题名 | Synergistic optimization of Liquid Chromatography and Mass Spectrometry parameters on Orbitrap Tribrid mass spectrometer for high efficient data-dependent proteomics |
作者 | |
通讯作者 | Tian, Ruijun |
发表日期 | 2020-09-13
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DOI | |
发表期刊 | |
ISSN | 1076-5174
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EISSN | 1096-9888
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卷号 | 56 |
摘要 | Steady improvement in Orbitrap-based mass spectrometry (MS) technologies has greatly advanced the peptide sequencing speed and depth. In-depth analysis of the performance of state-of-the-art MS and optimization of key parameters can improve sequencing efficiency. In this study, we first systematically compared the performance of two popular data-dependent acquisition approaches, with Orbitrap as the first-stage (MS1) mass analyzer and the same Orbitrap (high-high approach) or ion trap (high-low approach) as the second-stage (MS2) mass analyzer, on the Orbitrap Fusion mass spectrometer. High-high approach outperformed high-low approach in terms of better saturation of the scan cycle and higher MS2 identification rate. However, regardless of the acquisition method, there are still more than 60% of peptide features untargeted for MS2 scan. We then systematically optimized the MS parameters using the high-high approach. Increasing the isolation window in the high-high approach could facilitate faster scan speed, but decreased MS2 identification rate. On the contrary, increasing the injection time of MS2 scan could increase identification rate but decrease scan speed and the number of identified MS2 spectra. Dynamic exclusion time should be set properly according to the chromatography peak width. Furthermore, we found that the Orbitrap analyzer, rather than the analytical column, was easily saturated with higher loading amount, thus limited the dynamic range of MS1-based quantification. By using optimized parameters, 10 000 proteins and 110 000 unique peptides were identified by using 20 h of effective liquid chromatography (LC) gradient time. The study therefore illustrated the importance of synchronizing LC-MS precursor ion targeting, fragment ion detection, and chromatographic separation for high efficient data-dependent proteomics.;Steady improvement in Orbitrap-based mass spectrometry (MS) technologies has greatly advanced the peptide sequencing speed and depth. In-depth analysis of the performance of state-of-the-art MS and optimization of key parameters can improve sequencing efficiency. In this study, we first systematically compared the performance of two popular data-dependent acquisition approaches, with Orbitrap as the first-stage (MS1) mass analyzer and the same Orbitrap (high-high approach) or ion trap (high-low approach) as the second-stage (MS2) mass analyzer, on the Orbitrap Fusion mass spectrometer. High-high approach outperformed high-low approach in terms of better saturation of the scan cycle and higher MS2 identification rate. However, regardless of the acquisition method, there are still more than 60% of peptide features untargeted for MS2 scan. We then systematically optimized the MS parameters using the high-high approach. Increasing the isolation window in the high-high approach could facilitate faster scan speed, but decreased MS2 identification rate. On the contrary, increasing the injection time of MS2 scan could increase identification rate but decrease scan speed and the number of identified MS2 spectra. Dynamic exclusion time should be set properly according to the chromatography peak width. Furthermore, we found that the Orbitrap analyzer, rather than the analytical column, was easily saturated with higher loading amount, thus limited the dynamic range of MS1-based quantification. By using optimized parameters, 10 000 proteins and 110 000 unique peptides were identified by using 20 h of effective liquid chromatography (LC) gradient time. The study therefore illustrated the importance of synchronizing LC-MS precursor ion targeting, fragment ion detection, and chromatographic separation for high efficient data-dependent proteomics. |
关键词 | |
相关链接 | [来源记录] |
收录类别 | |
语种 | 英语
|
学校署名 | 第一
; 通讯
|
资助项目 | Shenzhen Innovation of Science and Technology Commission[JCYJ20170412154126026]
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WOS研究方向 | Biochemistry & Molecular Biology
; Chemistry
; Spectroscopy
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WOS类目 | Biochemical Research Methods
; Chemistry, Analytical
; Spectroscopy
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WOS记录号 | WOS:000568712400001
|
出版者 | |
EI入藏号 | 20203809201086
|
EI主题词 | Ions
; Liquid chromatography
; Mass spectrometers
; Molecular biology
; Peptides
|
EI分类号 | Biology:461.9
; Chemistry:801
|
ESI学科分类 | CHEMISTRY
|
来源库 | Web of Science
|
引用统计 |
被引频次[WOS]:10
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成果类型 | 期刊论文 |
条目标识符 | http://sustech.caswiz.com/handle/2SGJ60CL/186567 |
专题 | 理学院_化学系 南方科技大学医学院 生命科学学院_生物系 |
作者单位 | 1.Southern Univ Sci & Technol, Dept Chem, Shenzhen 518055, Peoples R China 2.Hong Kong Baptist Univ, Dept Chem, State Key Lab Environm & Biol Anal, Hong Kong, Hong Kong, Peoples R China 3.Beihang Univ, Beijing Adv Innovat Ctr Big Data Based Precis Med, Sch Med & Engn, Beijing 100191, Peoples R China 4.Beihang Univ, Key Lab Big Data Based Precis Med, Minist Ind & Informat Technol, Beijing, Peoples R China 5.Chinese Acad Sci, Inst Comp Technol, Key Lab Intelligent Informat Proc, Beijing 100190, Peoples R China 6.Southern Univ Sci & Technol, Guangdong Prov Key Lab Cell Microenvironm & Dis R, Shenzhen 518055, Peoples R China |
第一作者单位 | 化学系 |
通讯作者单位 | 化学系; 南方科技大学医学院 |
第一作者的第一单位 | 化学系 |
推荐引用方式 GB/T 7714 |
Huang, Peiwu,Liu, Chao,Gao, Weina,et al. Synergistic optimization of Liquid Chromatography and Mass Spectrometry parameters on Orbitrap Tribrid mass spectrometer for high efficient data-dependent proteomics[J]. JOURNAL OF MASS SPECTROMETRY,2020,56.
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APA |
Huang, Peiwu,Liu, Chao,Gao, Weina,Chu, Bizhu,Cai, Zongwei,&Tian, Ruijun.(2020).Synergistic optimization of Liquid Chromatography and Mass Spectrometry parameters on Orbitrap Tribrid mass spectrometer for high efficient data-dependent proteomics.JOURNAL OF MASS SPECTROMETRY,56.
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MLA |
Huang, Peiwu,et al."Synergistic optimization of Liquid Chromatography and Mass Spectrometry parameters on Orbitrap Tribrid mass spectrometer for high efficient data-dependent proteomics".JOURNAL OF MASS SPECTROMETRY 56(2020).
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