使用基于无标签和多重工作流的质谱测量进行蛋白质体规模组织映射.
Yumi Kwon1, Jongmin Woo1, Fengchao Yu2
1Environmental Molecular Sciences Laboratory, Pacific Northwest National Laboratory, Richland, WA 99354, United States.
bioRxiv : the preprint server for biology
|March 18, 2024
概括
空间蛋白质组映射方法的基准测试显示,无标签量化提供了深度蛋白质覆盖,而TMT-MS2在吞吐量方面表现出色. 这种比较有助于在复杂的组织微环境中发现生物标志物.
科学领域:
- 蛋白质组学是指蛋白质组学.
- 空间生物学 空间生物学
- 生物标志物发现发现
背景情况:
- 空间奥米克能够深入了解组织微环境.
- 蛋白质组规模组织映射对于识别诊断生物标志物和治疗点至关重要.
- 目前的蛋白质组映射方法在蛋白质覆盖和分析吞吐量方面面临挑战.
研究的目的:
- 为了对空间蛋白质组映射的三种蛋白质量测量方法进行基准测试:无标签,TMT-MS2和TMT-MS3.
- 以蛋白质覆盖,量化动态范围和分析吞吐量来评估它们的性能.
- 评估它们在识别差异丰富的蛋白质和空间共变的集群中的稳定性.
主要方法:
- 用于空间蛋白质组映射的无标签,TMT-MS2和TMT-MS3量化方法的基准测试.
- 基于蛋白质覆盖,空间分辨率,量化动态范围和吞吐量的性能评估.
- 应用深度蛋白质组绘图来研究胰腺小岛微环境.
主要成果:
- 无标签量化实现了最深的蛋白质覆盖 (约3500种蛋白质) 在50微米分辨率和最高动态范围.
- TMT-MS2方法显示了高的映射吞吐量 (>125像素/天).
- 无论是无标签的方法还是TMT-MS2方法,都为差异性蛋白质丰度和空间共同表达分析提供了可靠的量化.
结论:
- 空间蛋白质组映射方法为不同的研究需求提供了明显的优势.
- 深度蛋白质组映射可以识别细胞类型特定的蛋白质标记物.
- 空间共同表达分析揭示了组织微环境中的新型蛋白质模式,例如胰腺小岛.
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