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Updated: Jul 28, 2025

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为FFPE组织蛋白质学提供高通量工作流程

Ganesh P Pujari1, Kiran K Mangalaparthi1, Benjamin J Madden2

  • 1Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, Minnesota 55905, United States.

Journal of the American Society for Mass Spectrometry
|June 2, 2023
PubMed
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此摘要是机器生成的。

适应聚焦声学 (AFA) 技术简化和加速样本处理,用于激光捕获微解剖 (LCM) FFPE组织的高通量蛋白质组分析. 这种自动化工作流减少了处理时间,并改善了用于临床和研究应用的蛋白质和的识别.

科学领域:

  • 蛋白质组学是指蛋白质组学.
  • 生物技术是生物技术.
  • 分析化学 分析化学

背景情况:

  • 激光捕获微解剖 (LCM) 对于定甲嵌入 (FFPE) 组织的向蛋白质组分析至关重要.
  • 低蛋白质产量和繁重的加工阻碍了LCM样本的蛋白质组分析.
  • 需要自动化,以高效的高通量FPE基于蛋白质组学.

研究的目的:

  • 用适应焦点声学 (AFA) 来为FFPE组织样本处理提供简化,快速的工作流.
  • 为了评估AFA辅助的提取和消化,用于高通量蛋白质组分析.
  • 评估AFA对蛋白质和的回收和质量的影响.

主要方法:

  • 评估了三个工作流程:标准提取/一夜间消化,AFA辅助提取/一夜间消化,以及AFA辅助提取/同时消化.
  • 采用基于AFA的超声波用于96井和384井格式的自动样本处理.
  • 进行LC-MS/MS分析,在工作流程中比较蛋白质和的识别.

主要成果:

  • 基于 AFA 的工作流程实现了 LCM-FFPE 组织的自动化,高吞吐量处理.
  • 加速的素消化与AFA显著减少了整体处理时间.
  • AFA加速工作流程显示了略高的蛋白质和的识别,而不会影响质量.

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结论:

  • 基于 AFA 的工作流提供了快速,高通量样本处理LCM-FFPE组织.
  • 这种方法显著减少了样品处理,并且可以自动化.
  • 描述的工作流提高了临床和研究蛋白质组分析的效率.