IonStar enables high-precision, low-missing-data proteomics quantification in large biological cohorts

Xiaomeng Shen, Shichen Shen, Jun Li, Qiang Hu, Lei Nie, Chengjian Tu, Xue Wang, David J. Poulsen, Benjamin C. Orsburn, Jianmin Wang, Jun Qu

Research output: Contribution to journalArticlepeer-review

Abstract

Reproducible quantification of large biological cohorts is critical for clinical/pharmaceutical proteomics yet remains challenging because most prevalent methods suffer from drastically declined commonly quantified proteins and substantially deteriorated quantitative quality as cohort size expands. MS2-based data-independent acquisition approaches represent tremendous advancements in reproducible protein measurement, but often with limited depth. We developed IonStar, an MS1-based quantitative approach enabling in-depth, high-quality quantification of large cohorts by combining efficient/reproducible experimental procedures with unique data-processing components, such as efficient 3D chromatographic alignment, sensitive and selective direct ion current extraction, and stringent postfeature generation quality control. Compared with several popular label-free methods, IonStar exhibited far lower missing data (0.1%), superior quantitative accuracy/ precision [∼5% intragroup coefficient of variation (CV)], the widest protein abundance range, and the highest sensitivity/specificity for identifying protein changes (<5% false altered-protein discovery) in a benchmark sample set (n = 20). We demonstrated the usage of IonStar by a large-scale investigation of traumatic injuries and pharmacological treatments in rat brains (n = 100), quantifying >7,000 unique protein groups (>99.8% without missing data across the 100 samples) with a low false discovery rate (FDR), two or more unique peptides per protein, and high quantitative precision. IonStar represents a reliable and robust solution for precise and reproducible protein measurement in large cohorts.

Original languageEnglish (US)
Pages (from-to)E4767-E4776
JournalProceedings of the National Academy of Sciences of the United States of America
Volume115
Issue number21
DOIs
StatePublished - May 22 2018
Externally publishedYes

Keywords

  • Label-free quantification
  • Large-cohort analysis
  • MS1 ion current-based methods
  • Missing data
  • Quantitative proteomics

ASJC Scopus subject areas

  • General

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