BoxCarmax: A High-Selectivity Data-Independent Acquisition Mass Spectrometry Method for the Analysis of Protein Turnover and Complex Samples.
BoxCarmax: A High-Selectivity Data-Independent Acquisition Mass Spectrometry Method for the Analysis of Protein Turnover and Complex Samples.
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DOI:
10.1021/acs.analchem.0c04293
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发表时间:
2021-02-16
影响因子:
7.4
通讯作者:
Liu Y
中科院分区:
文献类型:
--
作者:
Salovska B;Li W;Di Y;Liu Y
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-labelled 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.
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影响因子:
16.6
作者:
Liu Y;Borel C;Li L;Müller T;Williams EG;Germain PL;Buljan M;Sajic T;Boersema PJ;Shao W;Faini M;Testa G;Beyer A;Antonarakis SE;Aebersold R
通讯作者:
Aebersold R
影响因子:
7.4
作者:
Haynes, Sarah E.;Majmudar, Jaimeen D.;Martin, Brent R.
通讯作者:
Martin, Brent R.
DOI:
10.1007/s13361-019-02243-1
发表时间:
2019-08-01
影响因子:
3.2
作者:
Li, Wenxue;Chi, Hao;Liu, Yansheng
通讯作者:
Liu, Yansheng
影响因子:
7
作者:
Bruderer, Roland;Muntel, Jan;Reiter, Lukas
通讯作者:
Reiter, Lukas
影响因子:
7
作者:
Huang, Ting;Bruderer, Roland;Reiter, Lukas
通讯作者:
Reiter, Lukas