A hybrid retention time alignment algorithm for SWATH-MS data.

Area of Science:

• Proteomics
• Analytical Chemistry
• Mass Spectrometry

Background:

• Data-independent acquisition (DIA) mass spectrometry (MS) is increasingly used for proteomics.
• Accurate alignment of chromatographic retention times across multiple DIA-MS samples is crucial for reliable peptide identification and quantification.
• Existing alignment methods face challenges in handling large retention time shifts and achieving fine-scale precision.

Purpose of the Study:

• To develop and evaluate a novel hybrid algorithm for robust LC-MS alignment in DIA-MS data.
• To improve peptide identification and enable accurate label-free relative quantification.
• To demonstrate the utility of alignment for noise reduction and feature screening in DIA-MS data analysis.

Main Methods:

• A hybrid algorithm combining profile-based dynamic time warping for coarse alignment and feature-based bipartite matching for fine-scale alignment was developed.
• The algorithm was tested on sequential windowed acquisition of all theoretical fragment ion mass spectra (SWATH) data, a type of DIA.
• Performance was evaluated by comparing aligned features with peptide identification results from DIA-Umpire.

Main Results:

• The hybrid alignment algorithm effectively corrects large retention time shifts and achieves fine-scale feature matching.
• An alignment-enabled denoising scheme reduced the number of pseudo-MS2 spectra from DIA-Umpire by up to 40% while maintaining identification numbers.
• The method demonstrated utility for accurate label-free relative quantification across multiple SWATH runs.

Conclusions:

• The novel hybrid algorithm provides accurate and robust LC-MS alignment for DIA-MS data.
• Alignment prior to identification can significantly improve data quality by reducing noise and facilitating feature screening.
• This approach enhances the reliability of peptide identification and enables precise label-free quantification in proteomics studies.