Characterization of a SILAC method for proteomic analysis of primary rat microglia. Issue 9 (13th April 2016)

• Record Type: Journal Article
• Authors: Zhang, Ping; Culver‐Cochran, Ashley E.; Stevens, Stanley M.; Liu, Bin
• Is Part Of: Proteomics. Volume 16:Issue 9(2016)
• Page Start: 1341
• Page End: 1346
• Publication Date: 2016-04-13
• Subjects: Cell biology -- Mass spectrometry -- Microglia -- Neuroimmune -- Pathway profiling -- SILAC; Proteins -- Separation -- Periodicals; Bioinformatics -- Periodicals; Proteomics -- Periodicals; Genomes -- Periodicals; Molecular genetics -- Periodicals; 572.605
• DOI: 10.1002/pmic.201500390
• Languages: English
• ISSNs: 1615-9853

Abstract

Abstract : Microglia play important and dynamic roles in mediating a variety of physiological and pathological processes during the development, normal function and degeneration of the central nervous system. Application of SILAC‐based proteomic analysis would greatly facilitate the identification of cellular pathways regulating the multifaceted phenotypes of microglia. We and others have successfully SILAC‐labeled immortalized murine microglial cell lines in previous studies. In this study, we report the development and evaluation of a SILAC‐labeled primary rat microglia model. Although the isotope labeling scheme for primary microglia is drastically different from that of immortalized cell lines, our de novo and uninterrupted primary culture labeling protocol (DUP‐SILAC) resulted in sufficient incorporation of SILAC labels for mass spectrometry‐based proteomic profiling. In addition, label incorporation did not alter their morphology and response to endotoxin stimulation. Proteomic analysis of the endotoxin‐stimulated SILAC‐labeled primary microglia identified expected as well as potentially novel activation markers and pro‐inflammatory pathways that could be quantified in a more physiologically relevant cellular model system compared to immortalized cell lines. The establishment of primary microglia SILAC model will further expand our capacity for global scale proteomic profiling of pathways under various physiological and pathological conditions. Proteomic MS data are available via ProteomeXchange with identifier PXD002759.