Synergistic optimization of Liquid Chromatography and Mass Spectrometry parameters on Orbitrap Tribrid mass spectrometer for high efficient data‐dependent proteomics. (13th September 2020) - Huang, Peiwu; Liu, Chao; Gao, Weina; Chu, Bizhu; Cai, Zongwei; Tian, Ruijun
Abstract: 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.