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Deep Proteome Profiling by Isobaric Labeling, Extensive Liquid Chromatography, Mass Spectrometry, and Software-assisted Quantification
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简单而强大的高通量血清蛋白质组学工作流程与低微流量LC-MS/MS.

Yoondam Seo1,2, Inseon Kang1, Hyeon-Jeong Lee1

  • 1Doping Control Center, Korea Institute of Science and Technology (KIST), Hwarang-Ro 14-Gil 5, Seongbuk-Gu, Seoul, 02792, Republic of Korea.

Analytical and bioanalytical chemistry
|October 18, 2024
PubMed
概括

这项研究引入了一种快速的,自动化的血清蛋白质学工作流程,用于高通量生物标志物发现. 该方法在最小的样本准备过程中获得可重现的结果,有助于识别与慢性病相关的蛋白质.

关键词:
慢性脏疾病 慢性脏疾病数据独立的获取获取数据.高通量的高通量.低微流量的微流量

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科学领域:

  • 蛋白质组学是指蛋白质组学.
  • 生物标志物发现发现
  • 临床化学 临床化学

背景情况:

  • 临床蛋白质组学促进了生物流体中的蛋白质识别和量化,以发现生物标志物.
  • 血清蛋白质组学需要提高大规模临床分析的吞吐量和可重复性.
  • 高通量自动化设备是昂贵的,而且其可访问性有限.

研究的目的:

  • 开发一个快速,高吞吐量和成本效益的血清蛋白质组学工作流.
  • 在没有自动化的情况下优化低微流量LC-MS/MS方法.
  • 评估该方法的可复制性和稳定性,用于临床样本分析.

主要方法:

  • 开发了一个快速,高通量,低微流的LC-MS/MS工作流程,省略了耗尽和淡化步骤.
  • 该方法应用于对235个血清样本的数据独立获取 (DIA) 分析.
  • 使用质量控制样本来评估18分钟DIA工作流程的可重复性和稳定性.

主要成果:

  • 该工作流始终确定了大约6000个和600个蛋白质组,包括33个FDA批准的生物标志物.
  • 18分钟的DIA工作流显示出可重复性和稳定性,即使使用2μL血清.
  • 在糖尿病患者慢性病的各个阶段,确定了五种失调的蛋白质.

结论:

  • 开发的工作流提供了高通量血清蛋白质学的快速,经济有效和可重复的方法.
  • 这种方法有助于在大型临床研究中发现和分析生物标志物.
  • 工作流成功地确定了慢性病进展的潜在蛋白质生物标志物.