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

Significance Reliable proteome-wide quantification in large biological cohorts is highly valuable for clinical and pharmaceutical research yet remains extremely challenging despite recent technical advancements. Specifically, elevated missing data levels and compromised quantitative quality are common issues for prevalent methods. Here, we describe an IonStar technique taking advantage of sensitive and selective MS1 ion current-base quantification via innovations in effective and reproducible quantitative feature generation. Compared with several label-free strategies, IonStar showed superior performance in large-cohort analysis, manifested by excellent accuracy/precision, extremely low missing data, and confident discovery of subtle protein changes. In a proof-of-concept study, we demonstrated that IonStar quantified >7,000 unique proteins in 100 brain samples with no missing data and excellent quantitative quality, which has not been achievable by existing methods. 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.