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Proc Natl Acad Sci U S A. 2018 May 22;115(21):E4767-E4776. doi: 10.1073/pnas.1800541115. Epub 2018 May 9.

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

Shen X1,2, Shen S1,2, Li J1,2, Hu Q3, Nie L4, Tu C1,2, Wang X2,5, Poulsen DJ6, Orsburn BC7, Wang J8, Qu J9,2.

Author information

1
Department of Pharmaceutical Sciences, University at Buffalo, The State University of New York, Buffalo, NY 14214.
2
Center of Excellence in Bioinformatics & Life Science, Buffalo, NY 14203.
3
Department of Biostatistics and Bioinformatics, Roswell Park Comprehensive Cancer Institute, Buffalo, NY 14263.
4
School of Pharmaceutical Sciences, Shandong University, Jinan 250012, China.
5
Department of Molecular and Cellular Biophysics, Roswell Park Comprehensive Cancer Institute, Buffalo, NY 14263.
6
Department of Neurosurgery, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, The State University of New York, Buffalo, NY 14203.
7
Cancer Research Technology Program, Frederick National Laboratory for Cancer Research, Frederick, MD 21701 orsburn@vt.edu jianmin.wang@roswellpark.org junqu@buffalo.edu.
8
Department of Biostatistics and Bioinformatics, Roswell Park Comprehensive Cancer Institute, Buffalo, NY 14263; orsburn@vt.edu jianmin.wang@roswellpark.org junqu@buffalo.edu.
9
Department of Pharmaceutical Sciences, University at Buffalo, The State University of New York, Buffalo, NY 14214; orsburn@vt.edu jianmin.wang@roswellpark.org junqu@buffalo.edu.

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.

KEYWORDS:

MS1 ion current-based methods; label-free quantification; large-cohort analysis; missing data; quantitative proteomics

Comment in

PMID:
29743190
DOI:
10.1073/pnas.1800541115
[Indexed for MEDLINE]
Free PMC Article

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