pathway

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Table of Contents

  • Introduction
  • Basic Protocol 1: Generate Lists of PPIs Within Signaling Pathway Networks
  • Basic Protocol 2: Detection of PPIs via In Situ Proximity Ligation Assay (PLA)
  • Reagents and Solutions
  • Commentary
  • Literature Cited
  • Figures
  • Tables
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Basic Protocol 1: Generate Lists of PPIs Within Signaling Pathway Networks

  • Computer with access to Internet

Basic Protocol 2: Detection of PPIs via In Situ Proximity Ligation Assay (PLA)

  • Cultured cells
  • Phosphate‐buffered saline (PBS)
  • 3% paraformaldehyde (PFA) in PBS
  • 0.2% Triton X‐100 in PBS
  • Duolink In Situ (Sigma‐Aldrich) containing:
  • Blocking solution
  • Antibody diluent
  • PLA probe (5×) stock: secondary antibody conjugates with a PLA oligonucleotide
  • Ligation buffer: contains oligonucleotides that hybridize to the PLA probes and all components, needed for ligation except the ligase
  • Ligase (1 U/μl)
  • Amplification (5×) buffer: contains all components needed for Rolling Circle Amplification except the polymerase (oligonucleotide probes labeled with a fluorophore that hybridizes to the RCA product are also included)
  • Polymerase (10 U/μl)
  • Pair of primary antibody for a given PPI (from different hosts)
  • Wash buffer (see recipe)
  • Wash buffer plus (see recipe)
  • Mounting medium with DAPI
  • Clear nail polish
  • 8‐well chamber slides (Thermo Fisher Scientific) and coverslips (Thermo Fisher Scientific)
  • 37°C incubator
  • Humidity chamber, e.g., a box with wet paper towels at the bottom
  • Staining jars
  • Shaker
  • Fluorescent or confocal microscope
  • Image analysis software, e.g., Blob Finder V3.2 (Allalou and Wahlby, ) or Cellprofiler (Carpenter et al., )
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Literature Cited

Literature Cited
  Allalou, A. and Wahlby, C. 2009. BlobFinder, a tool for fluorescence microscopy image cytometry. Comput. Methods Programs Biomed. 94:58‐65. doi: 10.1016/j.cmpb.2008.08.006.
  Barabasi, A.‐L., Gulbahce, N., and Loscalzo, J. 2011. Network medicine: A network‐based approach to human disease. Nat. Rev. Genet. 12:56‐68. doi: 10.1038/nrg2918.
  Carpenter, A.E., Jones, T.R., Lamprecht, M.R., Clarke, C., Kang, I.H., Friman, O., Guertin, D.A., Chang, J.H., Lindquist, R.A., Moffat, J., Golland, P., and Sabatini, D.M. 2006. CellProfiler: Image analysis software for identifying and quantifying cell phenotypes. Genome Biol. 7:R100. doi: 10.1186/gb‐2006‐7‐10‐r100.
  Chen, T.C., Lin, K.T., Chen, C.H., Lee, S.A., Lee, P.Y., Liu, Y.W., Kuo, Y.L., Wang, F.S., Lai, J.M., and Huang, C.Y. 2014. Using an in situ proximity ligation assay to systematically profile endogenous protein‐protein interactions in a pathway network. J. Proteome Res. 13:5339‐5346. doi: 10.1021/pr5002737.
  Croft, D. 2013. Building models using reactome pathways as templates. Methods Mol. Biol. 1021:273‐283. doi: 10.1007/978‐1‐62703‐450‐0_14.
  Fazekas, D., Koltai, M., Turei, D., Modos, D., Palfy, M., Dul, Z., Zsakai, L., Szalay‐Beko, M., Lenti, K., Farkas, I.J., Vellai, T., Csermely, P., and Korcsmaros, T. 2013. SignaLink 2—A signaling pathway resource with multi‐layered regulatory networks. BMC Syst. Biol. 7:7. doi: 10.1186/1752‐0509‐7‐7.
  Frolkis, A., Knox, C., Lim, E., Jewison, T., Law, V., Hau, D.D., Liu, P., Gautam, B., Ly, S., Guo, A.C., Xia, J., Liang, Y., Shrivastava, S., and Wishart, D.S. 2010. SMPDB: The small molecule pathway database. Nucleic Acids Res. 38:D480‐487. doi: 10.1093/nar/gkp1002.
  Jarvius, M., Paulsson, J., Weibrecht, I., Leuchowius, K.‐J., Andersson, A.‐C., Wählby, C., Gullberg, M., Botling, J., Sjöblom, T., Markova, B., Östman, A., Landegren, U., and Söderberg, O. 2007. In situ detection of phosphorylated platelet‐derived growth factor receptor β using a generalized proximity ligation method. Mol. Cell Proteomics 6:1500‐1509. doi: 10.1074/mcp.M700166‐MCP200.
  Joshi‐Tope, G., Vastrik, I., Gopinath, G.R., Matthews, L., Schmidt, E., Gillespie, M., D'Eustachio, P., Jassal, B., Lewis, S., Wu, G., Birney, E., and Stein, L. 2003. The genome knowledgebase: A resource for biologists and bioinformaticists. Cold Spring Harb. Symp. Quant. Biol. 68:237‐243. doi: 10.1101/sqb.2003.68.237.
  Kandasamy, K., Mohan, S.S., Raju, R., Keerthikumar, S., Kumar, G.S., Venugopal, A.K., Telikicherla, D., Navarro, J.D., Mathivanan, S., Pecquet, C., Gollapudi, S.K., Tattikota, S.G., Mohan, S., Padhukasahasram, H., Subbannayya, Y., Goel, R., Jacob, H.K., Zhong, J., Sekhar, R., Nanjappa, V., Balakrishnan, L., Subbaiah, R., Ramachandra, Y.L., Rahiman, B.A., Prasad, T.S., Lin, J.X., Houtman, J.C., Desiderio, S., Renauld, J.C., Constantinescu, S.N., Ohara, O., Hirano, T., Kubo, M., Singh, S., Khatri, P., Draghici, S., Bader, G.D., Sander, C., Leonard, W.J., and Pandey, A. 2010. NetPath: A public resource of curated signal transduction pathways. Genome Biol. 11:R3. doi: 10.1186/gb‐2010‐11‐1‐r3.
  Kanehisa, M., Goto, S., Sato, Y., Kawashima, M., Furumichi, M., and Tanabe, M. 2014. Data, information, knowledge and principle: Back to metabolism in KEGG. Nucleic Acids Res. 42:D199‐205. doi: 10.1093/nar/gkt1076.
  Kelder, T., van Iersel, M.P., Hanspers, K., Kutmon, M., Conklin, B.R., Evelo, C.T., and Pico, A.R. 2012. WikiPathways: Building research communities on biological pathways. Nucleic Acids Res. 40:D1301‐1307. doi: 10.1093/nar/gkr1074.
  Khatri, P., Sirota, M., and Butte, A.J. 2012. Ten years of pathway analysis: Current approaches and outstanding challenges. PLoS Comput. Biol. 8:e1002375. doi: 10.1371/journal.pcbi.1002375.
  Lee, S.A., Chan, C.H., Chen, T.C., Yang, C.Y., Huang, K.C., Tsai, C.H., Lai, J.M., Wang, F.S., Kao, C.Y., and Huang, C.Y. 2009. POINeT: Protein interactome with sub‐network analysis and hub prioritization. BMC Bioinformatics 10:114. doi: 10.1186/1471‐2105‐10‐114.
  Liu, C.H., Chen, T.C., Chau, G.Y., Jan, Y.H., Chen, C.H., Hsu, C.N., Lin, K.T., Juang, Y.L., Lu, P.J., Cheng, H.C., Chen, M.H., Chang, C.F., Ting, Y.S., Kao, C.Y., Hsiao, M., and Huang, C.Y. 2013. An analysis of protein‐protein interactions in cross‐talk pathways reveals CRKL as a novel prognostic marker in hepatocellular carcinoma. Mol. Cell Proteomics 12:1335‐1349. doi: 10.1074/mcp.O112.020404.
  Romero, P., Wagg, J., Green, M.L., Kaiser, D., Krummenacker, M., and Karp, P.D. 2005. Computational prediction of human metabolic pathways from the complete human genome. Genome Biol. 6:R2. doi: 10.1186/gb‐2004‐6‐1‐r2.
  Schaefer, C.F., Anthony, K., Krupa, S., Buchoff, J., Day, M., Hannay, T., and Buetow, K.H. 2009. PID: The pathway interaction database. Nucleic Acids Res. 37:D674‐679. doi: 10.1093/nar/gkn653.
  Shankavaram, U.T., Reinhold, W.C., Nishizuka, S., Major, S., Morita, D., Chary, K.K., Reimers, M.A., Scherf, U., Kahn, A., Dolginow, D., Cossman, J., Kaldjian, E.P., Scudiero, D.A., Petricoin, E., Liotta, L., Lee, J.K., and Weinstein, J.N. 2007. Transcript and protein expression profiles of the NCI‐60 cancer cell panel: An integromic microarray study. Mol. Cancer Ther. 6:820‐832. doi: 10.1158/1535‐7163.MCT‐06‐0650.
  Söderberg, O., Gullberg, M., Jarvius, M., Ridderstråle, K., Leuchowius, K.J., Jarvius, J., Wester, K., Hydbring, P., Bahram, F., and Larsson, L.G. 2006. Direct observation of individual endogenous protein complexes in situ by proximity ligation. Nat. Methods 3:995‐1000. doi: 10.1038/nmeth947.
  Su, A.I., Wiltshire, T., Batalov, S., Lapp, H., Ching, K.A., Block, D., Zhang, J., Soden, R., Hayakawa, M., Kreiman, G., Cooke, M.P., Walker, J.R., and Hogenesch, J.B. 2004. A gene atlas of the mouse and human protein‐encoding transcriptomes. Proc. Natl. Acad. Sci. U.S.A. 101:6062‐6067. doi: 10.1073/pnas.0400782101.
  Weibrecht, I., Leuchowius, K.J., Clausson, C.M., Conze, T., Jarvius, M., Howell, W.M., Kamali‐Moghaddam, M., and Soderberg, O. 2010. Proximity ligation assays: A recent addition to the proteomics toolbox. Exp. Rev. Proteomics 7:401‐409. doi: 10.1586/epr.10.10.
  Whirl‐Carrillo, M., McDonagh, E.M., Hebert, J.M., Gong, L., Sangkuhl, K., Thorn, C.F., Altman, R.B., and Klein, T.E. 2012. Pharmacogenomics knowledge for personalized medicine. Clin. Pharmacol. Ther. 92:414‐417. doi: 10.1038/clpt.2012.96.
  Yamamoto, S., Sakai, N., Nakamura, H., Fukagawa, H., Fukuda, K., and Takagi, T. 2011. INOH: Ontology‐based highly structured database of signal transduction pathways. Database (Oxford) 2011:bar052.
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