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BoxCarmax:一种用于分析蛋白质周转和复杂样品的高选择性数据非依赖性采集质谱方法。

BoxCarmax: A High-Selectivity Data-Independent Acquisition Mass Spectrometry Method for the Analysis of Protein Turnover and Complex Samples.

机构信息

Yale Cancer Biology Institute, Yale University, West Haven, Connecticut CT 06520, United States.

Department of Pharmacology, Yale University School of Medicine, New Haven, Connecticut CT 06510, United States.

出版信息

Anal Chem. 2021 Feb 16;93(6):3103-3111. doi: 10.1021/acs.analchem.0c04293. Epub 2021 Feb 3.

Abstract

The data-independent acquisition (DIA) performed in the latest high-resolution, high-speed mass spectrometers offers a powerful analytical tool for biological investigations. The DIA mass spectrometry (DIA-MS) combined with the isotopic labeling approach holds a particular promise for increasing the multiplexity of DIA-MS analysis, which could assist the relative protein quantification and the proteome-wide turnover profiling. However, the wide MS1 isolation windows employed in conventional DIA methods lead to a limited efficiency in identifying and quantifying isotope-labeled peptide pairs through peptide fragment ions. Here, we optimized a high-selectivity DIA-MS named BoxCarmax that supports the analysis of complex samples, such as those generated from Stable isotope labeling by amino acids in cell culture (SILAC) and pulse SILAC (pSILAC) experiments. BoxCarmax enables multiplexed acquisition at both MS1 and MS2 levels, through the integration of BoxCar and MSX features, as well as a gas-phase separation strategy. We found BoxCarmax significantly improved the quantitative accuracy in SILAC and pSILAC samples by mitigating the ratio suppression of isotope-peptide pairs. We further applied BoxCarmax to measure protein degradation regulation during serum starvation stress in cultured cells, revealing valuable biological insights. Our study offered an alternative and accurate approach for the MS analysis of protein turnover and complex samples.

摘要

数据非依赖性采集(DIA)在最新的高分辨率、高速质谱仪中提供了一种强大的生物研究分析工具。DIA 质谱(DIA-MS)与同位素标记方法相结合,对于提高 DIA-MS 分析的多重性具有特殊的意义,这可能有助于相对蛋白质定量和全蛋白质组周转率分析。然而,传统 DIA 方法中采用的宽 MS1 隔离窗口导致通过肽片段离子识别和定量同位素标记肽对的效率有限。在这里,我们优化了一种名为 BoxCarmax 的高选择性 DIA-MS,它支持复杂样品的分析,如稳定同位素标记通过细胞培养中的氨基酸(SILAC)和脉冲 SILAC(pSILAC)实验生成的样品。BoxCarmax 通过 BoxCar 和 MSX 特征的集成以及气相分离策略,在 MS1 和 MS2 水平上实现了多路采集。我们发现 BoxCarmax 通过减轻同位素肽对的比率抑制,显著提高了 SILAC 和 pSILAC 样品的定量准确性。我们进一步将 BoxCarmax 应用于测量培养细胞在血清饥饿应激过程中的蛋白质降解调节,揭示了有价值的生物学见解。我们的研究为蛋白质周转和复杂样品的 MS 分析提供了一种替代且准确的方法。

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