PXD018063

PXD018063 is an original dataset announced via ProteomeXchange.

Title	An 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
Description	Quantitative proteomics in large cohorts is highly valuable for biomedical/pharmaceutical investigations but often suffers from suboptimal reliability, accuracy, and reproducibility. Here we describe a new ultra-high-resolution (UHR) IonStar method achieving precise/reproducible protein measurement in large-cohorts while eliminating accuracy-related problems such as ratio compression, by taking advantage of the exceptional selectivity of UHR-MS1 detection (240k_FWHM). Using mixed-proteome sets reflecting large-cohort analysis using technical or biological replicates (N=56), we comprehensively compared the quantitative performances of UHR-IonStar with a state-of-the-art SWATH-MS method. The SWATH-MS employs a cutting-edge TripleTOF and a SpectronautTM package and was meticulously optimized to maximize sensitivity, reproducibility and proteome-coverage, by developing a highly extensive, customized spectral-library, reproducible/robust capillary-LC separation and narrow, variable-window acquisition. Comparing quantitative performances of the two distinct methods (i.e. MS1-vs.MS2-based) affords interesting and valuable observations. While the performances for higher-abundance proteins (i.e. higher 75% proteins in abundance) are quite similar by the two methods, UHR-IonStar showed markedly better quantitative accuracy, precision and reproducibility (e.g. R2>0.9 by UHR-IonStar vs. R2<0.4 by SWATH-MS) for proteins of lower 25% in abundance, and much improved sensitivity/selectivity for discovering significantly-altered proteins, especially these with changes ≤2.5 folds. Furthermore, UHR-IonStar showed more accurate protein quantification in single analysis of each individual sample in a large set, which is an inadequately-investigated albeit highly critical parameter for large-cohort analysis. Finally, we compared the two methods in measuring time courses of altered proteins in cancer cells (N=36) treated by paclitaxel, where dysregulated biological processes have been well-established. UHR-IonStar discovered more altered-proteins in biological processes and pathways that are known to be induced by paclitaxel, with substantially better significance scores. Additionally, UHR-IonStar showed markedly superior ability in accurately depicting the time courses of tubulin-α/β isoforms which are well-known to be consistently induced by paclitaxel. In summary, UHR-IonStar represents a reliable, robust and cost-effective solution for large-cohort proteomics quantification with excellent accuracy and precision.
HostingRepository	PRIDE
AnnounceDate	2021-06-24
AnnouncementXML	Submission_2021-06-24_08:25:39.503.xml
DigitalObjectIdentifier
ReviewLevel	Peer-reviewed dataset
DatasetOrigin	Original dataset
RepositorySupport	Unsupported dataset by repository
PrimarySubmitter	xue wang
SpeciesList	scientific name: Homo sapiens (Human); NCBI TaxID: 9606;
ModificationList	No PTMs are included in the dataset
Instrument	TripleTOF 6600

Revision	Datetime	Status	ChangeLog Entry
0	2020-03-17 01:41:47	ID requested
⏵ 1	2021-06-24 08:25:40	announced

Wang X, Jin L, Hu C, Shen S, Qian S, Ma M, Zhu X, Li F, Wang J, Tian Y, Qu J, 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. Anal Chem, 93(11):4884-4893(2021) [pubmed]

submitter keyword: Taxol-treated pancreatic cancer cells, SWATH-MS

Yu Tian
contact affiliation	Abbvie Bioresearch Center
contact email	yu.tian@abbvie.com
lab head
xue wang
contact affiliation	University at buffalo
contact email	xwang79@buffalo.edu
dataset submitter

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PRIDE project URI

• PRIDE
  1. PXD018063
     1. Label: PRIDE project
     2. Name: An 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