Analysis of membrane and hydrophilic proteins simultaneously derived from the mouse brain using cloud-point extraction
In this study, a temperature-induced phase fractionation known as cloud-point extraction (CPE) with the non-ionic surfactant Triton X-114 was used to simultaneously extract, concentrate, and fractionate hydrophobic and hydrophilic proteins from mouse brain tissue. Two bottom-up proteomic techniques...
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Veröffentlicht in: | Analytical and bioanalytical chemistry 2011-07, Vol.400 (9), p.2827-2836 |
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Sprache: | eng |
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Zusammenfassung: | In this study, a temperature-induced phase fractionation known as cloud-point extraction (CPE) with the non-ionic surfactant Triton X-114 was used to simultaneously extract, concentrate, and fractionate hydrophobic and hydrophilic proteins from mouse brain tissue. Two bottom-up proteomic techniques were used to comprehensively identify the extracted proteins. The first “shotgun”-based approach included tryptic digestion of the proteins followed by reversed-phase nanoliquid chromatography (RP-nanoLC) in combination with electrospray ionization (ESI) tandem mass spectrometry (MS/MS). In the second approach, the extracted intact proteins were first separated by one-dimensional (1D) gel electrophoresis and then in-gel digested with trypsin and analyzed with nanoLC-MS/MS. In total, 1,825 proteins were unambiguously identified and the percentage of membrane proteins was 26% which is at the reported genome expression levels of 20–30%. The protein overlap between the two approaches was high. The majority (77%) of the identifications in the first approach was also found by the second method. The protein overlap between the CPE-extracted hydrophilic and hydrophobic fractions was rather small (16–23%) for both methods, which indicates a good phase separation. A quantitative evaluation of the CPE with iTRAQ labeling and nanoLC-ESI-MS/MS analysis gave iTRAQ ratios at the expected levels and an overall variation of the entire method at 17–31%. The results indicate very reproducible sample preparation and analysis methods that readily can be applied on large-scale sample sets. |
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ISSN: | 1618-2642 1618-2650 1618-2650 |
DOI: | 10.1007/s00216-011-5037-9 |