4.6 Article

Glycosaminoglycanomics of Cultured Cells Using a Rapid and Sensitive LC-MS/MS Approach

期刊

ACS CHEMICAL BIOLOGY
卷 10, 期 5, 页码 1303-1310

出版社

AMER CHEMICAL SOC
DOI: 10.1021/acschembio.5b00011

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资金

  1. National Institutes of Health [GM38060, GM090127, HL096972, HL10172]
  2. China Scholarship Council
  3. Program for Changjiang Scholars and Innovative Research Team in University [IRT1188]

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Glycosaminoglycans (GAGs), a family of polysaccharides widely distributed in eukaryotic cells, are responsible for a wide array of biological functions. Quantitative disaccharide compositional analysis is one of the primary ways to characterize the GAG structure. This structural analysis is typically time-consuming (1-2 weeks) and labor intensive, requiring GAG recovery and multistep purification, prior to the enzymatic/chemical digestion of GAGs, and finally their analysis. Moreover, 10(5)-10(7) cells are usually required for compositional analysis. We report a sensitive, rapid, and quantitative analysis of GAGs present in a small number of cells. Commonly studied cell lines were selected based on phenotypic properties related to the biological functions of GAGs. These cells were lysed using a commercial surfactant reagent, sonicated, and digested with polysaccharide lyases. The resulting disaccharides were recovered by centrifugal filtration, labeled with 2-aminoacridone, and analyzed by liquid chromatography (LC)-mass spectrometry (MS). Using a highly sensitive MS method, multiple reaction monitoring (MRM), the limit of detection for each disaccharide was reduced to 0.5-1.0 pg, as compared with 1.0-5.0 ng obtained using standard LC-MS analysis. Sample preparation time was reduced to 1-2 days, and the cell number required was reduced to 5000 cells for complete GAG characterization to as few as 500 cells for the characterization of the major GAG disaccharide components. Our survey of the glycosaminoglycanomes of the 20 selected cell lines reveals major differences in their GAG amounts and compositions. Structure-function relationships are explored using these data, suggesting the utility of this method in cellular glycobiology.

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