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

Area of Science:

• Proteomics
• Biotechnology
• Analytical Chemistry

Background:

• Reproducible quantification of large biological cohorts is crucial for clinical and pharmaceutical proteomics.
• Current methods face challenges with declining protein quantification and data quality as cohort size increases.
• MS2-based data-independent acquisition offers reproducibility but limited proteome depth.

Purpose of the Study:

• To develop a novel MS1-based quantitative approach for in-depth, high-quality protein measurement in large cohorts.
• To address the limitations of existing label-free quantification methods in terms of missing data and quantitative accuracy.
• To provide a robust solution for precise and reproducible proteomic analysis in large-scale studies.

Main Methods:

• Developed IonStar, an MS1-based quantitative approach.
• Integrated efficient and reproducible experimental procedures.
• Implemented unique data-processing components: 3D chromatographic alignment, direct ion current extraction, and quality control.

Main Results:

• IonStar achieved significantly lower missing data (0.1%) compared to other label-free methods.
• Demonstrated superior quantitative accuracy and precision (∼5% intragroup CV) across a wide protein abundance range.
• Successfully quantified over 7,000 protein groups in a large rat brain cohort (n=100) with >99.8% data completeness and low FDR.

Conclusions:

• IonStar offers a reliable and robust solution for precise and reproducible protein quantification in large cohorts.
• The method overcomes key challenges in large-scale proteomics, enabling deeper and more accurate insights.
• IonStar is suitable for clinical, pharmaceutical, and large-scale biological investigations.