Orbitrap Exploris 480质谱在定量蛋白质组学应用中的优化和评测

基于数据依赖的扫描模式(data-dependent acquisition,DDA)和数据非依赖的扫描模式(data-independent acquisition,DIA)的非标记定量(label-free quantitative,LFQ)和同位素标记TMT(tandem mass tag)定量是蛋白质组学定量中...

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

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.