Back to Search
Start Over
IonStar enables high-precision, low-missing-data proteomics quantification in large biological cohorts.
- Source :
-
Proceedings of the National Academy of Sciences of the United States of America . 5/22/2018, Vol. 115 Issue 21, pE4767-E4776. 10p. - Publication Year :
- 2018
-
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 dataindependent 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. [ABSTRACT FROM AUTHOR]
- Subjects :
- *PROTEOMICS
*BIOLOGY
*COHORT analysis
*PREDICATE calculus
*MISSING data (Statistics)
Subjects
Details
- Language :
- English
- ISSN :
- 00278424
- Volume :
- 115
- Issue :
- 21
- Database :
- Academic Search Index
- Journal :
- Proceedings of the National Academy of Sciences of the United States of America
- Publication Type :
- Academic Journal
- Accession number :
- 129783849
- Full Text :
- https://doi.org/10.1073/pnas.1800541115