Improving Proteomic Dynamic Range with Multiple Accumulation Precursor Mass Spectrometry

IF 3.6 2区 生物学 Q1 BIOCHEMICAL RESEARCH METHODS
Teeradon Phlairaharn, , , Ariana E. Shannon, , , Xinlei Zeng, , , Dong-Jiunn Jeffery Truong, , , Erwin M. Schoof, , , Zilu Ye, , and , Brian C. Searle*, 
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Abstract

Orbitrap (OT)-based mass spectrometer platforms are a gold standard in high-resolution mass spectrometry, where their primary disadvantage is slower-scanning speed in comparison to time-of-flight or linear ion trap mass analyzers. In this study, we utilize long OT transients to extend the precursor dynamic range by modifying the selected ion monitoring method to multiplex several precursor m/z ranges into a single scan, which we call “multiple accumulation precursor mass spectrometry”. Our approach requires no software or hardware modifications and hides the additional ion accumulation steps during the time it takes to make other Orbitrap measurements, producing precursor spectra with nearly 2× dynamic range and essentially no consequences. We collected data using both data-dependent acquisition (DDA) and data-independent acquisition (DIA) methods to evaluate a range of approaches. With DDA, MAP-MS precursor quantification improves with higher quality measurements. At the same time, DIA detection is enhanced by up to 11% when combining precursor and tandem mass spectra for peptide detection.

Abstract Image

多重积累前体质谱法提高蛋白质组学动态范围。
基于Orbitrap (OT)的质谱仪平台是高分辨率质谱分析的黄金标准,与飞行时间或线性离子阱质谱仪相比,其主要缺点是扫描速度较慢。在本研究中,我们通过修改选定的离子监测方法,将多个前驱体的m/z范围复用到一次扫描中,从而利用长OT瞬态来扩展前驱体的动态范围,我们称之为“多重积累前驱体质谱”。我们的方法不需要修改软件或硬件,并且隐藏了在进行其他Orbitrap测量所需的时间内额外的离子积累步骤,产生了接近2倍动态范围的前驱光谱,基本上没有任何后果。我们使用数据依赖采集(DDA)和数据独立采集(DIA)方法收集数据,以评估一系列方法。使用DDA, MAP-MS前体定量得到了提高,测量质量更高。同时,将前体质谱与串联质谱相结合用于多肽检测时,DIA的检出率提高了11%。
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来源期刊
Journal of Proteome Research
Journal of Proteome Research 生物-生化研究方法
CiteScore
9.00
自引率
4.50%
发文量
251
审稿时长
3 months
期刊介绍: Journal of Proteome Research publishes content encompassing all aspects of global protein analysis and function, including the dynamic aspects of genomics, spatio-temporal proteomics, metabonomics and metabolomics, clinical and agricultural proteomics, as well as advances in methodology including bioinformatics. The theme and emphasis is on a multidisciplinary approach to the life sciences through the synergy between the different types of "omics".
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