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Proteomic Analysis of Secreted Proteins from Cell Microenvironment

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Plant Protein Secretion

Part of the book series: Methods in Molecular Biology ((MIMB,volume 1662))

Abstract

Cell microenvironment consists of various types of cells which communicate with each other by vast number of secreted proteins. An unbiased profiling of these secreted proteins on a global scale is often critical for understanding the intercellular signaling in an autocrine or paracrine manner. Mass spectrometry-based proteomics has become one of the most popular technology for characterization of the secreted proteins. In this chapter, we discuss the standard workflow for secreted proteins characterization, including harvesting secreted proteins from conditioned media, digesting the obtained proteins, liquid chromatography–mass spectrometry analysis, and downstream data analysis.

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References

  1. Zeng X, Yang P, Chen B et al (2013) Quantitative secretome analysis reveals the interactions between epithelia and tumor cells by in vitro modulating colon cancer microenvironment. J Proteome 89:51–70. doi:10.1016/j.jprot.2013.05.032

    Article  CAS  Google Scholar 

  2. Petersen TN, Brunak S, von Heijne G, Nielsen H (2011) SignalP 4.0: discriminating signal peptides from transmembrane regions. Nat Methods 8:785–786. doi:10.1038/nmeth.1701

    Article  CAS  PubMed  Google Scholar 

  3. Käll L, Krogh A, Sonnhammer ELL (2007) Advantages of combined transmembrane topology and signal peptide prediction--the Phobius web server. Nucleic Acids Res 35:W429–W432. doi:10.1093/nar/gkm256

    Article  PubMed  PubMed Central  Google Scholar 

  4. Mukherjee P, Mani S (2013) Methodologies to decipher the cell secretome. Biochim Biophys Acta 1834:2226–2232. doi:10.1016/j.bbapap.2013.01.022

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Bonnet M, Tournayre J, Cassar-Malek I (2016) Integrated data mining of transcriptomic and proteomic datasets to predict the secretome of adipose tissue and muscle in ruminants. Mol BioSyst 12:2722–2734. doi:10.1039/C6MB00224B

    Article  CAS  PubMed  Google Scholar 

  6. Ramilowski JA, Goldberg T, Harshbarger J et al (2015) A draft network of ligand-receptor-mediated multicellular signalling in human. Nat Commun 6:7866. doi:10.1038/ncomms8866

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Tian R (2014) Exploring intercellular signaling by proteomic approaches. Proteomics 14:498–512. doi:10.1002/pmic.201300259

    Article  CAS  PubMed  Google Scholar 

  8. Brown KJ, Formolo CA, Seol H et al (2012) Advances in the proteomic investigation of the cell secretome. Expert Rev Proteomics 9:337–345. doi:10.1586/epr.12.21

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Weng Y, Sui Z, Shan Y et al (2016) In-depth proteomic quantification of cell Secretome in serum-containing conditioned medium. Anal Chem 88:4971–4978. doi:10.1021/acs.analchem.6b00910

    Article  CAS  PubMed  Google Scholar 

  10. Eichelbaum K, Winter M, Diaz MB et al (2012) Selective enrichment of newly synthesized proteins for quantitative secretome analysis. Nat Biotechnol 30:984–990. doi:10.1038/nbt.2356

    Article  CAS  PubMed  Google Scholar 

  11. Liu P, Weng Y, Sui Z et al (2016) Quantitative secretomic analysis of pancreatic cancer cells in serum-containing conditioned medium. Sci Rep 6:37606. doi:10.1038/srep37606

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Meissner F, Scheltema RA, Mollenkopf H-J, Mann M (2013) Direct proteomic quantification of the secretome of activated immune cells. Science 340:475–478. doi:10.1126/science.1232578

    Article  CAS  PubMed  Google Scholar 

  13. Obenauf AC, Zou Y, Ji AL et al (2015) Therapy-induced tumour secretomes promote resistance and tumour progression. Nature 520:368–372. doi:10.1038/nature14336

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Roca-Rivada A, Belen Bravo S, Pérez-Sotelo D et al (2015) CILAIR-based Secretome analysis of obese visceral and subcutaneous adipose tissues reveals distinctive ECM remodeling and inflammation mediators. Sci Rep 5:12214. doi:10.1038/srep12214

    Article  PubMed  PubMed Central  Google Scholar 

  15. Cox TR, Rumney RMH, Schoof EM et al (2015) The hypoxic cancer secretome induces pre-metastatic bone lesions through lysyl oxidase. Nature 522:106–110. doi:10.1038/nature14492

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Blanco MA, LeRoy G, Khan Z et al (2012) Global secretome analysis identifies novel mediators of bone metastasis. Cell Res 22:1339–1355. doi:10.1038/cr.2012.89

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Jin L, Zhang Y, Li H et al (2012) Differential secretome analysis reveals CST6 as a suppressor of breast cancer bone metastasis. Cell Res 22:1356–1373. doi:10.1038/cr.2012.90

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Kaur A, Webster MR, Marchbank K et al (2016) sFRP2 in the aged microenvironment drives melanoma metastasis and therapy resistance. Nature 532:250–254. doi:10.1038/nature17392

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Ranganath SH, Levy O, Inamdar MS, Karp JM (2012) Harnessing the Mesenchymal stem cell Secretome for the treatment of cardiovascular disease. Cell Stem Cell 10:244–258. doi:10.1016/j.stem.2012.02.005

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Bárcena C, Stefanovic M, Tutusaus A et al (2015) Angiogenin secretion from Hepatoma cells activates hepatic stellate cells to amplify a self-sustained cycle promoting liver cancer. Sci Rep 5:7916. doi:10.1038/srep07916

    Article  PubMed  PubMed Central  Google Scholar 

  21. Eng JK, McCormack AL, Yates JR (1994) An approach to correlate tandem mass spectral data of peptides with amino acid sequences in a protein database. J Am Soc Mass Spectrom 5:976–989. doi:10.1016/1044-0305(94)80016-2

    Article  CAS  PubMed  Google Scholar 

  22. Käll L, Canterbury JD, Weston J et al (2007) Semi-supervised learning for peptide identification from shotgun proteomics datasets. Nat Methods 4:923–925. doi:10.1038/nmeth1113

    Article  PubMed  Google Scholar 

  23. Bendtsen JD, Jensen LJ, Blom N et al (2004) Feature-based prediction of non-classical and leaderless protein secretion. Protein Eng Des Sel 17:349–356. doi:10.1093/protein/gzh037

    Article  CAS  PubMed  Google Scholar 

  24. Gene Ontology Consortium TGO (2015) Gene ontology consortium: going forward. Nucleic Acids Res 43:D1049–D1056. doi:10.1093/nar/gku1179

    Article  Google Scholar 

  25. Eden E, Navon R, Steinfeld I et al (2009) GOrilla: a tool for discovery and visualization of enriched GO terms in ranked gene lists. BMC Bioinformatics 10:48. doi:10.1186/1471-2105-10-48

    Article  PubMed  PubMed Central  Google Scholar 

  26. Mi H, Poudel S, Muruganujan A et al (2016) PANTHER version 10: expanded protein families and functions, and analysis tools. Nucleic Acids Res 44:D336–D342. doi:10.1093/nar/gkv1194

    Article  CAS  PubMed  Google Scholar 

  27. Szklarczyk D, Franceschini A, Wyder S et al (2015) STRING v10: protein-protein interaction networks, integrated over the tree of life. Nucleic Acids Res 43:D447–D452. doi:10.1093/nar/gku1003

    Article  CAS  PubMed  Google Scholar 

  28. Orchard S, Ammari M, Aranda B et al (2014) The MIntAct project—IntAct as a common curation platform for 11 molecular interaction databases. Nucleic Acids Res 42:D358–D363. doi:10.1093/nar/gkt1115

    Article  CAS  PubMed  Google Scholar 

  29. Keshava Prasad TS, Goel R, Kandasamy K et al (2009) Human protein reference database--2009 update. Nucleic Acids Res 37:D767–D772. doi:10.1093/nar/gkn892

    Article  CAS  PubMed  Google Scholar 

  30. Stark C, Breitkreutz B-J, Chatr-Aryamontri A et al (2011) The BioGRID interaction database: 2011 update. Nucleic Acids Res 39:D698–D704. doi:10.1093/nar/gkq1116

    Article  CAS  PubMed  Google Scholar 

  31. Fabregat A, Sidiropoulos K, Garapati P et al (2016) The Reactome pathway knowledgebase. Nucleic Acids Res 44:D481–D487. doi:10.1093/nar/gkv1351

    Article  CAS  PubMed  Google Scholar 

  32. Krämer A, Green J, Pollard J, Tugendreich S (2014) Causal analysis approaches in ingenuity pathway analysis. Bioinformatics 30:523–530. doi:10.1093/bioinformatics/btt703

    Article  PubMed  Google Scholar 

  33. Tyanova S, Temu T, Carlson A et al (2015) Visualization of LC-MS/MS proteomics data in MaxQuant. Proteomics 15:1453–1456. doi:10.1002/pmic.201400449

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Gatto L, Breckels LM, Naake T, Gibb S (2015) Visualization of proteomics data using R and Bioconductor. Proteomics 15:1375–1389. doi:10.1002/pmic.201400392

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Wang R, Fabregat A, Ríos D et al (2012) PRIDE inspector: a tool to visualize and validate MS proteomics data. Nat Biotechnol 30:135–137. doi:10.1038/nbt.2112

    Article  PubMed  PubMed Central  Google Scholar 

  36. Chen T, Zhao J, Ma J, Zhu Y (2015) Web resources for mass spectrometry-based proteomics. Genomics Proteomics Bioinformatics 13:36–39. doi:10.1016/j.gpb.2015.01.004

    Article  PubMed  PubMed Central  Google Scholar 

  37. Tyanova S, Temu T, Sinitcyn P et al (2016) The Perseus computational platform for comprehensive analysis of (prote)omics data. Nat Methods 13:731–740. doi:10.1038/nmeth.3901

    Article  CAS  PubMed  Google Scholar 

  38. Chen W, Wang S, Adhikari S et al (2016) Simple and integrated Spintip-based technology applied for deep proteome profiling. Anal Chem 88:4864–4871. doi:10.1021/acs.analchem.6b00631

    Article  CAS  PubMed  Google Scholar 

  39. Rappsilber J, Mann M, Ishihama Y (2007) Protocol for micro-purification, enrichment, pre-fractionation and storage of peptides for proteomics using StageTips. Nat Protoc 2:1896–1906. doi:10.1038/nprot.2007.261

    Article  CAS  PubMed  Google Scholar 

  40. Yanbao Y, Madeline S, Rembert P (2014 A spinnable and automatable StageTip for high throughput peptide desalting and proteomics: protocol exchange. http://www.nature.com/protocolexchange/protocols/3421#/references. Accessed 28 Nov 2016

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Acknowledgments

This study was supported by grants from the Ministry of Science and Technology of China (2016YFA0501403), National Natural Science Foundation of China (No. 21575057), and the Shenzhen Innovation of Science and Technology Commission (JCYJ20150901153557178 and JSGG20160301103415523).

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Authors and Affiliations

  1. Department of Chemistry, Southern University of Science and Technology, 1088 Xueyuan Rd., Nanshan District, Guangdong, Shenzhen, 518055, People’s Republic of China

    Subash Adhikari, Lan Chen, Peiwu Huang & Ruijun Tian

  2. Shenzhen Key Laboratory of Cell Microenvironment, Southern University of Science and Technology, Shenzhen, 518055, People’s Republic of China

    Ruijun Tian

Authors
  1. Subash Adhikari
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  2. Lan Chen
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  3. Peiwu Huang
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  4. Ruijun Tian
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Corresponding author

Correspondence to Ruijun Tian .

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Editors and Affiliations

  1. School of Life Sciences, The Chinese University of Hong Kong, Shatin, Hong Kong, China

    Liwen Jiang

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Adhikari, S., Chen, L., Huang, P., Tian, R. (2017). Proteomic Analysis of Secreted Proteins from Cell Microenvironment. In: Jiang, L. (eds) Plant Protein Secretion. Methods in Molecular Biology, vol 1662. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-7262-3_4

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  • DOI: https://doi.org/10.1007/978-1-4939-7262-3_4

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  • Publisher Name: Humana Press, New York, NY

  • Print ISBN: 978-1-4939-7261-6

  • Online ISBN: 978-1-4939-7262-3

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