IonStar enables high-precision, low-missing-data proteomics quantification in large biological cohorts.
In: Proceedings of the National Academy of Sciences of the United States of America, Jg. 115 (2018-05-22), Heft 21, S. E4767-E4776
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Zugriff:
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.
Competing Interests: The authors declare no conflict of interest.
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IonStar enables high-precision, low-missing-data proteomics quantification in large biological cohorts.
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Autor/in / Beteiligte Person: | Shen, X ; Shen, S ; Li, J ; Hu, Q ; Nie, L ; Tu, C ; Wang, X ; Poulsen, DJ ; Orsburn, BC ; Wang, J ; Qu, J |
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Zeitschrift: | Proceedings of the National Academy of Sciences of the United States of America, Jg. 115 (2018-05-22), Heft 21, S. E4767-E4776 |
Veröffentlichung: | Washington, DC : National Academy of Sciences, 2018 |
Medientyp: | academicJournal |
ISSN: | 1091-6490 (electronic) |
DOI: | 10.1073/pnas.1800541115 |
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