Mass Spectrometry Core

质谱学和蛋白质组学核心将提供蛋白质鉴定、磷酸化位点 绘制图谱，并制定相对量化蛋白质/磷酸肽的策略，以帮助 结节性硬化症的途径和发病机制、LKB1/AMPK信号转导途径及其在结节性硬化症发病中的作用 Peutz-Jeghers综合征，以及对TSC1/TSC2/Tor/S6K信号通路的剖析。液相色谱联用 串联质谱学(LC/MS/MS)也将用于鉴定蛋白质和磷酸化位点 AS提供了蛋白质和磷酸化位点的定量信息。胰酶消化液中的多肽 将通过使用LTQ线性离子陷阱质谱仪的微毛细管反相LC/MS/MS进行分析 和一台QSTAR脉冲星四极飞行时间质谱仪。蛋白质及其磷酸化位点将是 用多肽串联质谱仪查询非冗余蛋白质数据库进行凝胶分离鉴定 光谱。蛋白质的相对定量将通过同位素标记不同来源的蛋白质来执行。 用细胞内氨基酸的稳定同位素标记分别研究轻氨基酸和重氨基酸的细胞状态 使用QSTAR质谱仪进行培养(SILAC)或标记细胞生长后的蛋白质和/或 全球内标技术(GIST)伯胺稳定同位素标记的多肽水平 试剂。轻/重肽对的比例将同时使用MSQuant和内部进行确定 开发了软件。生物信息学将用于生物信息的数据管理和数据挖掘 意义。

The Mass Spectrometry and Proteomics Core will provide protein identification, phosphorylation site mapping, and develop strategies for relative quantification of proteins/phosphopeptides in order to aid in the understanding of the Tuberous sclerosis pathway and pathogenesis, LKB1/AMPK signaling and its role in Peutz-Jeghers syndrome, and a dissection of Tsc1/Tsc2/Tor/S6K signaling. Liquid chromatography coupled to tandem mass spectrometry (LC/MS/MS) will be used to identify proteins and phosphorylation sites as well as provide quantitative information for proteins and phosphorylation sites. Peptides from tryptic digestions will be analyzed by microcapillary reversed-phase LC/MS/MS using a LTQ linear ion trap mass spectrometer and a QSTAR Pulsar quadrupole-TOF mass spectrometer. Proteins and their phosphorylation sites will be identified after gel separation by interrogating non-redundant protein databases with peptide tandem mass spectra. Relative quantitation of proteins will be performed by isotopically labeling the proteins from different cell states with light and heavy amino acids, respectively, using stable isotope labeling of amino acids in cell culture (SILAC) using the QSTAR mass spectrometer or labeling post cell growth at the protein and/or peptide level using global internal standard technology (GIST) primary amine stable isotope labeling reagents. Ratios of light to heavy peptide pairs will be determined using both MSQuant and in-house developed software. Bioinformatics will be used for data management and data mining for biological significance.