IonQuant Enables Accurate and Sensitive Label-Free Quantification With FDR-Controlled Match-Between-Runs

Area of Science:

• Proteomics
• Computational Biology
• Mass Spectrometry

Background:

• Missing values in label-free quantitative proteomics reduce analytical power.
• Match-between-runs (MBR) is a common strategy to address missing values by transferring peptide identifications across runs.
• Statistical confidence of MBR transfer has been underexplored.

Purpose of the Study:

• To develop and evaluate a mixture model-based approach for estimating the false discovery rate (FDR) of MBR peptide and protein identification transfer.
• To implement this FDR-controlled MBR approach in the IonQuant software tool.
• To compare the performance of IonQuant with FDR-controlled MBR against existing tools like MaxQuant.

Main Methods:

• Development of a mixture model for FDR estimation in MBR.
• Implementation of the FDR-controlled MBR algorithm within the IonQuant software.
• Benchmarking using diverse datasets from Orbitrap and timsTOF mass spectrometers, including single-cell proteomics experiments with ion mobility separation.

Main Results:

• IonQuant with FDR-controlled MBR demonstrated superior performance compared to MaxQuant, being 19-38 times faster.
• Quantified 6-18% more proteins with comparable or improved accuracy.
• Validated performance in challenging single-cell proteomics datasets.

Conclusions:

• FDR-controlled MBR is crucial for reliable label-free quantitative proteomics.
• IonQuant provides a fast and accurate solution for peptide and protein quantification.
• The integration within the FragPipe environment enhances usability for researchers.