Comparison of different processing pipelines for dia PASEF ® data

Significance of the Topic

The dia-PASEF technique integrates trapped ion mobility spectrometry with data-independent acquisition to enhance ion utilization, specificity, and sensitivity. This improvement supports comprehensive and accurate label-free quantification in complex proteomic samples.

Study Objectives and Overview

This study evaluates and contrasts multiple computational workflows for processing dia-PASEF datasets. Four main pipelines were assessed: Mobi-DYK/OpenSWATH with MaxQuant, Spectronaut 14 library generation and direct-DIA, and PEAKS Studio X+ with its Pulsar engine.

Methodology

Complex mixtures of tryptic digests from Arabidopsis thaliana, Yarrowia lipolytica, and Streptomyces coelicolor were prepared in two defined ratios. Spectral libraries were built from eight high-pH fractions analyzed on a Bruker timsTOF Pro using both DDA-PASEF and dia-PASEF acquisition modes. Data processing settings were harmonized across workflows with a 1% FDR threshold at the peptide level.

Used Instrumentation

• nanoElute nano-HPLC system (Bruker Daltonics)
• 250 mm IonOpticks pulled-emitter column
• timsTOF Pro mass spectrometer (Bruker Daltonics) operated in PASEF and dia-PASEF modes

Key Results and Discussion

Each pipeline successfully generated CCS-aware libraries exceeding 10 000 protein groups and 70 000 unique peptides. Library-based approaches achieved over 70% recovery in 90 min runs, while direct-DIA exceeded 55%. All workflows provided accurate quantification ratios, demonstrating robust performance across software platforms. Notably, newer software versions are expected to further improve results, particularly for the Mobi-DYK/OpenSWATH combination.

Benefits and Practical Applications

The incorporation of the ion mobility dimension in the processing workflows enhances selectivity and identification confidence. CCS-aware analysis enables deeper proteome coverage and precise quantitation, supporting applications in systems biology, biomarker discovery, and quality control.

Future Trends and Potential Applications

Ongoing software developments and updates will refine library generation and data analysis workflows. Integration of machine learning for spectral prediction, expansion of shared CCS databases, and real-time PASEF acquisition strategies are anticipated to further boost throughput and sensitivity.

Conclusion

All evaluated pipelines reliably support dia-PASEF data analysis and CCS-aware library construction. The choice of processing software may be guided by existing infrastructure and desired quantification depth, with prospects for continued performance gains through software enhancements.

References

• Chapelle M., Henry C., Millan-Oropeza A., Schmit P.-O., Kaspar S. Comparison of different processing pipelines for dia-PASEF data. Bruker application note, 2019.