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系统优化自动化聚丰富的高灵敏度的蛋白组学

Patricia Bortel¹, Ilaria Piga², Claire Koenig²

¹Faculty of Chemistry, Department of Analytical Chemistry, University of Vienna, Vienna, Austria; Vienna Doctoral School in Chemistry (DoSChem), University of Vienna, Vienna, Austria.

Molecular & cellular proteomics : MCP

|March 28, 2024

概括

优化在珠子上的蛋白质组学方法提高了低输入样本的覆盖率和灵敏度. 这种方法通过快速液体染色学-并联质谱学 (LC-MS/MS) 分析识别了超过16000个类.

科学领域:

蛋白质组学是指蛋白质组学.
质谱测量质量谱测量
生物化学生物化学

背景情况:

常规的蛋白组学需要改进覆盖范围,强度和灵敏度,特别是对于最小的输入.
需要自动化样品制备方法来进行高效的蛋白质组学分析.

研究的目的:

系统地优化用于自动化在珠子上的蛋白组学的实验参数,重点是低输入样本.
为了提高蛋白质组覆盖范围,稳定性和常规分析的灵敏性.

主要方法:

系统地优化关键实验参数用于自动化在珠子上的蛋白组学.
对类的鉴定,缩效率,部位定位和多重化的缩进行评估.
测试顺序丰富和逐步增加珠子的策略.
在 Orbitrap Exploris 480 和 Orbitrap Astral MS 仪器上应用优化策略.

主要成果:

优化的参数 (甘油酸,氧化,与珠的比率,结合时间,体积) 允许从30μg中在30分钟的LC-MS/MS内识别超过16,000个.
序列丰富和聚合分数增加了蛋白质组深度.
逐步的珠子添加增加了20%的蛋白质覆盖率.
在Orbitrap Astral MS.上使用狭窄窗口数据独立获取 (nDIA) 在30分钟LC-MS/MS中从50万个HeLa细胞中识别了超过32,000个类.

结论:

优化的自动化在珠子上的光蛋白学显著改善了从低输入样本中的光的识别.
开发的战略增强了蛋白质组的覆盖范围和灵敏度,适合在先进的质谱仪平台上进行常规分析.
这种方法为深度蛋白质组分析提供了强大而高效的工作流.

关键词:

轨道飞行器天文飞行器轨道飞行器探测器 (Exploris Exploris) 是一个探测器.自动化自动化自动化自动化数据独立获取数据独立获取数据光蛋白质组学光蛋白质组学光蛋白质组学序列丰富方式序列丰富方式

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