In:
Proceedings of the National Academy of Sciences, Proceedings of the National Academy of Sciences, Vol. 115, No. 21 ( 2018-05-22)
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.
Type of Medium:
Online Resource
ISSN:
0027-8424
,
1091-6490
DOI:
10.1073/pnas.1800541115
Language:
English
Publisher:
Proceedings of the National Academy of Sciences
Publication Date:
2018
detail.hit.zdb_id:
209104-5
detail.hit.zdb_id:
1461794-8
SSG:
11
SSG:
12
Permalink
