Ultra-High-Resolution IonStar Strategy Enhancing Accuracy and Precision of MS1-Based Proteomics and an Extensive Comparison with State-of-the-Art SWATH-MS in Large-Cohort Quantification.


Wang X(1)(2), Jin L(2), Hu C(2), Shen S(3), Qian S(1), Ma M(1), Zhu X(3), Li F(4), Wang J(5), Tian Y(2), Qu J(3)(1).
Author information:
(1)Department of Cell Stress Biology, Roswell Park Comprehensive Cancer Center, Buffalo, New York 14203, United States.
(2)AbbVie Bioresearch Center, Worcester, Massachusetts 01605, United States.
(3)Department of Pharmaceutical Sciences, University at Buffalo, SUNY, Buffalo, New York 14214, United States.
(4)Department of Pharmacology and Therapeutics, Roswell Park Comprehensive Cancer Center, Buffalo, New York 14203, United States.
(5)Department of Biostatistics and Bioinformatics, Roswell Park Comprehensive Cancer Center, Buffalo, New York 14203, United States.


Quantitative proteomics in large cohorts is highly valuable for clinical/pharmaceutical investigations but often suffers from severely compromised reliability, accuracy, and reproducibility. Here, we describe an ultra-high-resolution IonStar method achieving reproducible protein measurement in large cohorts while minimizing the ratio compression problem, by taking advantage of the exceptional selectivity of ultra-high-resolution (UHR)-MS1 detection (240k_FWHM@m/z = 200). Using mixed-proteome benchmark sets reflecting large-cohort analysis with technical or biological replicates (N = 56), we comprehensively compared the quantitative performances of UHR-IonStar vs a state-of-the-art SWATH-MS method, each with their own optimal analytical platforms. We confirmed a cutting-edge micro-liquid chromatography (LC)/Triple-TOF with Spectronaut outperforms nano-LC/Orbitrap for SWATH-MS, which was then meticulously developed/optimized to maximize sensitivity, reproducibility, and proteome coverage. While the two methods with distinct principles (i.e., MS1- vs MS2-based) showed similar depth-of-analysis (∼6700-7000 missing-data-free proteins quantified, 1% protein-false discovery rate (FDR) for entire set, 2 unique peptides/protein) and good accuracy/precision in quantifying high-abundance proteins, UHR-IonStar achieved substantially superior quantitative accuracy, precision, and reproducibility for lower-abundance proteins (a category that includes most regulatory proteins), as well as much-improved sensitivity/selectivity for discovering significantly altered proteins. Furthermore, compared to SWATH-MS, UHR-IonStar showed markedly higher accuracy for a single analysis of each sample across a large set, which is an inadequately investigated albeit critical parameter for large-cohort analysis. Finally, we compared UHR-IonStar vs SWATH-MS in measuring the time courses of altered proteins in paclitaxel-treated cells (N = 36), where dysregulated biological pathways have been very well established. UHR-IonStar discovered substantially more well-recognized biological processes/pathways induced by paclitaxel. Additionally, UHR-IonStar showed markedly superior ability than SWATH-MS in accurately depicting the time courses of well known to be paclitaxel-induced biomarkers. In summary, UHR-IonStar represents a reliable, robust, and cost-effective solution for large-cohort proteomic quantification with excellent accuracy and precision.