首页> 外文会议>ASMS Conference on Mass Spectrometry and Allied Topics >Single cell proteome profiling using highly sensitive LC-MS system and In-capillary sample preparation method
【24h】

Single cell proteome profiling using highly sensitive LC-MS system and In-capillary sample preparation method

机译:使用高敏感的LC-MS系统和毛细管样品制备方法的单细胞蛋白质组分析

获取原文

摘要

LC/MS-based proteomics has become a fairly mature technology, and often is applied for practical evaluations in both basic and clinical sciences. An issue is that the obtained data are ensemble averages of many cells so that the average results loose information on cellular heterogeneity. Single cell analyses are becoming more common place with other measurement areas such as genomics and transcriptomics based on signal amplification.1-3 These results demonstrate that individual cells have unique characters and should be individually analyzed in some cases. It remains difficult to achieve single cell proteome analysis, especially as one cannot comprehensively amplify MS signals of whole digested peptides. Here we optimize each procedure from sample preparation to LC-MS analysis to minimize loses and optimize signals. Overall, we detect hundreds of proteins from minuscule amount of mammalian cell lysates or single Aplysia Californica neurons. Our ability to perform single cell proteome analysis appears achievable in the near future. References: [1] T. Kalisky and S. Quake, Nature methods (2011), [2]F. Tang et al., Nature methods (2009), [3]R. Zenobi, Science (2013)
机译:基于LC / MS的蛋白质组学已成为一种相当成熟的技术,并且通常用于基础和临床科学的实际评估。问题是获得的数据是许多细胞的总结平均值,使得平均结果松散关于细胞异质性的信息。单细胞分析与基于信号放大的其他测量区域(如基因组学和转录组织)变得更常见的位置.1-3这些结果表明各个细胞具有独特的特征,并且应该在某些情况下单独分析。实现单细胞蛋白质组分析仍然难以难以全面地扩增全消化肽的MS信号。在这里,我们将每个过程从样品制备到LC-MS分析中优化,以最小化丢失并优化信号。总体而言,我们检测数百种蛋白质,来自哺乳动物细胞裂解物或单一APLYSIA CALIFORNICA神经元。我们在不久的将来可以实现我们进行单细胞蛋白质组分析的能力。参考文献:[1] T. Kalisky和S.地震,自然方法(2011),[2] F。 Tang等,自然方法(2009),[3] r。 Zenobi,Science(2013)

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号