Proteomic analysis of mouse astrocytes and their secretome by a combination of FASP and StageTip-based, high pH, reversed-phase fractionation

Proteomics

Volumn 14, Issue 13-14, 2014, Pages 1604-1609

  Han, Dohyun ^a   Jin, Jonghwa ^a   Woo, Jongmin ^a   Min, Hophil ^a   Kim, Youngsoo ^a  

^a Seoul National University College of Medicine   (South Korea)

Author keywords

Astrocyte; High pH fractionation; LC MS MS; Proteome profile; Secretome; Technology

Indexed keywords

DNA BINDING PROTEIN; HERMES ANTIGEN; LACTADHERIN; OSTEONECTIN; PROTEOME; SECRETOME; TENASCIN; THROMBOSPONDIN; UNCLASSIFIED DRUG;

ANIMAL CELL; ARTICLE; ASTROCYTE; ASTROCYTE CULTURE; BIOINFORMATICS; CELL LYSATE; CONTROLLED STUDY; FILTER; FRACTIONATION; LIQUID CHROMATOGRAPHY; MOUSE; NONHUMAN; PH; PRIORITY JOURNAL; PROTEIN SECRETION; PROTEOMICS; SIGNAL TRANSDUCTION; TANDEM MASS SPECTROMETRY; WHOLE CELL; ANIMAL; CELL LINE; CHEMISTRY; METABOLISM; PROCEDURES; REVERSED PHASE LIQUID CHROMATOGRAPHY;

ANIMALS; ASTROCYTES; CELL LINE; CHROMATOGRAPHY, REVERSE-PHASE; HYDROGEN-ION CONCENTRATION; MICE; PROTEOME; PROTEOMICS; TANDEM MASS SPECTROMETRY;

EID: 84903995739     PISSN: 16159853     EISSN: 16159861     Source Type: Journal    
DOI: 10.1002/pmic.201300495     Document Type: Article

References (23)

1. Sofroniew, M. V., Vinters, H. V., Astrocytes: biology and pathology. Acta Neuropathol. 2010, 119, 7-35.
2. Nedergaard, M., Ransom, B., Goldman, S. A., New roles for astrocytes: redefining the functional architecture of the brain. Trends Neurosci. 2003, 26, 523-530.
3. Molofsky, A. V., Krencik, R., Ullian, E. M., Tsai, H. H. et al., Astrocytes and disease: a neurodevelopmental perspective. Genes Dev. 2012, 26, 891-907.
4. Skorupa, A., Urbach, S., Vigy, O., King, M. A. et al., Angiogenin induces modifications in the astrocyte secretome: relevance to amyotrophic lateral sclerosis. J. Proteomics 2013, 91C, 274-285.
5. Yin, P., Knolhoff, A. M., Rosenberg, H. J., Millet, L. J. et al., Peptidomic analyses of mouse astrocytic cell lines and rat primary cultured astrocytes. J. Proteome Res. 2012, 11, 3965-3973.
6. Dowell, J. A., Johnson, J. A., Li, L., Identification of astrocyte secreted proteins with a combination of shotgun proteomics and bioinformatics. J. Proteome Res. 2009, 8, 4135-4143.
7. Greco, T. M., Seeholzer, S. H., Mak, A., Spruce, L., Ischiropoulos, H., Quantitative mass spectrometry-based proteomics reveals the dynamic range of primary mouse astrocyte protein secretion. J. Proteome Res. 2010, 9, 2764-2774.
8. Cahoy, J. D., Emery, B., Kaushal, A., Foo, L. C. et al., A transcriptome database for astrocytes, neurons, and oligodendrocytes: a new resource for understanding brain development and function. Journal Neurosci. 2008, 28, 264-278.
9. Wisniewski, J. R., Mann, M., Consecutive proteolytic digestion in an enzyme reactor increases depth of proteomic and phosphoproteomic analysis. Anal. Chem. 2012, 84, 2631-2637.
10. Han, D., Moon, S., Kim, Y., Min, H., Kim, Y., Characterization of the membrane proteome and N-glycoproteome in BV-2 mouse microglia by liquid chromatography-tandem mass spectrometry. BMC Genomics 2014, 15, 95.
11. Han, D., Moon, S., Kim, Y., Kim, J. et al., In-depth proteomic analysis of mouse microglia using a combination of FASP and StageTip-based, high pH, reversed-phase fractionation. Proteomics 2013, 13, 2984-2988.
12. Han, D., Moon, S., Kim, Y., Ho, W. K. et al., Comprehensive phosphoproteome analysis of INS-1 pancreatic beta-cells using various digestion strategies coupled with liquid chromatography-tandem mass spectrometry. J. Proteome Res. 2012, 11, 2206-2223.
13. Cox, J., Mann, M., MaxQuant enables high peptide identification rates, individualized p.p.b.-range mass accuracies and proteome-wide protein quantification. Nat. Biotechnol. 2008, 26, 1367-1372.
14. Cox, J., Neuhauser, N., Michalski, A., Scheltema, R. A. et al., Andromeda: a peptide search engine integrated into the MaxQuant environment. J. Proteome Res. 2011, 10, 1794-1805.
15. Wisniewski, J. R., Ostasiewicz, P., Dus, K., Zielinska, D. F. et al., Extensive quantitative remodeling of the proteome between normal colon tissue and adenocarcinoma. Mol. Syst. Biol. 2012, 8, 611.
16. Vizcaino, J. A., Cote, R. G., Csordas, A., Dianes, J. A. et al., The PRoteomics IDEntifications (PRIDE) database and associated tools: status in 2013. Nucleic Acids Res. 2013, 41, D1063-D1069.
17. Liu, Y., Han, S. S., Wu, Y., Tuohy, T. M. et al., CD44 expression identifies astrocyte-restricted precursor cells. Dev. Biol. 2004, 276, 31-46.
18. Sheng, J., Yang, S., Xu, L., Wu, C. et al., Bystin as a novel marker for reactive astrocytes in the adult rat brain following injury. Eur. J. Neurosci. 2004, 20, 873-884.
19. Bao, Y., Qin, L., Kim, E., Bhosle, S. et al., CD36 is involved in astrocyte activation and astroglial scar formation. J. Cereb. Blood Flow Metabolism 2012, 32, 1567-1577.
20. Lee, Y. S., Kang, J. W., Lee, Y. H., Kim, D. W., ID4 mediates proliferation of astrocytes after excitotoxic damage in the mouse hippocampus. Anat. Cell Biol. 2011, 44, 128-134.
21. Loei, H., Tan, H. T., Lim, T. K., Lim, K. H. et al., Mining the gastric cancer secretome: identification of GRN as a potential diagnostic marker for early gastric cancer. J. Proteome Res. 2012, 11, 1759-1772.
22. Zhang, Z., Chen, G., Zhou, W., Song, A. et al., Regulated ATP release from astrocytes through lysosome exocytosis. Nat. Cell Biol. 2007, 9, 945-953.
23. Tessier-Lavigne, M., Goodman, C. S., The molecular biology of axon guidance. Science 1996, 274, 1123-1133.