Optimization and Evaluation of Orbitrap Exploris 480 Mass Spectrometry for Quantitative Proteomics

被引:0
作者
Zhou Yue [1 ]
Yang Xiang-Yun [2 ]
Huang Min [2 ]
Li Xiang [3 ]
Guan Feng [1 ,3 ]
机构
[1] Jiangnan Univ, Sch Biotechnol, Key Lab Carbohydrate Chem & Biotechnol, Minist Educ, Wuxi 214122, Jiangsu, Peoples R China
[2] Themofisher Sci, Shanghai 200120, Peoples R China
[3] Northwest Univ, Coll Life Sci, Xian 710069, Peoples R China
关键词
quantitative proteomics; label-free quantitative; TMT; FAIMS; Orbitrap Exploris 480; PEPTIDE IDENTIFICATION; QUANTIFICATION; MIXTURES;
D O I
10.16476/j.pibb.2020.0245
中图分类号
Q5 [生物化学]; Q7 [分子生物学];
学科分类号
071010 ; 081704 ;
摘要
Data-dependent acquisition (DDA) and data-independent acquisition (DIA) based label-free quantification (LFQ) and tandem mass tag (TMT) based isotope labeling quantification are two key techniques for quantitative proteomics. Here we optimized the latest Orbitrap Exploris 480 mass spectrometer parameters in DDA, FAIMS DDA, FAIMS DIA based LFQ and TMT, and show its performance in cell line proteomics, single-cell proteomics, plasma proteomics and yeast proteomics. The results showed that the collision energy of 27, the resolution of the fragment spectrum of 15K, and maximum ion injection time of 22 ms are the best parameter combination for DDA experiment. For the proteomic analysis of ultra-low samples ranging from 200 pg-5 ng, the individual mass spectrometer parameters should be considered. We identified 1 259 and 1 725 proteins in 200 pg and 500 pg of HeLa cell lysate, respectively, achieving deep coverage of single-cell proteomics. In FAIMS DDA experiment, we chose CV-45V for 60 min or 90 min gradient, CV-45V-65V combinations for 120 min or 150 min gradient to obtain the optimal protein identifications, and identified 6 300, 6 994 and 7 500 proteins in 60 min, 120 min and 150 min from 293T proteome, respectively. In FAIMS DIA experiment, we used CV-45V-65V voltage sweeping, 60 isolation windows, and obtained the optimal proteins identifications and quantitative reproducibility. We quantified 7 019 proteins in 293T cell lysate and 1 077 proteins in depleted plasma in 60 min gradient. The combination of APD on, "Precursor Fit" threshold of 70 and Turbo TMT could simultaneously increase the number of protein identifications and the accuracy of TMT quantification. 10 989 peptides and 2 162 protein were quantified in TMT11-plex labeled yeast proteome. Taken together, the various LFQ methods and TMT quantification method optimized in this study showed high performance and various applications in quantitative proteomics.
引用
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页码:214 / 226
页数:13
相关论文
共 25 条
  • [1] Relative and Absolute Quantitation in Mass Spectrometry-Based Proteomics
    Ankney, J. Astor
    Muneer, Adil
    Chen, Xian
    [J]. ANNUAL REVIEW OF ANALYTICAL CHEMISTRY, VOL 11, 2018, 11 : 49 - 77
  • [2] A Compact Quadrupole-Orbitrap Mass Spectrometer with FAIMS Interface Improves Proteome Coverage in Short LC Gradients
    Bekker-Jensen, Dorte B.
    Martinez-Val, Ana
    Steigerwald, Sophia
    Ruther, Patrick
    Fort, Kyle L.
    Arrey, Tabiwang N.
    Harder, Alexander
    Makarov, Alexander
    Olsen, Jesper, V
    [J]. MOLECULAR & CELLULAR PROTEOMICS, 2020, 19 (04) : 716 - 729
  • [3] Quantitative profiling of proteins in complex mixtures using liquid chromatography and mass spectrometry
    Chelius, D
    Bondarenko, PV
    [J]. JOURNAL OF PROTEOME RESEARCH, 2002, 1 (04) : 317 - 323
  • [4] Protein Quantification in Label-Free LC-MS Experiments
    Clough, Timothy
    Key, Melissa
    Ott, Ilka
    Ragg, Susanne
    Schadow, Gunther
    Vitek, Olga
    [J]. JOURNAL OF PROTEOME RESEARCH, 2009, 8 (11) : 5275 - 5284
  • [5] Accurate Proteome-wide Label-free Quantification by Delayed Normalization and Maximal Peptide Ratio Extraction, Termed MaxLFQ
    Cox, Juergen
    Hein, Marco Y.
    Luber, Christian A.
    Paron, Igor
    Nagaraj, Nagarjuna
    Mann, Matthias
    [J]. MOLECULAR & CELLULAR PROTEOMICS, 2014, 13 (09) : 2513 - 2526
  • [6] DIA-NN: neural networks and interference correction enable deep proteome coverage in high throughput
    Demichev, Vadim
    Messner, Christoph B.
    Vernardis, Spyros I.
    Lilley, Kathryn S.
    Ralser, Markus
    [J]. NATURE METHODS, 2020, 17 (01) : 41 - +
  • [7] Faster SEQUEST Searching for Peptide Identification from Tandem Mass Spectra
    Diament, Benjamin J.
    Noble, William Stafford
    [J]. JOURNAL OF PROTEOME RESEARCH, 2011, 10 (09) : 3871 - 3879
  • [8] Evolution of Orbitrap Mass Spectrometry Instrumentation
    Eliuk, Shannon
    Makarov, Alexander
    [J]. ANNUAL REVIEW OF ANALYTICAL CHEMISTRY, VOL 8, 2015, 8 : 61 - 80
  • [9] Improved Precursor Characterization for Data-Dependent Mass Spectrometry
    Hebert, Alexander S.
    Thoeing, Christian
    Riley, Nicholas M.
    Kwiecien, Nicholas W.
    Shiskova, Evgenia
    Huguet, Romain
    Cardasis, Helene L.
    Kuehn, Andreas
    Eliuk, Shannon
    Zabrouskov, Vlad
    Westphall, Michael S.
    McAlister, Graeme C.
    Coon, Joshua J.
    [J]. ANALYTICAL CHEMISTRY, 2018, 90 (03) : 2333 - 2340
  • [10] Semi-supervised learning for peptide identification from shotgun proteomics datasets
    Kall, Lukas
    Canterbury, Jesse D.
    Weston, Jason
    Noble, William Stafford
    MacCoss, Michael J.
    [J]. NATURE METHODS, 2007, 4 (11) : 923 - 925